diff --git a/database/createDatabases.ps1 b/database/createDatabases.ps1 index f20593ed..01ec02e5 100644 --- a/database/createDatabases.ps1 +++ b/database/createDatabases.ps1 @@ -2,13 +2,13 @@ <# .SYNOPSIS - Create Databases Required for AIFabric + Create Databases Required for AICenter .DESCRIPTION - Create Databases required by AIFabric i.e ai_helper, ai_pkgmanager, ai_deployer, ai_trainer & ai_appmanager & the script also creates an user which has db_owner privileges on all these 5 databases + Create Databases required by AICenter i.e ai_helper, ai_pkgmanager, ai_deployer, ai_trainer & ai_appmanager & the script also creates an user which has db_owner privileges on all these 5 databases and the username and password that are generated are both logged to console as well as stored as an file in the current directory from where the script execution is triggered. .NOTES Name: ./createDatabases.ps1 - Author: AIFabric Team + Author: AICenter Team Pre-Requisites: script has to be executed throuh powershell in Administrator mode & before running script set execution policy to RemoteSigned by running "Set-ExecutionPolicy RemoteSigned" .EXAMPLE If SQL Server can be accessed through Windows Authentication then: diff --git a/gpu/attach_gpu_drivers.sh b/gpu/attach_gpu_drivers.sh new file mode 100644 index 00000000..19498bca --- /dev/null +++ b/gpu/attach_gpu_drivers.sh @@ -0,0 +1,110 @@ +#!/bin/bash + +function edit_daemon_json(){ + echo "################## Updating docker configuration ######################" + sudo bash -c ' +echo \ +"{ + \"default-runtime\": \"nvidia\", + \"exec-opts\": [\"native.cgroupdriver=systemd\"], + \"runtimes\": { + \"nvidia\": { + \"path\": \"/usr/bin/nvidia-container-runtime\", + \"runtimeArgs\": [] + } + } +}" > /etc/docker/daemon.json' + +} + +function kubernetes_cluster_up() { + count=0 + swapoff -a + while [ $count -lt 50 ]; do + sudo chmod +r /etc/kubernetes/admin.conf + export KUBECONFIG=/etc/kubernetes/admin.conf + result=$(kubectl get nodes| grep master) + if [[ "$result" == *"master"* ]]; then + echo "Kubernetes up after " $((count * 5)) "seconds" + break + else + echo "Kubernetes not up, retry : " $count + count=$(( $count + 1 )) + sleep 5 + fi + done + + if [ $count == 50 ]; then + echo "Kubernetes Failed to come up" + exit + fi +} + +function validate_gpu_updated() { + count=0 + while [ $count -lt 50 ]; do + result=$(kubectl describe nodes| grep nvidia.com/gpu) + if [[ "$result" == *"nvidia.com/gpu"* ]]; then + echo $result + echo "Node gpu info updated after " $((count * 5)) "seconds" + echo "##################### Successfully installed GPU #########################" + break + else + echo "kubectl gpu info not updated, retry : " $count + count=$(( $count + 1 )) + sleep 5 + fi + done + + if [ $count == 50 ]; then + echo "################## Failed to install gpu ####################" + swapoff -a + exit + fi +} + +function restart_docker() { + sudo pkill -SIGHUP dockerd + sudo systemctl restart docker + count=0 + while [ $count -lt 50 ]; do + result=$(sudo systemctl status docker| grep running) + if [[ "$result" == *"running"* ]]; then + echo "docker is up " $((count * 5)) "seconds" + break + else + echo "docker is not up, retry : " $count + count=$(( $count + 1 )) + sleep 5 + fi + done + + if [ $count == 50 ]; then + echo "Docker Failed to come up" + swapoff -a + exit + fi +} +echo "#################################### Ecko Shutdown #######################################" +sudo /opt/ekco/shutdown.sh + +echo "################################# Attach GPU Driver #####################################" +# edit json +edit_daemon_json +echo "################################# Restarting docker #####################################" +restart_docker +sleep 2 +# This is required because when kubeadm init start kubelet, its check docker cgroup driver and uses cgroupfs +# in case of discrepancy. So we have to change this driver and restart kubelet again +echo "################################# Restarting kubelet #####################################" +sudo sed -i 's/cgroup-driver=cgroupfs/cgroup-driver=systemd/' /var/lib/kubelet/kubeadm-flags.env +sudo systemctl restart kubelet +sleep 10 +kubernetes_cluster_up +kubectl apply -f nvidia-device-plugin.yaml +validate_gpu_updated + +echo "########################## Uncordon Node #######################################" +kubectl uncordon $(hostname | tr '[:upper:]' '[:lower:]') + +echo "################ GPU driver installation successful ###################" \ No newline at end of file diff --git a/gpu/nvidia-device-plugin.yaml b/gpu/nvidia-device-plugin.yaml new file mode 100644 index 00000000..c6fbc1e1 --- /dev/null +++ b/gpu/nvidia-device-plugin.yaml @@ -0,0 +1,61 @@ +# Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +apiVersion: apps/v1 +kind: DaemonSet +metadata: + name: nvidia-device-plugin-daemonset + namespace: kube-system +spec: + selector: + matchLabels: + name: nvidia-device-plugin-ds + updateStrategy: + type: RollingUpdate + template: + metadata: + # This annotation is deprecated. Kept here for backward compatibility + # See https://kubernetes.io/docs/tasks/administer-cluster/guaranteed-scheduling-critical-addon-pods/ + annotations: + scheduler.alpha.kubernetes.io/critical-pod: "" + labels: + name: nvidia-device-plugin-ds + spec: + tolerations: + # This toleration is deprecated. Kept here for backward compatibility + # See https://kubernetes.io/docs/tasks/administer-cluster/guaranteed-scheduling-critical-addon-pods/ + - key: CriticalAddonsOnly + operator: Exists + - key: nvidia.com/gpu + operator: Exists + effect: NoSchedule + # Mark this pod as a critical add-on; when enabled, the critical add-on + # scheduler reserves resources for critical add-on pods so that they can + # be rescheduled after a failure. + # See https://kubernetes.io/docs/tasks/administer-cluster/guaranteed-scheduling-critical-addon-pods/ + priorityClassName: "system-node-critical" + containers: + - image: nvidia/k8s-device-plugin:1.0.0-beta6 + name: nvidia-device-plugin-ctr + securityContext: + allowPrivilegeEscalation: false + capabilities: + drop: ["ALL"] + volumeMounts: + - name: device-plugin + mountPath: /var/lib/kubelet/device-plugins + volumes: + - name: device-plugin + hostPath: + path: /var/lib/kubelet/device-plugins \ No newline at end of file diff --git a/language_version_metadata/PYTHON36_CV__3__metadata.json b/language_version_metadata/PYTHON36_CV__3__metadata.json new file mode 100644 index 00000000..2ac0e782 --- /dev/null +++ b/language_version_metadata/PYTHON36_CV__3__metadata.json @@ -0,0 +1,20 @@ +[{ + "version": 3, + "imageType": "SERVING", + "processor": "CPU", + "mlPackageLanguage": "PYTHON36_CV", + "languageGroup": "CV", + "baseImage": "python36cv:22.4.0-3", + "runtimeImage": "", + "displayName": "Python 36 CV" +}, +{ + "version": 3, + "imageType": "SERVING", + "processor": "GPU", + "mlPackageLanguage": "PYTHON36_CV", + "languageGroup": "CV", + "baseImage": "python36cv:22.4.0-3", + "runtimeImage": "", + "displayName": "Python 36 CV" +}] \ No newline at end of file diff --git a/language_version_metadata/Python36_CV__2__metadata.json b/language_version_metadata/Python36_CV__2__metadata.json new file mode 100644 index 00000000..0d85b529 --- /dev/null +++ b/language_version_metadata/Python36_CV__2__metadata.json @@ -0,0 +1,20 @@ +[{ + "version": 2, + "imageType": "SERVING", + "processor": "CPU", + "mlPackageLanguage": "PYTHON36_CV", + "languageGroup": "CV", + "baseImage": "python36cv:v21.10.0-2", + "runtimeImage": "", + "displayName": "Python 36 CV" +}, +{ + "version": 2, + "imageType": "SERVING", + "processor": "GPU", + "mlPackageLanguage": "PYTHON36_CV", + "languageGroup": "CV", + "baseImage": "python36cv:v21.10.0-2", + "runtimeImage": "", + "displayName": "Python 36 CV" +}] \ No newline at end of file diff --git a/metadata/1040ScheduleC__10__metadata.json b/metadata/1040ScheduleC__10__metadata.json new file mode 100644 index 00000000..50b7d634 --- /dev/null +++ b/metadata/1040ScheduleC__10__metadata.json @@ -0,0 +1,33 @@ +{ + "name": "form_1040ScheduleC", + "retrainable": true, + "gpu": 0, + "processorType": "CPU", + "description": "Machine Learning model (available in Preview) for extracting commonly occurring data points from 1040ScheduleC. Please see more details including supported languages and sample documents in the official documentation page for Pre-trained Out of the Box models: https://docs.uipath.com/document-understanding/automation-cloud/latest/user-guide/out-of-the-box-pre-trained-ml-packages.", + "inputDescription": "ML Skills deployed using this ML Package can be integrated into RPA workflows using the Machine Learning Extractor activity in the Official feed or the MLModel activity in Connect feed. File formats accepted include pdf, tiff, jpg or png files. The activities also require an API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -> Other Services view.", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "changeLog": "", + "cpu": 1, + "inputType": "JSON", + "displayName": "1040ScheduleC", + "memory": 0, + "mlPackageLanguage": "PYTHON37_DU", + "projectId": "[project-id]", + "stagingUri": "[staging-uri]", + "projectName": "UiPath Document Understanding", + "projectDescription": "UiPath models to classify and extract information from images and pdfs.", + "tenantName": "UiPath", + "minAIFabricVersion": "22.10", + "languageVersion": 4, + "version": 10, + "customVersion": "24.10.3", + "imagePath": "du-semistructured:v24.10-3.27-rc02", + "parametersFileJSON": "{\"params\": [{\"name\": \"auto_retraining\",\"type\": [\"PIPELINE\"],\"value\": \"False\",\"help\": \"Enable this option when the Pipeline you are creating is a scheduled automatic pipeline, synchronized with automatic exports from Data Manager.\",\"options\": [\"True\",\"False\"]}],\"probes\": [{\"probe\": \"startup\",\"initialDelaySeconds\": \"120\",\"periodSeconds\": \"10\",\"timeoutSeconds\": \"2\",\"successThreshold\": \"1\",\"failureThreshold\": \"60\"}]}", + "settings": [ + { + "key": "package_type", + "type": "STRING", + "value": "1040_schedule_c" + } + ] +} diff --git a/metadata/1040ScheduleC__11__metadata.json b/metadata/1040ScheduleC__11__metadata.json new file mode 100644 index 00000000..448f90a0 --- /dev/null +++ b/metadata/1040ScheduleC__11__metadata.json @@ -0,0 +1,34 @@ +{ + "name": "form_1040ScheduleC", + "retrainable": true, + "gpu": 0, + "processorType": "CPU", + "description": "Machine Learning model (available in Preview) for extracting commonly occurring data points from 1040ScheduleC. Please see more details including supported languages and sample documents in the official documentation page for Pre-trained Out of the Box models: https://docs.uipath.com/document-understanding/automation-cloud/latest/user-guide/out-of-the-box-pre-trained-ml-packages.", + "inputDescription": "ML Skills deployed using this ML Package can be integrated into RPA workflows using the Machine Learning Extractor activity in the Official feed or the MLModel activity in Connect feed. File formats accepted include pdf, tiff, jpg or png files. The activities also require an API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -> Other Services view.", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "changeLog": "", + "cpu": 1, + "inputType": "JSON", + "displayName": "1040ScheduleC", + "memory": 0, + "mlPackageLanguage": "PYTHON37_DU", + "projectId": "[project-id]", + "stagingUri": "[staging-uri]", + "projectName": "UiPath Document Understanding", + "projectDescription": "UiPath models to classify and extract information from images and pdfs.", + "tenantName": "UiPath", + "minAIFabricVersion": "22.10", + "languageVersion": 4, + "version": 11, + "customVersion": "23.10.9", + "imagePath": "du-semistructured:v23.10-03.14-rc02", + "parametersFileJSON": "{\"params\": [{\"name\": \"auto_retraining\",\"type\": [\"PIPELINE\"],\"value\": \"False\",\"help\": \"Enable this option when the Pipeline you are creating is a scheduled automatic pipeline, synchronized with automatic exports from Data Manager.\",\"options\": [\"True\",\"False\"]}],\"probes\": [{\"probe\": \"startup\",\"initialDelaySeconds\": \"120\",\"periodSeconds\": \"10\",\"timeoutSeconds\": \"2\",\"successThreshold\": \"1\",\"failureThreshold\": \"60\"}]}", + "settings": [ + { + "key": "package_type", + "type": "STRING", + "value": "1040_schedule_c" + } + ], + "maxAIFabricVersion": "24.10" +} diff --git a/metadata/1040ScheduleC__12__metadata.json b/metadata/1040ScheduleC__12__metadata.json new file mode 100644 index 00000000..c64a88af --- /dev/null +++ b/metadata/1040ScheduleC__12__metadata.json @@ -0,0 +1,33 @@ +{ + "name": "form_1040ScheduleC", + "retrainable": true, + "gpu": 0, + "processorType": "CPU", + "description": "Machine Learning model (available in Preview) for extracting commonly occurring data points from 1040ScheduleC. Please see more details including supported languages and sample documents in the official documentation page for Pre-trained Out of the Box models: https://docs.uipath.com/document-understanding/automation-cloud/latest/user-guide/out-of-the-box-pre-trained-ml-packages.", + "inputDescription": "ML Skills deployed using this ML Package can be integrated into RPA workflows using the Machine Learning Extractor activity in the Official feed or the MLModel activity in Connect feed. File formats accepted include pdf, tiff, jpg or png files. The activities also require an API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -> Other Services view.", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "changeLog": "", + "cpu": 1, + "inputType": "JSON", + "displayName": "1040ScheduleC", + "memory": 0, + "mlPackageLanguage": "PYTHON37_DU", + "projectId": "[project-id]", + "stagingUri": "[staging-uri]", + "projectName": "UiPath Document Understanding", + "projectDescription": "UiPath models to classify and extract information from images and pdfs.", + "tenantName": "UiPath", + "minAIFabricVersion": "22.10", + "languageVersion": 4, + "version": 12, + "customVersion": "24.10.4", + "imagePath": "du-semistructured:v24.10-6.16-rc04", + "parametersFileJSON": "{\"params\": [{\"name\": \"auto_retraining\",\"type\": [\"PIPELINE\"],\"value\": \"False\",\"help\": \"Enable this option when the Pipeline you are creating is a scheduled automatic pipeline, synchronized with automatic exports from Data Manager.\",\"options\": [\"True\",\"False\"]}],\"probes\": [{\"probe\": \"startup\",\"initialDelaySeconds\": \"120\",\"periodSeconds\": \"10\",\"timeoutSeconds\": \"2\",\"successThreshold\": \"1\",\"failureThreshold\": \"60\"}]}", + "settings": [ + { + "key": "package_type", + "type": "STRING", + "value": "1040_schedule_c" + } + ] +} diff --git a/metadata/1040ScheduleC__13__metadata.json b/metadata/1040ScheduleC__13__metadata.json new file mode 100644 index 00000000..70d96083 --- /dev/null +++ b/metadata/1040ScheduleC__13__metadata.json @@ -0,0 +1,34 @@ +{ + "name": "form_1040ScheduleC", + "retrainable": true, + "gpu": 0, + "processorType": "CPU", + "description": "Machine Learning model (available in Preview) for extracting commonly occurring data points from 1040ScheduleC. Please see more details including supported languages and sample documents in the official documentation page for Pre-trained Out of the Box models: https://docs.uipath.com/document-understanding/automation-cloud/latest/user-guide/out-of-the-box-pre-trained-ml-packages.", + "inputDescription": "ML Skills deployed using this ML Package can be integrated into RPA workflows using the Machine Learning Extractor activity in the Official feed or the MLModel activity in Connect feed. File formats accepted include pdf, tiff, jpg or png files. The activities also require an API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -> Other Services view.", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "changeLog": "", + "cpu": 1, + "inputType": "JSON", + "displayName": "1040ScheduleC", + "memory": 0, + "mlPackageLanguage": "PYTHON37_DU", + "projectId": "[project-id]", + "stagingUri": "[staging-uri]", + "projectName": "UiPath Document Understanding", + "projectDescription": "UiPath models to classify and extract information from images and pdfs.", + "tenantName": "UiPath", + "minAIFabricVersion": "22.10", + "languageVersion": 4, + "version": 13, + "customVersion": "23.10.10", + "imagePath": "du-semistructured:v23.10-06.05-rc05", + "parametersFileJSON": "{\"params\": [{\"name\": \"auto_retraining\",\"type\": [\"PIPELINE\"],\"value\": \"False\",\"help\": \"Enable this option when the Pipeline you are creating is a scheduled automatic pipeline, synchronized with automatic exports from Data Manager.\",\"options\": [\"True\",\"False\"]}],\"probes\": [{\"probe\": \"startup\",\"initialDelaySeconds\": \"120\",\"periodSeconds\": \"10\",\"timeoutSeconds\": \"2\",\"successThreshold\": \"1\",\"failureThreshold\": \"60\"}]}", + "settings": [ + { + "key": "package_type", + "type": "STRING", + "value": "1040_schedule_c" + } + ], + "maxAIFabricVersion": "24.10" +} diff --git a/metadata/1040ScheduleC__14__metadata.json b/metadata/1040ScheduleC__14__metadata.json new file mode 100644 index 00000000..7ed287f4 --- /dev/null +++ b/metadata/1040ScheduleC__14__metadata.json @@ -0,0 +1,33 @@ +{ + "name": "form_1040ScheduleC", + "retrainable": true, + "gpu": 0, + "processorType": "CPU", + "description": "Machine Learning model (available in Preview) for extracting commonly occurring data points from 1040ScheduleC. Please see more details including supported languages and sample documents in the official documentation page for Pre-trained Out of the Box models: https://docs.uipath.com/document-understanding/automation-cloud/latest/user-guide/out-of-the-box-pre-trained-ml-packages.", + "inputDescription": "ML Skills deployed using this ML Package can be integrated into RPA workflows using the Machine Learning Extractor activity in the Official feed or the MLModel activity in Connect feed. File formats accepted include pdf, tiff, jpg or png files. The activities also require an API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -> Other Services view.", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "changeLog": "", + "cpu": 1, + "inputType": "JSON", + "displayName": "1040ScheduleC", + "memory": 0, + "mlPackageLanguage": "PYTHON37_DU", + "projectId": "[project-id]", + "stagingUri": "[staging-uri]", + "projectName": "UiPath Document Understanding", + "projectDescription": "UiPath models to classify and extract information from images and pdfs.", + "tenantName": "UiPath", + "minAIFabricVersion": "22.10", + "languageVersion": 4, + "version": 14, + "customVersion": "24.10.5", + "imagePath": "du-semistructured:v24.10-8.25-rc02", + "parametersFileJSON": "{\"params\": [{\"name\": \"auto_retraining\",\"type\": [\"PIPELINE\"],\"value\": \"False\",\"help\": \"Enable this option when the Pipeline you are creating is a scheduled automatic pipeline, synchronized with automatic exports from Data Manager.\",\"options\": [\"True\",\"False\"]}],\"probes\": [{\"probe\": \"startup\",\"initialDelaySeconds\": \"120\",\"periodSeconds\": \"10\",\"timeoutSeconds\": \"2\",\"successThreshold\": \"1\",\"failureThreshold\": \"60\"}]}", + "settings": [ + { + "key": "package_type", + "type": "STRING", + "value": "1040_schedule_c" + } + ] +} diff --git a/metadata/1040ScheduleC__15__metadata.json b/metadata/1040ScheduleC__15__metadata.json new file mode 100644 index 00000000..a2699efd --- /dev/null +++ b/metadata/1040ScheduleC__15__metadata.json @@ -0,0 +1,34 @@ +{ + "name": "form_1040ScheduleC", + "retrainable": true, + "gpu": 0, + "processorType": "CPU", + "description": "Machine Learning model (available in Preview) for extracting commonly occurring data points from 1040ScheduleC. Please see more details including supported languages and sample documents in the official documentation page for Pre-trained Out of the Box models: https://docs.uipath.com/document-understanding/automation-cloud/latest/user-guide/out-of-the-box-pre-trained-ml-packages.", + "inputDescription": "ML Skills deployed using this ML Package can be integrated into RPA workflows using the Machine Learning Extractor activity in the Official feed or the MLModel activity in Connect feed. File formats accepted include pdf, tiff, jpg or png files. The activities also require an API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -> Other Services view.", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "changeLog": "", + "cpu": 1, + "inputType": "JSON", + "displayName": "1040ScheduleC", + "memory": 0, + "mlPackageLanguage": "PYTHON37_DU", + "projectId": "[project-id]", + "stagingUri": "[staging-uri]", + "projectName": "UiPath Document Understanding", + "projectDescription": "UiPath models to classify and extract information from images and pdfs.", + "tenantName": "UiPath", + "minAIFabricVersion": "22.10", + "languageVersion": 4, + "version": 15, + "customVersion": "23.10.11", + "imagePath": "du-semistructured:v23.10-08.25-rc02", + "parametersFileJSON": "{\"params\": [{\"name\": \"auto_retraining\",\"type\": [\"PIPELINE\"],\"value\": \"False\",\"help\": \"Enable this option when the Pipeline you are creating is a scheduled automatic pipeline, synchronized with automatic exports from Data Manager.\",\"options\": [\"True\",\"False\"]}],\"probes\": [{\"probe\": \"startup\",\"initialDelaySeconds\": \"120\",\"periodSeconds\": \"10\",\"timeoutSeconds\": \"2\",\"successThreshold\": \"1\",\"failureThreshold\": \"60\"}]}", + "settings": [ + { + "key": "package_type", + "type": "STRING", + "value": "1040_schedule_c" + } + ], + "maxAIFabricVersion": "24.10" +} diff --git a/metadata/1040ScheduleC__16__metadata.json b/metadata/1040ScheduleC__16__metadata.json new file mode 100644 index 00000000..d89c3df2 --- /dev/null +++ b/metadata/1040ScheduleC__16__metadata.json @@ -0,0 +1,33 @@ +{ + "name": "form_1040ScheduleC", + "retrainable": true, + "gpu": 0, + "processorType": "CPU", + "description": "Machine Learning model (available in Preview) for extracting commonly occurring data points from 1040ScheduleC. Please see more details including supported languages and sample documents in the official documentation page for Pre-trained Out of the Box models: https://docs.uipath.com/document-understanding/automation-cloud/latest/user-guide/out-of-the-box-pre-trained-ml-packages.", + "inputDescription": "ML Skills deployed using this ML Package can be integrated into RPA workflows using the Machine Learning Extractor activity in the Official feed or the MLModel activity in Connect feed. File formats accepted include pdf, tiff, jpg or png files. The activities also require an API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -> Other Services view.", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "changeLog": "", + "cpu": 1, + "inputType": "JSON", + "displayName": "1040ScheduleC", + "memory": 0, + "mlPackageLanguage": "PYTHON37_DU", + "projectId": "[project-id]", + "stagingUri": "[staging-uri]", + "projectName": "UiPath Document Understanding", + "projectDescription": "UiPath models to classify and extract information from images and pdfs.", + "tenantName": "UiPath", + "minAIFabricVersion": "22.10", + "languageVersion": 4, + "version": 16, + "customVersion": "25.10.0", + "imagePath": "du-semistructured:v25.10-10.17-rc04", + "parametersFileJSON": "{\"params\": [{\"name\": \"auto_retraining\",\"type\": [\"PIPELINE\"],\"value\": \"False\",\"help\": \"Enable this option when the Pipeline you are creating is a scheduled automatic pipeline, synchronized with automatic exports from Data Manager.\",\"options\": [\"True\",\"False\"]}],\"probes\": [{\"probe\": \"startup\",\"initialDelaySeconds\": \"120\",\"periodSeconds\": \"10\",\"timeoutSeconds\": \"2\",\"successThreshold\": \"1\",\"failureThreshold\": \"60\"}]}", + "settings": [ + { + "key": "package_type", + "type": "STRING", + "value": "1040_schedule_c" + } + ] +} diff --git a/metadata/1040ScheduleC__8__metadata.json b/metadata/1040ScheduleC__8__metadata.json new file mode 100644 index 00000000..91568844 --- /dev/null +++ b/metadata/1040ScheduleC__8__metadata.json @@ -0,0 +1,33 @@ +{ + "name": "form_1040ScheduleC", + "retrainable": true, + "gpu": 0, + "processorType": "CPU", + "description": "Machine Learning model (available in Preview) for extracting commonly occurring data points from 1040ScheduleC. Please see more details including supported languages and sample documents in the official documentation page for Pre-trained Out of the Box models: https://docs.uipath.com/document-understanding/automation-cloud/latest/user-guide/out-of-the-box-pre-trained-ml-packages.", + "inputDescription": "ML Skills deployed using this ML Package can be integrated into RPA workflows using the Machine Learning Extractor activity in the Official feed or the MLModel activity in Connect feed. File formats accepted include pdf, tiff, jpg or png files. The activities also require an API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -> Other Services view.", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "changeLog": "", + "cpu": 1, + "inputType": "JSON", + "displayName": "1040ScheduleC", + "memory": 0, + "mlPackageLanguage": "PYTHON37_DU", + "projectId": "[project-id]", + "stagingUri": "[staging-uri]", + "projectName": "UiPath Document Understanding", + "projectDescription": "UiPath models to classify and extract information from images and pdfs.", + "tenantName": "UiPath", + "minAIFabricVersion": "22.10", + "languageVersion": 4, + "version": 8, + "customVersion": "24.10.2", + "imagePath": "du-semistructured:v24.10-1.27-rc02", + "parametersFileJSON": "{\"params\": [{\"name\": \"auto_retraining\",\"type\": [\"PIPELINE\"],\"value\": \"False\",\"help\": \"Enable this option when the Pipeline you are creating is a scheduled automatic pipeline, synchronized with automatic exports from Data Manager.\",\"options\": [\"True\",\"False\"]}],\"probes\": [{\"probe\": \"startup\",\"initialDelaySeconds\": \"120\",\"periodSeconds\": \"10\",\"timeoutSeconds\": \"2\",\"successThreshold\": \"1\",\"failureThreshold\": \"60\"}]}", + "settings": [ + { + "key": "package_type", + "type": "STRING", + "value": "1040_schedule_c" + } + ] +} \ No newline at end of file diff --git a/metadata/1040ScheduleC__9__metadata.json b/metadata/1040ScheduleC__9__metadata.json new file mode 100644 index 00000000..ed2e4c5e --- /dev/null +++ b/metadata/1040ScheduleC__9__metadata.json @@ -0,0 +1,34 @@ +{ + "name": "form_1040ScheduleC", + "retrainable": true, + "gpu": 0, + "processorType": "CPU", + "description": "Machine Learning model (available in Preview) for extracting commonly occurring data points from 1040ScheduleC. Please see more details including supported languages and sample documents in the official documentation page for Pre-trained Out of the Box models: https://docs.uipath.com/document-understanding/automation-cloud/latest/user-guide/out-of-the-box-pre-trained-ml-packages.", + "inputDescription": "ML Skills deployed using this ML Package can be integrated into RPA workflows using the Machine Learning Extractor activity in the Official feed or the MLModel activity in Connect feed. File formats accepted include pdf, tiff, jpg or png files. 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The activities also require an API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -> Other Services view.", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "changeLog": "", + "cpu": 1, + "inputType": "JSON", + "displayName": "1040x", + "memory": 0, + "mlPackageLanguage": "PYTHON37_DU", + "projectId": "[project-id]", + "stagingUri": "[staging-uri]", + "projectName": "UiPath Document Understanding", + "projectDescription": "UiPath models to classify and extract information from images and pdfs.", + "tenantName": "UiPath", + "minAIFabricVersion": "22.10", + "languageVersion": 4, + "version": 5, + "customVersion": "24.10.4", + "imagePath": "du-semistructured:v24.10-6.16-rc04", + "parametersFileJSON": "{\"params\": [{\"name\": \"auto_retraining\",\"type\": [\"PIPELINE\"],\"value\": \"False\",\"help\": \"Enable this option when the Pipeline you are creating is a scheduled automatic pipeline, synchronized with automatic exports from Data Manager.\",\"options\": [\"True\",\"False\"]}],\"probes\": [{\"probe\": \"startup\",\"initialDelaySeconds\": \"120\",\"periodSeconds\": \"10\",\"timeoutSeconds\": \"2\",\"successThreshold\": \"1\",\"failureThreshold\": \"60\"}]}", + "settings": [ + { + "key": "package_type", + "type": "STRING", + "value": "1040x" + } + ] +} diff --git a/metadata/1040x__6__metadata.json b/metadata/1040x__6__metadata.json new file mode 100644 index 00000000..11b4570b --- /dev/null +++ b/metadata/1040x__6__metadata.json @@ -0,0 +1,33 @@ +{ + "name": "form_1040x", + "retrainable": true, + "gpu": 0, + "processorType": "CPU", + "description": "Machine Learning model (available in Preview) for extracting commonly occurring data points from 1040x documents, including header fields and line items. Please see more details including supported languages and link to Activities guide in the About Licensing -> Document Understanding API Key section of the UiPath Automation Cloud Guide here: https://docs.uipath.com/automation-cloud/docs/about-licensing#section-document-understanding-api-key", + "inputDescription": "ML Skills deployed using this ML Package can be integrated into RPA workflows using the Machine Learning Extractor activity in the Official feed or the MLModel activity in Connect feed. File formats accepted include pdf, tiff, jpg or png files. The activities also require an API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -> Other Services view.", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "changeLog": "", + "cpu": 1, + "inputType": "JSON", + "displayName": "1040x", + "memory": 0, + "mlPackageLanguage": "PYTHON37_DU", + "projectId": "[project-id]", + "stagingUri": "[staging-uri]", + "projectName": "UiPath Document Understanding", + "projectDescription": "UiPath models to classify and extract information from images and pdfs.", + "tenantName": "UiPath", + "minAIFabricVersion": "22.10", + "languageVersion": 4, + "version": 6, + "customVersion": "24.10.5", + "imagePath": "du-semistructured:v24.10-8.25-rc02", + "parametersFileJSON": "{\"params\": [{\"name\": \"auto_retraining\",\"type\": [\"PIPELINE\"],\"value\": \"False\",\"help\": \"Enable this option when the Pipeline you are creating is a scheduled automatic pipeline, synchronized with automatic exports from Data Manager.\",\"options\": [\"True\",\"False\"]}],\"probes\": [{\"probe\": \"startup\",\"initialDelaySeconds\": \"120\",\"periodSeconds\": \"10\",\"timeoutSeconds\": \"2\",\"successThreshold\": \"1\",\"failureThreshold\": \"60\"}]}", + "settings": [ + { + "key": "package_type", + "type": "STRING", + "value": "1040x" + } + ] +} diff --git a/metadata/1040x__7__metadata.json b/metadata/1040x__7__metadata.json new file mode 100644 index 00000000..1fa3e9c4 --- /dev/null +++ b/metadata/1040x__7__metadata.json @@ -0,0 +1,33 @@ +{ + "name": "form_1040x", + "retrainable": true, + "gpu": 0, + "processorType": "CPU", + "description": "Machine Learning model (available in Preview) for extracting commonly occurring data points from 1040x documents, including header fields and line items. Please see more details including supported languages and link to Activities guide in the About Licensing -> Document Understanding API Key section of the UiPath Automation Cloud Guide here: https://docs.uipath.com/automation-cloud/docs/about-licensing#section-document-understanding-api-key", + "inputDescription": "ML Skills deployed using this ML Package can be integrated into RPA workflows using the Machine Learning Extractor activity in the Official feed or the MLModel activity in Connect feed. File formats accepted include pdf, tiff, jpg or png files. The activities also require an API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -> Other Services view.", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "changeLog": "", + "cpu": 1, + "inputType": "JSON", + "displayName": "1040x", + "memory": 0, + "mlPackageLanguage": "PYTHON37_DU", + "projectId": "[project-id]", + "stagingUri": "[staging-uri]", + "projectName": "UiPath Document Understanding", + "projectDescription": "UiPath models to classify and extract information from images and pdfs.", + "tenantName": "UiPath", + "minAIFabricVersion": "22.10", + "languageVersion": 4, + "version": 7, + "customVersion": "25.10.0", + "imagePath": "du-semistructured:v25.10-10.17-rc04", + "parametersFileJSON": "{\"params\": [{\"name\": \"auto_retraining\",\"type\": [\"PIPELINE\"],\"value\": \"False\",\"help\": \"Enable this option when the Pipeline you are creating is a scheduled automatic pipeline, synchronized with automatic exports from Data Manager.\",\"options\": [\"True\",\"False\"]}],\"probes\": [{\"probe\": \"startup\",\"initialDelaySeconds\": \"120\",\"periodSeconds\": \"10\",\"timeoutSeconds\": \"2\",\"successThreshold\": \"1\",\"failureThreshold\": \"60\"}]}", + "settings": [ + { + "key": "package_type", + "type": "STRING", + "value": "1040x" + } + ] +} diff --git a/metadata/3949a__3__metadata.json b/metadata/3949a__3__metadata.json new file mode 100644 index 00000000..99ee4b08 --- /dev/null +++ b/metadata/3949a__3__metadata.json @@ -0,0 +1,33 @@ +{ + "name": "form_3949a", + "retrainable": true, + "gpu": 0, + "processorType": "CPU", + "description": "Machine Learning model (available in Preview) for extracting commonly occurring data points from 3949a documents, including header fields and line items. Please see more details including supported languages and link to Activities guide in the About Licensing -> Document Understanding API Key section of the UiPath Automation Cloud Guide here: https://docs.uipath.com/automation-cloud/docs/about-licensing#section-document-understanding-api-key", + "inputDescription": "ML Skills deployed using this ML Package can be integrated into RPA workflows using the Machine Learning Extractor activity in the Official feed or the MLModel activity in Connect feed. File formats accepted include pdf, tiff, jpg or png files. The activities also require an API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -> Other Services view.", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "changeLog": "", + "cpu": 1, + "inputType": "JSON", + "displayName": "3949a", + "memory": 0, + "mlPackageLanguage": "PYTHON37_DU", + "projectId": "[project-id]", + "stagingUri": "[staging-uri]", + "projectName": "UiPath Document Understanding", + "projectDescription": "UiPath models to classify and extract information from images and pdfs.", + "tenantName": "UiPath", + "minAIFabricVersion": "22.10", + "languageVersion": 4, + "version": 3, + "customVersion": "24.10.2", + "imagePath": "du-semistructured:v24.10-1.27-rc02", + "parametersFileJSON": "{\"params\": [{\"name\": \"auto_retraining\",\"type\": [\"PIPELINE\"],\"value\": \"False\",\"help\": \"Enable this option when the Pipeline you are creating is a scheduled automatic pipeline, synchronized with automatic exports from Data Manager.\",\"options\": [\"True\",\"False\"]}],\"probes\": [{\"probe\": \"startup\",\"initialDelaySeconds\": \"120\",\"periodSeconds\": \"10\",\"timeoutSeconds\": \"2\",\"successThreshold\": \"1\",\"failureThreshold\": \"60\"}]}", + "settings": [ + { + "key": "package_type", + "type": "STRING", + "value": "3949a" + } + ] +} \ No newline at end of file diff --git a/metadata/3949a__4__metadata.json b/metadata/3949a__4__metadata.json new file mode 100644 index 00000000..e507f837 --- /dev/null +++ b/metadata/3949a__4__metadata.json @@ -0,0 +1,33 @@ +{ + "name": "form_3949a", + "retrainable": true, + "gpu": 0, + "processorType": "CPU", + "description": "Machine Learning model (available in Preview) for extracting commonly occurring data points from 3949a documents, including header fields and line items. Please see more details including supported languages and link to Activities guide in the About Licensing -> Document Understanding API Key section of the UiPath Automation Cloud Guide here: https://docs.uipath.com/automation-cloud/docs/about-licensing#section-document-understanding-api-key", + "inputDescription": "ML Skills deployed using this ML Package can be integrated into RPA workflows using the Machine Learning Extractor activity in the Official feed or the MLModel activity in Connect feed. File formats accepted include pdf, tiff, jpg or png files. The activities also require an API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -> Other Services view.", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "changeLog": "", + "cpu": 1, + "inputType": "JSON", + "displayName": "3949a", + "memory": 0, + "mlPackageLanguage": "PYTHON37_DU", + "projectId": "[project-id]", + "stagingUri": "[staging-uri]", + "projectName": "UiPath Document Understanding", + "projectDescription": "UiPath models to classify and extract information from images and pdfs.", + "tenantName": "UiPath", + "minAIFabricVersion": "22.10", + "languageVersion": 4, + "version": 4, + "customVersion": "24.10.3", + "imagePath": "du-semistructured:v24.10-3.27-rc02", + "parametersFileJSON": "{\"params\": [{\"name\": \"auto_retraining\",\"type\": [\"PIPELINE\"],\"value\": \"False\",\"help\": \"Enable this option when the Pipeline you are creating is a scheduled automatic pipeline, synchronized with automatic exports from Data Manager.\",\"options\": [\"True\",\"False\"]}],\"probes\": [{\"probe\": \"startup\",\"initialDelaySeconds\": \"120\",\"periodSeconds\": \"10\",\"timeoutSeconds\": \"2\",\"successThreshold\": \"1\",\"failureThreshold\": \"60\"}]}", + "settings": [ + { + "key": "package_type", + "type": "STRING", + "value": "3949a" + } + ] +} diff --git a/metadata/3949a__5__metadata.json b/metadata/3949a__5__metadata.json new file mode 100644 index 00000000..35b40b16 --- /dev/null +++ b/metadata/3949a__5__metadata.json @@ -0,0 +1,33 @@ +{ + "name": "form_3949a", + "retrainable": true, + "gpu": 0, + "processorType": "CPU", + "description": "Machine Learning model (available in Preview) for extracting commonly occurring data points from 3949a documents, including header fields and line items. Please see more details including supported languages and link to Activities guide in the About Licensing -> Document Understanding API Key section of the UiPath Automation Cloud Guide here: https://docs.uipath.com/automation-cloud/docs/about-licensing#section-document-understanding-api-key", + "inputDescription": "ML Skills deployed using this ML Package can be integrated into RPA workflows using the Machine Learning Extractor activity in the Official feed or the MLModel activity in Connect feed. File formats accepted include pdf, tiff, jpg or png files. The activities also require an API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -> Other Services view.", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "changeLog": "", + "cpu": 1, + "inputType": "JSON", + "displayName": "3949a", + "memory": 0, + "mlPackageLanguage": "PYTHON37_DU", + "projectId": "[project-id]", + "stagingUri": "[staging-uri]", + "projectName": "UiPath Document Understanding", + "projectDescription": "UiPath models to classify and extract information from images and pdfs.", + "tenantName": "UiPath", + "minAIFabricVersion": "22.10", + "languageVersion": 4, + "version": 5, + "customVersion": "24.10.4", + "imagePath": "du-semistructured:v24.10-6.16-rc04", + "parametersFileJSON": "{\"params\": [{\"name\": \"auto_retraining\",\"type\": [\"PIPELINE\"],\"value\": \"False\",\"help\": \"Enable this option when the Pipeline you are creating is a scheduled automatic pipeline, synchronized with automatic exports from Data Manager.\",\"options\": [\"True\",\"False\"]}],\"probes\": [{\"probe\": \"startup\",\"initialDelaySeconds\": \"120\",\"periodSeconds\": \"10\",\"timeoutSeconds\": \"2\",\"successThreshold\": \"1\",\"failureThreshold\": \"60\"}]}", + "settings": [ + { + "key": "package_type", + "type": "STRING", + "value": "3949a" + } + ] +} diff --git a/metadata/3949a__6__metadata.json b/metadata/3949a__6__metadata.json new file mode 100644 index 00000000..1ac2721d --- /dev/null +++ b/metadata/3949a__6__metadata.json @@ -0,0 +1,33 @@ +{ + "name": "form_3949a", + "retrainable": true, + "gpu": 0, + "processorType": "CPU", + "description": "Machine Learning model (available in Preview) for extracting commonly occurring data points from 3949a documents, including header fields and line items. Please see more details including supported languages and link to Activities guide in the About Licensing -> Document Understanding API Key section of the UiPath Automation Cloud Guide here: https://docs.uipath.com/automation-cloud/docs/about-licensing#section-document-understanding-api-key", + "inputDescription": "ML Skills deployed using this ML Package can be integrated into RPA workflows using the Machine Learning Extractor activity in the Official feed or the MLModel activity in Connect feed. File formats accepted include pdf, tiff, jpg or png files. The activities also require an API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -> Other Services view.", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "changeLog": "", + "cpu": 1, + "inputType": "JSON", + "displayName": "3949a", + "memory": 0, + "mlPackageLanguage": "PYTHON37_DU", + "projectId": "[project-id]", + "stagingUri": "[staging-uri]", + "projectName": "UiPath Document Understanding", + "projectDescription": "UiPath models to classify and extract information from images and pdfs.", + "tenantName": "UiPath", + "minAIFabricVersion": "22.10", + "languageVersion": 4, + "version": 6, + "customVersion": "24.10.5", + "imagePath": "du-semistructured:v24.10-8.25-rc02", + "parametersFileJSON": "{\"params\": [{\"name\": \"auto_retraining\",\"type\": [\"PIPELINE\"],\"value\": \"False\",\"help\": \"Enable this option when the Pipeline you are creating is a scheduled automatic pipeline, synchronized with automatic exports from Data Manager.\",\"options\": [\"True\",\"False\"]}],\"probes\": [{\"probe\": \"startup\",\"initialDelaySeconds\": \"120\",\"periodSeconds\": \"10\",\"timeoutSeconds\": \"2\",\"successThreshold\": \"1\",\"failureThreshold\": \"60\"}]}", + "settings": [ + { + "key": "package_type", + "type": "STRING", + "value": "3949a" + } + ] +} diff --git a/metadata/3949a__7__metadata.json b/metadata/3949a__7__metadata.json new file mode 100644 index 00000000..b9ed340c --- /dev/null +++ b/metadata/3949a__7__metadata.json @@ -0,0 +1,33 @@ +{ + "name": "form_3949a", + "retrainable": true, + "gpu": 0, + "processorType": "CPU", + "description": "Machine Learning model (available in Preview) for extracting commonly occurring data points from 3949a documents, including header fields and line items. Please see more details including supported languages and link to Activities guide in the About Licensing -> Document Understanding API Key section of the UiPath Automation Cloud Guide here: https://docs.uipath.com/automation-cloud/docs/about-licensing#section-document-understanding-api-key", + "inputDescription": "ML Skills deployed using this ML Package can be integrated into RPA workflows using the Machine Learning Extractor activity in the Official feed or the MLModel activity in Connect feed. File formats accepted include pdf, tiff, jpg or png files. The activities also require an API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -> Other Services view.", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "changeLog": "", + "cpu": 1, + "inputType": "JSON", + "displayName": "3949a", + "memory": 0, + "mlPackageLanguage": "PYTHON37_DU", + "projectId": "[project-id]", + "stagingUri": "[staging-uri]", + "projectName": "UiPath Document Understanding", + "projectDescription": "UiPath models to classify and extract information from images and pdfs.", + "tenantName": "UiPath", + "minAIFabricVersion": "22.10", + "languageVersion": 4, + "version": 7, + "customVersion": "25.10.0", + "imagePath": "du-semistructured:v25.10-10.17-rc04", + "parametersFileJSON": "{\"params\": [{\"name\": \"auto_retraining\",\"type\": [\"PIPELINE\"],\"value\": \"False\",\"help\": \"Enable this option when the Pipeline you are creating is a scheduled automatic pipeline, synchronized with automatic exports from Data Manager.\",\"options\": [\"True\",\"False\"]}],\"probes\": [{\"probe\": \"startup\",\"initialDelaySeconds\": \"120\",\"periodSeconds\": \"10\",\"timeoutSeconds\": \"2\",\"successThreshold\": \"1\",\"failureThreshold\": \"60\"}]}", + "settings": [ + { + "key": "package_type", + "type": "STRING", + "value": "3949a" + } + ] +} diff --git a/metadata/4506T__25__metadata.json b/metadata/4506T__25__metadata.json new file mode 100644 index 00000000..02d619cc --- /dev/null +++ b/metadata/4506T__25__metadata.json @@ -0,0 +1,34 @@ +{ + "name": "form_4506T", + "retrainable": true, + "gpu": 0, + "processorType": "CPU", + "description": "Machine Learning model for extracting commonly occurring data points from 4506T documents, including header fields and line items. 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The activities also require an API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -> Other Services view.", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "changeLog": "", + "cpu": 1, + "inputType": "JSON", + "displayName": "4506T", + "memory": 0, + "mlPackageLanguage": "PYTHON37_DU", + "projectId": "[project-id]", + "stagingUri": "[staging-uri]", + "projectName": "UiPath Document Understanding", + "projectDescription": "UiPath models to classify and extract information from images and pdfs.", + "tenantName": "UiPath", + "minAIFabricVersion": "22.10", + "languageVersion": 4, + "version": 25, + "customVersion": "22.10.14", + "imagePath": "du-semistructured:v22.10-10.10-rc01", + "parametersFileJSON": "{\"params\": [{\"name\": \"auto_retraining\",\"type\": [\"PIPELINE\"],\"value\": \"False\",\"help\": \"Enable this option when the Pipeline you are creating is a scheduled automatic pipeline, synchronized with automatic exports from Data Manager.\",\"options\": [\"True\",\"False\"]}],\"probes\": [{\"probe\": \"startup\",\"initialDelaySeconds\": \"120\",\"periodSeconds\": \"10\",\"timeoutSeconds\": \"2\",\"successThreshold\": \"1\",\"failureThreshold\": \"60\"}]}", + "settings": [ + { + "key": "package_type", + "type": "STRING", + "value": "4506t" + } + ], + "maxAIFabricVersion": "23.4" +} \ No newline at end of file diff --git a/metadata/PurchaseOrders__2__metadata.json b/metadata/4506T__2__metadata.json similarity index 74% rename from metadata/PurchaseOrders__2__metadata.json rename to metadata/4506T__2__metadata.json index 98a0db3c..58feac8e 100644 --- a/metadata/PurchaseOrders__2__metadata.json +++ b/metadata/4506T__2__metadata.json @@ -1,21 +1,26 @@ { - "changeLog": "Release v2020.8", - "cpu": 0, - "description": "Machine Learning model for extracting commonly occurring data points from Purchase Orders, including header fields and line items. 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The activities also require an API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -> Other Services view.", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "changeLog": "", + "cpu": 1, "inputType": "JSON", + "displayName": "4506T", "memory": 0, "mlPackageLanguage": "PYTHON37_DU", - "name": "PurchaseOrders", - "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", - "processorType": "GPU", - "projectId": "[project-id]", - "retrainable": true, + "projectId": "[project-id]", "stagingUri": "[staging-uri]", "projectName": "UiPath Document Understanding", "projectDescription": "UiPath models to classify and extract information from images and pdfs.", "tenantName": "UiPath", - "imagePath": "registry.replicated.com/aif-core/purchaseorders:2" -} + "minAIFabricVersion": "22.4", + "languageVersion": 4, + "version": 2, + "customVersion": "22.4.1", + "contentUri": "https:///publicmodels/AIC/4506T/22.4.1/4506t_package.zip", + "maxAIFabricVersion": "22.4" +} \ No newline at end of file diff --git a/metadata/4506T__31__metadata.json b/metadata/4506T__31__metadata.json new file mode 100644 index 00000000..36b85948 --- /dev/null +++ b/metadata/4506T__31__metadata.json @@ -0,0 +1,33 @@ +{ + "name": "form_4506T", + "retrainable": true, + "gpu": 0, + "processorType": "CPU", + "description": "Machine Learning model for extracting commonly occurring data points from 4506T documents, including header fields and line items. 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The activities also require an API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -> Other Services view.", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "changeLog": "", + "cpu": 1, + "inputType": "JSON", + "displayName": "4506T", + "memory": 0, + "mlPackageLanguage": "PYTHON37_DU", + "projectId": "[project-id]", + "stagingUri": "[staging-uri]", + "projectName": "UiPath Document Understanding", + "projectDescription": "UiPath models to classify and extract information from images and pdfs.", + "tenantName": "UiPath", + "minAIFabricVersion": "22.10", + "languageVersion": 4, + "version": 31, + "customVersion": "24.10.2", + "imagePath": "du-semistructured:v24.10-1.27-rc02", + "parametersFileJSON": "{\"params\": [{\"name\": \"auto_retraining\",\"type\": [\"PIPELINE\"],\"value\": \"False\",\"help\": \"Enable this option when the Pipeline you are creating is a scheduled automatic pipeline, synchronized with automatic exports from Data Manager.\",\"options\": [\"True\",\"False\"]}],\"probes\": [{\"probe\": \"startup\",\"initialDelaySeconds\": \"120\",\"periodSeconds\": \"10\",\"timeoutSeconds\": \"2\",\"successThreshold\": \"1\",\"failureThreshold\": \"60\"}]}", + "settings": [ + { + "key": "package_type", + "type": "STRING", + "value": "4506t" + } + ] +} \ No newline at end of file diff --git a/metadata/4506T__32__metadata.json b/metadata/4506T__32__metadata.json new file mode 100644 index 00000000..ede0cef9 --- /dev/null +++ b/metadata/4506T__32__metadata.json @@ -0,0 +1,34 @@ +{ + "name": "form_4506T", + "retrainable": true, + "gpu": 0, + "processorType": "CPU", + "description": "Machine Learning model for extracting commonly occurring data points from 4506T documents, including header fields and line items. 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The activities also require an API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -> Other Services view.", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "changeLog": "", + "cpu": 1, + "inputType": "JSON", + "displayName": "9465", + "memory": 0, + "mlPackageLanguage": "PYTHON37_DU", + "projectId": "[project-id]", + "stagingUri": "[staging-uri]", + "projectName": "UiPath Document Understanding", + "projectDescription": "UiPath models to classify and extract information from images and pdfs.", + "tenantName": "UiPath", + "minAIFabricVersion": "22.10", + "languageVersion": 4, + "version": 5, + "customVersion": "24.10.4", + "imagePath": "du-semistructured:v24.10-6.16-rc04", + "parametersFileJSON": "{\"params\": [{\"name\": \"auto_retraining\",\"type\": [\"PIPELINE\"],\"value\": \"False\",\"help\": \"Enable this option when the Pipeline you are creating is a scheduled automatic pipeline, synchronized with automatic exports from Data Manager.\",\"options\": [\"True\",\"False\"]}],\"probes\": [{\"probe\": \"startup\",\"initialDelaySeconds\": \"120\",\"periodSeconds\": \"10\",\"timeoutSeconds\": \"2\",\"successThreshold\": \"1\",\"failureThreshold\": \"60\"}]}", + "settings": [ + { + "key": "package_type", + "type": "STRING", + "value": "9465" + } + ] +} diff --git a/metadata/9465__6__metadata.json b/metadata/9465__6__metadata.json new file mode 100644 index 00000000..ad4ebc43 --- /dev/null +++ b/metadata/9465__6__metadata.json @@ -0,0 +1,33 @@ +{ + "name": "form_9465", + "retrainable": true, + "gpu": 0, + "processorType": "CPU", + "description": "Machine Learning model (available in Preview) for extracting commonly occurring data points from 9465 documents, including header fields and line items. Please see more details including supported languages and link to Activities guide in the About Licensing -> Document Understanding API Key section of the UiPath Automation Cloud Guide here: https://docs.uipath.com/automation-cloud/docs/about-licensing#section-document-understanding-api-key", + "inputDescription": "ML Skills deployed using this ML Package can be integrated into RPA workflows using the Machine Learning Extractor activity in the Official feed or the MLModel activity in Connect feed. File formats accepted include pdf, tiff, jpg or png files. The activities also require an API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -> Other Services view.", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "changeLog": "", + "cpu": 1, + "inputType": "JSON", + "displayName": "9465", + "memory": 0, + "mlPackageLanguage": "PYTHON37_DU", + "projectId": "[project-id]", + "stagingUri": "[staging-uri]", + "projectName": "UiPath Document Understanding", + "projectDescription": "UiPath models to classify and extract information from images and pdfs.", + "tenantName": "UiPath", + "minAIFabricVersion": "22.10", + "languageVersion": 4, + "version": 6, + "customVersion": "24.10.5", + "imagePath": "du-semistructured:v24.10-8.25-rc02", + "parametersFileJSON": "{\"params\": [{\"name\": \"auto_retraining\",\"type\": [\"PIPELINE\"],\"value\": \"False\",\"help\": \"Enable this option when the Pipeline you are creating is a scheduled automatic pipeline, synchronized with automatic exports from Data Manager.\",\"options\": [\"True\",\"False\"]}],\"probes\": [{\"probe\": \"startup\",\"initialDelaySeconds\": \"120\",\"periodSeconds\": \"10\",\"timeoutSeconds\": \"2\",\"successThreshold\": \"1\",\"failureThreshold\": \"60\"}]}", + "settings": [ + { + "key": "package_type", + "type": "STRING", + "value": "9465" + } + ] +} diff --git a/metadata/9465__7__metadata.json b/metadata/9465__7__metadata.json new file mode 100644 index 00000000..684c4b38 --- /dev/null +++ b/metadata/9465__7__metadata.json @@ -0,0 +1,33 @@ +{ + "name": "form_9465", + "retrainable": true, + "gpu": 0, + "processorType": "CPU", + "description": "Machine Learning model (available in Preview) for extracting commonly occurring data points from 9465 documents, including header fields and line items. Please see more details including supported languages and link to Activities guide in the About Licensing -> Document Understanding API Key section of the UiPath Automation Cloud Guide here: https://docs.uipath.com/automation-cloud/docs/about-licensing#section-document-understanding-api-key", + "inputDescription": "ML Skills deployed using this ML Package can be integrated into RPA workflows using the Machine Learning Extractor activity in the Official feed or the MLModel activity in Connect feed. File formats accepted include pdf, tiff, jpg or png files. The activities also require an API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -> Other Services view.", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "changeLog": "", + "cpu": 1, + "inputType": "JSON", + "displayName": "9465", + "memory": 0, + "mlPackageLanguage": "PYTHON37_DU", + "projectId": "[project-id]", + "stagingUri": "[staging-uri]", + "projectName": "UiPath Document Understanding", + "projectDescription": "UiPath models to classify and extract information from images and pdfs.", + "tenantName": "UiPath", + "minAIFabricVersion": "22.10", + "languageVersion": 4, + "version": 7, + "customVersion": "25.10.0", + "imagePath": "du-semistructured:v25.10-10.17-rc04", + "parametersFileJSON": "{\"params\": [{\"name\": \"auto_retraining\",\"type\": [\"PIPELINE\"],\"value\": \"False\",\"help\": \"Enable this option when the Pipeline you are creating is a scheduled automatic pipeline, synchronized with automatic exports from Data Manager.\",\"options\": [\"True\",\"False\"]}],\"probes\": [{\"probe\": \"startup\",\"initialDelaySeconds\": \"120\",\"periodSeconds\": \"10\",\"timeoutSeconds\": \"2\",\"successThreshold\": \"1\",\"failureThreshold\": \"60\"}]}", + "settings": [ + { + "key": "package_type", + "type": "STRING", + "value": "9465" + } + ] +} diff --git a/metadata/990__25__metadata.json b/metadata/990__25__metadata.json new file mode 100644 index 00000000..8e983b5b --- /dev/null +++ b/metadata/990__25__metadata.json @@ -0,0 +1,34 @@ +{ + "name": "form_990", + "retrainable": true, + "gpu": 0, + "processorType": "CPU", + "description": "Machine Learning model for extracting commonly occurring data points from 990 documents, including header fields and line items. Please see more details including supported languages and link to Activities guide in the About Licensing -> Document Understanding API Key section of the UiPath Automation Cloud Guide here: https://docs.uipath.com/automation-cloud/docs/about-licensing#section-document-understanding-api-key", + "inputDescription": "ML Skills deployed using this ML Package can be integrated into RPA workflows using the Machine Learning Extractor activity in the Official feed or the MLModel activity in Connect feed. File formats accepted include pdf, tiff, jpg or png files. The activities also require an API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -> Other Services view.", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "changeLog": "", + "cpu": 1, + "inputType": "JSON", + "displayName": "990", + "memory": 0, + "mlPackageLanguage": "PYTHON37_DU", + "projectId": "[project-id]", + "stagingUri": "[staging-uri]", + "projectName": "UiPath Document Understanding", + "projectDescription": "UiPath models to classify and extract information from images and pdfs.", + "tenantName": "UiPath", + "minAIFabricVersion": "22.10", + "languageVersion": 4, + "version": 25, + "customVersion": "22.10.14", + "imagePath": "du-semistructured:v22.10-10.10-rc01", + "parametersFileJSON": "{\"params\": [{\"name\": \"auto_retraining\",\"type\": [\"PIPELINE\"],\"value\": \"False\",\"help\": \"Enable this option when the Pipeline you are creating is a scheduled automatic pipeline, synchronized with automatic exports from Data Manager.\",\"options\": [\"True\",\"False\"]}],\"probes\": [{\"probe\": \"startup\",\"initialDelaySeconds\": \"120\",\"periodSeconds\": \"10\",\"timeoutSeconds\": \"2\",\"successThreshold\": \"1\",\"failureThreshold\": \"60\"}]}", + "settings": [ + { + "key": "package_type", + "type": "STRING", + "value": "990" + } + ], + "maxAIFabricVersion": "23.4" +} \ No newline at end of file diff --git a/metadata/Receipts__2__metadata.json b/metadata/990__2__metadata.json similarity index 58% rename from metadata/Receipts__2__metadata.json rename to metadata/990__2__metadata.json index 10257e8a..7264bc6d 100644 --- a/metadata/Receipts__2__metadata.json +++ b/metadata/990__2__metadata.json @@ -1,21 +1,26 @@ { - "changeLog": "Release v2020.7", - "cpu": 0, - "description": "Machine Learning model for extracting commonly occurring data points from Receipts, including header fields and line items. 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The activities also require an API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -> Other Services view.", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "changeLog": "", + "cpu": 1, + "inputType": "JSON", + "displayName": "Acord25", + "memory": 0, + "mlPackageLanguage": "PYTHON37_DU", + "projectId": "[project-id]", + "stagingUri": "[staging-uri]", + "projectName": "UiPath Document Understanding", + "projectDescription": "UiPath models to classify and extract information from images and pdfs.", + "tenantName": "UiPath", + "minAIFabricVersion": "22.10", + "languageVersion": 4, + "version": 30, + "customVersion": "23.4.11", + "imagePath": "du-semistructured:v23.4-01.27-rc01", + "parametersFileJSON": "{\"params\": [{\"name\": \"auto_retraining\",\"type\": [\"PIPELINE\"],\"value\": \"False\",\"help\": \"Enable this option when the Pipeline you are creating is a scheduled automatic pipeline, synchronized with automatic exports from Data Manager.\",\"options\": [\"True\",\"False\"]}],\"probes\": [{\"probe\": \"startup\",\"initialDelaySeconds\": \"120\",\"periodSeconds\": \"10\",\"timeoutSeconds\": \"2\",\"successThreshold\": \"1\",\"failureThreshold\": \"60\"}]}", + "settings": [ + { + "key": "package_type", + "type": "STRING", + "value": "acord25" + } + ], + "maxAIFabricVersion": "23.10" +} \ No newline at end of file diff --git a/metadata/Acord25__31__metadata.json b/metadata/Acord25__31__metadata.json new file mode 100644 index 00000000..c57c21c0 --- /dev/null +++ b/metadata/Acord25__31__metadata.json @@ -0,0 +1,34 @@ +{ + "name": "Acord25", + "retrainable": true, + "gpu": 0, + "processorType": "CPU", + "description": "Machine Learning model for extracting commonly occurring data points from Acord25 documents, including header fields and line items. 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The activities also require an API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -> Other Services view.", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "changeLog": "", + "cpu": 1, + "inputType": "JSON", + "displayName": "Acord25", + "memory": 0, + "mlPackageLanguage": "PYTHON37_DU", + "projectId": "[project-id]", + "stagingUri": "[staging-uri]", + "projectName": "UiPath Document Understanding", + "projectDescription": "UiPath models to classify and extract information from images and pdfs.", + "tenantName": "UiPath", + "minAIFabricVersion": "22.10", + "languageVersion": 4, + "version": 31, + "customVersion": "23.10.8", + "imagePath": "du-semistructured:v23.10-01.28-rc07", + "parametersFileJSON": "{\"params\": [{\"name\": \"auto_retraining\",\"type\": [\"PIPELINE\"],\"value\": \"False\",\"help\": \"Enable this option when the Pipeline you are creating is a scheduled automatic pipeline, synchronized with automatic exports from Data Manager.\",\"options\": [\"True\",\"False\"]}],\"probes\": [{\"probe\": \"startup\",\"initialDelaySeconds\": \"120\",\"periodSeconds\": \"10\",\"timeoutSeconds\": \"2\",\"successThreshold\": \"1\",\"failureThreshold\": \"60\"}]}", + "settings": [ + { + "key": "package_type", + "type": "STRING", + "value": "acord25" + } + ], + "maxAIFabricVersion": "24.10" +} \ No newline at end of file diff --git a/metadata/Acord25__32__metadata.json b/metadata/Acord25__32__metadata.json new file mode 100644 index 00000000..60e50dbb --- /dev/null +++ b/metadata/Acord25__32__metadata.json @@ -0,0 +1,33 @@ +{ + "name": "Acord25", + "retrainable": true, + "gpu": 0, + "processorType": "CPU", + "description": "Machine Learning model for extracting commonly occurring data points from Acord25 documents, including header fields and line items. Please see more details including supported languages and link to Activities guide in the About Licensing -> Document Understanding API Key section of the UiPath Automation Cloud Guide here: https://docs.uipath.com/automation-cloud/docs/about-licensing#section-document-understanding-api-key", + "inputDescription": "ML Skills deployed using this ML Package can be integrated into RPA workflows using the Machine Learning Extractor activity in the Official feed or the MLModel activity in Connect feed. File formats accepted include pdf, tiff, jpg or png files. 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Please see more details including supported languages and link to Activities guide in the About Licensing -> Document Understanding API Key section of the UiPath Automation Cloud Guide here: https://docs.uipath.com/automation-cloud/docs/about-licensing#section-document-understanding-api-key", + "inputDescription": "ML Skills deployed using this ML Package can be integrated into RPA workflows using the Machine Learning Extractor activity in the Official feed or the MLModel activity in Connect feed. File formats accepted include pdf, tiff, jpg or png files. The activities also require an API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -> Other Services view.", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "changeLog": "", + "cpu": 1, + "inputType": "JSON", + "displayName": "Acord25", + "memory": 0, + "mlPackageLanguage": "PYTHON37_DU", + "projectId": "[project-id]", + "stagingUri": "[staging-uri]", + "projectName": "UiPath Document Understanding", + "projectDescription": "UiPath models to classify and extract information from images and pdfs.", + "tenantName": "UiPath", + "minAIFabricVersion": "22.10", + "languageVersion": 4, + "version": 33, + "customVersion": "23.10.9", + "imagePath": "du-semistructured:v23.10-03.14-rc02", + "parametersFileJSON": "{\"params\": [{\"name\": \"auto_retraining\",\"type\": [\"PIPELINE\"],\"value\": \"False\",\"help\": \"Enable this option when the Pipeline you are creating is a scheduled automatic pipeline, synchronized with automatic exports from Data Manager.\",\"options\": [\"True\",\"False\"]}],\"probes\": [{\"probe\": \"startup\",\"initialDelaySeconds\": \"120\",\"periodSeconds\": \"10\",\"timeoutSeconds\": \"2\",\"successThreshold\": \"1\",\"failureThreshold\": \"60\"}]}", + "settings": [ + { + "key": "package_type", + "type": "STRING", + "value": "acord25" + } + ], + "maxAIFabricVersion": "24.10" +} diff --git a/metadata/Acord25__34__metadata.json b/metadata/Acord25__34__metadata.json new file mode 100644 index 00000000..63e2c9d6 --- /dev/null +++ b/metadata/Acord25__34__metadata.json @@ -0,0 +1,34 @@ +{ + "name": "Acord25", + "retrainable": true, + "gpu": 0, + "processorType": "CPU", + "description": "Machine Learning model for extracting commonly occurring data points from Acord25 documents, including header fields and line items. Please see more details including supported languages and link to Activities guide in the About Licensing -> Document Understanding API Key section of the UiPath Automation Cloud Guide here: https://docs.uipath.com/automation-cloud/docs/about-licensing#section-document-understanding-api-key", + "inputDescription": "ML Skills deployed using this ML Package can be integrated into RPA workflows using the Machine Learning Extractor activity in the Official feed or the MLModel activity in Connect feed. File formats accepted include pdf, tiff, jpg or png files. The activities also require an API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -> Other Services view.", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "changeLog": "", + "cpu": 1, + "inputType": "JSON", + "displayName": "Acord25", + "memory": 0, + "mlPackageLanguage": "PYTHON37_DU", + "projectId": "[project-id]", + "stagingUri": "[staging-uri]", + "projectName": "UiPath Document Understanding", + "projectDescription": "UiPath models to classify and extract information from images and pdfs.", + "tenantName": "UiPath", + "minAIFabricVersion": "22.10", + "languageVersion": 4, + "version": 34, + "customVersion": "23.4.12", + "imagePath": "du-semistructured:v23.4-03.12-rc02", + "parametersFileJSON": "{\"params\": [{\"name\": \"auto_retraining\",\"type\": [\"PIPELINE\"],\"value\": \"False\",\"help\": \"Enable this option when the Pipeline you are creating is a scheduled automatic pipeline, synchronized with automatic exports from Data Manager.\",\"options\": [\"True\",\"False\"]}],\"probes\": [{\"probe\": \"startup\",\"initialDelaySeconds\": \"120\",\"periodSeconds\": \"10\",\"timeoutSeconds\": \"2\",\"successThreshold\": \"1\",\"failureThreshold\": \"60\"}]}", + "settings": [ + { + "key": "package_type", + "type": "STRING", + "value": "acord25" + } + ], + "maxAIFabricVersion": "23.10" +} diff --git a/metadata/Acord25__35__metadata.json b/metadata/Acord25__35__metadata.json new file mode 100644 index 00000000..41585641 --- /dev/null +++ b/metadata/Acord25__35__metadata.json @@ -0,0 +1,33 @@ +{ + "name": "Acord25", + "retrainable": true, + "gpu": 0, + "processorType": "CPU", + "description": "Machine Learning model for extracting commonly occurring data points from Acord25 documents, including header fields and line items. Please see more details including supported languages and link to Activities guide in the About Licensing -> Document Understanding API Key section of the UiPath Automation Cloud Guide here: https://docs.uipath.com/automation-cloud/docs/about-licensing#section-document-understanding-api-key", + "inputDescription": "ML Skills deployed using this ML Package can be integrated into RPA workflows using the Machine Learning Extractor activity in the Official feed or the MLModel activity in Connect feed. File formats accepted include pdf, tiff, jpg or png files. The activities also require an API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -> Other Services view.", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "changeLog": "", + "cpu": 1, + "inputType": "JSON", + "displayName": "Acord25", + "memory": 0, + "mlPackageLanguage": "PYTHON37_DU", + "projectId": "[project-id]", + "stagingUri": "[staging-uri]", + "projectName": "UiPath Document Understanding", + "projectDescription": "UiPath models to classify and extract information from images and pdfs.", + "tenantName": "UiPath", + "minAIFabricVersion": "22.10", + "languageVersion": 4, + "version": 35, + "customVersion": "24.10.4", + "imagePath": "du-semistructured:v24.10-6.16-rc04", + "parametersFileJSON": "{\"params\": [{\"name\": \"auto_retraining\",\"type\": [\"PIPELINE\"],\"value\": \"False\",\"help\": \"Enable this option when the Pipeline you are creating is a scheduled automatic pipeline, synchronized with automatic exports from Data Manager.\",\"options\": [\"True\",\"False\"]}],\"probes\": [{\"probe\": \"startup\",\"initialDelaySeconds\": \"120\",\"periodSeconds\": \"10\",\"timeoutSeconds\": \"2\",\"successThreshold\": \"1\",\"failureThreshold\": \"60\"}]}", + "settings": [ + { + "key": "package_type", + "type": "STRING", + "value": "acord25" + } + ] +} diff --git a/metadata/Acord25__36__metadata.json b/metadata/Acord25__36__metadata.json new file mode 100644 index 00000000..e635c7c4 --- /dev/null +++ b/metadata/Acord25__36__metadata.json @@ -0,0 +1,34 @@ +{ + "name": "Acord25", + "retrainable": true, + "gpu": 0, + "processorType": "CPU", + "description": "Machine Learning model for extracting commonly occurring data points from Acord25 documents, including header fields and line items. Please see more details including supported languages and link to Activities guide in the About Licensing -> Document Understanding API Key section of the UiPath Automation Cloud Guide here: https://docs.uipath.com/automation-cloud/docs/about-licensing#section-document-understanding-api-key", + "inputDescription": "ML Skills deployed using this ML Package can be integrated into RPA workflows using the Machine Learning Extractor activity in the Official feed or the MLModel activity in Connect feed. File formats accepted include pdf, tiff, jpg or png files. The activities also require an API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -> Other Services view.", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "changeLog": "", + "cpu": 1, + "inputType": "JSON", + "displayName": "Acord25", + "memory": 0, + "mlPackageLanguage": "PYTHON37_DU", + "projectId": "[project-id]", + "stagingUri": "[staging-uri]", + "projectName": "UiPath Document Understanding", + "projectDescription": "UiPath models to classify and extract information from images and pdfs.", + "tenantName": "UiPath", + "minAIFabricVersion": "22.10", + "languageVersion": 4, + "version": 36, + "customVersion": "23.10.10", + "imagePath": "du-semistructured:v23.10-06.05-rc05", + "parametersFileJSON": "{\"params\": [{\"name\": \"auto_retraining\",\"type\": [\"PIPELINE\"],\"value\": \"False\",\"help\": \"Enable this option when the Pipeline you are creating is a scheduled automatic pipeline, synchronized with automatic exports from Data Manager.\",\"options\": [\"True\",\"False\"]}],\"probes\": [{\"probe\": \"startup\",\"initialDelaySeconds\": \"120\",\"periodSeconds\": \"10\",\"timeoutSeconds\": \"2\",\"successThreshold\": \"1\",\"failureThreshold\": \"60\"}]}", + "settings": [ + { + "key": "package_type", + "type": "STRING", + "value": "acord25" + } + ], + "maxAIFabricVersion": "24.10" +} diff --git a/metadata/Acord25__37__metadata.json b/metadata/Acord25__37__metadata.json new file mode 100644 index 00000000..5bdb6d1e --- /dev/null +++ b/metadata/Acord25__37__metadata.json @@ -0,0 +1,33 @@ +{ + "name": "Acord25", + "retrainable": true, + "gpu": 0, + "processorType": "CPU", + "description": "Machine Learning model for extracting commonly occurring data points from Acord25 documents, including header fields and line items. Please see more details including supported languages and link to Activities guide in the About Licensing -> Document Understanding API Key section of the UiPath Automation Cloud Guide here: https://docs.uipath.com/automation-cloud/docs/about-licensing#section-document-understanding-api-key", + "inputDescription": "ML Skills deployed using this ML Package can be integrated into RPA workflows using the Machine Learning Extractor activity in the Official feed or the MLModel activity in Connect feed. File formats accepted include pdf, tiff, jpg or png files. The activities also require an API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -> Other Services view.", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "changeLog": "", + "cpu": 1, + "inputType": "JSON", + "displayName": "Acord25", + "memory": 0, + "mlPackageLanguage": "PYTHON37_DU", + "projectId": "[project-id]", + "stagingUri": "[staging-uri]", + "projectName": "UiPath Document Understanding", + "projectDescription": "UiPath models to classify and extract information from images and pdfs.", + "tenantName": "UiPath", + "minAIFabricVersion": "22.10", + "languageVersion": 4, + "version": 37, + "customVersion": "24.10.5", + "imagePath": "du-semistructured:v24.10-8.25-rc02", + "parametersFileJSON": "{\"params\": [{\"name\": \"auto_retraining\",\"type\": [\"PIPELINE\"],\"value\": \"False\",\"help\": \"Enable this option when the Pipeline you are creating is a scheduled automatic pipeline, synchronized with automatic exports from Data Manager.\",\"options\": [\"True\",\"False\"]}],\"probes\": [{\"probe\": \"startup\",\"initialDelaySeconds\": \"120\",\"periodSeconds\": \"10\",\"timeoutSeconds\": \"2\",\"successThreshold\": \"1\",\"failureThreshold\": \"60\"}]}", + "settings": [ + { + "key": "package_type", + "type": "STRING", + "value": "acord25" + } + ] +} diff --git a/metadata/Acord25__38__metadata.json b/metadata/Acord25__38__metadata.json new file mode 100644 index 00000000..c4e7214f --- /dev/null +++ b/metadata/Acord25__38__metadata.json @@ -0,0 +1,34 @@ +{ + "name": "Acord25", + "retrainable": true, + "gpu": 0, + "processorType": "CPU", + "description": "Machine Learning model for extracting commonly occurring data points from Acord25 documents, including header fields and line items. Please see more details including supported languages and link to Activities guide in the About Licensing -> Document Understanding API Key section of the UiPath Automation Cloud Guide here: https://docs.uipath.com/automation-cloud/docs/about-licensing#section-document-understanding-api-key", + "inputDescription": "ML Skills deployed using this ML Package can be integrated into RPA workflows using the Machine Learning Extractor activity in the Official feed or the MLModel activity in Connect feed. File formats accepted include pdf, tiff, jpg or png files. The activities also require an API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -> Other Services view.", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "changeLog": "", + "cpu": 1, + "inputType": "JSON", + "displayName": "Acord25", + "memory": 0, + "mlPackageLanguage": "PYTHON37_DU", + "projectId": "[project-id]", + "stagingUri": "[staging-uri]", + "projectName": "UiPath Document Understanding", + "projectDescription": "UiPath models to classify and extract information from images and pdfs.", + "tenantName": "UiPath", + "minAIFabricVersion": "22.10", + "languageVersion": 4, + "version": 38, + "customVersion": "23.10.11", + "imagePath": "du-semistructured:v23.10-08.25-rc02", + "parametersFileJSON": "{\"params\": [{\"name\": \"auto_retraining\",\"type\": [\"PIPELINE\"],\"value\": \"False\",\"help\": \"Enable this option when the Pipeline you are creating is a scheduled automatic pipeline, synchronized with automatic exports from Data Manager.\",\"options\": [\"True\",\"False\"]}],\"probes\": [{\"probe\": \"startup\",\"initialDelaySeconds\": \"120\",\"periodSeconds\": \"10\",\"timeoutSeconds\": \"2\",\"successThreshold\": \"1\",\"failureThreshold\": \"60\"}]}", + "settings": [ + { + "key": "package_type", + "type": "STRING", + "value": "acord25" + } + ], + "maxAIFabricVersion": "24.10" +} diff --git a/metadata/Acord25__39__metadata.json b/metadata/Acord25__39__metadata.json new file mode 100644 index 00000000..e1186c9c --- /dev/null +++ b/metadata/Acord25__39__metadata.json @@ -0,0 +1,33 @@ +{ + "name": "Acord25", + "retrainable": true, + "gpu": 0, + "processorType": "CPU", + "description": "Machine Learning model for extracting commonly occurring data points from Acord25 documents, including header fields and line items. Please see more details including supported languages and link to Activities guide in the About Licensing -> Document Understanding API Key section of the UiPath Automation Cloud Guide here: https://docs.uipath.com/automation-cloud/docs/about-licensing#section-document-understanding-api-key", + "inputDescription": "ML Skills deployed using this ML Package can be integrated into RPA workflows using the Machine Learning Extractor activity in the Official feed or the MLModel activity in Connect feed. File formats accepted include pdf, tiff, jpg or png files. The activities also require an API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -> Other Services view.", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "changeLog": "", + "cpu": 1, + "inputType": "JSON", + "displayName": "Acord25", + "memory": 0, + "mlPackageLanguage": "PYTHON37_DU", + "projectId": "[project-id]", + "stagingUri": "[staging-uri]", + "projectName": "UiPath Document Understanding", + "projectDescription": "UiPath models to classify and extract information from images and pdfs.", + "tenantName": "UiPath", + "minAIFabricVersion": "22.10", + "languageVersion": 4, + "version": 39, + "customVersion": "25.10.0", + "imagePath": "du-semistructured:v25.10-10.17-rc04", + "parametersFileJSON": "{\"params\": [{\"name\": \"auto_retraining\",\"type\": [\"PIPELINE\"],\"value\": \"False\",\"help\": \"Enable this option when the Pipeline you are creating is a scheduled automatic pipeline, synchronized with automatic exports from Data Manager.\",\"options\": [\"True\",\"False\"]}],\"probes\": [{\"probe\": \"startup\",\"initialDelaySeconds\": \"120\",\"periodSeconds\": \"10\",\"timeoutSeconds\": \"2\",\"successThreshold\": \"1\",\"failureThreshold\": \"60\"}]}", + "settings": [ + { + "key": "package_type", + "type": "STRING", + "value": "acord25" + } + ] +} diff --git a/metadata/AustralianInvoices__1__metadata.json b/metadata/AustralianInvoices__10__metadata.json similarity index 60% rename from metadata/AustralianInvoices__1__metadata.json rename to metadata/AustralianInvoices__10__metadata.json index 61b06cc8..18d50274 100644 --- a/metadata/AustralianInvoices__1__metadata.json +++ b/metadata/AustralianInvoices__10__metadata.json @@ -1,21 +1,26 @@ { - "changeLog": "", - "cpu": 0, - "description": "Machine Learning model for extracting commonly occurring data points from Australian Invoices, including header fields and line items. Please see more details including supported languages and link to Activities guide in the About Licensing -> Document Understanding API Key section of the UiPath Automation Cloud Guide here: https://docs.uipath.com/automation-cloud/docs/about-licensing#section-document-understanding-api-key", - "displayName": "AustralianInvoices", + "name": "AustralianInvoices", + "retrainable": true, "gpu": 0, + "processorType": "CPU", + "description": "Machine Learning model for extracting commonly occurring data points from Invoices from Australia, including header fields and line items. Please see more details including supported languages in the ML Packages section of the Document Understanding Guide: https://docs.uipath.com/document-understanding/docs/ml-packages", "inputDescription": "ML Skills deployed using this ML Package can be integrated into RPA workflows using the Machine Learning Extractor activity in the Official feed or the MLModel activity in Connect feed. File formats accepted include pdf, tiff, jpg or png files. The activities also require an API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -> Other Services view.", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "changeLog": "", + "cpu": 1, "inputType": "JSON", + "displayName": "AustralianInvoices", "memory": 0, - "mlPackageLanguage": "PYTHON36_DU", - "name": "AustralianInvoices", - "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", - "processorType": "GPU", - "projectId": "[project-id]", - "retrainable": true, + "mlPackageLanguage": "PYTHON37_DU", + "projectId": "[project-id]", "stagingUri": "[staging-uri]", "projectName": "UiPath Document Understanding", "projectDescription": "UiPath models to classify and extract information from images and pdfs.", "tenantName": "UiPath", - "imagePath": "registry.replicated.com/aif-core/australianinvoices:1" -} + "minAIFabricVersion": "22.4", + "languageVersion": 4, + "version": 10, + "customVersion": "22.4.1", + "contentUri": "https:///publicmodels/AIC/AustralianInvoices/22.4.1/invoices_au_package.zip", + "maxAIFabricVersion": "22.4" +} \ No newline at end of file diff --git a/metadata/AustralianInvoices__29__metadata.json b/metadata/AustralianInvoices__29__metadata.json new file mode 100644 index 00000000..885ebe7d --- /dev/null +++ b/metadata/AustralianInvoices__29__metadata.json @@ -0,0 +1,34 @@ +{ + "name": "AustralianInvoices", + "retrainable": true, + "gpu": 0, + "processorType": "CPU", + "description": "Machine Learning model for extracting commonly occurring data points from Invoices from Australia, including header fields and line items. Please see more details including supported languages in the ML Packages section of the Document Understanding Guide: https://docs.uipath.com/document-understanding/docs/ml-packages", + "inputDescription": "ML Skills deployed using this ML Package can be integrated into RPA workflows using the Machine Learning Extractor activity in the Official feed or the MLModel activity in Connect feed. File formats accepted include pdf, tiff, jpg or png files. The activities also require an API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -> Other Services view.", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "changeLog": "", + "cpu": 1, + "inputType": "JSON", + "displayName": "AustralianInvoices", + "memory": 0, + "mlPackageLanguage": "PYTHON37_DU", + "projectId": "[project-id]", + "stagingUri": "[staging-uri]", + "projectName": "UiPath Document Understanding", + "projectDescription": "UiPath models to classify and extract information from images and pdfs.", + "tenantName": "UiPath", + "minAIFabricVersion": "22.10", + "languageVersion": 4, + "version": 29, + "customVersion": "22.10.14", + "imagePath": "du-semistructured:v22.10-10.10-rc01", + "parametersFileJSON": "{\"params\": [{\"name\": \"auto_retraining\",\"type\": [\"PIPELINE\"],\"value\": \"False\",\"help\": \"Enable this option when the Pipeline you are creating is a scheduled automatic pipeline, synchronized with automatic exports from Data Manager.\",\"options\": [\"True\",\"False\"]}],\"probes\": [{\"probe\": \"startup\",\"initialDelaySeconds\": \"120\",\"periodSeconds\": \"10\",\"timeoutSeconds\": \"2\",\"successThreshold\": \"1\",\"failureThreshold\": \"60\"}]}", + "settings": [ + { + "key": "package_type", + "type": "STRING", + "value": "invoices_au" + } + ], + "maxAIFabricVersion": "23.4" +} \ No newline at end of file diff --git a/metadata/AustralianInvoices__32__metadata.json b/metadata/AustralianInvoices__32__metadata.json new file mode 100644 index 00000000..e15a3641 --- /dev/null +++ b/metadata/AustralianInvoices__32__metadata.json @@ -0,0 +1,34 @@ +{ + "name": "AustralianInvoices", + "retrainable": true, + "gpu": 0, + "processorType": "CPU", + "description": "Machine Learning model for extracting commonly occurring data points from Invoices from Australia, including header fields and line items. Please see more details including supported languages in the ML Packages section of the Document Understanding Guide: https://docs.uipath.com/document-understanding/docs/ml-packages", + "inputDescription": "ML Skills deployed using this ML Package can be integrated into RPA workflows using the Machine Learning Extractor activity in the Official feed or the MLModel activity in Connect feed. File formats accepted include pdf, tiff, jpg or png files. The activities also require an API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -> Other Services view.", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "changeLog": "", + "cpu": 1, + "inputType": "JSON", + "displayName": "AustralianInvoices", + "memory": 0, + "mlPackageLanguage": "PYTHON37_DU", + "projectId": "[project-id]", + "stagingUri": "[staging-uri]", + "projectName": "UiPath Document Understanding", + "projectDescription": "UiPath models to classify and extract information from images and pdfs.", + "tenantName": "UiPath", + "minAIFabricVersion": "22.10", + "languageVersion": 4, + "version": 32, + "customVersion": "23.4.11", + "imagePath": "du-semistructured:v23.4-01.27-rc01", + "parametersFileJSON": "{\"params\": [{\"name\": \"auto_retraining\",\"type\": [\"PIPELINE\"],\"value\": \"False\",\"help\": \"Enable this option when the Pipeline you are creating is a scheduled automatic pipeline, synchronized with automatic exports from Data Manager.\",\"options\": [\"True\",\"False\"]}],\"probes\": [{\"probe\": \"startup\",\"initialDelaySeconds\": \"120\",\"periodSeconds\": \"10\",\"timeoutSeconds\": \"2\",\"successThreshold\": \"1\",\"failureThreshold\": \"60\"}]}", + "settings": [ + { + "key": "package_type", + "type": "STRING", + "value": "invoices_au" + } + ], + "maxAIFabricVersion": "23.10" +} \ No newline at end of file diff --git a/metadata/AustralianInvoices__33__metadata.json b/metadata/AustralianInvoices__33__metadata.json new file mode 100644 index 00000000..d7b48e4e --- /dev/null +++ b/metadata/AustralianInvoices__33__metadata.json @@ -0,0 +1,34 @@ +{ + "name": "AustralianInvoices", + "retrainable": true, + "gpu": 0, + "processorType": "CPU", + "description": "Machine Learning model for extracting commonly occurring data points from Invoices from Australia, including header fields and line items. Please see more details including supported languages in the ML Packages section of the Document Understanding Guide: https://docs.uipath.com/document-understanding/docs/ml-packages", + "inputDescription": "ML Skills deployed using this ML Package can be integrated into RPA workflows using the Machine Learning Extractor activity in the Official feed or the MLModel activity in Connect feed. File formats accepted include pdf, tiff, jpg or png files. The activities also require an API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -> Other Services view.", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "changeLog": "", + "cpu": 1, + "inputType": "JSON", + "displayName": "AustralianInvoices", + "memory": 0, + "mlPackageLanguage": "PYTHON37_DU", + "projectId": "[project-id]", + "stagingUri": "[staging-uri]", + "projectName": "UiPath Document Understanding", + "projectDescription": "UiPath models to classify and extract information from images and pdfs.", + "tenantName": "UiPath", + "minAIFabricVersion": "22.10", + "languageVersion": 4, + "version": 33, + "customVersion": "23.4.12", + "imagePath": "du-semistructured:v23.4-03.12-rc02", + "parametersFileJSON": "{\"params\": [{\"name\": \"auto_retraining\",\"type\": [\"PIPELINE\"],\"value\": \"False\",\"help\": \"Enable this option when the Pipeline you are creating is a scheduled automatic pipeline, synchronized with automatic exports from Data Manager.\",\"options\": [\"True\",\"False\"]}],\"probes\": [{\"probe\": \"startup\",\"initialDelaySeconds\": \"120\",\"periodSeconds\": \"10\",\"timeoutSeconds\": \"2\",\"successThreshold\": \"1\",\"failureThreshold\": \"60\"}]}", + "settings": [ + { + "key": "package_type", + "type": "STRING", + "value": "invoices_au" + } + ], + "maxAIFabricVersion": "23.10" +} diff --git a/metadata/AustralianInvoices__3__metadata.json b/metadata/AustralianInvoices__3__metadata.json deleted file mode 100644 index 02617853..00000000 --- a/metadata/AustralianInvoices__3__metadata.json +++ /dev/null @@ -1,21 +0,0 @@ -{ - "name": "AustralianInvoices", - "retrainable": true, - "gpu": 1, - "processorType": "GPU", - "description": "Machine Learning model(available in Preview) for extracting commonly occurring data points from Invoices from Australia, including header fields and line items. Please see more details including supported languages and link to Activities guide in the About Licensing -\u003e Document Understanding API Key section of the UiPath Automation Cloud Guide here: https://docs.uipath.com/automation-cloud/docs/about-licensing#section-document-understanding-api-key", - "inputDescription": "ML Skills deployed using this ML Package can be integrated into RPA workflows using the Machine Learning Extractor activity in the Official feed or the MLModel activity in Connect feed. File formats accepted include pdf, tiff, jpg or png files. The activities also require an API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -\u003e Other Services view.", - "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", - "changeLog": "Model: 20.10.4", - "cpu": 0, - "inputType": "JSON", - "displayName": "AustralianInvoices", - "memory": 0, - "mlPackageLanguage": "PYTHON37_DU", - "projectId": "[project-id]", - "stagingUri": "[staging-uri]", - "projectName": "UiPath Document Understanding", - "projectDescription": "UiPath models to classify and extract information from images and pdfs.", - "tenantName": "UiPath", - "imagePath": "registry.replicated.com/aif-core/australianinvoices:3" -} diff --git a/metadata/BankStatements__23__metadata.json b/metadata/BankStatements__23__metadata.json new file mode 100644 index 00000000..f66d9e00 --- /dev/null +++ b/metadata/BankStatements__23__metadata.json @@ -0,0 +1,33 @@ +{ + "name": "BankStatements", + "retrainable": true, + "gpu": 0, + "processorType": "CPU", + "description": "Machine Learning model for extracting commonly occurring data points from Bank Statements, including header fields and line items. Please see more details including supported languages in the ML Packages section of the Document Understanding Guide: https://docs.uipath.com/document-understanding/docs/ml-packages", + "inputDescription": "ML Skills deployed using this ML Package can be integrated into RPA workflows using the Machine Learning Extractor activity in the Official feed or the MLModel activity in Connect feed. File formats accepted include pdf, tiff, jpg or png files. The activities also require an API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -> Other Services view.", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "cpu": 1, + "inputType": "JSON", + "displayName": "BankStatements", + "memory": 0, + "mlPackageLanguage": "PYTHON37_DU", + "projectId": "[project-id]", + "stagingUri": "[staging-uri]", + "projectName": "UiPath Document Understanding", + "projectDescription": "UiPath models to classify and extract information from images and pdfs.", + "tenantName": "UiPath", + "minAIFabricVersion": "22.10", + "languageVersion": 4, + "version": 23, + "customVersion": "22.10.14", + "imagePath": "du-semistructured:v22.10-10.10-rc01", + "parametersFileJSON": "{\"params\": [{\"name\": \"auto_retraining\",\"type\": [\"PIPELINE\"],\"value\": \"False\",\"help\": \"Enable this option when the Pipeline you are creating is a scheduled automatic pipeline, synchronized with automatic exports from Data Manager.\",\"options\": [\"True\",\"False\"]}],\"probes\": [{\"probe\": \"startup\",\"initialDelaySeconds\": \"120\",\"periodSeconds\": \"10\",\"timeoutSeconds\": \"2\",\"successThreshold\": \"1\",\"failureThreshold\": \"60\"}]}", + "settings": [ + { + "key": "package_type", + "type": "STRING", + "value": "bank_statements" + } + ], + "maxAIFabricVersion": "23.4" +} \ No newline at end of file diff --git a/metadata/BankStatements__29__metadata.json b/metadata/BankStatements__29__metadata.json new file mode 100644 index 00000000..52f69a9f --- /dev/null +++ b/metadata/BankStatements__29__metadata.json @@ -0,0 +1,32 @@ +{ + "name": "BankStatements", + "retrainable": true, + "gpu": 0, + "processorType": "CPU", + "description": "Machine Learning model for extracting commonly occurring data points from Bank Statements, including header fields and line items. Please see more details including supported languages in the ML Packages section of the Document Understanding Guide: https://docs.uipath.com/document-understanding/docs/ml-packages", + "inputDescription": "ML Skills deployed using this ML Package can be integrated into RPA workflows using the Machine Learning Extractor activity in the Official feed or the MLModel activity in Connect feed. File formats accepted include pdf, tiff, jpg or png files. The activities also require an API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -> Other Services view.", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "cpu": 1, + "inputType": "JSON", + "displayName": "BankStatements", + "memory": 0, + "mlPackageLanguage": "PYTHON37_DU", + "projectId": "[project-id]", + "stagingUri": "[staging-uri]", + "projectName": "UiPath Document Understanding", + "projectDescription": "UiPath models to classify and extract information from images and pdfs.", + "tenantName": "UiPath", + "minAIFabricVersion": "22.10", + "languageVersion": 4, + "version": 29, + "customVersion": "24.10.2", + "imagePath": "du-semistructured:v24.10-1.27-rc02", + "parametersFileJSON": "{\"params\": [{\"name\": \"auto_retraining\",\"type\": [\"PIPELINE\"],\"value\": \"False\",\"help\": \"Enable this option when the Pipeline you are creating is a scheduled automatic pipeline, synchronized with automatic exports from Data Manager.\",\"options\": [\"True\",\"False\"]}],\"probes\": [{\"probe\": \"startup\",\"initialDelaySeconds\": \"120\",\"periodSeconds\": \"10\",\"timeoutSeconds\": \"2\",\"successThreshold\": \"1\",\"failureThreshold\": \"60\"}]}", + "settings": [ + { + "key": "package_type", + "type": "STRING", + "value": "bank_statements" + } + ] +} \ No newline at end of file diff --git a/metadata/BankStatements__30__metadata.json b/metadata/BankStatements__30__metadata.json new file mode 100644 index 00000000..c5e82694 --- /dev/null +++ b/metadata/BankStatements__30__metadata.json @@ -0,0 +1,33 @@ +{ + "name": "BankStatements", + "retrainable": true, + "gpu": 0, + "processorType": "CPU", + "description": "Machine Learning model for extracting commonly occurring data points from Bank Statements, including header fields and line items. Please see more details including supported languages in the ML Packages section of the Document Understanding Guide: https://docs.uipath.com/document-understanding/docs/ml-packages", + "inputDescription": "ML Skills deployed using this ML Package can be integrated into RPA workflows using the Machine Learning Extractor activity in the Official feed or the MLModel activity in Connect feed. File formats accepted include pdf, tiff, jpg or png files. The activities also require an API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -> Other Services view.", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "cpu": 1, + "inputType": "JSON", + "displayName": "BankStatements", + "memory": 0, + "mlPackageLanguage": "PYTHON37_DU", + "projectId": "[project-id]", + "stagingUri": "[staging-uri]", + "projectName": "UiPath Document Understanding", + "projectDescription": "UiPath models to classify and extract information from images and pdfs.", + "tenantName": "UiPath", + "minAIFabricVersion": "22.10", + "languageVersion": 4, + "version": 30, + "customVersion": "23.4.11", + "imagePath": "du-semistructured:v23.4-01.27-rc01", + "parametersFileJSON": "{\"params\": [{\"name\": \"auto_retraining\",\"type\": [\"PIPELINE\"],\"value\": \"False\",\"help\": \"Enable this option when the Pipeline you are creating is a scheduled automatic pipeline, synchronized with automatic exports from Data Manager.\",\"options\": [\"True\",\"False\"]}],\"probes\": [{\"probe\": \"startup\",\"initialDelaySeconds\": \"120\",\"periodSeconds\": \"10\",\"timeoutSeconds\": \"2\",\"successThreshold\": \"1\",\"failureThreshold\": \"60\"}]}", + "settings": [ + { + "key": "package_type", + "type": "STRING", + "value": "bank_statements" + } + ], + "maxAIFabricVersion": "23.10" +} \ No newline at end of file diff --git a/metadata/BankStatements__31__metadata.json b/metadata/BankStatements__31__metadata.json new file mode 100644 index 00000000..0c33adfc --- /dev/null +++ b/metadata/BankStatements__31__metadata.json @@ -0,0 +1,33 @@ +{ + "name": "BankStatements", + "retrainable": true, + "gpu": 0, + "processorType": "CPU", + "description": "Machine Learning model for extracting commonly occurring data points from Bank Statements, including header fields and line items. Please see more details including supported languages in the ML Packages section of the Document Understanding Guide: https://docs.uipath.com/document-understanding/docs/ml-packages", + "inputDescription": "ML Skills deployed using this ML Package can be integrated into RPA workflows using the Machine Learning Extractor activity in the Official feed or the MLModel activity in Connect feed. File formats accepted include pdf, tiff, jpg or png files. The activities also require an API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -> Other Services view.", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "cpu": 1, + "inputType": "JSON", + "displayName": "BankStatements", + "memory": 0, + "mlPackageLanguage": "PYTHON37_DU", + "projectId": "[project-id]", + "stagingUri": "[staging-uri]", + "projectName": "UiPath Document Understanding", + "projectDescription": "UiPath models to classify and extract information from images and pdfs.", + "tenantName": "UiPath", + "minAIFabricVersion": "22.10", + "languageVersion": 4, + "version": 31, + "customVersion": "23.10.8", + "imagePath": "du-semistructured:v23.10-01.28-rc07", + "parametersFileJSON": "{\"params\": [{\"name\": \"auto_retraining\",\"type\": [\"PIPELINE\"],\"value\": \"False\",\"help\": \"Enable this option when the Pipeline you are creating is a scheduled automatic pipeline, synchronized with automatic exports from Data Manager.\",\"options\": [\"True\",\"False\"]}],\"probes\": [{\"probe\": \"startup\",\"initialDelaySeconds\": \"120\",\"periodSeconds\": \"10\",\"timeoutSeconds\": \"2\",\"successThreshold\": \"1\",\"failureThreshold\": \"60\"}]}", + "settings": [ + { + "key": "package_type", + "type": "STRING", + "value": "bank_statements" + } + ], + "maxAIFabricVersion": "24.10" +} \ No newline at end of file diff --git a/metadata/BankStatements__32__metadata.json b/metadata/BankStatements__32__metadata.json new file mode 100644 index 00000000..c1edda28 --- /dev/null +++ b/metadata/BankStatements__32__metadata.json @@ -0,0 +1,32 @@ +{ + "name": "BankStatements", + "retrainable": true, + "gpu": 0, + "processorType": "CPU", + "description": "Machine Learning model for extracting commonly occurring data points from Bank Statements, including header fields and line items. Please see more details including supported languages in the ML Packages section of the Document Understanding Guide: https://docs.uipath.com/document-understanding/docs/ml-packages", + "inputDescription": "ML Skills deployed using this ML Package can be integrated into RPA workflows using the Machine Learning Extractor activity in the Official feed or the MLModel activity in Connect feed. File formats accepted include pdf, tiff, jpg or png files. The activities also require an API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -> Other Services view.", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "cpu": 1, + "inputType": "JSON", + "displayName": "BankStatements", + "memory": 0, + "mlPackageLanguage": "PYTHON37_DU", + "projectId": "[project-id]", + "stagingUri": "[staging-uri]", + "projectName": "UiPath Document Understanding", + "projectDescription": "UiPath models to classify and extract information from images and pdfs.", + "tenantName": "UiPath", + "minAIFabricVersion": "22.10", + "languageVersion": 4, + "version": 32, + "customVersion": "24.10.3", + "imagePath": "du-semistructured:v24.10-3.27-rc02", + "parametersFileJSON": "{\"params\": [{\"name\": \"auto_retraining\",\"type\": [\"PIPELINE\"],\"value\": \"False\",\"help\": \"Enable this option when the Pipeline you are creating is a scheduled automatic pipeline, synchronized with automatic exports from Data Manager.\",\"options\": [\"True\",\"False\"]}],\"probes\": [{\"probe\": \"startup\",\"initialDelaySeconds\": \"120\",\"periodSeconds\": \"10\",\"timeoutSeconds\": \"2\",\"successThreshold\": \"1\",\"failureThreshold\": \"60\"}]}", + "settings": [ + { + "key": "package_type", + "type": "STRING", + "value": "bank_statements" + } + ] +} diff --git a/metadata/BankStatements__33__metadata.json b/metadata/BankStatements__33__metadata.json new file mode 100644 index 00000000..0d3ff252 --- /dev/null +++ b/metadata/BankStatements__33__metadata.json @@ -0,0 +1,33 @@ +{ + "name": "BankStatements", + "retrainable": true, + "gpu": 0, + "processorType": "CPU", + "description": "Machine Learning model for extracting commonly occurring data points from Bank Statements, including header fields and line items. Please see more details including supported languages in the ML Packages section of the Document Understanding Guide: https://docs.uipath.com/document-understanding/docs/ml-packages", + "inputDescription": "ML Skills deployed using this ML Package can be integrated into RPA workflows using the Machine Learning Extractor activity in the Official feed or the MLModel activity in Connect feed. File formats accepted include pdf, tiff, jpg or png files. The activities also require an API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -> Other Services view.", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "cpu": 1, + "inputType": "JSON", + "displayName": "BankStatements", + "memory": 0, + "mlPackageLanguage": "PYTHON37_DU", + "projectId": "[project-id]", + "stagingUri": "[staging-uri]", + "projectName": "UiPath Document Understanding", + "projectDescription": "UiPath models to classify and extract information from images and pdfs.", + "tenantName": "UiPath", + "minAIFabricVersion": "22.10", + "languageVersion": 4, + "version": 33, + "customVersion": "23.10.9", + "imagePath": "du-semistructured:v23.10-03.14-rc02", + "parametersFileJSON": "{\"params\": [{\"name\": \"auto_retraining\",\"type\": [\"PIPELINE\"],\"value\": \"False\",\"help\": \"Enable this option when the Pipeline you are creating is a scheduled automatic pipeline, synchronized with automatic exports from Data Manager.\",\"options\": [\"True\",\"False\"]}],\"probes\": [{\"probe\": \"startup\",\"initialDelaySeconds\": \"120\",\"periodSeconds\": \"10\",\"timeoutSeconds\": \"2\",\"successThreshold\": \"1\",\"failureThreshold\": \"60\"}]}", + "settings": [ + { + "key": "package_type", + "type": "STRING", + "value": "bank_statements" + } + ], + "maxAIFabricVersion": "24.10" +} diff --git a/metadata/BankStatements__34__metadata.json b/metadata/BankStatements__34__metadata.json new file mode 100644 index 00000000..1972941c --- /dev/null +++ b/metadata/BankStatements__34__metadata.json @@ -0,0 +1,33 @@ +{ + "name": "BankStatements", + "retrainable": true, + "gpu": 0, + "processorType": "CPU", + "description": "Machine Learning model for extracting commonly occurring data points from Bank Statements, including header fields and line items. Please see more details including supported languages in the ML Packages section of the Document Understanding Guide: https://docs.uipath.com/document-understanding/docs/ml-packages", + "inputDescription": "ML Skills deployed using this ML Package can be integrated into RPA workflows using the Machine Learning Extractor activity in the Official feed or the MLModel activity in Connect feed. File formats accepted include pdf, tiff, jpg or png files. The activities also require an API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -> Other Services view.", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "cpu": 1, + "inputType": "JSON", + "displayName": "BankStatements", + "memory": 0, + "mlPackageLanguage": "PYTHON37_DU", + "projectId": "[project-id]", + "stagingUri": "[staging-uri]", + "projectName": "UiPath Document Understanding", + "projectDescription": "UiPath models to classify and extract information from images and pdfs.", + "tenantName": "UiPath", + "minAIFabricVersion": "22.10", + "languageVersion": 4, + "version": 34, + "customVersion": "23.4.12", + "imagePath": "du-semistructured:v23.4-03.12-rc02", + "parametersFileJSON": "{\"params\": [{\"name\": \"auto_retraining\",\"type\": [\"PIPELINE\"],\"value\": \"False\",\"help\": \"Enable this option when the Pipeline you are creating is a scheduled automatic pipeline, synchronized with automatic exports from Data Manager.\",\"options\": [\"True\",\"False\"]}],\"probes\": [{\"probe\": \"startup\",\"initialDelaySeconds\": \"120\",\"periodSeconds\": \"10\",\"timeoutSeconds\": \"2\",\"successThreshold\": \"1\",\"failureThreshold\": \"60\"}]}", + "settings": [ + { + "key": "package_type", + "type": "STRING", + "value": "bank_statements" + } + ], + "maxAIFabricVersion": "23.10" +} diff --git a/metadata/BankStatements__35__metadata.json b/metadata/BankStatements__35__metadata.json new file mode 100644 index 00000000..9ae5ac9a --- /dev/null +++ b/metadata/BankStatements__35__metadata.json @@ -0,0 +1,32 @@ +{ + "name": "BankStatements", + "retrainable": true, + "gpu": 0, + "processorType": "CPU", + "description": "Machine Learning model for extracting commonly occurring data points from Bank Statements, including header fields and line items. Please see more details including supported languages in the ML Packages section of the Document Understanding Guide: https://docs.uipath.com/document-understanding/docs/ml-packages", + "inputDescription": "ML Skills deployed using this ML Package can be integrated into RPA workflows using the Machine Learning Extractor activity in the Official feed or the MLModel activity in Connect feed. File formats accepted include pdf, tiff, jpg or png files. The activities also require an API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -> Other Services view.", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "cpu": 1, + "inputType": "JSON", + "displayName": "BankStatements", + "memory": 0, + "mlPackageLanguage": "PYTHON37_DU", + "projectId": "[project-id]", + "stagingUri": "[staging-uri]", + "projectName": "UiPath Document Understanding", + "projectDescription": "UiPath models to classify and extract information from images and pdfs.", + "tenantName": "UiPath", + "minAIFabricVersion": "22.10", + "languageVersion": 4, + "version": 35, + "customVersion": "24.10.4", + "imagePath": "du-semistructured:v24.10-6.16-rc04", + "parametersFileJSON": "{\"params\": [{\"name\": \"auto_retraining\",\"type\": [\"PIPELINE\"],\"value\": \"False\",\"help\": \"Enable this option when the Pipeline you are creating is a scheduled automatic pipeline, synchronized with automatic exports from Data Manager.\",\"options\": [\"True\",\"False\"]}],\"probes\": [{\"probe\": \"startup\",\"initialDelaySeconds\": \"120\",\"periodSeconds\": \"10\",\"timeoutSeconds\": \"2\",\"successThreshold\": \"1\",\"failureThreshold\": \"60\"}]}", + "settings": [ + { + "key": "package_type", + "type": "STRING", + "value": "bank_statements" + } + ] +} diff --git a/metadata/BankStatements__36__metadata.json b/metadata/BankStatements__36__metadata.json new file mode 100644 index 00000000..2c37f9ac --- /dev/null +++ b/metadata/BankStatements__36__metadata.json @@ -0,0 +1,33 @@ +{ + "name": "BankStatements", + "retrainable": true, + "gpu": 0, + "processorType": "CPU", + "description": "Machine Learning model for extracting commonly occurring data points from Bank Statements, including header fields and line items. Please see more details including supported languages in the ML Packages section of the Document Understanding Guide: https://docs.uipath.com/document-understanding/docs/ml-packages", + "inputDescription": "ML Skills deployed using this ML Package can be integrated into RPA workflows using the Machine Learning Extractor activity in the Official feed or the MLModel activity in Connect feed. File formats accepted include pdf, tiff, jpg or png files. The activities also require an API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -> Other Services view.", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "cpu": 1, + "inputType": "JSON", + "displayName": "BankStatements", + "memory": 0, + "mlPackageLanguage": "PYTHON37_DU", + "projectId": "[project-id]", + "stagingUri": "[staging-uri]", + "projectName": "UiPath Document Understanding", + "projectDescription": "UiPath models to classify and extract information from images and pdfs.", + "tenantName": "UiPath", + "minAIFabricVersion": "22.10", + "languageVersion": 4, + "version": 36, + "customVersion": "23.10.10", + "imagePath": "du-semistructured:v23.10-06.05-rc05", + "parametersFileJSON": "{\"params\": [{\"name\": \"auto_retraining\",\"type\": [\"PIPELINE\"],\"value\": \"False\",\"help\": \"Enable this option when the Pipeline you are creating is a scheduled automatic pipeline, synchronized with automatic exports from Data Manager.\",\"options\": [\"True\",\"False\"]}],\"probes\": [{\"probe\": \"startup\",\"initialDelaySeconds\": \"120\",\"periodSeconds\": \"10\",\"timeoutSeconds\": \"2\",\"successThreshold\": \"1\",\"failureThreshold\": \"60\"}]}", + "settings": [ + { + "key": "package_type", + "type": "STRING", + "value": "bank_statements" + } + ], + "maxAIFabricVersion": "24.10" +} diff --git a/metadata/BankStatements__37__metadata.json b/metadata/BankStatements__37__metadata.json new file mode 100644 index 00000000..b88b3160 --- /dev/null +++ b/metadata/BankStatements__37__metadata.json @@ -0,0 +1,32 @@ +{ + "name": "BankStatements", + "retrainable": true, + "gpu": 0, + "processorType": "CPU", + "description": "Machine Learning model for extracting commonly occurring data points from Bank Statements, including header fields and line items. Please see more details including supported languages in the ML Packages section of the Document Understanding Guide: https://docs.uipath.com/document-understanding/docs/ml-packages", + "inputDescription": "ML Skills deployed using this ML Package can be integrated into RPA workflows using the Machine Learning Extractor activity in the Official feed or the MLModel activity in Connect feed. File formats accepted include pdf, tiff, jpg or png files. The activities also require an API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -> Other Services view.", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "cpu": 1, + "inputType": "JSON", + "displayName": "BankStatements", + "memory": 0, + "mlPackageLanguage": "PYTHON37_DU", + "projectId": "[project-id]", + "stagingUri": "[staging-uri]", + "projectName": "UiPath Document Understanding", + "projectDescription": "UiPath models to classify and extract information from images and pdfs.", + "tenantName": "UiPath", + "minAIFabricVersion": "22.10", + "languageVersion": 4, + "version": 37, + "customVersion": "24.10.5", + "imagePath": "du-semistructured:v24.10-8.25-rc02", + "parametersFileJSON": "{\"params\": [{\"name\": \"auto_retraining\",\"type\": [\"PIPELINE\"],\"value\": \"False\",\"help\": \"Enable this option when the Pipeline you are creating is a scheduled automatic pipeline, synchronized with automatic exports from Data Manager.\",\"options\": [\"True\",\"False\"]}],\"probes\": [{\"probe\": \"startup\",\"initialDelaySeconds\": \"120\",\"periodSeconds\": \"10\",\"timeoutSeconds\": \"2\",\"successThreshold\": \"1\",\"failureThreshold\": \"60\"}]}", + "settings": [ + { + "key": "package_type", + "type": "STRING", + "value": "bank_statements" + } + ] +} diff --git a/metadata/BankStatements__38__metadata.json b/metadata/BankStatements__38__metadata.json new file mode 100644 index 00000000..20a75ebb --- /dev/null +++ b/metadata/BankStatements__38__metadata.json @@ -0,0 +1,33 @@ +{ + "name": "BankStatements", + "retrainable": true, + "gpu": 0, + "processorType": "CPU", + "description": "Machine Learning model for extracting commonly occurring data points from Bank Statements, including header fields and line items. Please see more details including supported languages in the ML Packages section of the Document Understanding Guide: https://docs.uipath.com/document-understanding/docs/ml-packages", + "inputDescription": "ML Skills deployed using this ML Package can be integrated into RPA workflows using the Machine Learning Extractor activity in the Official feed or the MLModel activity in Connect feed. File formats accepted include pdf, tiff, jpg or png files. The activities also require an API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -> Other Services view.", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "cpu": 1, + "inputType": "JSON", + "displayName": "BankStatements", + "memory": 0, + "mlPackageLanguage": "PYTHON37_DU", + "projectId": "[project-id]", + "stagingUri": "[staging-uri]", + "projectName": "UiPath Document Understanding", + "projectDescription": "UiPath models to classify and extract information from images and pdfs.", + "tenantName": "UiPath", + "minAIFabricVersion": "22.10", + "languageVersion": 4, + "version": 38, + "customVersion": "23.10.11", + "imagePath": "du-semistructured:v23.10-08.25-rc02", + "parametersFileJSON": "{\"params\": [{\"name\": \"auto_retraining\",\"type\": [\"PIPELINE\"],\"value\": \"False\",\"help\": \"Enable this option when the Pipeline you are creating is a scheduled automatic pipeline, synchronized with automatic exports from Data Manager.\",\"options\": [\"True\",\"False\"]}],\"probes\": [{\"probe\": \"startup\",\"initialDelaySeconds\": \"120\",\"periodSeconds\": \"10\",\"timeoutSeconds\": \"2\",\"successThreshold\": \"1\",\"failureThreshold\": \"60\"}]}", + "settings": [ + { + "key": "package_type", + "type": "STRING", + "value": "bank_statements" + } + ], + "maxAIFabricVersion": "24.10" +} diff --git a/metadata/BankStatements__39__metadata.json b/metadata/BankStatements__39__metadata.json new file mode 100644 index 00000000..847547b8 --- /dev/null +++ b/metadata/BankStatements__39__metadata.json @@ -0,0 +1,32 @@ +{ + "name": "BankStatements", + "retrainable": true, + "gpu": 0, + "processorType": "CPU", + "description": "Machine Learning model for extracting commonly occurring data points from Bank Statements, including header fields and line items. Please see more details including supported languages in the ML Packages section of the Document Understanding Guide: https://docs.uipath.com/document-understanding/docs/ml-packages", + "inputDescription": "ML Skills deployed using this ML Package can be integrated into RPA workflows using the Machine Learning Extractor activity in the Official feed or the MLModel activity in Connect feed. File formats accepted include pdf, tiff, jpg or png files. The activities also require an API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -> Other Services view.", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "cpu": 1, + "inputType": "JSON", + "displayName": "BankStatements", + "memory": 0, + "mlPackageLanguage": "PYTHON37_DU", + "projectId": "[project-id]", + "stagingUri": "[staging-uri]", + "projectName": "UiPath Document Understanding", + "projectDescription": "UiPath models to classify and extract information from images and pdfs.", + "tenantName": "UiPath", + "minAIFabricVersion": "22.10", + "languageVersion": 4, + "version": 39, + "customVersion": "25.10.0", + "imagePath": "du-semistructured:v25.10-10.17-rc04", + "parametersFileJSON": "{\"params\": [{\"name\": \"auto_retraining\",\"type\": [\"PIPELINE\"],\"value\": \"False\",\"help\": \"Enable this option when the Pipeline you are creating is a scheduled automatic pipeline, synchronized with automatic exports from Data Manager.\",\"options\": [\"True\",\"False\"]}],\"probes\": [{\"probe\": \"startup\",\"initialDelaySeconds\": \"120\",\"periodSeconds\": \"10\",\"timeoutSeconds\": \"2\",\"successThreshold\": \"1\",\"failureThreshold\": \"60\"}]}", + "settings": [ + { + "key": "package_type", + "type": "STRING", + "value": "bank_statements" + } + ] +} diff --git a/metadata/BillsOfLading__23__metadata.json b/metadata/BillsOfLading__23__metadata.json new file mode 100644 index 00000000..f02f118c --- /dev/null +++ b/metadata/BillsOfLading__23__metadata.json @@ -0,0 +1,33 @@ +{ + "name": "BillsOfLading", + "retrainable": true, + "gpu": 0, + "processorType": "CPU", + "description": "Machine Learning model for extracting commonly occurring data points from Bills of Lading, including header fields and line items. Please see more details including supported languages in the ML Packages section of the Document Understanding Guide: https://docs.uipath.com/document-understanding/docs/ml-packages", + "inputDescription": "ML Skills deployed using this ML Package can be integrated into RPA workflows using the Machine Learning Extractor activity in the Official feed or the MLModel activity in Connect feed. File formats accepted include pdf, tiff, jpg or png files. The activities also require an API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -> Other Services view.", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "cpu": 1, + "inputType": "JSON", + "displayName": "BillsOfLading", + "memory": 0, + "mlPackageLanguage": "PYTHON37_DU", + "projectId": "[project-id]", + "stagingUri": "[staging-uri]", + "projectName": "UiPath Document Understanding", + "projectDescription": "UiPath models to classify and extract information from images and pdfs.", + "tenantName": "UiPath", + "minAIFabricVersion": "22.10", + "languageVersion": 4, + "version": 23, + "customVersion": "22.10.14", + "imagePath": "du-semistructured:v22.10-10.10-rc01", + "parametersFileJSON": "{\"params\": [{\"name\": \"auto_retraining\",\"type\": [\"PIPELINE\"],\"value\": \"False\",\"help\": \"Enable this option when the Pipeline you are creating is a scheduled automatic pipeline, synchronized with automatic exports from Data Manager.\",\"options\": [\"True\",\"False\"]}],\"probes\": [{\"probe\": \"startup\",\"initialDelaySeconds\": \"120\",\"periodSeconds\": \"10\",\"timeoutSeconds\": \"2\",\"successThreshold\": \"1\",\"failureThreshold\": \"60\"}]}", + "settings": [ + { + "key": "package_type", + "type": "STRING", + "value": "bills_of_lading" + } + ], + "maxAIFabricVersion": "23.4" +} \ No newline at end of file diff --git a/metadata/BillsOfLading__29__metadata.json b/metadata/BillsOfLading__29__metadata.json new file mode 100644 index 00000000..302d64c9 --- /dev/null +++ b/metadata/BillsOfLading__29__metadata.json @@ -0,0 +1,32 @@ +{ + "name": "BillsOfLading", + "retrainable": true, + "gpu": 0, + "processorType": "CPU", + "description": "Machine Learning model for extracting commonly occurring data points from Bills of Lading, including header fields and line items. Please see more details including supported languages in the ML Packages section of the Document Understanding Guide: https://docs.uipath.com/document-understanding/docs/ml-packages", + "inputDescription": "ML Skills deployed using this ML Package can be integrated into RPA workflows using the Machine Learning Extractor activity in the Official feed or the MLModel activity in Connect feed. File formats accepted include pdf, tiff, jpg or png files. The activities also require an API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -> Other Services view.", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "cpu": 1, + "inputType": "JSON", + "displayName": "BillsOfLading", + "memory": 0, + "mlPackageLanguage": "PYTHON37_DU", + "projectId": "[project-id]", + "stagingUri": "[staging-uri]", + "projectName": "UiPath Document Understanding", + "projectDescription": "UiPath models to classify and extract information from images and pdfs.", + "tenantName": "UiPath", + "minAIFabricVersion": "22.10", + "languageVersion": 4, + "version": 29, + "customVersion": "24.10.2", + "imagePath": "du-semistructured:v24.10-1.27-rc02", + "parametersFileJSON": "{\"params\": [{\"name\": \"auto_retraining\",\"type\": [\"PIPELINE\"],\"value\": \"False\",\"help\": \"Enable this option when the Pipeline you are creating is a scheduled automatic pipeline, synchronized with automatic exports from Data Manager.\",\"options\": [\"True\",\"False\"]}],\"probes\": [{\"probe\": \"startup\",\"initialDelaySeconds\": \"120\",\"periodSeconds\": \"10\",\"timeoutSeconds\": \"2\",\"successThreshold\": \"1\",\"failureThreshold\": \"60\"}]}", + "settings": [ + { + "key": "package_type", + "type": "STRING", + "value": "bills_of_lading" + } + ] +} \ No newline at end of file diff --git a/metadata/BillsOfLading__30__metadata.json b/metadata/BillsOfLading__30__metadata.json new file mode 100644 index 00000000..316a58bc --- /dev/null +++ b/metadata/BillsOfLading__30__metadata.json @@ -0,0 +1,33 @@ +{ + "name": "BillsOfLading", + "retrainable": true, + "gpu": 0, + "processorType": "CPU", + "description": "Machine Learning model for extracting commonly occurring data points from Bills of Lading, including header fields and line items. Please see more details including supported languages in the ML Packages section of the Document Understanding Guide: https://docs.uipath.com/document-understanding/docs/ml-packages", + "inputDescription": "ML Skills deployed using this ML Package can be integrated into RPA workflows using the Machine Learning Extractor activity in the Official feed or the MLModel activity in Connect feed. File formats accepted include pdf, tiff, jpg or png files. The activities also require an API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -> Other Services view.", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "cpu": 1, + "inputType": "JSON", + "displayName": "BillsOfLading", + "memory": 0, + "mlPackageLanguage": "PYTHON37_DU", + "projectId": "[project-id]", + "stagingUri": "[staging-uri]", + "projectName": "UiPath Document Understanding", + "projectDescription": "UiPath models to classify and extract information from images and pdfs.", + "tenantName": "UiPath", + "minAIFabricVersion": "22.10", + "languageVersion": 4, + "version": 30, + "customVersion": "23.4.11", + "imagePath": "du-semistructured:v23.4-01.27-rc01", + "parametersFileJSON": "{\"params\": [{\"name\": \"auto_retraining\",\"type\": [\"PIPELINE\"],\"value\": \"False\",\"help\": \"Enable this option when the Pipeline you are creating is a scheduled automatic pipeline, synchronized with automatic exports from Data Manager.\",\"options\": [\"True\",\"False\"]}],\"probes\": [{\"probe\": \"startup\",\"initialDelaySeconds\": \"120\",\"periodSeconds\": \"10\",\"timeoutSeconds\": \"2\",\"successThreshold\": \"1\",\"failureThreshold\": \"60\"}]}", + "settings": [ + { + "key": "package_type", + "type": "STRING", + "value": "bills_of_lading" + } + ], + "maxAIFabricVersion": "23.10" +} \ No newline at end of file diff --git a/metadata/BillsOfLading__31__metadata.json b/metadata/BillsOfLading__31__metadata.json new file mode 100644 index 00000000..27a0c452 --- /dev/null +++ b/metadata/BillsOfLading__31__metadata.json @@ -0,0 +1,33 @@ +{ + "name": "BillsOfLading", + "retrainable": true, + "gpu": 0, + "processorType": "CPU", + "description": "Machine Learning model for extracting commonly occurring data points from Bills of Lading, including header fields and line items. Please see more details including supported languages in the ML Packages section of the Document Understanding Guide: https://docs.uipath.com/document-understanding/docs/ml-packages", + "inputDescription": "ML Skills deployed using this ML Package can be integrated into RPA workflows using the Machine Learning Extractor activity in the Official feed or the MLModel activity in Connect feed. File formats accepted include pdf, tiff, jpg or png files. The activities also require an API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -> Other Services view.", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "cpu": 1, + "inputType": "JSON", + "displayName": "BillsOfLading", + "memory": 0, + "mlPackageLanguage": "PYTHON37_DU", + "projectId": "[project-id]", + "stagingUri": "[staging-uri]", + "projectName": "UiPath Document Understanding", + "projectDescription": "UiPath models to classify and extract information from images and pdfs.", + "tenantName": "UiPath", + "minAIFabricVersion": "22.10", + "languageVersion": 4, + "version": 31, + "customVersion": "23.10.8", + "imagePath": "du-semistructured:v23.10-01.28-rc07", + "parametersFileJSON": "{\"params\": [{\"name\": \"auto_retraining\",\"type\": [\"PIPELINE\"],\"value\": \"False\",\"help\": \"Enable this option when the Pipeline you are creating is a scheduled automatic pipeline, synchronized with automatic exports from Data Manager.\",\"options\": [\"True\",\"False\"]}],\"probes\": [{\"probe\": \"startup\",\"initialDelaySeconds\": \"120\",\"periodSeconds\": \"10\",\"timeoutSeconds\": \"2\",\"successThreshold\": \"1\",\"failureThreshold\": \"60\"}]}", + "settings": [ + { + "key": "package_type", + "type": "STRING", + "value": "bills_of_lading" + } + ], + "maxAIFabricVersion": "24.10" +} \ No newline at end of file diff --git a/metadata/BillsOfLading__32__metadata.json b/metadata/BillsOfLading__32__metadata.json new file mode 100644 index 00000000..0210759b --- /dev/null +++ b/metadata/BillsOfLading__32__metadata.json @@ -0,0 +1,32 @@ +{ + "name": "BillsOfLading", + "retrainable": true, + "gpu": 0, + "processorType": "CPU", + "description": "Machine Learning model for extracting commonly occurring data points from Bills of Lading, including header fields and line items. Please see more details including supported languages in the ML Packages section of the Document Understanding Guide: https://docs.uipath.com/document-understanding/docs/ml-packages", + "inputDescription": "ML Skills deployed using this ML Package can be integrated into RPA workflows using the Machine Learning Extractor activity in the Official feed or the MLModel activity in Connect feed. File formats accepted include pdf, tiff, jpg or png files. The activities also require an API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -> Other Services view.", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "cpu": 1, + "inputType": "JSON", + "displayName": "BillsOfLading", + "memory": 0, + "mlPackageLanguage": "PYTHON37_DU", + "projectId": "[project-id]", + "stagingUri": "[staging-uri]", + "projectName": "UiPath Document Understanding", + "projectDescription": "UiPath models to classify and extract information from images and pdfs.", + "tenantName": "UiPath", + "minAIFabricVersion": "22.10", + "languageVersion": 4, + "version": 32, + "customVersion": "24.10.3", + "imagePath": "du-semistructured:v24.10-3.27-rc02", + "parametersFileJSON": "{\"params\": [{\"name\": \"auto_retraining\",\"type\": [\"PIPELINE\"],\"value\": \"False\",\"help\": \"Enable this option when the Pipeline you are creating is a scheduled automatic pipeline, synchronized with automatic exports from Data Manager.\",\"options\": [\"True\",\"False\"]}],\"probes\": [{\"probe\": \"startup\",\"initialDelaySeconds\": \"120\",\"periodSeconds\": \"10\",\"timeoutSeconds\": \"2\",\"successThreshold\": \"1\",\"failureThreshold\": \"60\"}]}", + "settings": [ + { + "key": "package_type", + "type": "STRING", + "value": "bills_of_lading" + } + ] +} diff --git a/metadata/BillsOfLading__33__metadata.json b/metadata/BillsOfLading__33__metadata.json new file mode 100644 index 00000000..320dc764 --- /dev/null +++ b/metadata/BillsOfLading__33__metadata.json @@ -0,0 +1,33 @@ +{ + "name": "BillsOfLading", + "retrainable": true, + "gpu": 0, + "processorType": "CPU", + "description": "Machine Learning model for extracting commonly occurring data points from Bills of Lading, including header fields and line items. Please see more details including supported languages in the ML Packages section of the Document Understanding Guide: https://docs.uipath.com/document-understanding/docs/ml-packages", + "inputDescription": "ML Skills deployed using this ML Package can be integrated into RPA workflows using the Machine Learning Extractor activity in the Official feed or the MLModel activity in Connect feed. File formats accepted include pdf, tiff, jpg or png files. The activities also require an API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -> Other Services view.", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "cpu": 1, + "inputType": "JSON", + "displayName": "BillsOfLading", + "memory": 0, + "mlPackageLanguage": "PYTHON37_DU", + "projectId": "[project-id]", + "stagingUri": "[staging-uri]", + "projectName": "UiPath Document Understanding", + "projectDescription": "UiPath models to classify and extract information from images and pdfs.", + "tenantName": "UiPath", + "minAIFabricVersion": "22.10", + "languageVersion": 4, + "version": 33, + "customVersion": "23.10.9", + "imagePath": "du-semistructured:v23.10-03.14-rc02", + "parametersFileJSON": "{\"params\": [{\"name\": \"auto_retraining\",\"type\": [\"PIPELINE\"],\"value\": \"False\",\"help\": \"Enable this option when the Pipeline you are creating is a scheduled automatic pipeline, synchronized with automatic exports from Data Manager.\",\"options\": [\"True\",\"False\"]}],\"probes\": [{\"probe\": \"startup\",\"initialDelaySeconds\": \"120\",\"periodSeconds\": \"10\",\"timeoutSeconds\": \"2\",\"successThreshold\": \"1\",\"failureThreshold\": \"60\"}]}", + "settings": [ + { + "key": "package_type", + "type": "STRING", + "value": "bills_of_lading" + } + ], + "maxAIFabricVersion": "24.10" +} diff --git a/metadata/BillsOfLading__34__metadata.json b/metadata/BillsOfLading__34__metadata.json new file mode 100644 index 00000000..afe51155 --- /dev/null +++ b/metadata/BillsOfLading__34__metadata.json @@ -0,0 +1,33 @@ +{ + "name": "BillsOfLading", + "retrainable": true, + "gpu": 0, + "processorType": "CPU", + "description": "Machine Learning model for extracting commonly occurring data points from Bills of Lading, including header fields and line items. Please see more details including supported languages in the ML Packages section of the Document Understanding Guide: https://docs.uipath.com/document-understanding/docs/ml-packages", + "inputDescription": "ML Skills deployed using this ML Package can be integrated into RPA workflows using the Machine Learning Extractor activity in the Official feed or the MLModel activity in Connect feed. File formats accepted include pdf, tiff, jpg or png files. The activities also require an API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -> Other Services view.", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "cpu": 1, + "inputType": "JSON", + "displayName": "BillsOfLading", + "memory": 0, + "mlPackageLanguage": "PYTHON37_DU", + "projectId": "[project-id]", + "stagingUri": "[staging-uri]", + "projectName": "UiPath Document Understanding", + "projectDescription": "UiPath models to classify and extract information from images and pdfs.", + "tenantName": "UiPath", + "minAIFabricVersion": "22.10", + "languageVersion": 4, + "version": 34, + "customVersion": "23.4.12", + "imagePath": "du-semistructured:v23.4-03.12-rc02", + "parametersFileJSON": "{\"params\": [{\"name\": \"auto_retraining\",\"type\": [\"PIPELINE\"],\"value\": \"False\",\"help\": \"Enable this option when the Pipeline you are creating is a scheduled automatic pipeline, synchronized with automatic exports from Data Manager.\",\"options\": [\"True\",\"False\"]}],\"probes\": [{\"probe\": \"startup\",\"initialDelaySeconds\": \"120\",\"periodSeconds\": \"10\",\"timeoutSeconds\": \"2\",\"successThreshold\": \"1\",\"failureThreshold\": \"60\"}]}", + "settings": [ + { + "key": "package_type", + "type": "STRING", + "value": "bills_of_lading" + } + ], + "maxAIFabricVersion": "23.10" +} diff --git a/metadata/BillsOfLading__35__metadata.json b/metadata/BillsOfLading__35__metadata.json new file mode 100644 index 00000000..d9dc8c2a --- /dev/null +++ b/metadata/BillsOfLading__35__metadata.json @@ -0,0 +1,32 @@ +{ + "name": "BillsOfLading", + "retrainable": true, + "gpu": 0, + "processorType": "CPU", + "description": "Machine Learning model for extracting commonly occurring data points from Bills of Lading, including header fields and line items. Please see more details including supported languages in the ML Packages section of the Document Understanding Guide: https://docs.uipath.com/document-understanding/docs/ml-packages", + "inputDescription": "ML Skills deployed using this ML Package can be integrated into RPA workflows using the Machine Learning Extractor activity in the Official feed or the MLModel activity in Connect feed. File formats accepted include pdf, tiff, jpg or png files. The activities also require an API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -> Other Services view.", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "cpu": 1, + "inputType": "JSON", + "displayName": "BillsOfLading", + "memory": 0, + "mlPackageLanguage": "PYTHON37_DU", + "projectId": "[project-id]", + "stagingUri": "[staging-uri]", + "projectName": "UiPath Document Understanding", + "projectDescription": "UiPath models to classify and extract information from images and pdfs.", + "tenantName": "UiPath", + "minAIFabricVersion": "22.10", + "languageVersion": 4, + "version": 35, + "customVersion": "24.10.4", + "imagePath": "du-semistructured:v24.10-6.16-rc04", + "parametersFileJSON": "{\"params\": [{\"name\": \"auto_retraining\",\"type\": [\"PIPELINE\"],\"value\": \"False\",\"help\": \"Enable this option when the Pipeline you are creating is a scheduled automatic pipeline, synchronized with automatic exports from Data Manager.\",\"options\": [\"True\",\"False\"]}],\"probes\": [{\"probe\": \"startup\",\"initialDelaySeconds\": \"120\",\"periodSeconds\": \"10\",\"timeoutSeconds\": \"2\",\"successThreshold\": \"1\",\"failureThreshold\": \"60\"}]}", + "settings": [ + { + "key": "package_type", + "type": "STRING", + "value": "bills_of_lading" + } + ] +} diff --git a/metadata/BillsOfLading__36__metadata.json b/metadata/BillsOfLading__36__metadata.json new file mode 100644 index 00000000..a313c0e1 --- /dev/null +++ b/metadata/BillsOfLading__36__metadata.json @@ -0,0 +1,33 @@ +{ + "name": "BillsOfLading", + "retrainable": true, + "gpu": 0, + "processorType": "CPU", + "description": "Machine Learning model for extracting commonly occurring data points from Bills of Lading, including header fields and line items. Please see more details including supported languages in the ML Packages section of the Document Understanding Guide: https://docs.uipath.com/document-understanding/docs/ml-packages", + "inputDescription": "ML Skills deployed using this ML Package can be integrated into RPA workflows using the Machine Learning Extractor activity in the Official feed or the MLModel activity in Connect feed. File formats accepted include pdf, tiff, jpg or png files. The activities also require an API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -> Other Services view.", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "cpu": 1, + "inputType": "JSON", + "displayName": "BillsOfLading", + "memory": 0, + "mlPackageLanguage": "PYTHON37_DU", + "projectId": "[project-id]", + "stagingUri": "[staging-uri]", + "projectName": "UiPath Document Understanding", + "projectDescription": "UiPath models to classify and extract information from images and pdfs.", + "tenantName": "UiPath", + "minAIFabricVersion": "22.10", + "languageVersion": 4, + "version": 36, + "customVersion": "23.10.10", + "imagePath": "du-semistructured:v23.10-06.05-rc05", + "parametersFileJSON": "{\"params\": [{\"name\": \"auto_retraining\",\"type\": [\"PIPELINE\"],\"value\": \"False\",\"help\": \"Enable this option when the Pipeline you are creating is a scheduled automatic pipeline, synchronized with automatic exports from Data Manager.\",\"options\": [\"True\",\"False\"]}],\"probes\": [{\"probe\": \"startup\",\"initialDelaySeconds\": \"120\",\"periodSeconds\": \"10\",\"timeoutSeconds\": \"2\",\"successThreshold\": \"1\",\"failureThreshold\": \"60\"}]}", + "settings": [ + { + "key": "package_type", + "type": "STRING", + "value": "bills_of_lading" + } + ], + "maxAIFabricVersion": "24.10" +} diff --git a/metadata/BillsOfLading__37__metadata.json b/metadata/BillsOfLading__37__metadata.json new file mode 100644 index 00000000..03294233 --- /dev/null +++ b/metadata/BillsOfLading__37__metadata.json @@ -0,0 +1,32 @@ +{ + "name": "BillsOfLading", + "retrainable": true, + "gpu": 0, + "processorType": "CPU", + "description": "Machine Learning model for extracting commonly occurring data points from Bills of Lading, including header fields and line items. Please see more details including supported languages in the ML Packages section of the Document Understanding Guide: https://docs.uipath.com/document-understanding/docs/ml-packages", + "inputDescription": "ML Skills deployed using this ML Package can be integrated into RPA workflows using the Machine Learning Extractor activity in the Official feed or the MLModel activity in Connect feed. File formats accepted include pdf, tiff, jpg or png files. The activities also require an API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -> Other Services view.", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "cpu": 1, + "inputType": "JSON", + "displayName": "BillsOfLading", + "memory": 0, + "mlPackageLanguage": "PYTHON37_DU", + "projectId": "[project-id]", + "stagingUri": "[staging-uri]", + "projectName": "UiPath Document Understanding", + "projectDescription": "UiPath models to classify and extract information from images and pdfs.", + "tenantName": "UiPath", + "minAIFabricVersion": "22.10", + "languageVersion": 4, + "version": 37, + "customVersion": "24.10.5", + "imagePath": "du-semistructured:v24.10-8.25-rc02", + "parametersFileJSON": "{\"params\": [{\"name\": \"auto_retraining\",\"type\": [\"PIPELINE\"],\"value\": \"False\",\"help\": \"Enable this option when the Pipeline you are creating is a scheduled automatic pipeline, synchronized with automatic exports from Data Manager.\",\"options\": [\"True\",\"False\"]}],\"probes\": [{\"probe\": \"startup\",\"initialDelaySeconds\": \"120\",\"periodSeconds\": \"10\",\"timeoutSeconds\": \"2\",\"successThreshold\": \"1\",\"failureThreshold\": \"60\"}]}", + "settings": [ + { + "key": "package_type", + "type": "STRING", + "value": "bills_of_lading" + } + ] +} diff --git a/metadata/BillsOfLading__38__metadata.json b/metadata/BillsOfLading__38__metadata.json new file mode 100644 index 00000000..f329cb4b --- /dev/null +++ b/metadata/BillsOfLading__38__metadata.json @@ -0,0 +1,33 @@ +{ + "name": "BillsOfLading", + "retrainable": true, + "gpu": 0, + "processorType": "CPU", + "description": "Machine Learning model for extracting commonly occurring data points from Bills of Lading, including header fields and line items. Please see more details including supported languages in the ML Packages section of the Document Understanding Guide: https://docs.uipath.com/document-understanding/docs/ml-packages", + "inputDescription": "ML Skills deployed using this ML Package can be integrated into RPA workflows using the Machine Learning Extractor activity in the Official feed or the MLModel activity in Connect feed. File formats accepted include pdf, tiff, jpg or png files. The activities also require an API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -> Other Services view.", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "cpu": 1, + "inputType": "JSON", + "displayName": "BillsOfLading", + "memory": 0, + "mlPackageLanguage": "PYTHON37_DU", + "projectId": "[project-id]", + "stagingUri": "[staging-uri]", + "projectName": "UiPath Document Understanding", + "projectDescription": "UiPath models to classify and extract information from images and pdfs.", + "tenantName": "UiPath", + "minAIFabricVersion": "22.10", + "languageVersion": 4, + "version": 38, + "customVersion": "23.10.11", + "imagePath": "du-semistructured:v23.10-08.25-rc02", + "parametersFileJSON": "{\"params\": [{\"name\": \"auto_retraining\",\"type\": [\"PIPELINE\"],\"value\": \"False\",\"help\": \"Enable this option when the Pipeline you are creating is a scheduled automatic pipeline, synchronized with automatic exports from Data Manager.\",\"options\": [\"True\",\"False\"]}],\"probes\": [{\"probe\": \"startup\",\"initialDelaySeconds\": \"120\",\"periodSeconds\": \"10\",\"timeoutSeconds\": \"2\",\"successThreshold\": \"1\",\"failureThreshold\": \"60\"}]}", + "settings": [ + { + "key": "package_type", + "type": "STRING", + "value": "bills_of_lading" + } + ], + "maxAIFabricVersion": "24.10" +} diff --git a/metadata/BillsOfLading__39__metadata.json b/metadata/BillsOfLading__39__metadata.json new file mode 100644 index 00000000..24516e96 --- /dev/null +++ b/metadata/BillsOfLading__39__metadata.json @@ -0,0 +1,32 @@ +{ + "name": "BillsOfLading", + "retrainable": true, + "gpu": 0, + "processorType": "CPU", + "description": "Machine Learning model for extracting commonly occurring data points from Bills of Lading, including header fields and line items. Please see more details including supported languages in the ML Packages section of the Document Understanding Guide: https://docs.uipath.com/document-understanding/docs/ml-packages", + "inputDescription": "ML Skills deployed using this ML Package can be integrated into RPA workflows using the Machine Learning Extractor activity in the Official feed or the MLModel activity in Connect feed. File formats accepted include pdf, tiff, jpg or png files. The activities also require an API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -> Other Services view.", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "cpu": 1, + "inputType": "JSON", + "displayName": "BillsOfLading", + "memory": 0, + "mlPackageLanguage": "PYTHON37_DU", + "projectId": "[project-id]", + "stagingUri": "[staging-uri]", + "projectName": "UiPath Document Understanding", + "projectDescription": "UiPath models to classify and extract information from images and pdfs.", + "tenantName": "UiPath", + "minAIFabricVersion": "22.10", + "languageVersion": 4, + "version": 39, + "customVersion": "25.10.0", + "imagePath": "du-semistructured:v25.10-10.17-rc04", + "parametersFileJSON": "{\"params\": [{\"name\": \"auto_retraining\",\"type\": [\"PIPELINE\"],\"value\": \"False\",\"help\": \"Enable this option when the Pipeline you are creating is a scheduled automatic pipeline, synchronized with automatic exports from Data Manager.\",\"options\": [\"True\",\"False\"]}],\"probes\": [{\"probe\": \"startup\",\"initialDelaySeconds\": \"120\",\"periodSeconds\": \"10\",\"timeoutSeconds\": \"2\",\"successThreshold\": \"1\",\"failureThreshold\": \"60\"}]}", + "settings": [ + { + "key": "package_type", + "type": "STRING", + "value": "bills_of_lading" + } + ] +} diff --git a/metadata/CMS1500__18__metadata.json b/metadata/CMS1500__18__metadata.json new file mode 100644 index 00000000..3cd6dd37 --- /dev/null +++ b/metadata/CMS1500__18__metadata.json @@ -0,0 +1,33 @@ +{ + "name": "CMS1500", + "retrainable": true, + "gpu": 0, + "processorType": "CPU", + "description": "Machine Learning model for extracting commonly occurring data points from CMS 1500. 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The activities also require an API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -> Other Services view.", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "changeLog": "", + "cpu": 1, + "inputType": "JSON", + "displayName": "CMS1500", + "memory": 0, + "mlPackageLanguage": "PYTHON37_DU", + "projectId": "[project-id]", + "stagingUri": "[staging-uri]", + "projectName": "UiPath Document Understanding", + "projectDescription": "UiPath models to classify and extract information from images and pdfs.", + "tenantName": "UiPath", + "minAIFabricVersion": "22.10", + "languageVersion": 4, + "version": 18, + "customVersion": "24.10.2", + "imagePath": "du-semistructured:v24.10-1.27-rc02", + "parametersFileJSON": "{\"params\": [{\"name\": \"auto_retraining\",\"type\": [\"PIPELINE\"],\"value\": \"False\",\"help\": \"Enable this option when the Pipeline you are creating is a scheduled automatic pipeline, synchronized with automatic exports from Data Manager.\",\"options\": [\"True\",\"False\"]}],\"probes\": [{\"probe\": \"startup\",\"initialDelaySeconds\": \"120\",\"periodSeconds\": \"10\",\"timeoutSeconds\": \"2\",\"successThreshold\": \"1\",\"failureThreshold\": \"60\"}]}", + "settings": [ + { + "key": "package_type", + "type": "STRING", + "value": "cms1500" + } + ] +} \ No newline at end of file diff --git a/metadata/CMS1500__19__metadata.json b/metadata/CMS1500__19__metadata.json new file mode 100644 index 00000000..ccfc5ec8 --- /dev/null +++ b/metadata/CMS1500__19__metadata.json @@ -0,0 +1,34 @@ +{ + "name": "CMS1500", + "retrainable": true, + "gpu": 0, + "processorType": "CPU", + "description": "Machine Learning model for extracting commonly occurring data points from CMS 1500. 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The activities also require an API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -> Other Services view.", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "changeLog": "", + "cpu": 1, + "inputType": "JSON", + "displayName": "CMS1500", + "memory": 0, + "mlPackageLanguage": "PYTHON37_DU", + "projectId": "[project-id]", + "stagingUri": "[staging-uri]", + "projectName": "UiPath Document Understanding", + "projectDescription": "UiPath models to classify and extract information from images and pdfs.", + "tenantName": "UiPath", + "minAIFabricVersion": "22.10", + "languageVersion": 4, + "version": 19, + "customVersion": "23.4.11", + "imagePath": "du-semistructured:v23.4-01.27-rc01", + "parametersFileJSON": "{\"params\": [{\"name\": \"auto_retraining\",\"type\": [\"PIPELINE\"],\"value\": \"False\",\"help\": \"Enable this option when the Pipeline you are creating is a scheduled automatic pipeline, synchronized with automatic exports from Data Manager.\",\"options\": [\"True\",\"False\"]}],\"probes\": [{\"probe\": \"startup\",\"initialDelaySeconds\": \"120\",\"periodSeconds\": \"10\",\"timeoutSeconds\": \"2\",\"successThreshold\": \"1\",\"failureThreshold\": \"60\"}]}", + "settings": [ + { + "key": "package_type", + "type": "STRING", + "value": "cms1500" + } + ], + "maxAIFabricVersion": "23.10" +} \ No newline at end of file diff --git a/metadata/CMS1500__20__metadata.json b/metadata/CMS1500__20__metadata.json new file mode 100644 index 00000000..ab4f5b06 --- /dev/null +++ b/metadata/CMS1500__20__metadata.json @@ -0,0 +1,34 @@ +{ + "name": "CMS1500", + "retrainable": true, + "gpu": 0, + "processorType": "CPU", + "description": "Machine Learning model for extracting commonly occurring data points from CMS 1500. 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The activities also require an API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -> Other Services view.", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "changeLog": "", + "cpu": 1, + "inputType": "JSON", + "displayName": "CMS1500", + "memory": 0, + "mlPackageLanguage": "PYTHON37_DU", + "projectId": "[project-id]", + "stagingUri": "[staging-uri]", + "projectName": "UiPath Document Understanding", + "projectDescription": "UiPath models to classify and extract information from images and pdfs.", + "tenantName": "UiPath", + "minAIFabricVersion": "22.10", + "languageVersion": 4, + "version": 20, + "customVersion": "23.10.8", + "imagePath": "du-semistructured:v23.10-01.28-rc07", + "parametersFileJSON": "{\"params\": [{\"name\": \"auto_retraining\",\"type\": [\"PIPELINE\"],\"value\": \"False\",\"help\": \"Enable this option when the Pipeline you are creating is a scheduled automatic pipeline, synchronized with automatic exports from Data Manager.\",\"options\": [\"True\",\"False\"]}],\"probes\": [{\"probe\": \"startup\",\"initialDelaySeconds\": \"120\",\"periodSeconds\": \"10\",\"timeoutSeconds\": \"2\",\"successThreshold\": \"1\",\"failureThreshold\": \"60\"}]}", + "settings": [ + { + "key": "package_type", + "type": "STRING", + "value": "cms1500" + } + ], + "maxAIFabricVersion": "24.10" +} \ No newline at end of file diff --git a/metadata/CMS1500__21__metadata.json b/metadata/CMS1500__21__metadata.json new file mode 100644 index 00000000..22c85d57 --- /dev/null +++ b/metadata/CMS1500__21__metadata.json @@ -0,0 +1,33 @@ +{ + "name": "CMS1500", + "retrainable": true, + "gpu": 0, + "processorType": "CPU", + "description": "Machine Learning model for extracting commonly occurring data points from CMS 1500. 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The activities also require an API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -> Other Services view.", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "changeLog": "", + "cpu": 1, + "inputType": "JSON", + "displayName": "CMS1500", + "memory": 0, + "mlPackageLanguage": "PYTHON37_DU", + "projectId": "[project-id]", + "stagingUri": "[staging-uri]", + "projectName": "UiPath Document Understanding", + "projectDescription": "UiPath models to classify and extract information from images and pdfs.", + "tenantName": "UiPath", + "minAIFabricVersion": "22.10", + "languageVersion": 4, + "version": 21, + "customVersion": "24.10.3", + "imagePath": "du-semistructured:v24.10-3.27-rc02", + "parametersFileJSON": "{\"params\": [{\"name\": \"auto_retraining\",\"type\": [\"PIPELINE\"],\"value\": \"False\",\"help\": \"Enable this option when the Pipeline you are creating is a scheduled automatic pipeline, synchronized with automatic exports from Data Manager.\",\"options\": [\"True\",\"False\"]}],\"probes\": [{\"probe\": \"startup\",\"initialDelaySeconds\": \"120\",\"periodSeconds\": \"10\",\"timeoutSeconds\": \"2\",\"successThreshold\": \"1\",\"failureThreshold\": \"60\"}]}", + "settings": [ + { + "key": "package_type", + "type": "STRING", + "value": "cms1500" + } + ] +} diff --git a/metadata/CMS1500__22__metadata.json b/metadata/CMS1500__22__metadata.json new file mode 100644 index 00000000..08d6356a --- /dev/null +++ b/metadata/CMS1500__22__metadata.json @@ -0,0 +1,34 @@ +{ + "name": "CMS1500", + "retrainable": true, + "gpu": 0, + "processorType": "CPU", + "description": "Machine Learning model for extracting commonly occurring data points from CMS 1500. 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The activities also require an API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -> Other Services view.", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "changeLog": "", + "cpu": 1, + "inputType": "JSON", + "displayName": "CertificatesOfOrigin", + "memory": 0, + "mlPackageLanguage": "PYTHON37_DU", + "projectId": "[project-id]", + "stagingUri": "[staging-uri]", + "projectName": "UiPath Document Understanding", + "projectDescription": "UiPath models to classify and extract information from images and pdfs.", + "tenantName": "UiPath", + "minAIFabricVersion": "22.10", + "languageVersion": 4, + "version": 22, + "customVersion": "23.10.9", + "imagePath": "du-semistructured:v23.10-03.14-rc02", + "parametersFileJSON": "{\"params\": [{\"name\": \"auto_retraining\",\"type\": [\"PIPELINE\"],\"value\": \"False\",\"help\": \"Enable this option when the Pipeline you are creating is a scheduled automatic pipeline, synchronized with automatic exports from Data Manager.\",\"options\": [\"True\",\"False\"]}],\"probes\": [{\"probe\": \"startup\",\"initialDelaySeconds\": \"120\",\"periodSeconds\": \"10\",\"timeoutSeconds\": \"2\",\"successThreshold\": \"1\",\"failureThreshold\": \"60\"}]}", + "settings": [ + { + "key": "package_type", + "type": "STRING", + "value": "certificates_origin" + } + ], + "maxAIFabricVersion": "24.10" +} diff --git a/metadata/CertificatesOfOrigin__23__metadata.json b/metadata/CertificatesOfOrigin__23__metadata.json new file mode 100644 index 00000000..7ca99ae6 --- /dev/null +++ b/metadata/CertificatesOfOrigin__23__metadata.json @@ -0,0 +1,34 @@ +{ + "name": "CertificatesOfOrigin", + "retrainable": true, + "gpu": 0, + "processorType": "CPU", + "description": "Machine Learning model for extracting commonly occurring data points from Certificates of Origin. 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The activities also require an API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -> Other Services view.", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "changeLog": "", + "cpu": 1, + "inputType": "JSON", + "displayName": "CertificatesOfOrigin", + "memory": 0, + "mlPackageLanguage": "PYTHON37_DU", + "projectId": "[project-id]", + "stagingUri": "[staging-uri]", + "projectName": "UiPath Document Understanding", + "projectDescription": "UiPath models to classify and extract information from images and pdfs.", + "tenantName": "UiPath", + "minAIFabricVersion": "22.10", + "languageVersion": 4, + "version": 23, + "customVersion": "23.4.12", + "imagePath": "du-semistructured:v23.4-03.12-rc02", + "parametersFileJSON": "{\"params\": [{\"name\": \"auto_retraining\",\"type\": [\"PIPELINE\"],\"value\": \"False\",\"help\": \"Enable this option when the Pipeline you are creating is a scheduled automatic pipeline, synchronized with automatic exports from Data Manager.\",\"options\": [\"True\",\"False\"]}],\"probes\": [{\"probe\": \"startup\",\"initialDelaySeconds\": \"120\",\"periodSeconds\": \"10\",\"timeoutSeconds\": \"2\",\"successThreshold\": \"1\",\"failureThreshold\": \"60\"}]}", + "settings": [ + { + "key": "package_type", + "type": "STRING", + "value": "certificates_origin" + } + ], + "maxAIFabricVersion": "23.10" +} diff --git a/metadata/CertificatesOfOrigin__24__metadata.json b/metadata/CertificatesOfOrigin__24__metadata.json new file mode 100644 index 00000000..e86c8899 --- /dev/null +++ b/metadata/CertificatesOfOrigin__24__metadata.json @@ -0,0 +1,33 @@ +{ + "name": "CertificatesOfOrigin", + "retrainable": true, + "gpu": 0, + "processorType": "CPU", + "description": "Machine Learning model for extracting commonly occurring data points from Certificates of Origin. See the documentation page for this model for more details https://docs.uipath.com/document-understanding/automation-cloud/latest/user-guide/certificates-origin-ml-package", + "inputDescription": "ML Skills deployed using this ML Package can be integrated into RPA workflows using the Machine Learning Extractor activity in the Official feed or the MLModel activity in Connect feed. Accepted file formats: pdf, tiff, jpg or png files. The activities also require an API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -> Other Services view.", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "changeLog": "", + "cpu": 1, + "inputType": "JSON", + "displayName": "CertificatesOfOrigin", + "memory": 0, + "mlPackageLanguage": "PYTHON37_DU", + "projectId": "[project-id]", + "stagingUri": "[staging-uri]", + "projectName": "UiPath Document Understanding", + "projectDescription": "UiPath models to classify and extract information from images and pdfs.", + "tenantName": "UiPath", + "minAIFabricVersion": "22.10", + "languageVersion": 4, + "version": 24, + "customVersion": "24.10.4", + "imagePath": "du-semistructured:v24.10-6.16-rc04", + "parametersFileJSON": "{\"params\": [{\"name\": \"auto_retraining\",\"type\": [\"PIPELINE\"],\"value\": \"False\",\"help\": \"Enable this option when the Pipeline you are creating is a scheduled automatic pipeline, synchronized with automatic exports from Data Manager.\",\"options\": [\"True\",\"False\"]}],\"probes\": [{\"probe\": \"startup\",\"initialDelaySeconds\": \"120\",\"periodSeconds\": \"10\",\"timeoutSeconds\": \"2\",\"successThreshold\": \"1\",\"failureThreshold\": \"60\"}]}", + "settings": [ + { + "key": "package_type", + "type": "STRING", + "value": "certificates_origin" + } + ] +} diff --git a/metadata/CertificatesOfOrigin__25__metadata.json b/metadata/CertificatesOfOrigin__25__metadata.json new file mode 100644 index 00000000..f2e510ea --- /dev/null +++ b/metadata/CertificatesOfOrigin__25__metadata.json @@ -0,0 +1,34 @@ +{ + "name": "CertificatesOfOrigin", + "retrainable": true, + "gpu": 0, + "processorType": "CPU", + "description": "Machine Learning model for extracting commonly occurring data points from Certificates of Origin. 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The activities also require an API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -> Other Services view.", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "changeLog": "", + "cpu": 1, + "inputType": "JSON", + "displayName": "CertificatesOfOrigin", + "memory": 0, + "mlPackageLanguage": "PYTHON37_DU", + "projectId": "[project-id]", + "stagingUri": "[staging-uri]", + "projectName": "UiPath Document Understanding", + "projectDescription": "UiPath models to classify and extract information from images and pdfs.", + "tenantName": "UiPath", + "minAIFabricVersion": "22.10", + "languageVersion": 4, + "version": 25, + "customVersion": "23.10.10", + "imagePath": "du-semistructured:v23.10-06.05-rc05", + "parametersFileJSON": "{\"params\": [{\"name\": \"auto_retraining\",\"type\": [\"PIPELINE\"],\"value\": \"False\",\"help\": \"Enable this option when the Pipeline you are creating is a scheduled automatic pipeline, synchronized with automatic exports from Data Manager.\",\"options\": [\"True\",\"False\"]}],\"probes\": [{\"probe\": \"startup\",\"initialDelaySeconds\": \"120\",\"periodSeconds\": \"10\",\"timeoutSeconds\": \"2\",\"successThreshold\": \"1\",\"failureThreshold\": \"60\"}]}", + "settings": [ + { + "key": "package_type", + "type": "STRING", + "value": "certificates_origin" + } + ], + "maxAIFabricVersion": "24.10" +} diff --git a/metadata/CertificatesOfOrigin__26__metadata.json b/metadata/CertificatesOfOrigin__26__metadata.json new file mode 100644 index 00000000..0734ada0 --- /dev/null +++ b/metadata/CertificatesOfOrigin__26__metadata.json @@ -0,0 +1,33 @@ +{ + "name": "CertificatesOfOrigin", + "retrainable": true, + "gpu": 0, + "processorType": "CPU", + "description": "Machine Learning model for extracting commonly occurring data points from Certificates of Origin. 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The activities also require an API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -> Other Services view.", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "changeLog": "", + "cpu": 1, + "inputType": "JSON", + "displayName": "CertificatesOfOrigin", + "memory": 0, + "mlPackageLanguage": "PYTHON37_DU", + "projectId": "[project-id]", + "stagingUri": "[staging-uri]", + "projectName": "UiPath Document Understanding", + "projectDescription": "UiPath models to classify and extract information from images and pdfs.", + "tenantName": "UiPath", + "minAIFabricVersion": "22.10", + "languageVersion": 4, + "version": 26, + "customVersion": "24.10.5", + "imagePath": "du-semistructured:v24.10-8.25-rc02", + "parametersFileJSON": "{\"params\": [{\"name\": \"auto_retraining\",\"type\": [\"PIPELINE\"],\"value\": \"False\",\"help\": \"Enable this option when the Pipeline you are creating is a scheduled automatic pipeline, synchronized with automatic exports from Data Manager.\",\"options\": [\"True\",\"False\"]}],\"probes\": [{\"probe\": \"startup\",\"initialDelaySeconds\": \"120\",\"periodSeconds\": \"10\",\"timeoutSeconds\": \"2\",\"successThreshold\": \"1\",\"failureThreshold\": \"60\"}]}", + "settings": [ + { + "key": "package_type", + "type": "STRING", + "value": "certificates_origin" + } + ] +} diff --git a/metadata/CertificatesOfOrigin__27__metadata.json b/metadata/CertificatesOfOrigin__27__metadata.json new file mode 100644 index 00000000..95369034 --- /dev/null +++ b/metadata/CertificatesOfOrigin__27__metadata.json @@ -0,0 +1,34 @@ +{ + "name": "CertificatesOfOrigin", + "retrainable": true, + "gpu": 0, + "processorType": "CPU", + "description": "Machine Learning model for extracting commonly occurring data points from Certificates of Origin. See the documentation page for this model for more details https://docs.uipath.com/document-understanding/automation-cloud/latest/user-guide/certificates-origin-ml-package", + "inputDescription": "ML Skills deployed using this ML Package can be integrated into RPA workflows using the Machine Learning Extractor activity in the Official feed or the MLModel activity in Connect feed. Accepted file formats: pdf, tiff, jpg or png files. The activities also require an API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -> Other Services view.", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "changeLog": "", + "cpu": 1, + "inputType": "JSON", + "displayName": "CertificatesOfOrigin", + "memory": 0, + "mlPackageLanguage": "PYTHON37_DU", + "projectId": "[project-id]", + "stagingUri": "[staging-uri]", + "projectName": "UiPath Document Understanding", + "projectDescription": "UiPath models to classify and extract information from images and pdfs.", + "tenantName": "UiPath", + "minAIFabricVersion": "22.10", + "languageVersion": 4, + "version": 27, + "customVersion": "23.10.11", + "imagePath": "du-semistructured:v23.10-08.25-rc02", + "parametersFileJSON": "{\"params\": [{\"name\": \"auto_retraining\",\"type\": [\"PIPELINE\"],\"value\": \"False\",\"help\": \"Enable this option when the Pipeline you are creating is a scheduled automatic pipeline, synchronized with automatic exports from Data Manager.\",\"options\": [\"True\",\"False\"]}],\"probes\": [{\"probe\": \"startup\",\"initialDelaySeconds\": \"120\",\"periodSeconds\": \"10\",\"timeoutSeconds\": \"2\",\"successThreshold\": \"1\",\"failureThreshold\": \"60\"}]}", + "settings": [ + { + "key": "package_type", + "type": "STRING", + "value": "certificates_origin" + } + ], + "maxAIFabricVersion": "24.10" +} diff --git a/metadata/CertificatesOfOrigin__28__metadata.json b/metadata/CertificatesOfOrigin__28__metadata.json new file mode 100644 index 00000000..51635c10 --- /dev/null +++ b/metadata/CertificatesOfOrigin__28__metadata.json @@ -0,0 +1,33 @@ +{ + "name": "CertificatesOfOrigin", + "retrainable": true, + "gpu": 0, + "processorType": "CPU", + "description": "Machine Learning model for extracting commonly occurring data points from Certificates of Origin. See the documentation page for this model for more details https://docs.uipath.com/document-understanding/automation-cloud/latest/user-guide/certificates-origin-ml-package", + "inputDescription": "ML Skills deployed using this ML Package can be integrated into RPA workflows using the Machine Learning Extractor activity in the Official feed or the MLModel activity in Connect feed. Accepted file formats: pdf, tiff, jpg or png files. The activities also require an API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -> Other Services view.", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "changeLog": "", + "cpu": 1, + "inputType": "JSON", + "displayName": "CertificatesOfOrigin", + "memory": 0, + "mlPackageLanguage": "PYTHON37_DU", + "projectId": "[project-id]", + "stagingUri": "[staging-uri]", + "projectName": "UiPath Document Understanding", + "projectDescription": "UiPath models to classify and extract information from images and pdfs.", + "tenantName": "UiPath", + "minAIFabricVersion": "22.10", + "languageVersion": 4, + "version": 28, + "customVersion": "25.10.0", + "imagePath": "du-semistructured:v25.10-10.17-rc04", + "parametersFileJSON": "{\"params\": [{\"name\": \"auto_retraining\",\"type\": [\"PIPELINE\"],\"value\": \"False\",\"help\": \"Enable this option when the Pipeline you are creating is a scheduled automatic pipeline, synchronized with automatic exports from Data Manager.\",\"options\": [\"True\",\"False\"]}],\"probes\": [{\"probe\": \"startup\",\"initialDelaySeconds\": \"120\",\"periodSeconds\": \"10\",\"timeoutSeconds\": \"2\",\"successThreshold\": \"1\",\"failureThreshold\": \"60\"}]}", + "settings": [ + { + "key": "package_type", + "type": "STRING", + "value": "certificates_origin" + } + ] +} diff --git a/metadata/Checks__23__metadata.json b/metadata/Checks__23__metadata.json new file mode 100644 index 00000000..c503c846 --- /dev/null +++ b/metadata/Checks__23__metadata.json @@ -0,0 +1,33 @@ +{ + "name": "Checks", + "retrainable": true, + "gpu": 0, + "processorType": "CPU", + "description": "Machine Learning model for extracting commonly occurring data points from Checks, including header fields and line items. 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The activities also require an API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -> Other Services view.", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "cpu": 1, + "inputType": "JSON", + "displayName": "Checks", + "memory": 0, + "mlPackageLanguage": "PYTHON37_DU", + "projectId": "[project-id]", + "stagingUri": "[staging-uri]", + "projectName": "UiPath Document Understanding", + "projectDescription": "UiPath models to classify and extract information from images and pdfs.", + "tenantName": "UiPath", + "minAIFabricVersion": "22.10", + "languageVersion": 4, + "version": 23, + "customVersion": "22.10.14", + "imagePath": "du-semistructured:v22.10-10.10-rc01", + "parametersFileJSON": "{\"params\": [{\"name\": \"auto_retraining\",\"type\": [\"PIPELINE\"],\"value\": \"False\",\"help\": \"Enable this option when the Pipeline you are creating is a scheduled automatic pipeline, synchronized with automatic exports from Data Manager.\",\"options\": [\"True\",\"False\"]}],\"probes\": [{\"probe\": \"startup\",\"initialDelaySeconds\": \"120\",\"periodSeconds\": \"10\",\"timeoutSeconds\": \"2\",\"successThreshold\": \"1\",\"failureThreshold\": \"60\"}]}", + "settings": [ + { + "key": "package_type", + "type": "STRING", + "value": "checks" + } + ], + "maxAIFabricVersion": "23.4" +} \ No newline at end of file diff --git a/metadata/Checks__29__metadata.json b/metadata/Checks__29__metadata.json new file mode 100644 index 00000000..ebad510a --- /dev/null +++ b/metadata/Checks__29__metadata.json @@ -0,0 +1,32 @@ +{ + "name": "Checks", + "retrainable": true, + "gpu": 0, + "processorType": "CPU", + "description": "Machine Learning model for extracting commonly occurring data points from Checks, including header fields and line items. 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The activities also require an API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -> Other Services view.", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "cpu": 1, + "inputType": "JSON", + "displayName": "Checks", + "memory": 0, + "mlPackageLanguage": "PYTHON37_DU", + "projectId": "[project-id]", + "stagingUri": "[staging-uri]", + "projectName": "UiPath Document Understanding", + "projectDescription": "UiPath models to classify and extract information from images and pdfs.", + "tenantName": "UiPath", + "minAIFabricVersion": "22.10", + "languageVersion": 4, + "version": 29, + "customVersion": "24.10.2", + "imagePath": "du-semistructured:v24.10-1.27-rc02", + "parametersFileJSON": "{\"params\": [{\"name\": \"auto_retraining\",\"type\": [\"PIPELINE\"],\"value\": \"False\",\"help\": \"Enable this option when the Pipeline you are creating is a scheduled automatic pipeline, synchronized with automatic exports from Data Manager.\",\"options\": [\"True\",\"False\"]}],\"probes\": [{\"probe\": \"startup\",\"initialDelaySeconds\": \"120\",\"periodSeconds\": \"10\",\"timeoutSeconds\": \"2\",\"successThreshold\": \"1\",\"failureThreshold\": \"60\"}]}", + "settings": [ + { + "key": "package_type", + "type": "STRING", + "value": "checks" + } + ] +} \ No newline at end of file diff --git a/metadata/Checks__30__metadata.json b/metadata/Checks__30__metadata.json new file mode 100644 index 00000000..535422b3 --- /dev/null +++ b/metadata/Checks__30__metadata.json @@ -0,0 +1,33 @@ +{ + "name": "Checks", + "retrainable": true, + "gpu": 0, + "processorType": "CPU", + "description": "Machine Learning model for extracting commonly occurring data points from Checks, including header fields and line items. 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The activities also require an API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -> Other Services view.", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "cpu": 1, + "inputType": "JSON", + "displayName": "Checks", + "memory": 0, + "mlPackageLanguage": "PYTHON37_DU", + "projectId": "[project-id]", + "stagingUri": "[staging-uri]", + "projectName": "UiPath Document Understanding", + "projectDescription": "UiPath models to classify and extract information from images and pdfs.", + "tenantName": "UiPath", + "minAIFabricVersion": "22.10", + "languageVersion": 4, + "version": 30, + "customVersion": "23.4.11", + "imagePath": "du-semistructured:v23.4-01.27-rc01", + "parametersFileJSON": "{\"params\": [{\"name\": \"auto_retraining\",\"type\": [\"PIPELINE\"],\"value\": \"False\",\"help\": \"Enable this option when the Pipeline you are creating is a scheduled automatic pipeline, synchronized with automatic exports from Data Manager.\",\"options\": [\"True\",\"False\"]}],\"probes\": [{\"probe\": \"startup\",\"initialDelaySeconds\": \"120\",\"periodSeconds\": \"10\",\"timeoutSeconds\": \"2\",\"successThreshold\": \"1\",\"failureThreshold\": \"60\"}]}", + "settings": [ + { + "key": "package_type", + "type": "STRING", + "value": "checks" + } + ], + "maxAIFabricVersion": "23.10" +} \ No newline at end of file diff --git a/metadata/Checks__31__metadata.json b/metadata/Checks__31__metadata.json new file mode 100644 index 00000000..96231144 --- /dev/null +++ b/metadata/Checks__31__metadata.json @@ -0,0 +1,33 @@ +{ + "name": "Checks", + "retrainable": true, + "gpu": 0, + "processorType": "CPU", + "description": "Machine Learning model for extracting commonly occurring data points from Checks, including header fields and line items. 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The activities also require an API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -> Other Services view.", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "cpu": 1, + "inputType": "JSON", + "displayName": "Checks", + "memory": 0, + "mlPackageLanguage": "PYTHON37_DU", + "projectId": "[project-id]", + "stagingUri": "[staging-uri]", + "projectName": "UiPath Document Understanding", + "projectDescription": "UiPath models to classify and extract information from images and pdfs.", + "tenantName": "UiPath", + "minAIFabricVersion": "22.10", + "languageVersion": 4, + "version": 31, + "customVersion": "23.10.8", + "imagePath": "du-semistructured:v23.10-01.28-rc07", + "parametersFileJSON": "{\"params\": [{\"name\": \"auto_retraining\",\"type\": [\"PIPELINE\"],\"value\": \"False\",\"help\": \"Enable this option when the Pipeline you are creating is a scheduled automatic pipeline, synchronized with automatic exports from Data Manager.\",\"options\": [\"True\",\"False\"]}],\"probes\": [{\"probe\": \"startup\",\"initialDelaySeconds\": \"120\",\"periodSeconds\": \"10\",\"timeoutSeconds\": \"2\",\"successThreshold\": \"1\",\"failureThreshold\": \"60\"}]}", + "settings": [ + { + "key": "package_type", + "type": "STRING", + "value": "checks" + } + ], + "maxAIFabricVersion": "24.10" +} \ No newline at end of file diff --git a/metadata/Checks__32__metadata.json b/metadata/Checks__32__metadata.json new file mode 100644 index 00000000..527c82b2 --- /dev/null +++ b/metadata/Checks__32__metadata.json @@ -0,0 +1,32 @@ +{ + "name": "Checks", + "retrainable": true, + "gpu": 0, + "processorType": "CPU", + "description": "Machine Learning model for extracting commonly occurring data points from Checks, including header fields and line items. Please see more details including supported languages in the ML Packages section of the Document Understanding Guide: https://docs.uipath.com/document-understanding/docs/ml-packages", + "inputDescription": "ML Skills deployed using this ML Package can be integrated into RPA workflows using the Machine Learning Extractor activity in the Official feed or the MLModel activity in Connect feed. File formats accepted include pdf, tiff, jpg or png files. The activities also require an API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -> Other Services view.", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "cpu": 1, + "inputType": "JSON", + "displayName": "Checks", + "memory": 0, + "mlPackageLanguage": "PYTHON37_DU", + "projectId": "[project-id]", + "stagingUri": "[staging-uri]", + "projectName": "UiPath Document Understanding", + "projectDescription": "UiPath models to classify and extract information from images and pdfs.", + "tenantName": "UiPath", + "minAIFabricVersion": "22.10", + "languageVersion": 4, + "version": 32, + "customVersion": "24.10.3", + "imagePath": "du-semistructured:v24.10-3.27-rc02", + "parametersFileJSON": "{\"params\": [{\"name\": \"auto_retraining\",\"type\": [\"PIPELINE\"],\"value\": \"False\",\"help\": \"Enable this option when the Pipeline you are creating is a scheduled automatic pipeline, synchronized with automatic exports from Data Manager.\",\"options\": [\"True\",\"False\"]}],\"probes\": [{\"probe\": \"startup\",\"initialDelaySeconds\": \"120\",\"periodSeconds\": \"10\",\"timeoutSeconds\": \"2\",\"successThreshold\": \"1\",\"failureThreshold\": \"60\"}]}", + "settings": [ + { + "key": "package_type", + "type": "STRING", + "value": "checks" + } + ] +} diff --git a/metadata/Checks__33__metadata.json b/metadata/Checks__33__metadata.json new file mode 100644 index 00000000..23c26bb5 --- /dev/null +++ b/metadata/Checks__33__metadata.json @@ -0,0 +1,33 @@ +{ + "name": "Checks", + "retrainable": true, + "gpu": 0, + "processorType": "CPU", + "description": "Machine Learning model for extracting commonly occurring data points from Checks, including header fields and line items. Please see more details including supported languages in the ML Packages section of the Document Understanding Guide: https://docs.uipath.com/document-understanding/docs/ml-packages", + "inputDescription": "ML Skills deployed using this ML Package can be integrated into RPA workflows using the Machine Learning Extractor activity in the Official feed or the MLModel activity in Connect feed. File formats accepted include pdf, tiff, jpg or png files. The activities also require an API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -> Other Services view.", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "cpu": 1, + "inputType": "JSON", + "displayName": "Checks", + "memory": 0, + "mlPackageLanguage": "PYTHON37_DU", + "projectId": "[project-id]", + "stagingUri": "[staging-uri]", + "projectName": "UiPath Document Understanding", + "projectDescription": "UiPath models to classify and extract information from images and pdfs.", + "tenantName": "UiPath", + "minAIFabricVersion": "22.10", + "languageVersion": 4, + "version": 33, + "customVersion": "23.10.9", + "imagePath": "du-semistructured:v23.10-03.14-rc02", + "parametersFileJSON": "{\"params\": [{\"name\": \"auto_retraining\",\"type\": [\"PIPELINE\"],\"value\": \"False\",\"help\": \"Enable this option when the Pipeline you are creating is a scheduled automatic pipeline, synchronized with automatic exports from Data Manager.\",\"options\": [\"True\",\"False\"]}],\"probes\": [{\"probe\": \"startup\",\"initialDelaySeconds\": \"120\",\"periodSeconds\": \"10\",\"timeoutSeconds\": \"2\",\"successThreshold\": \"1\",\"failureThreshold\": \"60\"}]}", + "settings": [ + { + "key": "package_type", + "type": "STRING", + "value": "checks" + } + ], + "maxAIFabricVersion": "24.10" +} diff --git a/metadata/Checks__34__metadata.json b/metadata/Checks__34__metadata.json new file mode 100644 index 00000000..89f337f0 --- /dev/null +++ b/metadata/Checks__34__metadata.json @@ -0,0 +1,33 @@ +{ + "name": "Checks", + "retrainable": true, + "gpu": 0, + "processorType": "CPU", + "description": "Machine Learning model for extracting commonly occurring data points from Checks, including header fields and line items. 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The activities also require an API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -> Other Services view.", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "cpu": 1, + "inputType": "JSON", + "displayName": "Checks", + "memory": 0, + "mlPackageLanguage": "PYTHON37_DU", + "projectId": "[project-id]", + "stagingUri": "[staging-uri]", + "projectName": "UiPath Document Understanding", + "projectDescription": "UiPath models to classify and extract information from images and pdfs.", + "tenantName": "UiPath", + "minAIFabricVersion": "22.10", + "languageVersion": 4, + "version": 34, + "customVersion": "23.4.12", + "imagePath": "du-semistructured:v23.4-03.12-rc02", + "parametersFileJSON": "{\"params\": [{\"name\": \"auto_retraining\",\"type\": [\"PIPELINE\"],\"value\": \"False\",\"help\": \"Enable this option when the Pipeline you are creating is a scheduled automatic pipeline, synchronized with automatic exports from Data Manager.\",\"options\": [\"True\",\"False\"]}],\"probes\": [{\"probe\": \"startup\",\"initialDelaySeconds\": \"120\",\"periodSeconds\": \"10\",\"timeoutSeconds\": \"2\",\"successThreshold\": \"1\",\"failureThreshold\": \"60\"}]}", + "settings": [ + { + "key": "package_type", + "type": "STRING", + "value": "checks" + } + ], + "maxAIFabricVersion": "23.10" +} diff --git a/metadata/Checks__35__metadata.json b/metadata/Checks__35__metadata.json new file mode 100644 index 00000000..8fa26f8f --- /dev/null +++ b/metadata/Checks__35__metadata.json @@ -0,0 +1,32 @@ +{ + "name": "Checks", + "retrainable": true, + "gpu": 0, + "processorType": "CPU", + "description": "Machine Learning model for extracting commonly occurring data points from Checks, including header fields and line items. 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See the documentation page for this model for more details https://docs.uipath.com/document-understanding/automation-cloud/latest/user-guide/children-product-certificates-ml-package", + "inputDescription": "ML Skills deployed using this ML Package can be integrated into RPA workflows using the Machine Learning Extractor activity in the Official feed or the MLModel activity in Connect feed. Accepted file formats: pdf, tiff, jpg or png files. The activities also require an API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -> Other Services view.", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "changeLog": "", + "cpu": 1, + "inputType": "JSON", + "displayName": "ChildrenProductCertificates", + "memory": 0, + "mlPackageLanguage": "PYTHON37_DU", + "projectId": "[project-id]", + "stagingUri": "[staging-uri]", + "projectName": "UiPath Document Understanding", + "projectDescription": "UiPath models to classify and extract information from images and pdfs.", + "tenantName": "UiPath", + "minAIFabricVersion": "22.10", + "languageVersion": 4, + "version": 21, + "customVersion": "24.10.3", + "imagePath": "du-semistructured:v24.10-3.27-rc02", + "parametersFileJSON": "{\"params\": [{\"name\": \"auto_retraining\",\"type\": [\"PIPELINE\"],\"value\": \"False\",\"help\": \"Enable this option when the Pipeline you are creating is a scheduled automatic pipeline, synchronized with automatic exports from Data Manager.\",\"options\": [\"True\",\"False\"]}],\"probes\": [{\"probe\": \"startup\",\"initialDelaySeconds\": \"120\",\"periodSeconds\": \"10\",\"timeoutSeconds\": \"2\",\"successThreshold\": \"1\",\"failureThreshold\": \"60\"}]}", + "settings": [ + { + "key": "package_type", + "type": "STRING", + "value": "children_product_certificates" + } + ] +} diff --git a/metadata/ChildrenProductCertificates__22__metadata.json b/metadata/ChildrenProductCertificates__22__metadata.json new file mode 100644 index 00000000..68b05aa5 --- /dev/null +++ b/metadata/ChildrenProductCertificates__22__metadata.json @@ -0,0 +1,34 @@ +{ + "name": "ChildrenProductCertificates", + "retrainable": true, + "gpu": 0, + "processorType": "CPU", + "description": "Machine Learning model for extracting commonly occurring data points from Children Product Certificates. See the documentation page for this model for more details https://docs.uipath.com/document-understanding/automation-cloud/latest/user-guide/children-product-certificates-ml-package", + "inputDescription": "ML Skills deployed using this ML Package can be integrated into RPA workflows using the Machine Learning Extractor activity in the Official feed or the MLModel activity in Connect feed. Accepted file formats: pdf, tiff, jpg or png files. The activities also require an API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -> Other Services view.", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "changeLog": "", + "cpu": 1, + "inputType": "JSON", + "displayName": "ChildrenProductCertificates", + "memory": 0, + "mlPackageLanguage": "PYTHON37_DU", + "projectId": "[project-id]", + "stagingUri": "[staging-uri]", + "projectName": "UiPath Document Understanding", + "projectDescription": "UiPath models to classify and extract information from images and pdfs.", + "tenantName": "UiPath", + "minAIFabricVersion": "22.10", + "languageVersion": 4, + "version": 22, + "customVersion": "23.10.9", + "imagePath": "du-semistructured:v23.10-03.14-rc02", + "parametersFileJSON": "{\"params\": [{\"name\": \"auto_retraining\",\"type\": [\"PIPELINE\"],\"value\": \"False\",\"help\": \"Enable this option when the Pipeline you are creating is a scheduled automatic pipeline, synchronized with automatic exports from Data Manager.\",\"options\": [\"True\",\"False\"]}],\"probes\": [{\"probe\": \"startup\",\"initialDelaySeconds\": \"120\",\"periodSeconds\": \"10\",\"timeoutSeconds\": \"2\",\"successThreshold\": \"1\",\"failureThreshold\": \"60\"}]}", + "settings": [ + { + "key": "package_type", + "type": "STRING", + "value": "children_product_certificates" + } + ], + "maxAIFabricVersion": "24.10" +} diff --git a/metadata/ChildrenProductCertificates__23__metadata.json b/metadata/ChildrenProductCertificates__23__metadata.json new file mode 100644 index 00000000..cf9967a5 --- /dev/null +++ b/metadata/ChildrenProductCertificates__23__metadata.json @@ -0,0 +1,34 @@ +{ + "name": "ChildrenProductCertificates", + "retrainable": true, + "gpu": 0, + "processorType": "CPU", + "description": "Machine Learning model for extracting commonly occurring data points from Children Product Certificates. See the documentation page for this model for more details https://docs.uipath.com/document-understanding/automation-cloud/latest/user-guide/children-product-certificates-ml-package", + "inputDescription": "ML Skills deployed using this ML Package can be integrated into RPA workflows using the Machine Learning Extractor activity in the Official feed or the MLModel activity in Connect feed. Accepted file formats: pdf, tiff, jpg or png files. The activities also require an API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -> Other Services view.", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "changeLog": "", + "cpu": 1, + "inputType": "JSON", + "displayName": "ChildrenProductCertificates", + "memory": 0, + "mlPackageLanguage": "PYTHON37_DU", + "projectId": "[project-id]", + "stagingUri": "[staging-uri]", + "projectName": "UiPath Document Understanding", + "projectDescription": "UiPath models to classify and extract information from images and pdfs.", + "tenantName": "UiPath", + "minAIFabricVersion": "22.10", + "languageVersion": 4, + "version": 23, + "customVersion": "23.4.12", + "imagePath": "du-semistructured:v23.4-03.12-rc02", + "parametersFileJSON": "{\"params\": [{\"name\": \"auto_retraining\",\"type\": [\"PIPELINE\"],\"value\": \"False\",\"help\": \"Enable this option when the Pipeline you are creating is a scheduled automatic pipeline, synchronized with automatic exports from Data Manager.\",\"options\": [\"True\",\"False\"]}],\"probes\": [{\"probe\": \"startup\",\"initialDelaySeconds\": \"120\",\"periodSeconds\": \"10\",\"timeoutSeconds\": \"2\",\"successThreshold\": \"1\",\"failureThreshold\": \"60\"}]}", + "settings": [ + { + "key": "package_type", + "type": "STRING", + "value": "children_product_certificates" + } + ], + "maxAIFabricVersion": "23.10" +} diff --git a/metadata/ChildrenProductCertificates__24__metadata.json b/metadata/ChildrenProductCertificates__24__metadata.json new file mode 100644 index 00000000..8e480454 --- /dev/null +++ b/metadata/ChildrenProductCertificates__24__metadata.json @@ -0,0 +1,33 @@ +{ + "name": "ChildrenProductCertificates", + "retrainable": true, + "gpu": 0, + "processorType": "CPU", + "description": "Machine Learning model for extracting commonly occurring data points from Children Product Certificates. See the documentation page for this model for more details https://docs.uipath.com/document-understanding/automation-cloud/latest/user-guide/children-product-certificates-ml-package", + "inputDescription": "ML Skills deployed using this ML Package can be integrated into RPA workflows using the Machine Learning Extractor activity in the Official feed or the MLModel activity in Connect feed. Accepted file formats: pdf, tiff, jpg or png files. The activities also require an API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -> Other Services view.", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "changeLog": "", + "cpu": 1, + "inputType": "JSON", + "displayName": "ChildrenProductCertificates", + "memory": 0, + "mlPackageLanguage": "PYTHON37_DU", + "projectId": "[project-id]", + "stagingUri": "[staging-uri]", + "projectName": "UiPath Document Understanding", + "projectDescription": "UiPath models to classify and extract information from images and pdfs.", + "tenantName": "UiPath", + "minAIFabricVersion": "22.10", + "languageVersion": 4, + "version": 24, + "customVersion": "24.10.4", + "imagePath": "du-semistructured:v24.10-6.16-rc04", + "parametersFileJSON": "{\"params\": [{\"name\": \"auto_retraining\",\"type\": [\"PIPELINE\"],\"value\": \"False\",\"help\": \"Enable this option when the Pipeline you are creating is a scheduled automatic pipeline, synchronized with automatic exports from Data Manager.\",\"options\": [\"True\",\"False\"]}],\"probes\": [{\"probe\": \"startup\",\"initialDelaySeconds\": \"120\",\"periodSeconds\": \"10\",\"timeoutSeconds\": \"2\",\"successThreshold\": \"1\",\"failureThreshold\": \"60\"}]}", + "settings": [ + { + "key": "package_type", + "type": "STRING", + "value": "children_product_certificates" + } + ] +} diff --git a/metadata/ChildrenProductCertificates__25__metadata.json b/metadata/ChildrenProductCertificates__25__metadata.json new file mode 100644 index 00000000..9009ebc6 --- /dev/null +++ b/metadata/ChildrenProductCertificates__25__metadata.json @@ -0,0 +1,34 @@ +{ + "name": "ChildrenProductCertificates", + "retrainable": true, + "gpu": 0, + "processorType": "CPU", + "description": "Machine Learning model for extracting commonly occurring data points from Children Product Certificates. See the documentation page for this model for more details https://docs.uipath.com/document-understanding/automation-cloud/latest/user-guide/children-product-certificates-ml-package", + "inputDescription": "ML Skills deployed using this ML Package can be integrated into RPA workflows using the Machine Learning Extractor activity in the Official feed or the MLModel activity in Connect feed. Accepted file formats: pdf, tiff, jpg or png files. The activities also require an API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -> Other Services view.", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "changeLog": "", + "cpu": 1, + "inputType": "JSON", + "displayName": "ChildrenProductCertificates", + "memory": 0, + "mlPackageLanguage": "PYTHON37_DU", + "projectId": "[project-id]", + "stagingUri": "[staging-uri]", + "projectName": "UiPath Document Understanding", + "projectDescription": "UiPath models to classify and extract information from images and pdfs.", + "tenantName": "UiPath", + "minAIFabricVersion": "22.10", + "languageVersion": 4, + "version": 25, + "customVersion": "23.10.10", + "imagePath": "du-semistructured:v23.10-06.05-rc05", + "parametersFileJSON": "{\"params\": [{\"name\": \"auto_retraining\",\"type\": [\"PIPELINE\"],\"value\": \"False\",\"help\": \"Enable this option when the Pipeline you are creating is a scheduled automatic pipeline, synchronized with automatic exports from Data Manager.\",\"options\": [\"True\",\"False\"]}],\"probes\": [{\"probe\": \"startup\",\"initialDelaySeconds\": \"120\",\"periodSeconds\": \"10\",\"timeoutSeconds\": \"2\",\"successThreshold\": \"1\",\"failureThreshold\": \"60\"}]}", + "settings": [ + { + "key": "package_type", + "type": "STRING", + "value": "children_product_certificates" + } + ], + "maxAIFabricVersion": "24.10" +} diff --git a/metadata/ChildrenProductCertificates__26__metadata.json b/metadata/ChildrenProductCertificates__26__metadata.json new file mode 100644 index 00000000..100a2713 --- /dev/null +++ b/metadata/ChildrenProductCertificates__26__metadata.json @@ -0,0 +1,33 @@ +{ + "name": "ChildrenProductCertificates", + "retrainable": true, + "gpu": 0, + "processorType": "CPU", + "description": "Machine Learning model for extracting commonly occurring data points from Children Product Certificates. See the documentation page for this model for more details https://docs.uipath.com/document-understanding/automation-cloud/latest/user-guide/children-product-certificates-ml-package", + "inputDescription": "ML Skills deployed using this ML Package can be integrated into RPA workflows using the Machine Learning Extractor activity in the Official feed or the MLModel activity in Connect feed. Accepted file formats: pdf, tiff, jpg or png files. The activities also require an API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -> Other Services view.", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "changeLog": "", + "cpu": 1, + "inputType": "JSON", + "displayName": "ChildrenProductCertificates", + "memory": 0, + "mlPackageLanguage": "PYTHON37_DU", + "projectId": "[project-id]", + "stagingUri": "[staging-uri]", + "projectName": "UiPath Document Understanding", + "projectDescription": "UiPath models to classify and extract information from images and pdfs.", + "tenantName": "UiPath", + "minAIFabricVersion": "22.10", + "languageVersion": 4, + "version": 26, + "customVersion": "24.10.5", + "imagePath": "du-semistructured:v24.10-8.25-rc02", + "parametersFileJSON": "{\"params\": [{\"name\": \"auto_retraining\",\"type\": [\"PIPELINE\"],\"value\": \"False\",\"help\": \"Enable this option when the Pipeline you are creating is a scheduled automatic pipeline, synchronized with automatic exports from Data Manager.\",\"options\": [\"True\",\"False\"]}],\"probes\": [{\"probe\": \"startup\",\"initialDelaySeconds\": \"120\",\"periodSeconds\": \"10\",\"timeoutSeconds\": \"2\",\"successThreshold\": \"1\",\"failureThreshold\": \"60\"}]}", + "settings": [ + { + "key": "package_type", + "type": "STRING", + "value": "children_product_certificates" + } + ] +} diff --git a/metadata/ChildrenProductCertificates__27__metadata.json b/metadata/ChildrenProductCertificates__27__metadata.json new file mode 100644 index 00000000..14db4051 --- /dev/null +++ b/metadata/ChildrenProductCertificates__27__metadata.json @@ -0,0 +1,34 @@ +{ + "name": "ChildrenProductCertificates", + "retrainable": true, + "gpu": 0, + "processorType": "CPU", + "description": "Machine Learning model for extracting commonly occurring data points from Children Product Certificates. See the documentation page for this model for more details https://docs.uipath.com/document-understanding/automation-cloud/latest/user-guide/children-product-certificates-ml-package", + "inputDescription": "ML Skills deployed using this ML Package can be integrated into RPA workflows using the Machine Learning Extractor activity in the Official feed or the MLModel activity in Connect feed. Accepted file formats: pdf, tiff, jpg or png files. The activities also require an API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -> Other Services view.", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "changeLog": "", + "cpu": 1, + "inputType": "JSON", + "displayName": "ChildrenProductCertificates", + "memory": 0, + "mlPackageLanguage": "PYTHON37_DU", + "projectId": "[project-id]", + "stagingUri": "[staging-uri]", + "projectName": "UiPath Document Understanding", + "projectDescription": "UiPath models to classify and extract information from images and pdfs.", + "tenantName": "UiPath", + "minAIFabricVersion": "22.10", + "languageVersion": 4, + "version": 27, + "customVersion": "23.10.11", + "imagePath": "du-semistructured:v23.10-08.25-rc02", + "parametersFileJSON": "{\"params\": [{\"name\": \"auto_retraining\",\"type\": [\"PIPELINE\"],\"value\": \"False\",\"help\": \"Enable this option when the Pipeline you are creating is a scheduled automatic pipeline, synchronized with automatic exports from Data Manager.\",\"options\": [\"True\",\"False\"]}],\"probes\": [{\"probe\": \"startup\",\"initialDelaySeconds\": \"120\",\"periodSeconds\": \"10\",\"timeoutSeconds\": \"2\",\"successThreshold\": \"1\",\"failureThreshold\": \"60\"}]}", + "settings": [ + { + "key": "package_type", + "type": "STRING", + "value": "children_product_certificates" + } + ], + "maxAIFabricVersion": "24.10" +} diff --git a/metadata/ChildrenProductCertificates__28__metadata.json b/metadata/ChildrenProductCertificates__28__metadata.json new file mode 100644 index 00000000..fa636999 --- /dev/null +++ b/metadata/ChildrenProductCertificates__28__metadata.json @@ -0,0 +1,33 @@ +{ + "name": "ChildrenProductCertificates", + "retrainable": true, + "gpu": 0, + "processorType": "CPU", + "description": "Machine Learning model for extracting commonly occurring data points from Children Product Certificates. See the documentation page for this model for more details https://docs.uipath.com/document-understanding/automation-cloud/latest/user-guide/children-product-certificates-ml-package", + "inputDescription": "ML Skills deployed using this ML Package can be integrated into RPA workflows using the Machine Learning Extractor activity in the Official feed or the MLModel activity in Connect feed. Accepted file formats: pdf, tiff, jpg or png files. The activities also require an API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -> Other Services view.", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "changeLog": "", + "cpu": 1, + "inputType": "JSON", + "displayName": "ChildrenProductCertificates", + "memory": 0, + "mlPackageLanguage": "PYTHON37_DU", + "projectId": "[project-id]", + "stagingUri": "[staging-uri]", + "projectName": "UiPath Document Understanding", + "projectDescription": "UiPath models to classify and extract information from images and pdfs.", + "tenantName": "UiPath", + "minAIFabricVersion": "22.10", + "languageVersion": 4, + "version": 28, + "customVersion": "25.10.0", + "imagePath": "du-semistructured:v25.10-10.17-rc04", + "parametersFileJSON": "{\"params\": [{\"name\": \"auto_retraining\",\"type\": [\"PIPELINE\"],\"value\": \"False\",\"help\": \"Enable this option when the Pipeline you are creating is a scheduled automatic pipeline, synchronized with automatic exports from Data Manager.\",\"options\": [\"True\",\"False\"]}],\"probes\": [{\"probe\": \"startup\",\"initialDelaySeconds\": \"120\",\"periodSeconds\": \"10\",\"timeoutSeconds\": \"2\",\"successThreshold\": \"1\",\"failureThreshold\": \"60\"}]}", + "settings": [ + { + "key": "package_type", + "type": "STRING", + "value": "children_product_certificates" + } + ] +} diff --git a/metadata/ComputerVision__122100703__metadata.json b/metadata/ComputerVision__122100703__metadata.json new file mode 100644 index 00000000..222bc8c2 --- /dev/null +++ b/metadata/ComputerVision__122100703__metadata.json @@ -0,0 +1,26 @@ +{ + "changeLog": "Release v2022.10.14", + "cpu": 0, + "description": "Backend server for the AI Computer Vision solution that detects User Interface Elements from a provided Application screenshot", + "displayName": "ComputerVision", + "gpu": 1, + "inputDescription": "Please use the Computer Vision Activities included in the UIAutomation package. For more information please visit the official documentation. https://docs.uipath.com/activities/other/latest/ui-automation/computer-vision-activities ", + "inputType": "JSON", + "memory": 0, + "mlPackageLanguage": "PYTHON36_CV", + "name": "ComputerVision", + "outputDescription": "Please use the Computer Vision Activities included in the UIAutomation package. For more information please visit the official documentation.", + "processorType": "GPU", + "projectId": "[project-id]", + "retrainable": false, + "stagingUri": "[staging-uri]", + "projectName": "UiPath Computer Vision", + "projectDescription": "Backend server for the AI Computer Vision solution that detects User Interface Elements from a provided Application screenshot", + "tenantName": "UiPath", + "minAIFabricVersion": "22.10", + "languageVersion": 3, + "version": 122100703, + "customVersion": "22.10.14", + "imagePath": "cv2210:22.10.7.3", + "parametersFileJSON": "{\"probes\": [{\"probe\": \"startup\",\"initialDelaySeconds\": \"0\",\"periodSeconds\": \"1\",\"timeoutSeconds\": \"1\",\"successThreshold\": \"1\",\"failureThreshold\": \"200\"}]}" +} diff --git a/metadata/ComputerVision__223041305__metadata.json b/metadata/ComputerVision__223041305__metadata.json new file mode 100644 index 00000000..2163483f --- /dev/null +++ b/metadata/ComputerVision__223041305__metadata.json @@ -0,0 +1,26 @@ +{ + "changeLog": "Release v2023.04.12", + "cpu": 0, + "description": "Backend server for the AI Computer Vision solution that detects User Interface Elements from a provided Application screenshot", + "displayName": "ComputerVision", + "gpu": 1, + "inputDescription": "Please use the Computer Vision Activities included in the UIAutomation package. For more information please visit the official documentation. https://docs.uipath.com/activities/other/latest/ui-automation/computer-vision-activities ", + "inputType": "JSON", + "memory": 0, + "mlPackageLanguage": "PYTHON36_CV", + "name": "ComputerVision", + "outputDescription": "Please use the Computer Vision Activities included in the UIAutomation package. For more information please visit the official documentation. https://docs.uipath.com/activities/other/latest/ui-automation/computer-vision-activities ", + "processorType": "GPU", + "projectId": "[project-id]", + "retrainable": false, + "stagingUri": "[staging-uri]", + "projectName": "UiPath Computer Vision", + "projectDescription": "Backend server for the AI Computer Vision solution that detects User Interface Elements from a provided Application screenshot", + "tenantName": "UiPath", + "minAIFabricVersion": "22.10", + "languageVersion": 3, + "version": 223041305, + "customVersion": "23.04.10", + "imagePath": "cv2304:23.4.13.5", + "parametersFileJSON": "{\"probes\": [{\"probe\": \"startup\",\"initialDelaySeconds\": \"0\",\"periodSeconds\": \"1\",\"timeoutSeconds\": \"1\",\"successThreshold\": \"1\",\"failureThreshold\": \"200\"}]}" +} diff --git a/metadata/ComputerVision__223100517__metadata.json b/metadata/ComputerVision__223100517__metadata.json new file mode 100644 index 00000000..15ff9775 --- /dev/null +++ b/metadata/ComputerVision__223100517__metadata.json @@ -0,0 +1,26 @@ +{ + "changeLog": "Release v2023.10.9", + "cpu": 0, + "description": "Backend server for the AI Computer Vision solution that detects User Interface Elements from a provided Application screenshot", + "displayName": "ComputerVision", + "gpu": 1, + "inputDescription": "Please use the Computer Vision Activities included in the UIAutomation package. For more information please visit the official documentation. https://docs.uipath.com/activities/other/latest/ui-automation/computer-vision-activities ", + "inputType": "JSON", + "memory": 0, + "mlPackageLanguage": "PYTHON36_CV", + "name": "ComputerVision", + "outputDescription": "Please use the Computer Vision Activities included in the UIAutomation package. For more information please visit the official documentation. https://docs.uipath.com/activities/other/latest/ui-automation/computer-vision-activities ", + "processorType": "GPU", + "projectId": "[project-id]", + "retrainable": false, + "stagingUri": "[staging-uri]", + "projectName": "UiPath Computer Vision", + "projectDescription": "Backend server for the AI Computer Vision solution that detects User Interface Elements from a provided Application screenshot", + "tenantName": "UiPath", + "minAIFabricVersion": "22.10", + "languageVersion": 3, + "version": 223100517, + "customVersion": "23.10.7", + "imagePath": "cv2310:23.10.5.17", + "parametersFileJSON": "{\"probes\": [{\"probe\": \"startup\",\"initialDelaySeconds\": \"0\",\"periodSeconds\": \"1\",\"timeoutSeconds\": \"1\",\"successThreshold\": \"1\",\"failureThreshold\": \"600\"}]}" +} diff --git a/metadata/ComputerVision__224100002__metadata.json b/metadata/ComputerVision__224100002__metadata.json new file mode 100644 index 00000000..c84a9506 --- /dev/null +++ b/metadata/ComputerVision__224100002__metadata.json @@ -0,0 +1,26 @@ +{ + "changeLog": "Release v2023.10.9", + "cpu": 0, + "description": "Backend server for the AI Computer Vision solution that detects User Interface Elements from a provided Application screenshot", + "displayName": "ComputerVision", + "gpu": 1, + "inputDescription": "Please use the Computer Vision Activities included in the UIAutomation package. For more information please visit the official documentation. https://docs.uipath.com/activities/other/latest/ui-automation/computer-vision-activities ", + "inputType": "JSON", + "memory": 0, + "mlPackageLanguage": "PYTHON36_CV", + "name": "ComputerVision", + "outputDescription": "Please use the Computer Vision Activities included in the UIAutomation package. For more information please visit the official documentation. https://docs.uipath.com/activities/other/latest/ui-automation/computer-vision-activities ", + "processorType": "GPU", + "projectId": "[project-id]", + "retrainable": false, + "stagingUri": "[staging-uri]", + "projectName": "UiPath Computer Vision", + "projectDescription": "Backend server for the AI Computer Vision solution that detects User Interface Elements from a provided Application screenshot", + "tenantName": "UiPath", + "minAIFabricVersion": "22.10", + "languageVersion": 3, + "version": 224100002, + "customVersion": "23.10.7.1", + "imagePath": "cv2410:24.10.0.2", + "parametersFileJSON": "{\"probes\": [{\"probe\": \"startup\",\"initialDelaySeconds\": \"0\",\"periodSeconds\": \"1\",\"timeoutSeconds\": \"1\",\"successThreshold\": \"1\",\"failureThreshold\": \"600\"}]}" +} diff --git a/metadata/ComputerVision__1__metadata.json b/metadata/ComputerVision__2__metadata.json similarity index 66% rename from metadata/ComputerVision__1__metadata.json rename to metadata/ComputerVision__2__metadata.json index 89cd9475..06093c50 100644 --- a/metadata/ComputerVision__1__metadata.json +++ b/metadata/ComputerVision__2__metadata.json @@ -1,21 +1,24 @@ -{ - "changeLog": "Release v2020.10", - "cpu": 0, - "description": "Backend server for the Computer Vision solution that detects User Interface Elements from a provided Application screenshot", - "displayName": "ComputerVision", - "gpu": 0, - "inputDescription": "Please use the Computer Vision Activities included in the UIAutomation package. For more information please visit the official documentation.", - "inputType": "JSON", - "memory": 0, - "mlPackageLanguage": "PYTHON36_CV", - "name": "ComputerVision", - "outputDescription": "Please use the Computer Vision Activities included in the UIAutomation package. For more information please visit the official documentation.", - "processorType": "GPU", - "projectId": "[project-id]", - "retrainable": false, - "stagingUri": "[staging-uri]", - "projectName": "UiPath Document Understanding", - "projectDescription": "UiPath models to classify and extract information from images and pdfs.", - "tenantName": "UiPath", - "imagePath": "registry.replicated.com/aif-core/computervision:1" +{ + "changeLog": "Release v2021.2", + "cpu": 0, + "description": "Backend server for the Computer Vision solution that detects User Interface Elements from a provided Application screenshot", + "displayName": "ComputerVision", + "gpu": 0, + "inputDescription": "Please use the Computer Vision Activities included in the UIAutomation package. For more information please visit the official documentation.", + "inputType": "JSON", + "memory": 0, + "mlPackageLanguage": "PYTHON36_CV", + "name": "ComputerVision", + "outputDescription": "Please use the Computer Vision Activities included in the UIAutomation package. For more information please visit the official documentation.", + "processorType": "GPU", + "projectId": "[project-id]", + "retrainable": false, + "stagingUri": "[staging-uri]", + "projectName": "UiPath Computer Vision", + "projectDescription": "Backend server for the Computer Vision solution that detects User Interface Elements from a provided Application screenshot", + "tenantName": "UiPath", + "minAIFabricVersion": "v21.10", + "languageVersion": 2, + "version": 2, + "contentUri": "https:///publicmodels/AIC/ComputerVision/2/cv_server.zip" } \ No newline at end of file diff --git a/metadata/ComputerVision__3__metadata.json b/metadata/ComputerVision__3__metadata.json new file mode 100644 index 00000000..c8a65f5d --- /dev/null +++ b/metadata/ComputerVision__3__metadata.json @@ -0,0 +1,26 @@ +{ + "changeLog": "Release v2022.04.07001", + "cpu": 0, + "description": "Backend server for the Computer Vision solution that detects User Interface Elements from a provided Application screenshot", + "displayName": "ComputerVision", + "gpu": 1, + "inputDescription": "Please use the Computer Vision Activities included in the UIAutomation package. For more information please visit the official documentation.", + "inputType": "JSON", + "memory": 0, + "mlPackageLanguage": "PYTHON36_CV", + "name": "ComputerVision", + "outputDescription": "Please use the Computer Vision Activities included in the UIAutomation package. For more information please visit the official documentation.", + "processorType": "GPU", + "projectId": "[project-id]", + "retrainable": false, + "stagingUri": "[staging-uri]", + "projectName": "UiPath Computer Vision", + "projectDescription": "Backend server for the Computer Vision solution that detects User Interface Elements from a provided Application screenshot", + "tenantName": "UiPath", + "minAIFabricVersion": "v21.10", + "languageVersion": 3, + "version": 3, + "baseImageCpu": "python36cv:22.4.0-3", + "baseImageGpu": "python36cv:22.4.0-3", + "contentUri": "https:///publicmodels/AIC/ComputerVision/3/cv_server.zip" +} \ No newline at end of file diff --git a/metadata/CustomNamedEntityRecognition__10__metadata.json b/metadata/CustomNamedEntityRecognition__10__metadata.json new file mode 100644 index 00000000..44925610 --- /dev/null +++ b/metadata/CustomNamedEntityRecognition__10__metadata.json @@ -0,0 +1,23 @@ +{ + "name": "CustomNamedEntityRecognition", + "retrainable": true, + "gpu": 0, + "processorType": "CPU", + "description": "This model allows you to bring your own dataset tagged with entities you want to extract. The training and evaluation datasets need to be in CoNLL or JSON format as highlighted in documentation : https://docs.uipath.com/ai-fabric/v0/docs/aic-cloud-custom-named-entity-recognition#dataset-format", + "inputDescription": "Text in one of the 100 languages here (https://docs.uipath.com/ai-fabric/v0/docs/aic-cloud-custom-named-entity-recognition#languages) from which entities will be extracted.", + "outputDescription": "A Json Response with list of named entities in the text. Each element in the list has the following items in the prediction: 1. Text that was recognized; 2. Starting and ending positions of the text, character-wise; 3. Type of the named entity; 4.Confidence.", + "changeLog": "model 22.6.0", + "cpu": 1, + "inputType": "JSON", + "displayName": "CustomNamedEntityRecognition", + "memory": 0, + "mlPackageLanguage": "PYTHON38", + "projectId": "[project-id]", + "stagingUri": "[staging-uri]", + "projectName": "UiPath Language Analysis", + "projectDescription": "Curated Models from UiPath to analyze language in Emails, Documents, Web Pages, Text Messages, Portals and more", + "tenantName": "UiPath", + "minAIFabricVersion": "v22.4", + "version": 10, + "contentUri": "https:///publicmodels/AIC/CustomNamedEntityRecognition/10/ner_package.zip" +} \ No newline at end of file diff --git a/metadata/CustomNamedEntityRecognition__11__metadata.json b/metadata/CustomNamedEntityRecognition__11__metadata.json new file mode 100644 index 00000000..2e29d227 --- /dev/null +++ b/metadata/CustomNamedEntityRecognition__11__metadata.json @@ -0,0 +1,23 @@ +{ + "name": "CustomNamedEntityRecognition", + "retrainable": true, + "gpu": 0, + "processorType": "CPU", + "description": "This model allows you to bring your own dataset tagged with entities you want to extract. The training and evaluation datasets need to be in CoNLL or JSON format as highlighted in documentation : https://docs.uipath.com/ai-fabric/v0/docs/aic-cloud-custom-named-entity-recognition#dataset-format", + "inputDescription": "Text in one of the 100 languages here (https://docs.uipath.com/ai-fabric/v0/docs/aic-cloud-custom-named-entity-recognition#languages) from which entities will be extracted.", + "outputDescription": "A Json Response with list of named entities in the text. Each element in the list has the following items in the prediction: 1. Text that was recognized; 2. Starting and ending positions of the text, character-wise; 3. Type of the named entity; 4.Confidence.", + "changeLog": "model 22.6.0", + "cpu": 1, + "inputType": "JSON", + "displayName": "CustomNamedEntityRecognition", + "memory": 0, + "mlPackageLanguage": "PYTHON38", + "projectId": "[project-id]", + "stagingUri": "[staging-uri]", + "projectName": "UiPath Language Analysis", + "projectDescription": "Curated Models from UiPath to analyze language in Emails, Documents, Web Pages, Text Messages, Portals and more", + "tenantName": "UiPath", + "minAIFabricVersion": "v22.4", + "version": 11, + "contentUri": "https:///publicmodels/AIC/CustomNamedEntityRecognition/11/ner_package.zip" +} \ No newline at end of file diff --git a/metadata/CustomNamedEntityRecognition__8__metadata.json b/metadata/CustomNamedEntityRecognition__8__metadata.json new file mode 100644 index 00000000..64be98d1 --- /dev/null +++ b/metadata/CustomNamedEntityRecognition__8__metadata.json @@ -0,0 +1,23 @@ +{ + "name": "CustomNamedEntityRecognition", + "retrainable": true, + "gpu": 0, + "processorType": "CPU", + "description": "This model allows you to bring your own dataset tagged with entities you want to extract. The training and evaluation datasets need to be in CoNLL or JSON format as highlighted in documentation : https://docs.uipath.com/ai-fabric/v0/docs/aic-cloud-custom-named-entity-recognition#dataset-format", + "inputDescription": "Text in one of the 100 languages here (https://docs.uipath.com/ai-fabric/v0/docs/aic-cloud-custom-named-entity-recognition#languages) from which entities will be extracted.", + "outputDescription": "A Json Response with list of named entities in the text. Each element in the list has the following items in the prediction: 1. Text that was recognized; 2. Starting and ending positions of the text, character-wise; 3. Type of the named entity; 4.Confidence.", + "changeLog": "model 22.4.0", + "cpu": 1, + "inputType": "JSON", + "displayName": "CustomNamedEntityRecognition", + "memory": 0, + "mlPackageLanguage": "PYTHON38", + "projectId": "[project-id]", + "stagingUri": "[staging-uri]", + "projectName": "UiPath Language Analysis", + "projectDescription": "Curated Models from UiPath to analyze language in Emails, Documents, Web Pages, Text Messages, Portals and more", + "tenantName": "UiPath", + "minAIFabricVersion": "v22.4", + "version": 8, + "contentUri": "https:///publicmodels/AIC/CustomNamedEntityRecognition/8/ner_package.zip" +} \ No newline at end of file diff --git a/metadata/CustomNamedEntityRecognition__9__metadata.json b/metadata/CustomNamedEntityRecognition__9__metadata.json new file mode 100644 index 00000000..0472e9e1 --- /dev/null +++ b/metadata/CustomNamedEntityRecognition__9__metadata.json @@ -0,0 +1,23 @@ +{ + "name": "CustomNamedEntityRecognition", + "retrainable": true, + "gpu": 0, + "processorType": "CPU", + "description": "This model allows you to bring your own dataset tagged with entities you want to extract. The training and evaluation datasets need to be in CoNLL or JSON format as highlighted in documentation : https://docs.uipath.com/ai-fabric/v0/docs/aic-cloud-custom-named-entity-recognition#dataset-format", + "inputDescription": "Text in one of the 100 languages here (https://docs.uipath.com/ai-fabric/v0/docs/aic-cloud-custom-named-entity-recognition#languages) from which entities will be extracted.", + "outputDescription": "A Json Response with list of named entities in the text. Each element in the list has the following items in the prediction: 1. Text that was recognized; 2. Starting and ending positions of the text, character-wise; 3. Type of the named entity; 4.Confidence.", + "changeLog": "model 22.6.0", + "cpu": 1, + "inputType": "JSON", + "displayName": "CustomNamedEntityRecognition", + "memory": 0, + "mlPackageLanguage": "PYTHON38", + "projectId": "[project-id]", + "stagingUri": "[staging-uri]", + "projectName": "UiPath Language Analysis", + "projectDescription": "Curated Models from UiPath to analyze language in Emails, Documents, Web Pages, Text Messages, Portals and more", + "tenantName": "UiPath", + "minAIFabricVersion": "v22.4", + "version": 9, + "contentUri": "https:///publicmodels/AIC/CustomNamedEntityRecognition/9/ner_package.zip" +} \ No newline at end of file diff --git a/metadata/DeliveryNotes__3__metadata.json b/metadata/DeliveryNotes__3__metadata.json new file mode 100644 index 00000000..0db642d3 --- /dev/null +++ b/metadata/DeliveryNotes__3__metadata.json @@ -0,0 +1,26 @@ +{ + "name": "DeliveryNotes", + "retrainable": true, + "gpu": 0, + "processorType": "CPU", + "description": "Machine Learning model for extracting commonly occurring data points from Delivery Notes, including header fields and line items. Please see more details including supported languages and link to Activities guide in the About Licensing -> Document Understanding API Key section of the UiPath Automation Cloud Guide here: https://docs.uipath.com/automation-cloud/docs/about-licensing#section-document-understanding-api-key", + "inputDescription": "ML Skills deployed using this ML Package can be integrated into RPA workflows using the Machine Learning Extractor activity in the Official feed or the MLModel activity in Connect feed. File formats accepted include pdf, tiff, jpg or png files. The activities also require an API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -> Other Services view.", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "changeLog": "", + "cpu": 1, + "inputType": "JSON", + "displayName": "DeliveryNotes", + "memory": 0, + "mlPackageLanguage": "PYTHON37_DU", + "projectId": "[project-id]", + "stagingUri": "[staging-uri]", + "projectName": "UiPath Document Understanding", + "projectDescription": "UiPath models to classify and extract information from images and pdfs.", + "tenantName": "UiPath", + "minAIFabricVersion": "22.4", + "languageVersion": 4, + "version": 3, + "customVersion": "22.4.1", + "contentUri": "https:///publicmodels/AIC/DeliveryNotes/22.4.1/delivery_notes_package.zip", + "maxAIFabricVersion": "22.4" +} \ No newline at end of file diff --git a/metadata/DocumentClassifier__25__metadata.json b/metadata/DocumentClassifier__25__metadata.json new file mode 100644 index 00000000..d3be7473 --- /dev/null +++ b/metadata/DocumentClassifier__25__metadata.json @@ -0,0 +1,27 @@ +{ + "changeLog": "", + "cpu": 1, + "description": "Generic Machine Learning package for document classification. Use this package to create custom document classification Machine Learning models.", + "displayName": "DocumentClassifier", + "gpu": 0, + "inputDescription": "ML Skills deployed using this ML Package can be integrated into RPA workflows using the Machine Learning Classifier activity in the Official feed. File formats accepted include pdf, tiff, jpg or png files. The activity also requires an API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -> Other Services view", + "inputType": "JSON", + "memory": 0, + "mlPackageLanguage": "PYTHON37_DU", + "name": "DocumentClassifier", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "processorType": "CPU", + "retrainable": true, + "stagingUri": "[staging-uri]", + "projectId": "[project-id]", + "projectName": "UiPath Document Understanding", + "projectDescription": "UiPath models to classify and extract information from images and pdfs.", + "tenantName": "UiPath", + "minAIFabricVersion": "22.10", + "languageVersion": 4, + "version": 25, + "customVersion": "22.10.14", + "imagePath": "du-ml-document-type-text-classifier:v22.10-10.10-rc01", + "parametersFileJSON": "{\"params\": [{\"name\": \"auto_retraining\",\"type\": [\"PIPELINE\"],\"value\": \"False\",\"help\": \"Enable this option when the Pipeline you are creating is a scheduled automatic pipeline, synchronized with automatic exports from Data Manager.\",\"options\": [\"True\",\"False\"]}],\"probes\": [{\"probe\": \"startup\",\"initialDelaySeconds\": \"120\",\"periodSeconds\": \"10\",\"timeoutSeconds\": \"2\",\"successThreshold\": \"1\",\"failureThreshold\": \"60\"}]}", + "maxAIFabricVersion": "23.4" +} \ No newline at end of file diff --git a/metadata/DocumentClassifier__31__metadata.json b/metadata/DocumentClassifier__31__metadata.json new file mode 100644 index 00000000..04ebad5f --- /dev/null +++ b/metadata/DocumentClassifier__31__metadata.json @@ -0,0 +1,26 @@ +{ + "changeLog": "", + "cpu": 1, + "description": "Generic Machine Learning package for document classification. Use this package to create custom document classification Machine Learning models.", + "displayName": "DocumentClassifier", + "gpu": 0, + "inputDescription": "ML Skills deployed using this ML Package can be integrated into RPA workflows using the Machine Learning Classifier activity in the Official feed. File formats accepted include pdf, tiff, jpg or png files. The activity also requires an API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -> Other Services view", + "inputType": "JSON", + "memory": 0, + "mlPackageLanguage": "PYTHON37_DU", + "name": "DocumentClassifier", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "processorType": "CPU", + "retrainable": true, + "stagingUri": "[staging-uri]", + "projectId": "[project-id]", + "projectName": "UiPath Document Understanding", + "projectDescription": "UiPath models to classify and extract information from images and pdfs.", + "tenantName": "UiPath", + "minAIFabricVersion": "22.10", + "languageVersion": 4, + "version": 31, + "customVersion": "24.10.2", + "imagePath": "du-ml-document-type-text-classifier:v24.10-1.27-rc02", + "parametersFileJSON": "{\"params\": [{\"name\": \"auto_retraining\",\"type\": [\"PIPELINE\"],\"value\": \"False\",\"help\": \"Enable this option when the Pipeline you are creating is a scheduled automatic pipeline, synchronized with automatic exports from Data Manager.\",\"options\": [\"True\",\"False\"]}],\"probes\": [{\"probe\": \"startup\",\"initialDelaySeconds\": \"120\",\"periodSeconds\": \"10\",\"timeoutSeconds\": \"2\",\"successThreshold\": \"1\",\"failureThreshold\": \"60\"}]}" +} \ No newline at end of file diff --git a/metadata/DocumentClassifier__32__metadata.json b/metadata/DocumentClassifier__32__metadata.json new file mode 100644 index 00000000..e5953a3f --- /dev/null +++ b/metadata/DocumentClassifier__32__metadata.json @@ -0,0 +1,27 @@ +{ + "changeLog": "", + "cpu": 1, + "description": "Generic Machine Learning package for document classification. Use this package to create custom document classification Machine Learning models.", + "displayName": "DocumentClassifier", + "gpu": 0, + "inputDescription": "ML Skills deployed using this ML Package can be integrated into RPA workflows using the Machine Learning Classifier activity in the Official feed. File formats accepted include pdf, tiff, jpg or png files. The activity also requires an API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -> Other Services view", + "inputType": "JSON", + "memory": 0, + "mlPackageLanguage": "PYTHON37_DU", + "name": "DocumentClassifier", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "processorType": "CPU", + "retrainable": true, + "stagingUri": "[staging-uri]", + "projectId": "[project-id]", + "projectName": "UiPath Document Understanding", + "projectDescription": "UiPath models to classify and extract information from images and pdfs.", + "tenantName": "UiPath", + "minAIFabricVersion": "22.10", + "languageVersion": 4, + "version": 32, + "customVersion": "23.4.11", + "imagePath": "du-ml-document-type-text-classifier:v23.4-01.27-rc01", + "parametersFileJSON": "{\"params\": [{\"name\": \"auto_retraining\",\"type\": [\"PIPELINE\"],\"value\": \"False\",\"help\": \"Enable this option when the Pipeline you are creating is a scheduled automatic pipeline, synchronized with automatic exports from Data Manager.\",\"options\": [\"True\",\"False\"]}],\"probes\": [{\"probe\": \"startup\",\"initialDelaySeconds\": \"120\",\"periodSeconds\": \"10\",\"timeoutSeconds\": \"2\",\"successThreshold\": \"1\",\"failureThreshold\": \"60\"}]}", + "maxAIFabricVersion": "23.10" +} \ No newline at end of file diff --git a/metadata/DocumentClassifier__33__metadata.json b/metadata/DocumentClassifier__33__metadata.json new file mode 100644 index 00000000..bf3df068 --- /dev/null +++ b/metadata/DocumentClassifier__33__metadata.json @@ -0,0 +1,27 @@ +{ + "changeLog": "", + "cpu": 1, + "description": "Generic Machine Learning package for document classification. Use this package to create custom document classification Machine Learning models.", + "displayName": "DocumentClassifier", + "gpu": 0, + "inputDescription": "ML Skills deployed using this ML Package can be integrated into RPA workflows using the Machine Learning Classifier activity in the Official feed. File formats accepted include pdf, tiff, jpg or png files. The activity also requires an API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -> Other Services view", + "inputType": "JSON", + "memory": 0, + "mlPackageLanguage": "PYTHON37_DU", + "name": "DocumentClassifier", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "processorType": "CPU", + "retrainable": true, + "stagingUri": "[staging-uri]", + "projectId": "[project-id]", + "projectName": "UiPath Document Understanding", + "projectDescription": "UiPath models to classify and extract information from images and pdfs.", + "tenantName": "UiPath", + "minAIFabricVersion": "22.10", + "languageVersion": 4, + "version": 33, + "customVersion": "23.10.8", + "imagePath": "du-ml-document-type-text-classifier:v23.10-01.28-rc07", + "parametersFileJSON": "{\"params\": [{\"name\": \"auto_retraining\",\"type\": [\"PIPELINE\"],\"value\": \"False\",\"help\": \"Enable this option when the Pipeline you are creating is a scheduled automatic pipeline, synchronized with automatic exports from Data Manager.\",\"options\": [\"True\",\"False\"]}],\"probes\": [{\"probe\": \"startup\",\"initialDelaySeconds\": \"120\",\"periodSeconds\": \"10\",\"timeoutSeconds\": \"2\",\"successThreshold\": \"1\",\"failureThreshold\": \"60\"}]}", + "maxAIFabricVersion": "24.10" +} \ No newline at end of file diff --git a/metadata/DocumentClassifier__34__metadata.json b/metadata/DocumentClassifier__34__metadata.json new file mode 100644 index 00000000..8c1a9f27 --- /dev/null +++ b/metadata/DocumentClassifier__34__metadata.json @@ -0,0 +1,26 @@ +{ + "changeLog": "", + "cpu": 1, + "description": "Generic Machine Learning package for document classification. Use this package to create custom document classification Machine Learning models.", + "displayName": "DocumentClassifier", + "gpu": 0, + "inputDescription": "ML Skills deployed using this ML Package can be integrated into RPA workflows using the Machine Learning Classifier activity in the Official feed. File formats accepted include pdf, tiff, jpg or png files. The activity also requires an API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -> Other Services view", + "inputType": "JSON", + "memory": 0, + "mlPackageLanguage": "PYTHON37_DU", + "name": "DocumentClassifier", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "processorType": "CPU", + "retrainable": true, + "stagingUri": "[staging-uri]", + "projectId": "[project-id]", + "projectName": "UiPath Document Understanding", + "projectDescription": "UiPath models to classify and extract information from images and pdfs.", + "tenantName": "UiPath", + "minAIFabricVersion": "22.10", + "languageVersion": 4, + "version": 34, + "customVersion": "24.10.3", + "imagePath": "du-ml-document-type-text-classifier:v24.10-3.11-rc01", + "parametersFileJSON": "{\"params\": [{\"name\": \"auto_retraining\",\"type\": [\"PIPELINE\"],\"value\": \"False\",\"help\": \"Enable this option when the Pipeline you are creating is a scheduled automatic pipeline, synchronized with automatic exports from Data Manager.\",\"options\": [\"True\",\"False\"]}],\"probes\": [{\"probe\": \"startup\",\"initialDelaySeconds\": \"120\",\"periodSeconds\": \"10\",\"timeoutSeconds\": \"2\",\"successThreshold\": \"1\",\"failureThreshold\": \"60\"}]}" +} diff --git a/metadata/DocumentClassifier__35__metadata.json b/metadata/DocumentClassifier__35__metadata.json new file mode 100644 index 00000000..c25e308e --- /dev/null +++ b/metadata/DocumentClassifier__35__metadata.json @@ -0,0 +1,27 @@ +{ + "changeLog": "", + "cpu": 1, + "description": "Generic Machine Learning package for document classification. Use this package to create custom document classification Machine Learning models.", + "displayName": "DocumentClassifier", + "gpu": 0, + "inputDescription": "ML Skills deployed using this ML Package can be integrated into RPA workflows using the Machine Learning Classifier activity in the Official feed. File formats accepted include pdf, tiff, jpg or png files. 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Use this package to create custom document classification Machine Learning models.", + "displayName": "DocumentClassifier", + "gpu": 0, + "inputDescription": "ML Skills deployed using this ML Package can be integrated into RPA workflows using the Machine Learning Classifier activity in the Official feed. File formats accepted include pdf, tiff, jpg or png files. 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Use this package to create custom document classification Machine Learning models.", + "displayName": "DocumentClassifier", + "gpu": 0, + "inputDescription": "ML Skills deployed using this ML Package can be integrated into RPA workflows using the Machine Learning Classifier activity in the Official feed. File formats accepted include pdf, tiff, jpg or png files. 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The activities also require an API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -> Other Services view.", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "changeLog": "", + "cpu": 1, + "inputType": "JSON", + "displayName": "DocumentUnderstanding", + "memory": 0, + "mlPackageLanguage": "PYTHON37_DU", + "projectId": "[project-id]", + "stagingUri": "[staging-uri]", + "projectName": "UiPath Document Understanding", + "projectDescription": "UiPath models to classify and extract information from images and pdfs.", + "tenantName": "UiPath", + "minAIFabricVersion": "22.10", + "languageVersion": 4, + "version": 45, + "customVersion": "23.4.12", + "imagePath": "du-semistructured:v23.4-03.12-rc02", + "parametersFileJSON": "{\"params\": [{\"name\": \"auto_retraining\",\"type\": [\"PIPELINE\"],\"value\": \"False\",\"help\": \"Enable this option when the Pipeline you are creating is a scheduled automatic pipeline, synchronized with automatic exports from Data Manager.\",\"options\": [\"True\",\"False\"]}],\"probes\": [{\"probe\": \"startup\",\"initialDelaySeconds\": \"120\",\"periodSeconds\": \"10\",\"timeoutSeconds\": \"2\",\"successThreshold\": \"1\",\"failureThreshold\": \"60\"}]}", + "settings": [ + { + "key": "package_type", + "type": "STRING", + "value": "du" + } + ], + "maxAIFabricVersion": "23.10" +} diff --git a/metadata/DocumentUnderstanding__2__metadata.json b/metadata/DocumentUnderstanding__46__metadata.json similarity index 51% rename from metadata/DocumentUnderstanding__2__metadata.json rename to metadata/DocumentUnderstanding__46__metadata.json index 9766b3bd..5dcfc851 100644 --- a/metadata/DocumentUnderstanding__2__metadata.json +++ b/metadata/DocumentUnderstanding__46__metadata.json @@ -1,21 +1,33 @@ { - "changeLog": "Release v2020.7", - "cpu": 0, - "description": "Machine Learning model for extracting commonly occurring data points from Semi-structured or Structured documents, including regular fields, table columns and classification fields. 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The activities also require an API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -> Other Services view.", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "changeLog": "", + "cpu": 1, + "inputType": "JSON", + "displayName": "EUDeclarationOfConformity", + "memory": 0, + "mlPackageLanguage": "PYTHON37_DU", + "projectId": "[project-id]", + "stagingUri": "[staging-uri]", + "projectName": "UiPath Document Understanding", + "projectDescription": "UiPath models to classify and extract information from images and pdfs.", + "tenantName": "UiPath", + "minAIFabricVersion": "22.10", + "languageVersion": 4, + "version": 21, + "customVersion": "24.10.3", + "imagePath": "du-semistructured:v24.10-3.27-rc02", + "parametersFileJSON": "{\"params\": [{\"name\": \"auto_retraining\",\"type\": [\"PIPELINE\"],\"value\": \"False\",\"help\": \"Enable this option when the Pipeline you are creating is a scheduled automatic pipeline, synchronized with automatic exports from Data Manager.\",\"options\": [\"True\",\"False\"]}],\"probes\": [{\"probe\": \"startup\",\"initialDelaySeconds\": \"120\",\"periodSeconds\": \"10\",\"timeoutSeconds\": \"2\",\"successThreshold\": \"1\",\"failureThreshold\": \"60\"}]}", + "settings": [ + { + "key": "package_type", + "type": "STRING", + "value": "eu_declaration_conformity" + } + ] +} diff --git a/metadata/EUDeclarationOfConformity__22__metadata.json b/metadata/EUDeclarationOfConformity__22__metadata.json new file mode 100644 index 00000000..846db316 --- /dev/null +++ b/metadata/EUDeclarationOfConformity__22__metadata.json @@ -0,0 +1,34 @@ +{ + "name": "EUDeclarationOfConformity", + "retrainable": true, + "gpu": 0, + "processorType": "CPU", + "description": "Machine Learning model for extracting commonly occurring data points from EU Declaration of Conformity. 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The activities also require an API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -> Other Services view.", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "changeLog": "", + "cpu": 1, + "inputType": "JSON", + "displayName": "EUDeclarationOfConformity", + "memory": 0, + "mlPackageLanguage": "PYTHON37_DU", + "projectId": "[project-id]", + "stagingUri": "[staging-uri]", + "projectName": "UiPath Document Understanding", + "projectDescription": "UiPath models to classify and extract information from images and pdfs.", + "tenantName": "UiPath", + "minAIFabricVersion": "22.10", + "languageVersion": 4, + "version": 22, + "customVersion": "23.10.9", + "imagePath": "du-semistructured:v23.10-03.14-rc02", + "parametersFileJSON": "{\"params\": [{\"name\": \"auto_retraining\",\"type\": [\"PIPELINE\"],\"value\": \"False\",\"help\": \"Enable this option when the Pipeline you are creating is a scheduled automatic pipeline, synchronized with automatic exports from Data Manager.\",\"options\": [\"True\",\"False\"]}],\"probes\": [{\"probe\": \"startup\",\"initialDelaySeconds\": \"120\",\"periodSeconds\": \"10\",\"timeoutSeconds\": \"2\",\"successThreshold\": \"1\",\"failureThreshold\": \"60\"}]}", + "settings": [ + { + "key": "package_type", + "type": "STRING", + "value": "eu_declaration_conformity" + } + ], + "maxAIFabricVersion": "24.10" +} diff --git a/metadata/EUDeclarationOfConformity__23__metadata.json b/metadata/EUDeclarationOfConformity__23__metadata.json new file mode 100644 index 00000000..bd951249 --- /dev/null +++ b/metadata/EUDeclarationOfConformity__23__metadata.json @@ -0,0 +1,34 @@ +{ + "name": "EUDeclarationOfConformity", + "retrainable": true, + "gpu": 0, + "processorType": "CPU", + "description": "Machine Learning model for extracting commonly occurring data points from EU Declaration of Conformity. See the documentation page for this model for more details https://docs.uipath.com/document-understanding/automation-cloud/latest/user-guide/eu-declaration-conformity-ml-package", + "inputDescription": "ML Skills deployed using this ML Package can be integrated into RPA workflows using the Machine Learning Extractor activity in the Official feed or the MLModel activity in Connect feed. Accpted file formats: pdf, tiff, jpg or png files. The activities also require an API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -> Other Services view.", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "changeLog": "", + "cpu": 1, + "inputType": "JSON", + "displayName": "EUDeclarationOfConformity", + "memory": 0, + "mlPackageLanguage": "PYTHON37_DU", + "projectId": "[project-id]", + "stagingUri": "[staging-uri]", + "projectName": "UiPath Document Understanding", + "projectDescription": "UiPath models to classify and extract information from images and pdfs.", + "tenantName": "UiPath", + "minAIFabricVersion": "22.10", + "languageVersion": 4, + "version": 23, + "customVersion": "23.4.12", + "imagePath": "du-semistructured:v23.4-03.12-rc02", + "parametersFileJSON": "{\"params\": [{\"name\": \"auto_retraining\",\"type\": [\"PIPELINE\"],\"value\": \"False\",\"help\": \"Enable this option when the Pipeline you are creating is a scheduled automatic pipeline, synchronized with automatic exports from Data Manager.\",\"options\": [\"True\",\"False\"]}],\"probes\": [{\"probe\": \"startup\",\"initialDelaySeconds\": \"120\",\"periodSeconds\": \"10\",\"timeoutSeconds\": \"2\",\"successThreshold\": \"1\",\"failureThreshold\": \"60\"}]}", + "settings": [ + { + "key": "package_type", + "type": "STRING", + "value": "eu_declaration_conformity" + } + ], + "maxAIFabricVersion": "23.10" +} diff --git a/metadata/EUDeclarationOfConformity__24__metadata.json b/metadata/EUDeclarationOfConformity__24__metadata.json new file mode 100644 index 00000000..919505e9 --- /dev/null +++ b/metadata/EUDeclarationOfConformity__24__metadata.json @@ -0,0 +1,33 @@ +{ + "name": "EUDeclarationOfConformity", + "retrainable": true, + "gpu": 0, + "processorType": "CPU", + "description": "Machine Learning model for extracting commonly occurring data points from EU Declaration of Conformity. See the documentation page for this model for more details https://docs.uipath.com/document-understanding/automation-cloud/latest/user-guide/eu-declaration-conformity-ml-package", + "inputDescription": "ML Skills deployed using this ML Package can be integrated into RPA workflows using the Machine Learning Extractor activity in the Official feed or the MLModel activity in Connect feed. Accpted file formats: pdf, tiff, jpg or png files. The activities also require an API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -> Other Services view.", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "changeLog": "", + "cpu": 1, + "inputType": "JSON", + "displayName": "EUDeclarationOfConformity", + "memory": 0, + "mlPackageLanguage": "PYTHON37_DU", + "projectId": "[project-id]", + "stagingUri": "[staging-uri]", + "projectName": "UiPath Document Understanding", + "projectDescription": "UiPath models to classify and extract information from images and pdfs.", + "tenantName": "UiPath", + "minAIFabricVersion": "22.10", + "languageVersion": 4, + "version": 24, + "customVersion": "24.10.4", + "imagePath": "du-semistructured:v24.10-6.16-rc04", + "parametersFileJSON": "{\"params\": [{\"name\": \"auto_retraining\",\"type\": [\"PIPELINE\"],\"value\": \"False\",\"help\": \"Enable this option when the Pipeline you are creating is a scheduled automatic pipeline, synchronized with automatic exports from Data Manager.\",\"options\": [\"True\",\"False\"]}],\"probes\": [{\"probe\": \"startup\",\"initialDelaySeconds\": \"120\",\"periodSeconds\": \"10\",\"timeoutSeconds\": \"2\",\"successThreshold\": \"1\",\"failureThreshold\": \"60\"}]}", + "settings": [ + { + "key": "package_type", + "type": "STRING", + "value": "eu_declaration_conformity" + } + ] +} diff --git a/metadata/EUDeclarationOfConformity__25__metadata.json b/metadata/EUDeclarationOfConformity__25__metadata.json new file mode 100644 index 00000000..9c5e1084 --- /dev/null +++ b/metadata/EUDeclarationOfConformity__25__metadata.json @@ -0,0 +1,34 @@ +{ + "name": "EUDeclarationOfConformity", + "retrainable": true, + "gpu": 0, + "processorType": "CPU", + "description": "Machine Learning model for extracting commonly occurring data points from EU Declaration of Conformity. See the documentation page for this model for more details https://docs.uipath.com/document-understanding/automation-cloud/latest/user-guide/eu-declaration-conformity-ml-package", + "inputDescription": "ML Skills deployed using this ML Package can be integrated into RPA workflows using the Machine Learning Extractor activity in the Official feed or the MLModel activity in Connect feed. Accpted file formats: pdf, tiff, jpg or png files. The activities also require an API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -> Other Services view.", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "changeLog": "", + "cpu": 1, + "inputType": "JSON", + "displayName": "EUDeclarationOfConformity", + "memory": 0, + "mlPackageLanguage": "PYTHON37_DU", + "projectId": "[project-id]", + "stagingUri": "[staging-uri]", + "projectName": "UiPath Document Understanding", + "projectDescription": "UiPath models to classify and extract information from images and pdfs.", + "tenantName": "UiPath", + "minAIFabricVersion": "22.10", + "languageVersion": 4, + "version": 25, + "customVersion": "23.10.10", + "imagePath": "du-semistructured:v23.10-06.05-rc05", + "parametersFileJSON": "{\"params\": [{\"name\": \"auto_retraining\",\"type\": [\"PIPELINE\"],\"value\": \"False\",\"help\": \"Enable this option when the Pipeline you are creating is a scheduled automatic pipeline, synchronized with automatic exports from Data Manager.\",\"options\": [\"True\",\"False\"]}],\"probes\": [{\"probe\": \"startup\",\"initialDelaySeconds\": \"120\",\"periodSeconds\": \"10\",\"timeoutSeconds\": \"2\",\"successThreshold\": \"1\",\"failureThreshold\": \"60\"}]}", + "settings": [ + { + "key": "package_type", + "type": "STRING", + "value": "eu_declaration_conformity" + } + ], + "maxAIFabricVersion": "24.10" +} diff --git a/metadata/EUDeclarationOfConformity__26__metadata.json b/metadata/EUDeclarationOfConformity__26__metadata.json new file mode 100644 index 00000000..de789edd --- /dev/null +++ b/metadata/EUDeclarationOfConformity__26__metadata.json @@ -0,0 +1,33 @@ +{ + "name": "EUDeclarationOfConformity", + "retrainable": true, + "gpu": 0, + "processorType": "CPU", + "description": "Machine Learning model for extracting commonly occurring data points from EU Declaration of Conformity. See the documentation page for this model for more details https://docs.uipath.com/document-understanding/automation-cloud/latest/user-guide/eu-declaration-conformity-ml-package", + "inputDescription": "ML Skills deployed using this ML Package can be integrated into RPA workflows using the Machine Learning Extractor activity in the Official feed or the MLModel activity in Connect feed. Accpted file formats: pdf, tiff, jpg or png files. The activities also require an API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -> Other Services view.", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "changeLog": "", + "cpu": 1, + "inputType": "JSON", + "displayName": "EUDeclarationOfConformity", + "memory": 0, + "mlPackageLanguage": "PYTHON37_DU", + "projectId": "[project-id]", + "stagingUri": "[staging-uri]", + "projectName": "UiPath Document Understanding", + "projectDescription": "UiPath models to classify and extract information from images and pdfs.", + "tenantName": "UiPath", + "minAIFabricVersion": "22.10", + "languageVersion": 4, + "version": 26, + "customVersion": "24.10.5", + "imagePath": "du-semistructured:v24.10-8.25-rc02", + "parametersFileJSON": "{\"params\": [{\"name\": \"auto_retraining\",\"type\": [\"PIPELINE\"],\"value\": \"False\",\"help\": \"Enable this option when the Pipeline you are creating is a scheduled automatic pipeline, synchronized with automatic exports from Data Manager.\",\"options\": [\"True\",\"False\"]}],\"probes\": [{\"probe\": \"startup\",\"initialDelaySeconds\": \"120\",\"periodSeconds\": \"10\",\"timeoutSeconds\": \"2\",\"successThreshold\": \"1\",\"failureThreshold\": \"60\"}]}", + "settings": [ + { + "key": "package_type", + "type": "STRING", + "value": "eu_declaration_conformity" + } + ] +} diff --git a/metadata/EUDeclarationOfConformity__27__metadata.json b/metadata/EUDeclarationOfConformity__27__metadata.json new file mode 100644 index 00000000..f4cce01f --- /dev/null +++ b/metadata/EUDeclarationOfConformity__27__metadata.json @@ -0,0 +1,34 @@ +{ + "name": "EUDeclarationOfConformity", + "retrainable": true, + "gpu": 0, + "processorType": "CPU", + "description": "Machine Learning model for extracting commonly occurring data points from EU Declaration of Conformity. See the documentation page for this model for more details https://docs.uipath.com/document-understanding/automation-cloud/latest/user-guide/eu-declaration-conformity-ml-package", + "inputDescription": "ML Skills deployed using this ML Package can be integrated into RPA workflows using the Machine Learning Extractor activity in the Official feed or the MLModel activity in Connect feed. Accpted file formats: pdf, tiff, jpg or png files. The activities also require an API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -> Other Services view.", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "changeLog": "", + "cpu": 1, + "inputType": "JSON", + "displayName": "EUDeclarationOfConformity", + "memory": 0, + "mlPackageLanguage": "PYTHON37_DU", + "projectId": "[project-id]", + "stagingUri": "[staging-uri]", + "projectName": "UiPath Document Understanding", + "projectDescription": "UiPath models to classify and extract information from images and pdfs.", + "tenantName": "UiPath", + "minAIFabricVersion": "22.10", + "languageVersion": 4, + "version": 27, + "customVersion": "23.10.11", + "imagePath": "du-semistructured:v23.10-08.25-rc02", + "parametersFileJSON": "{\"params\": [{\"name\": \"auto_retraining\",\"type\": [\"PIPELINE\"],\"value\": \"False\",\"help\": \"Enable this option when the Pipeline you are creating is a scheduled automatic pipeline, synchronized with automatic exports from Data Manager.\",\"options\": [\"True\",\"False\"]}],\"probes\": [{\"probe\": \"startup\",\"initialDelaySeconds\": \"120\",\"periodSeconds\": \"10\",\"timeoutSeconds\": \"2\",\"successThreshold\": \"1\",\"failureThreshold\": \"60\"}]}", + "settings": [ + { + "key": "package_type", + "type": "STRING", + "value": "eu_declaration_conformity" + } + ], + "maxAIFabricVersion": "24.10" +} diff --git a/metadata/EUDeclarationOfConformity__28__metadata.json b/metadata/EUDeclarationOfConformity__28__metadata.json new file mode 100644 index 00000000..fd87d3ff --- /dev/null +++ b/metadata/EUDeclarationOfConformity__28__metadata.json @@ -0,0 +1,33 @@ +{ + "name": "EUDeclarationOfConformity", + "retrainable": true, + "gpu": 0, + "processorType": "CPU", + "description": "Machine Learning model for extracting commonly occurring data points from EU Declaration of Conformity. See the documentation page for this model for more details https://docs.uipath.com/document-understanding/automation-cloud/latest/user-guide/eu-declaration-conformity-ml-package", + "inputDescription": "ML Skills deployed using this ML Package can be integrated into RPA workflows using the Machine Learning Extractor activity in the Official feed or the MLModel activity in Connect feed. Accpted file formats: pdf, tiff, jpg or png files. The activities also require an API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -> Other Services view.", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "changeLog": "", + "cpu": 1, + "inputType": "JSON", + "displayName": "EUDeclarationOfConformity", + "memory": 0, + "mlPackageLanguage": "PYTHON37_DU", + "projectId": "[project-id]", + "stagingUri": "[staging-uri]", + "projectName": "UiPath Document Understanding", + "projectDescription": "UiPath models to classify and extract information from images and pdfs.", + "tenantName": "UiPath", + "minAIFabricVersion": "22.10", + "languageVersion": 4, + "version": 28, + "customVersion": "25.10.0", + "imagePath": "du-semistructured:v25.10-10.17-rc04", + "parametersFileJSON": "{\"params\": [{\"name\": \"auto_retraining\",\"type\": [\"PIPELINE\"],\"value\": \"False\",\"help\": \"Enable this option when the Pipeline you are creating is a scheduled automatic pipeline, synchronized with automatic exports from Data Manager.\",\"options\": [\"True\",\"False\"]}],\"probes\": [{\"probe\": \"startup\",\"initialDelaySeconds\": \"120\",\"periodSeconds\": \"10\",\"timeoutSeconds\": \"2\",\"successThreshold\": \"1\",\"failureThreshold\": \"60\"}]}", + "settings": [ + { + "key": "package_type", + "type": "STRING", + "value": "eu_declaration_conformity" + } + ] +} diff --git a/metadata/EnglishTextClassification__1__metadata.json b/metadata/EnglishTextClassification__1__metadata.json deleted file mode 100644 index aa342d3e..00000000 --- a/metadata/EnglishTextClassification__1__metadata.json +++ /dev/null @@ -1,21 +0,0 @@ -{ - "changeLog": "", - "cpu": 0, - "description": "This is the preview version of a generic, retrainable model for English Classification. This ML Package must be retrained, if deployed without training first, deployment will fail with an error stating that the model is not trained. This model is a deep learning architecture for language classification. It is based on RoBERTa, a self-supervised method for pretraining natural language processing systems. A GPU can be used both at serving time and training time. A GPU delivers ~5-10x improvement in speed. The original paper can be found here https://arxiv.org/abs/1907.11692 by Yinhan Liu, Myle Ott et al. The model was open-sourced by Facebook AI Research.", - "displayName": "EnglishTextClassification", - "gpu": 0, - "inputDescription": "Text to be classified as String: 'I loved this movie.'", - "inputType": "JSON", - "memory": 0, - "mlPackageLanguage": "PYTHON36", - "name": "EnglishTextClassification", - "outputDescription": "JSON with pedicted class name, associated confidence on that class prediction (between 0-1). For example: {\"prediction\": \"Positive\", \"confidence\": 0.9422031841278076,}", - "processorType": "GPU", - "retrainable": true, - "stagingUri": "[staging-uri]", - "projectId": "[project-id]", - "projectName": "Language Analysis", - "projectDescription": "Models for analyzing text including language detection, sentiment analysis, and named-entity recognition.", - "tenantName": "Open-Source Packages", - "imagePath": "registry.replicated.com/aif-core/englishtextclassification:1" -} diff --git a/metadata/EnglishTextClassification__2__metadata.json b/metadata/EnglishTextClassification__2__metadata.json index 641bc559..c361dbfa 100644 --- a/metadata/EnglishTextClassification__2__metadata.json +++ b/metadata/EnglishTextClassification__2__metadata.json @@ -17,7 +17,8 @@ "projectName": "Language Analysis", "projectDescription": "Models for analyzing text including language detection, sentiment analysis, and named-entity recognition.", "tenantName": "Open-Source Packages", - "minAIFabricVersion": "v20.7.1", + "minAIFabricVersion": "v21.10", "languageVersion": 0, - "imagePath": "registry.replicated.com/aif-core/englishtextclassification:2" + "version": 2, + "contentUri": "https:///publicmodels/AIC/EnglishTextClassification/2/EnglishTextClassificationAG.zip" } diff --git a/metadata/FM1003__25__metadata.json b/metadata/FM1003__25__metadata.json new file mode 100644 index 00000000..9c57ec74 --- /dev/null +++ b/metadata/FM1003__25__metadata.json @@ -0,0 +1,34 @@ +{ + "name": "FM1003", + "retrainable": true, + "gpu": 0, + "processorType": "CPU", + "description": "Machine Learning model (available in Preview) for extracting commonly occurring data points from FM1003 documents, including header fields and line items. Please see more details including supported languages and link to Activities guide in the About Licensing -> Document Understanding API Key section of the UiPath Automation Cloud Guide here: https://docs.uipath.com/automation-cloud/docs/about-licensing#section-document-understanding-api-key", + "inputDescription": "ML Skills deployed using this ML Package can be integrated into RPA workflows using the Machine Learning Extractor activity in the Official feed or the MLModel activity in Connect feed. File formats accepted include pdf, tiff, jpg or png files. The activities also require an API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -> Other Services view.", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "changeLog": "", + "cpu": 1, + "inputType": "JSON", + "displayName": "FM1003", + "memory": 0, + "mlPackageLanguage": "PYTHON37_DU", + "projectId": "[project-id]", + "stagingUri": "[staging-uri]", + "projectName": "UiPath Document Understanding", + "projectDescription": "UiPath models to classify and extract information from images and pdfs.", + "tenantName": "UiPath", + "minAIFabricVersion": "22.10", + "languageVersion": 4, + "version": 25, + "customVersion": "22.10.14", + "imagePath": "du-semistructured:v22.10-10.10-rc01", + "parametersFileJSON": "{\"params\": [{\"name\": \"auto_retraining\",\"type\": [\"PIPELINE\"],\"value\": \"False\",\"help\": \"Enable this option when the Pipeline you are creating is a scheduled automatic pipeline, synchronized with automatic exports from Data Manager.\",\"options\": [\"True\",\"False\"]}],\"probes\": [{\"probe\": \"startup\",\"initialDelaySeconds\": \"120\",\"periodSeconds\": \"10\",\"timeoutSeconds\": \"2\",\"successThreshold\": \"1\",\"failureThreshold\": \"60\"}]}", + "settings": [ + { + "key": "package_type", + "type": "STRING", + "value": "fm1003" + } + ], + "maxAIFabricVersion": "23.4" +} \ No newline at end of file diff --git a/metadata/FM1003__2__metadata.json b/metadata/FM1003__2__metadata.json new file mode 100644 index 00000000..ff1d6529 --- /dev/null +++ b/metadata/FM1003__2__metadata.json @@ -0,0 +1,26 @@ +{ + "name": "FM1003", + "retrainable": true, + "gpu": 0, + "processorType": "CPU", + "description": "Machine Learning model(available in Preview) for extracting commonly occurring data points from FM1003 documents, including header fields and line items. 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The activities also require an API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -> Other Services view.", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "changeLog": "", + "cpu": 1, + "inputType": "JSON", + "displayName": "FM1003", + "memory": 0, + "mlPackageLanguage": "PYTHON37_DU", + "projectId": "[project-id]", + "stagingUri": "[staging-uri]", + "projectName": "UiPath Document Understanding", + "projectDescription": "UiPath models to classify and extract information from images and pdfs.", + "tenantName": "UiPath", + "minAIFabricVersion": "22.4", + "languageVersion": 4, + "version": 2, + "customVersion": "22.4.1", + "contentUri": "https:///publicmodels/AIC/FM1003/22.4.1/fm1003_package.zip", + "maxAIFabricVersion": "22.4" +} \ No newline at end of file diff --git a/metadata/FM1003__31__metadata.json b/metadata/FM1003__31__metadata.json new file mode 100644 index 00000000..f904d7bb --- /dev/null +++ b/metadata/FM1003__31__metadata.json @@ -0,0 +1,33 @@ +{ + "name": "FM1003", + "retrainable": true, + "gpu": 0, + "processorType": "CPU", + "description": "Machine Learning model (available in Preview) for extracting commonly occurring data points from FM1003 documents, including header fields and line items. Please see more details including supported languages and link to Activities guide in the About Licensing -> Document Understanding API Key section of the UiPath Automation Cloud Guide here: https://docs.uipath.com/automation-cloud/docs/about-licensing#section-document-understanding-api-key", + "inputDescription": "ML Skills deployed using this ML Package can be integrated into RPA workflows using the Machine Learning Extractor activity in the Official feed or the MLModel activity in Connect feed. File formats accepted include pdf, tiff, jpg or png files. The activities also require an API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -> Other Services view.", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "changeLog": "", + "cpu": 1, + "inputType": "JSON", + "displayName": "FM1003", + "memory": 0, + "mlPackageLanguage": "PYTHON37_DU", + "projectId": "[project-id]", + "stagingUri": "[staging-uri]", + "projectName": "UiPath Document Understanding", + "projectDescription": "UiPath models to classify and extract information from images and pdfs.", + "tenantName": "UiPath", + "minAIFabricVersion": "22.10", + "languageVersion": 4, + "version": 31, + "customVersion": "24.10.2", + "imagePath": "du-semistructured:v24.10-1.27-rc02", + "parametersFileJSON": "{\"params\": [{\"name\": \"auto_retraining\",\"type\": [\"PIPELINE\"],\"value\": \"False\",\"help\": \"Enable this option when the Pipeline you are creating is a scheduled automatic pipeline, synchronized with automatic exports from Data Manager.\",\"options\": [\"True\",\"False\"]}],\"probes\": [{\"probe\": \"startup\",\"initialDelaySeconds\": \"120\",\"periodSeconds\": \"10\",\"timeoutSeconds\": \"2\",\"successThreshold\": \"1\",\"failureThreshold\": \"60\"}]}", + "settings": [ + { + "key": "package_type", + "type": "STRING", + "value": "fm1003" + } + ] +} \ No newline at end of file diff --git a/metadata/FM1003__32__metadata.json b/metadata/FM1003__32__metadata.json new file mode 100644 index 00000000..248e5ade --- /dev/null +++ b/metadata/FM1003__32__metadata.json @@ -0,0 +1,34 @@ +{ + "name": "FM1003", + "retrainable": true, + "gpu": 0, + "processorType": "CPU", + "description": "Machine Learning model (available in Preview) for extracting commonly occurring data points from FM1003 documents, including header fields and line items. 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The activities also require an API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -> Other Services view.", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "cpu": 1, + "inputType": "JSON", + "displayName": "FinancialStatements", + "memory": 0, + "mlPackageLanguage": "PYTHON37_DU", + "projectId": "[project-id]", + "stagingUri": "[staging-uri]", + "projectName": "UiPath Document Understanding", + "projectDescription": "UiPath models to classify and extract information from images and pdfs.", + "tenantName": "UiPath", + "minAIFabricVersion": "22.10", + "languageVersion": 4, + "version": 23, + "customVersion": "22.10.14", + "imagePath": "du-semistructured:v22.10-10.10-rc01", + "parametersFileJSON": "{\"params\": [{\"name\": \"auto_retraining\",\"type\": [\"PIPELINE\"],\"value\": \"False\",\"help\": \"Enable this option when the Pipeline you are creating is a scheduled automatic pipeline, synchronized with automatic exports from Data Manager.\",\"options\": [\"True\",\"False\"]}],\"probes\": [{\"probe\": \"startup\",\"initialDelaySeconds\": \"120\",\"periodSeconds\": \"10\",\"timeoutSeconds\": \"2\",\"successThreshold\": \"1\",\"failureThreshold\": \"60\"}]}", + "settings": [ + { + "key": "package_type", + "type": "STRING", + "value": "financial_statements" + } + ], + "maxAIFabricVersion": "23.4" +} \ No newline at end of file diff --git a/metadata/FinancialStatements__29__metadata.json b/metadata/FinancialStatements__29__metadata.json new file mode 100644 index 00000000..99abaa3a --- /dev/null +++ b/metadata/FinancialStatements__29__metadata.json @@ -0,0 +1,32 @@ +{ + "name": "FinancialStatements", + "retrainable": true, + "gpu": 0, + "processorType": "CPU", + "description": "Machine Learning model for extracting commonly occurring data points from Financial Statements, including header fields and line items. Please see more details including supported languages in the ML Packages section of the Document Understanding Guide: https://docs.uipath.com/document-understanding/docs/ml-packages", + "inputDescription": "ML Skills deployed using this ML Package can be integrated into RPA workflows using the Machine Learning Extractor activity in the Official feed or the MLModel activity in Connect feed. File formats accepted include pdf, tiff, jpg or png files. The activities also require an API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -> Other Services view.", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "cpu": 1, + "inputType": "JSON", + "displayName": "FinancialStatements", + "memory": 0, + "mlPackageLanguage": "PYTHON37_DU", + "projectId": "[project-id]", + "stagingUri": "[staging-uri]", + "projectName": "UiPath Document Understanding", + "projectDescription": "UiPath models to classify and extract information from images and pdfs.", + "tenantName": "UiPath", + "minAIFabricVersion": "22.10", + "languageVersion": 4, + "version": 29, + "customVersion": "24.10.2", + "imagePath": "du-semistructured:v24.10-1.27-rc02", + "parametersFileJSON": "{\"params\": [{\"name\": \"auto_retraining\",\"type\": [\"PIPELINE\"],\"value\": \"False\",\"help\": \"Enable this option when the Pipeline you are creating is a scheduled automatic pipeline, synchronized with automatic exports from Data Manager.\",\"options\": [\"True\",\"False\"]}],\"probes\": [{\"probe\": \"startup\",\"initialDelaySeconds\": \"120\",\"periodSeconds\": \"10\",\"timeoutSeconds\": \"2\",\"successThreshold\": \"1\",\"failureThreshold\": \"60\"}]}", + "settings": [ + { + "key": "package_type", + "type": "STRING", + "value": "financial_statements" + } + ] +} \ No newline at end of file diff --git a/metadata/FinancialStatements__30__metadata.json b/metadata/FinancialStatements__30__metadata.json new file mode 100644 index 00000000..353c97fc --- /dev/null +++ b/metadata/FinancialStatements__30__metadata.json @@ -0,0 +1,33 @@ +{ + "name": "FinancialStatements", + "retrainable": true, + "gpu": 0, + "processorType": "CPU", + "description": "Machine Learning model for extracting commonly occurring data points from Financial Statements, including header fields and line items. Please see more details including supported languages in the ML Packages section of the Document Understanding Guide: https://docs.uipath.com/document-understanding/docs/ml-packages", + "inputDescription": "ML Skills deployed using this ML Package can be integrated into RPA workflows using the Machine Learning Extractor activity in the Official feed or the MLModel activity in Connect feed. File formats accepted include pdf, tiff, jpg or png files. The activities also require an API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -> Other Services view.", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "cpu": 1, + "inputType": "JSON", + "displayName": "FinancialStatements", + "memory": 0, + "mlPackageLanguage": "PYTHON37_DU", + "projectId": "[project-id]", + "stagingUri": "[staging-uri]", + "projectName": "UiPath Document Understanding", + "projectDescription": "UiPath models to classify and extract information from images and pdfs.", + "tenantName": "UiPath", + "minAIFabricVersion": "22.10", + "languageVersion": 4, + "version": 30, + "customVersion": "23.4.11", + "imagePath": "du-semistructured:v23.4-01.27-rc01", + "parametersFileJSON": "{\"params\": [{\"name\": \"auto_retraining\",\"type\": [\"PIPELINE\"],\"value\": \"False\",\"help\": \"Enable this option when the Pipeline you are creating is a scheduled automatic pipeline, synchronized with automatic exports from Data Manager.\",\"options\": [\"True\",\"False\"]}],\"probes\": [{\"probe\": \"startup\",\"initialDelaySeconds\": \"120\",\"periodSeconds\": \"10\",\"timeoutSeconds\": \"2\",\"successThreshold\": \"1\",\"failureThreshold\": \"60\"}]}", + "settings": [ + { + "key": "package_type", + "type": "STRING", + "value": "financial_statements" + } + ], + "maxAIFabricVersion": "23.10" +} \ No newline at end of file diff --git a/metadata/FinancialStatements__31__metadata.json b/metadata/FinancialStatements__31__metadata.json new file mode 100644 index 00000000..0731345b --- /dev/null +++ b/metadata/FinancialStatements__31__metadata.json @@ -0,0 +1,33 @@ +{ + "name": "FinancialStatements", + "retrainable": true, + "gpu": 0, + "processorType": "CPU", + "description": "Machine Learning model for extracting commonly occurring data points from Financial Statements, including header fields and line items. Please see more details including supported languages in the ML Packages section of the Document Understanding Guide: https://docs.uipath.com/document-understanding/docs/ml-packages", + "inputDescription": "ML Skills deployed using this ML Package can be integrated into RPA workflows using the Machine Learning Extractor activity in the Official feed or the MLModel activity in Connect feed. File formats accepted include pdf, tiff, jpg or png files. The activities also require an API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -> Other Services view.", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "cpu": 1, + "inputType": "JSON", + "displayName": "FinancialStatements", + "memory": 0, + "mlPackageLanguage": "PYTHON37_DU", + "projectId": "[project-id]", + "stagingUri": "[staging-uri]", + "projectName": "UiPath Document Understanding", + "projectDescription": "UiPath models to classify and extract information from images and pdfs.", + "tenantName": "UiPath", + "minAIFabricVersion": "22.10", + "languageVersion": 4, + "version": 31, + "customVersion": "23.10.8", + "imagePath": "du-semistructured:v23.10-01.28-rc07", + "parametersFileJSON": "{\"params\": [{\"name\": \"auto_retraining\",\"type\": [\"PIPELINE\"],\"value\": \"False\",\"help\": \"Enable this option when the Pipeline you are creating is a scheduled automatic pipeline, synchronized with automatic exports from Data Manager.\",\"options\": [\"True\",\"False\"]}],\"probes\": [{\"probe\": \"startup\",\"initialDelaySeconds\": \"120\",\"periodSeconds\": \"10\",\"timeoutSeconds\": \"2\",\"successThreshold\": \"1\",\"failureThreshold\": \"60\"}]}", + "settings": [ + { + "key": "package_type", + "type": "STRING", + "value": "financial_statements" + } + ], + "maxAIFabricVersion": "24.10" +} \ No newline at end of file diff --git a/metadata/FinancialStatements__32__metadata.json b/metadata/FinancialStatements__32__metadata.json new file mode 100644 index 00000000..89c1860a --- /dev/null +++ b/metadata/FinancialStatements__32__metadata.json @@ -0,0 +1,32 @@ +{ + "name": "FinancialStatements", + "retrainable": true, + "gpu": 0, + "processorType": "CPU", + "description": "Machine Learning model for extracting commonly occurring data points from Financial Statements, including header fields and line items. Please see more details including supported languages in the ML Packages section of the Document Understanding Guide: https://docs.uipath.com/document-understanding/docs/ml-packages", + "inputDescription": "ML Skills deployed using this ML Package can be integrated into RPA workflows using the Machine Learning Extractor activity in the Official feed or the MLModel activity in Connect feed. File formats accepted include pdf, tiff, jpg or png files. The activities also require an API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -> Other Services view.", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "cpu": 1, + "inputType": "JSON", + "displayName": "FinancialStatements", + "memory": 0, + "mlPackageLanguage": "PYTHON37_DU", + "projectId": "[project-id]", + "stagingUri": "[staging-uri]", + "projectName": "UiPath Document Understanding", + "projectDescription": "UiPath models to classify and extract information from images and pdfs.", + "tenantName": "UiPath", + "minAIFabricVersion": "22.10", + "languageVersion": 4, + "version": 32, + "customVersion": "24.10.3", + "imagePath": "du-semistructured:v24.10-3.27-rc02", + "parametersFileJSON": "{\"params\": [{\"name\": \"auto_retraining\",\"type\": [\"PIPELINE\"],\"value\": \"False\",\"help\": \"Enable this option when the Pipeline you are creating is a scheduled automatic pipeline, synchronized with automatic exports from Data Manager.\",\"options\": [\"True\",\"False\"]}],\"probes\": [{\"probe\": \"startup\",\"initialDelaySeconds\": \"120\",\"periodSeconds\": \"10\",\"timeoutSeconds\": \"2\",\"successThreshold\": \"1\",\"failureThreshold\": \"60\"}]}", + "settings": [ + { + "key": "package_type", + "type": "STRING", + "value": "financial_statements" + } + ] +} diff --git a/metadata/FinancialStatements__33__metadata.json b/metadata/FinancialStatements__33__metadata.json new file mode 100644 index 00000000..72d475ec --- /dev/null +++ b/metadata/FinancialStatements__33__metadata.json @@ -0,0 +1,33 @@ +{ + "name": "FinancialStatements", + "retrainable": true, + "gpu": 0, + "processorType": "CPU", + "description": "Machine Learning model for extracting commonly occurring data points from Financial Statements, including header fields and line items. Please see more details including supported languages in the ML Packages section of the Document Understanding Guide: https://docs.uipath.com/document-understanding/docs/ml-packages", + "inputDescription": "ML Skills deployed using this ML Package can be integrated into RPA workflows using the Machine Learning Extractor activity in the Official feed or the MLModel activity in Connect feed. File formats accepted include pdf, tiff, jpg or png files. The activities also require an API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -> Other Services view.", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "cpu": 1, + "inputType": "JSON", + "displayName": "FinancialStatements", + "memory": 0, + "mlPackageLanguage": "PYTHON37_DU", + "projectId": "[project-id]", + "stagingUri": "[staging-uri]", + "projectName": "UiPath Document Understanding", + "projectDescription": "UiPath models to classify and extract information from images and pdfs.", + "tenantName": "UiPath", + "minAIFabricVersion": "22.10", + "languageVersion": 4, + "version": 33, + "customVersion": "23.10.9", + "imagePath": "du-semistructured:v23.10-03.14-rc02", + "parametersFileJSON": "{\"params\": [{\"name\": \"auto_retraining\",\"type\": [\"PIPELINE\"],\"value\": \"False\",\"help\": \"Enable this option when the Pipeline you are creating is a scheduled automatic pipeline, synchronized with automatic exports from Data Manager.\",\"options\": [\"True\",\"False\"]}],\"probes\": [{\"probe\": \"startup\",\"initialDelaySeconds\": \"120\",\"periodSeconds\": \"10\",\"timeoutSeconds\": \"2\",\"successThreshold\": \"1\",\"failureThreshold\": \"60\"}]}", + "settings": [ + { + "key": "package_type", + "type": "STRING", + "value": "financial_statements" + } + ], + "maxAIFabricVersion": "24.10" +} diff --git a/metadata/FinancialStatements__34__metadata.json b/metadata/FinancialStatements__34__metadata.json new file mode 100644 index 00000000..c6951b1a --- /dev/null +++ b/metadata/FinancialStatements__34__metadata.json @@ -0,0 +1,33 @@ +{ + "name": "FinancialStatements", + "retrainable": true, + "gpu": 0, + "processorType": "CPU", + "description": "Machine Learning model for extracting commonly occurring data points from Financial Statements, including header fields and line items. Please see more details including supported languages in the ML Packages section of the Document Understanding Guide: https://docs.uipath.com/document-understanding/docs/ml-packages", + "inputDescription": "ML Skills deployed using this ML Package can be integrated into RPA workflows using the Machine Learning Extractor activity in the Official feed or the MLModel activity in Connect feed. File formats accepted include pdf, tiff, jpg or png files. The activities also require an API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -> Other Services view.", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "cpu": 1, + "inputType": "JSON", + "displayName": "FinancialStatements", + "memory": 0, + "mlPackageLanguage": "PYTHON37_DU", + "projectId": "[project-id]", + "stagingUri": "[staging-uri]", + "projectName": "UiPath Document Understanding", + "projectDescription": "UiPath models to classify and extract information from images and pdfs.", + "tenantName": "UiPath", + "minAIFabricVersion": "22.10", + "languageVersion": 4, + "version": 34, + "customVersion": "23.4.12", + "imagePath": "du-semistructured:v23.4-03.12-rc02", + "parametersFileJSON": "{\"params\": [{\"name\": \"auto_retraining\",\"type\": [\"PIPELINE\"],\"value\": \"False\",\"help\": \"Enable this option when the Pipeline you are creating is a scheduled automatic pipeline, synchronized with automatic exports from Data Manager.\",\"options\": [\"True\",\"False\"]}],\"probes\": [{\"probe\": \"startup\",\"initialDelaySeconds\": \"120\",\"periodSeconds\": \"10\",\"timeoutSeconds\": \"2\",\"successThreshold\": \"1\",\"failureThreshold\": \"60\"}]}", + "settings": [ + { + "key": "package_type", + "type": "STRING", + "value": "financial_statements" + } + ], + "maxAIFabricVersion": "23.10" +} diff --git a/metadata/FinancialStatements__35__metadata.json b/metadata/FinancialStatements__35__metadata.json new file mode 100644 index 00000000..ee5455bb --- /dev/null +++ b/metadata/FinancialStatements__35__metadata.json @@ -0,0 +1,32 @@ +{ + "name": "FinancialStatements", + "retrainable": true, + "gpu": 0, + "processorType": "CPU", + "description": "Machine Learning model for extracting commonly occurring data points from Financial Statements, including header fields and line items. Please see more details including supported languages in the ML Packages section of the Document Understanding Guide: https://docs.uipath.com/document-understanding/docs/ml-packages", + "inputDescription": "ML Skills deployed using this ML Package can be integrated into RPA workflows using the Machine Learning Extractor activity in the Official feed or the MLModel activity in Connect feed. File formats accepted include pdf, tiff, jpg or png files. The activities also require an API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -> Other Services view.", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "cpu": 1, + "inputType": "JSON", + "displayName": "FinancialStatements", + "memory": 0, + "mlPackageLanguage": "PYTHON37_DU", + "projectId": "[project-id]", + "stagingUri": "[staging-uri]", + "projectName": "UiPath Document Understanding", + "projectDescription": "UiPath models to classify and extract information from images and pdfs.", + "tenantName": "UiPath", + "minAIFabricVersion": "22.10", + "languageVersion": 4, + "version": 35, + "customVersion": "24.10.4", + "imagePath": "du-semistructured:v24.10-6.16-rc04", + "parametersFileJSON": "{\"params\": [{\"name\": \"auto_retraining\",\"type\": [\"PIPELINE\"],\"value\": \"False\",\"help\": \"Enable this option when the Pipeline you are creating is a scheduled automatic pipeline, synchronized with automatic exports from Data Manager.\",\"options\": [\"True\",\"False\"]}],\"probes\": [{\"probe\": \"startup\",\"initialDelaySeconds\": \"120\",\"periodSeconds\": \"10\",\"timeoutSeconds\": \"2\",\"successThreshold\": \"1\",\"failureThreshold\": \"60\"}]}", + "settings": [ + { + "key": "package_type", + "type": "STRING", + "value": "financial_statements" + } + ] +} diff --git a/metadata/FinancialStatements__36__metadata.json b/metadata/FinancialStatements__36__metadata.json new file mode 100644 index 00000000..6040c661 --- /dev/null +++ b/metadata/FinancialStatements__36__metadata.json @@ -0,0 +1,33 @@ +{ + "name": "FinancialStatements", + "retrainable": true, + "gpu": 0, + "processorType": "CPU", + "description": "Machine Learning model for extracting commonly occurring data points from Financial Statements, including header fields and line items. Please see more details including supported languages in the ML Packages section of the Document Understanding Guide: https://docs.uipath.com/document-understanding/docs/ml-packages", + "inputDescription": "ML Skills deployed using this ML Package can be integrated into RPA workflows using the Machine Learning Extractor activity in the Official feed or the MLModel activity in Connect feed. File formats accepted include pdf, tiff, jpg or png files. The activities also require an API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -> Other Services view.", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "cpu": 1, + "inputType": "JSON", + "displayName": "FinancialStatements", + "memory": 0, + "mlPackageLanguage": "PYTHON37_DU", + "projectId": "[project-id]", + "stagingUri": "[staging-uri]", + "projectName": "UiPath Document Understanding", + "projectDescription": "UiPath models to classify and extract information from images and pdfs.", + "tenantName": "UiPath", + "minAIFabricVersion": "22.10", + "languageVersion": 4, + "version": 36, + "customVersion": "23.10.10", + "imagePath": "du-semistructured:v23.10-06.05-rc05", + "parametersFileJSON": "{\"params\": [{\"name\": \"auto_retraining\",\"type\": [\"PIPELINE\"],\"value\": \"False\",\"help\": \"Enable this option when the Pipeline you are creating is a scheduled automatic pipeline, synchronized with automatic exports from Data Manager.\",\"options\": [\"True\",\"False\"]}],\"probes\": [{\"probe\": \"startup\",\"initialDelaySeconds\": \"120\",\"periodSeconds\": \"10\",\"timeoutSeconds\": \"2\",\"successThreshold\": \"1\",\"failureThreshold\": \"60\"}]}", + "settings": [ + { + "key": "package_type", + "type": "STRING", + "value": "financial_statements" + } + ], + "maxAIFabricVersion": "24.10" +} diff --git a/metadata/FinancialStatements__37__metadata.json b/metadata/FinancialStatements__37__metadata.json new file mode 100644 index 00000000..69eef757 --- /dev/null +++ b/metadata/FinancialStatements__37__metadata.json @@ -0,0 +1,32 @@ +{ + "name": "FinancialStatements", + "retrainable": true, + "gpu": 0, + "processorType": "CPU", + "description": "Machine Learning model for extracting commonly occurring data points from Financial Statements, including header fields and line items. Please see more details including supported languages in the ML Packages section of the Document Understanding Guide: https://docs.uipath.com/document-understanding/docs/ml-packages", + "inputDescription": "ML Skills deployed using this ML Package can be integrated into RPA workflows using the Machine Learning Extractor activity in the Official feed or the MLModel activity in Connect feed. File formats accepted include pdf, tiff, jpg or png files. The activities also require an API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -> Other Services view.", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "cpu": 1, + "inputType": "JSON", + "displayName": "FinancialStatements", + "memory": 0, + "mlPackageLanguage": "PYTHON37_DU", + "projectId": "[project-id]", + "stagingUri": "[staging-uri]", + "projectName": "UiPath Document Understanding", + "projectDescription": "UiPath models to classify and extract information from images and pdfs.", + "tenantName": "UiPath", + "minAIFabricVersion": "22.10", + "languageVersion": 4, + "version": 37, + "customVersion": "24.10.5", + "imagePath": "du-semistructured:v24.10-8.25-rc02", + "parametersFileJSON": "{\"params\": [{\"name\": \"auto_retraining\",\"type\": [\"PIPELINE\"],\"value\": \"False\",\"help\": \"Enable this option when the Pipeline you are creating is a scheduled automatic pipeline, synchronized with automatic exports from Data Manager.\",\"options\": [\"True\",\"False\"]}],\"probes\": [{\"probe\": \"startup\",\"initialDelaySeconds\": \"120\",\"periodSeconds\": \"10\",\"timeoutSeconds\": \"2\",\"successThreshold\": \"1\",\"failureThreshold\": \"60\"}]}", + "settings": [ + { + "key": "package_type", + "type": "STRING", + "value": "financial_statements" + } + ] +} diff --git a/metadata/FinancialStatements__38__metadata.json b/metadata/FinancialStatements__38__metadata.json new file mode 100644 index 00000000..a9c72ec0 --- /dev/null +++ b/metadata/FinancialStatements__38__metadata.json @@ -0,0 +1,33 @@ +{ + "name": "FinancialStatements", + "retrainable": true, + "gpu": 0, + "processorType": "CPU", + "description": "Machine Learning model for extracting commonly occurring data points from Financial Statements, including header fields and line items. Please see more details including supported languages in the ML Packages section of the Document Understanding Guide: https://docs.uipath.com/document-understanding/docs/ml-packages", + "inputDescription": "ML Skills deployed using this ML Package can be integrated into RPA workflows using the Machine Learning Extractor activity in the Official feed or the MLModel activity in Connect feed. File formats accepted include pdf, tiff, jpg or png files. The activities also require an API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -> Other Services view.", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "cpu": 1, + "inputType": "JSON", + "displayName": "FinancialStatements", + "memory": 0, + "mlPackageLanguage": "PYTHON37_DU", + "projectId": "[project-id]", + "stagingUri": "[staging-uri]", + "projectName": "UiPath Document Understanding", + "projectDescription": "UiPath models to classify and extract information from images and pdfs.", + "tenantName": "UiPath", + "minAIFabricVersion": "22.10", + "languageVersion": 4, + "version": 38, + "customVersion": "23.10.11", + "imagePath": "du-semistructured:v23.10-08.25-rc02", + "parametersFileJSON": "{\"params\": [{\"name\": \"auto_retraining\",\"type\": [\"PIPELINE\"],\"value\": \"False\",\"help\": \"Enable this option when the Pipeline you are creating is a scheduled automatic pipeline, synchronized with automatic exports from Data Manager.\",\"options\": [\"True\",\"False\"]}],\"probes\": [{\"probe\": \"startup\",\"initialDelaySeconds\": \"120\",\"periodSeconds\": \"10\",\"timeoutSeconds\": \"2\",\"successThreshold\": \"1\",\"failureThreshold\": \"60\"}]}", + "settings": [ + { + "key": "package_type", + "type": "STRING", + "value": "financial_statements" + } + ], + "maxAIFabricVersion": "24.10" +} diff --git a/metadata/FinancialStatements__39__metadata.json b/metadata/FinancialStatements__39__metadata.json new file mode 100644 index 00000000..3a826eea --- /dev/null +++ b/metadata/FinancialStatements__39__metadata.json @@ -0,0 +1,32 @@ +{ + "name": "FinancialStatements", + "retrainable": true, + "gpu": 0, + "processorType": "CPU", + "description": "Machine Learning model for extracting commonly occurring data points from Financial Statements, including header fields and line items. Please see more details including supported languages in the ML Packages section of the Document Understanding Guide: https://docs.uipath.com/document-understanding/docs/ml-packages", + "inputDescription": "ML Skills deployed using this ML Package can be integrated into RPA workflows using the Machine Learning Extractor activity in the Official feed or the MLModel activity in Connect feed. File formats accepted include pdf, tiff, jpg or png files. The activities also require an API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -> Other Services view.", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "cpu": 1, + "inputType": "JSON", + "displayName": "FinancialStatements", + "memory": 0, + "mlPackageLanguage": "PYTHON37_DU", + "projectId": "[project-id]", + "stagingUri": "[staging-uri]", + "projectName": "UiPath Document Understanding", + "projectDescription": "UiPath models to classify and extract information from images and pdfs.", + "tenantName": "UiPath", + "minAIFabricVersion": "22.10", + "languageVersion": 4, + "version": 39, + "customVersion": "25.10.0", + "imagePath": "du-semistructured:v25.10-10.17-rc04", + "parametersFileJSON": "{\"params\": [{\"name\": \"auto_retraining\",\"type\": [\"PIPELINE\"],\"value\": \"False\",\"help\": \"Enable this option when the Pipeline you are creating is a scheduled automatic pipeline, synchronized with automatic exports from Data Manager.\",\"options\": [\"True\",\"False\"]}],\"probes\": [{\"probe\": \"startup\",\"initialDelaySeconds\": \"120\",\"periodSeconds\": \"10\",\"timeoutSeconds\": \"2\",\"successThreshold\": \"1\",\"failureThreshold\": \"60\"}]}", + "settings": [ + { + "key": "package_type", + "type": "STRING", + "value": "financial_statements" + } + ] +} diff --git a/metadata/FormExtractor__1__metadata.json b/metadata/FormExtractor__1__metadata.json index 888627fa..ca35e3f5 100644 --- a/metadata/FormExtractor__1__metadata.json +++ b/metadata/FormExtractor__1__metadata.json @@ -1,5 +1,5 @@ { - "changeLog": "Release v2020.8", + "changeLog": "v21-7-18", "cpu": 0, "description": "This Package provides the Endpoint required by the Form Extractor activity. Please see more details in the Form Extractor activity documentation here: https://docs.uipath.com/activities/docs/form-extractor", "displayName": "FormExtractor", @@ -11,11 +11,14 @@ "name": "FormExtractor", "outputDescription": "Please refer to the documentation of the Form Extractor Activity.", "processorType": "CPU", - "projectId": "[project-id]", + "projectId": "[project-id]", "retrainable": false, "stagingUri": "[staging-uri]", "projectName": "UiPath Document Understanding", "projectDescription": "UiPath models to classify and extract information from images and pdfs.", "tenantName": "UiPath", - "imagePath": "registry.replicated.com/aif-core/formextractor:1" + "minAIFabricVersion": "v21.10", + "languageVersion": 3, + "version": 1, + "contentUri": "https:///publicmodels/AIC/FormExtractor/1/FE-du-svc-v21-7-18-main-4a0e8c.zip" } diff --git a/metadata/FormExtractor__2__metadata.json b/metadata/FormExtractor__2__metadata.json new file mode 100644 index 00000000..117d1e21 --- /dev/null +++ b/metadata/FormExtractor__2__metadata.json @@ -0,0 +1,24 @@ +{ + "changeLog": "v21-7-18", + "cpu": 0, + "description": "This Package provides the Endpoint required by the Form Extractor activity. Please see more details in the Form Extractor activity documentation here: https://docs.uipath.com/activities/docs/form-extractor", + "displayName": "FormExtractor", + "gpu": 0, + "inputDescription": "ML Skills deployed using this package are queried directly by Form Extractor Activity. For document types, languages supported and other information about the Form Extractor product please refer to the documentation here: https://docs.uipath.com/activities/docs/form-extractor.", + "inputType": "JSON", + "memory": 0, + "mlPackageLanguage": "PYTHON37_DU", + "name": "FormExtractor", + "outputDescription": "Please refer to the documentation of the Form Extractor Activity.", + "processorType": "CPU", + "projectId": "[project-id]", + "retrainable": false, + "stagingUri": "[staging-uri]", + "projectName": "UiPath Document Understanding", + "projectDescription": "UiPath models to classify and extract information from images and pdfs.", + "tenantName": "UiPath", + "minAIFabricVersion": "v21.10", + "languageVersion": 3, + "version": 2, + "contentUri": "https:///publicmodels/AIC/FormExtractor/2/FE-du-svc-v21-7-18-main-4a0e8c.zip" +} diff --git a/metadata/HandwritingRecognition__2__metadata.json b/metadata/HandwritingRecognition__2__metadata.json new file mode 100644 index 00000000..950b83e8 --- /dev/null +++ b/metadata/HandwritingRecognition__2__metadata.json @@ -0,0 +1,24 @@ +{ + "changeLog": "Model: 21.4.0", + "cpu": 0, + "description": "[DEPRECATED] This Package provides the Handwriting recognition capability required by the Intelligent Form Extractor Package. Deploying this ML Package is required prior to deploying the Intelligent Form Extractor Package. For detailed instructions about the steps required to correctly configure and deploy the Intelligent Form Extractor, see the Out-of-the-box Packages documentation for AI Center here: https://docs.uipath.com/ai-fabric/docs/uipath-document-understanding", + "displayName": "HandwritingRecognition", + "gpu": 0, + "inputDescription": "ML Skills deployed using this package are queried directly by Intelligent Form Extractor Package also hosted in AI Center. For document types, languages supported and other information about the Intelligent Form Extractor product please refer to the documentation here: https://docs.uipath.com/activities/docs/intelligent-form-extractor.", + "inputType": "JSON", + "memory": 0, + "mlPackageLanguage": "PYTHON37_DU", + "name": "HandwritingRecognition", + "outputDescription": "Please refer to the documentation of the Intelligent Form Extractor Activity.", + "processorType": "CPU", + "projectId": "[project-id]", + "retrainable": false, + "stagingUri": "[staging-uri]", + "projectName": "UiPath Document Understanding", + "projectDescription": "UiPath models to classify and extract information from images and pdfs.", + "tenantName": "UiPath", + "minAIFabricVersion": "v21.10", + "languageVersion": 3, + "version": 2, + "contentUri": "https:///publicmodels/AIC/HandwritingRecognition/2/hwocr_aif_package.zip" +} diff --git a/metadata/HandwritingRecognition__1__metadata.json b/metadata/HandwritingRecognition__3__metadata.json similarity index 77% rename from metadata/HandwritingRecognition__1__metadata.json rename to metadata/HandwritingRecognition__3__metadata.json index 316ac9cb..227896da 100644 --- a/metadata/HandwritingRecognition__1__metadata.json +++ b/metadata/HandwritingRecognition__3__metadata.json @@ -1,21 +1,24 @@ { - "changeLog": "Release v2020.10", + "changeLog": "Model: 21.4.0", "cpu": 0, - "description": "This Package provides the Handwriting recognition capability required by the Intelligent Form Extractor Package. Deploying this ML Package is required prior to deploying the Intelligent Form Extractor Package. For detailed instructions about the steps required to correctly configure and deploy the Intelligent Form Extractor, see the Out-of-the-box Packages documentation for AI Fabric here: https://docs.uipath.com/ai-fabric/docs/uipath-document-understanding", + "description": "This Package provides the Handwriting recognition capability required by the Intelligent Form Extractor Package. Deploying this ML Package is required prior to deploying the Intelligent Form Extractor Package. For detailed instructions about the steps required to correctly configure and deploy the Intelligent Form Extractor, see the Out-of-the-box Packages documentation for AI Center here: https://docs.uipath.com/ai-fabric/docs/uipath-document-understanding", "displayName": "HandwritingRecognition", "gpu": 0, - "inputDescription": "ML Skills deployed using this package are queried directly by Intelligent Form Extractor Package also hosted in AI Fabric. For document types, languages supported and other information about the Intelligent Form Extractor product please refer to the documentation here: https://docs.uipath.com/activities/docs/intelligent-form-extractor.", + "inputDescription": "ML Skills deployed using this package are queried directly by Intelligent Form Extractor Package also hosted in AI Center. For document types, languages supported and other information about the Intelligent Form Extractor product please refer to the documentation here: https://docs.uipath.com/activities/docs/intelligent-form-extractor.", "inputType": "JSON", "memory": 0, "mlPackageLanguage": "PYTHON37_DU", "name": "HandwritingRecognition", "outputDescription": "Please refer to the documentation of the Intelligent Form Extractor Activity.", "processorType": "CPU", - "projectId": "[project-id]", + "projectId": "[project-id]", "retrainable": false, "stagingUri": "[staging-uri]", "projectName": "UiPath Document Understanding", "projectDescription": "UiPath models to classify and extract information from images and pdfs.", "tenantName": "UiPath", - "imagePath": "registry.replicated.com/aif-core/handwritingrecognition:1" + "minAIFabricVersion": "v21.10", + "languageVersion": 3, + "version": 3, + "contentUri": "https:///publicmodels/AIC/HandwritingRecognition/3/hwocr_aif_package.zip" } diff --git a/metadata/I9__25__metadata.json b/metadata/I9__25__metadata.json new file mode 100644 index 00000000..e9848275 --- /dev/null +++ b/metadata/I9__25__metadata.json @@ -0,0 +1,34 @@ +{ + "name": "I9", + "retrainable": true, + "gpu": 0, + "processorType": "CPU", + "description": "Machine Learning model for extracting commonly occurring data points from I9 documents, including header fields and line items. Please see more details including supported languages and link to Activities guide in the About Licensing -> Document Understanding API Key section of the UiPath Automation Cloud Guide here: https://docs.uipath.com/automation-cloud/docs/about-licensing#section-document-understanding-api-key", + "inputDescription": "ML Skills deployed using this ML Package can be integrated into RPA workflows using the Machine Learning Extractor activity in the Official feed or the MLModel activity in Connect feed. File formats accepted include pdf, tiff, jpg or png files. The activities also require an API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -> Other Services view.", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "changeLog": "", + "cpu": 1, + "inputType": "JSON", + "displayName": "I9", + "memory": 0, + "mlPackageLanguage": "PYTHON37_DU", + "projectId": "[project-id]", + "stagingUri": "[staging-uri]", + "projectName": "UiPath Document Understanding", + "projectDescription": "UiPath models to classify and extract information from images and pdfs.", + "tenantName": "UiPath", + "minAIFabricVersion": "22.10", + "languageVersion": 4, + "version": 25, + "customVersion": "22.10.14", + "imagePath": "du-semistructured:v22.10-10.10-rc01", + "parametersFileJSON": "{\"params\": [{\"name\": \"auto_retraining\",\"type\": [\"PIPELINE\"],\"value\": \"False\",\"help\": \"Enable this option when the Pipeline you are creating is a scheduled automatic pipeline, synchronized with automatic exports from Data Manager.\",\"options\": [\"True\",\"False\"]}],\"probes\": [{\"probe\": \"startup\",\"initialDelaySeconds\": \"120\",\"periodSeconds\": \"10\",\"timeoutSeconds\": \"2\",\"successThreshold\": \"1\",\"failureThreshold\": \"60\"}]}", + "settings": [ + { + "key": "package_type", + "type": "STRING", + "value": "i9" + } + ], + "maxAIFabricVersion": "23.4" +} \ No newline at end of file diff --git a/metadata/I9__2__metadata.json b/metadata/I9__2__metadata.json new file mode 100644 index 00000000..2a9bbac1 --- /dev/null +++ b/metadata/I9__2__metadata.json @@ -0,0 +1,26 @@ +{ + "name": "I9", + "retrainable": true, + "gpu": 0, + "processorType": "CPU", + "description": "Machine Learning model for extracting commonly occurring data points from I9 documents, including header fields and line items. Please see more details including supported languages and link to Activities guide in the About Licensing -> Document Understanding API Key section of the UiPath Automation Cloud Guide here: https://docs.uipath.com/automation-cloud/docs/about-licensing#section-document-understanding-api-key", + "inputDescription": "ML Skills deployed using this ML Package can be integrated into RPA workflows using the Machine Learning Extractor activity in the Official feed or the MLModel activity in Connect feed. File formats accepted include pdf, tiff, jpg or png files. The activities also require an API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -> Other Services view.", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "changeLog": "", + "cpu": 1, + "inputType": "JSON", + "displayName": "I9", + "memory": 0, + "mlPackageLanguage": "PYTHON37_DU", + "projectId": "[project-id]", + "stagingUri": "[staging-uri]", + "projectName": "UiPath Document Understanding", + "projectDescription": "UiPath models to classify and extract information from images and pdfs.", + "tenantName": "UiPath", + "minAIFabricVersion": "22.4", + "languageVersion": 4, + "version": 2, + "customVersion": "22.4.1", + "contentUri": "https:///publicmodels/AIC/I9/22.4.1/i9_package.zip", + "maxAIFabricVersion": "22.4" +} \ No newline at end of file diff --git a/metadata/I9__31__metadata.json b/metadata/I9__31__metadata.json new file mode 100644 index 00000000..f83cca7f --- /dev/null +++ b/metadata/I9__31__metadata.json @@ -0,0 +1,33 @@ +{ + "name": "I9", + "retrainable": true, + "gpu": 0, + "processorType": "CPU", + "description": "Machine Learning model for extracting commonly occurring data points from I9 documents, including header fields and line items. Please see more details including supported languages and link to Activities guide in the About Licensing -> Document Understanding API Key section of the UiPath Automation Cloud Guide here: https://docs.uipath.com/automation-cloud/docs/about-licensing#section-document-understanding-api-key", + "inputDescription": "ML Skills deployed using this ML Package can be integrated into RPA workflows using the Machine Learning Extractor activity in the Official feed or the MLModel activity in Connect feed. File formats accepted include pdf, tiff, jpg or png files. The activities also require an API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -> Other Services view.", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "changeLog": "", + "cpu": 1, + "inputType": "JSON", + "displayName": "I9", + "memory": 0, + "mlPackageLanguage": "PYTHON37_DU", + "projectId": "[project-id]", + "stagingUri": "[staging-uri]", + "projectName": "UiPath Document Understanding", + "projectDescription": "UiPath models to classify and extract information from images and pdfs.", + "tenantName": "UiPath", + "minAIFabricVersion": "22.10", + "languageVersion": 4, + "version": 31, + "customVersion": "24.10.2", + "imagePath": "du-semistructured:v24.10-1.27-rc02", + "parametersFileJSON": "{\"params\": [{\"name\": \"auto_retraining\",\"type\": [\"PIPELINE\"],\"value\": \"False\",\"help\": \"Enable this option when the Pipeline you are creating is a scheduled automatic pipeline, synchronized with automatic exports from Data Manager.\",\"options\": [\"True\",\"False\"]}],\"probes\": [{\"probe\": \"startup\",\"initialDelaySeconds\": \"120\",\"periodSeconds\": \"10\",\"timeoutSeconds\": \"2\",\"successThreshold\": \"1\",\"failureThreshold\": \"60\"}]}", + "settings": [ + { + "key": "package_type", + "type": "STRING", + "value": "i9" + } + ] +} \ No newline at end of file diff --git a/metadata/I9__32__metadata.json b/metadata/I9__32__metadata.json new file mode 100644 index 00000000..6da6f56b --- /dev/null +++ b/metadata/I9__32__metadata.json @@ -0,0 +1,34 @@ +{ + "name": "I9", + "retrainable": true, + "gpu": 0, + "processorType": "CPU", + "description": "Machine Learning model for extracting commonly occurring data points from I9 documents, including header fields and line items. Please see more details including supported languages and link to Activities guide in the About Licensing -> Document Understanding API Key section of the UiPath Automation Cloud Guide here: https://docs.uipath.com/automation-cloud/docs/about-licensing#section-document-understanding-api-key", + "inputDescription": "ML Skills deployed using this ML Package can be integrated into RPA workflows using the Machine Learning Extractor activity in the Official feed or the MLModel activity in Connect feed. File formats accepted include pdf, tiff, jpg or png files. The activities also require an API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -> Other Services view.", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "changeLog": "", + "cpu": 1, + "inputType": "JSON", + "displayName": "I9", + "memory": 0, + "mlPackageLanguage": "PYTHON37_DU", + "projectId": "[project-id]", + "stagingUri": "[staging-uri]", + "projectName": "UiPath Document Understanding", + "projectDescription": "UiPath models to classify and extract information from images and pdfs.", + "tenantName": "UiPath", + "minAIFabricVersion": "22.10", + "languageVersion": 4, + "version": 32, + "customVersion": "23.4.11", + "imagePath": "du-semistructured:v23.4-01.27-rc01", + "parametersFileJSON": "{\"params\": [{\"name\": \"auto_retraining\",\"type\": [\"PIPELINE\"],\"value\": \"False\",\"help\": \"Enable this option when the Pipeline you are creating is a scheduled automatic pipeline, synchronized with automatic exports from Data Manager.\",\"options\": [\"True\",\"False\"]}],\"probes\": [{\"probe\": \"startup\",\"initialDelaySeconds\": \"120\",\"periodSeconds\": \"10\",\"timeoutSeconds\": \"2\",\"successThreshold\": \"1\",\"failureThreshold\": \"60\"}]}", + "settings": [ + { + "key": "package_type", + "type": "STRING", + "value": "i9" + } + ], + "maxAIFabricVersion": "23.10" +} \ No newline at end of file diff --git a/metadata/I9__33__metadata.json b/metadata/I9__33__metadata.json new file mode 100644 index 00000000..a93508f3 --- /dev/null +++ b/metadata/I9__33__metadata.json @@ -0,0 +1,34 @@ +{ + "name": "I9", + "retrainable": true, + "gpu": 0, + "processorType": "CPU", + "description": "Machine Learning model for extracting commonly occurring data points from I9 documents, including header fields and line items. Please see more details including supported languages and link to Activities guide in the About Licensing -> Document Understanding API Key section of the UiPath Automation Cloud Guide here: https://docs.uipath.com/automation-cloud/docs/about-licensing#section-document-understanding-api-key", + "inputDescription": "ML Skills deployed using this ML Package can be integrated into RPA workflows using the Machine Learning Extractor activity in the Official feed or the MLModel activity in Connect feed. File formats accepted include pdf, tiff, jpg or png files. The activities also require an API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -> Other Services view.", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "changeLog": "", + "cpu": 1, + "inputType": "JSON", + "displayName": "I9", + "memory": 0, + "mlPackageLanguage": "PYTHON37_DU", + "projectId": "[project-id]", + "stagingUri": "[staging-uri]", + "projectName": "UiPath Document Understanding", + "projectDescription": "UiPath models to classify and extract information from images and pdfs.", + "tenantName": "UiPath", + "minAIFabricVersion": "22.10", + "languageVersion": 4, + "version": 33, + "customVersion": "23.10.8", + "imagePath": "du-semistructured:v23.10-01.28-rc07", + "parametersFileJSON": "{\"params\": [{\"name\": \"auto_retraining\",\"type\": [\"PIPELINE\"],\"value\": \"False\",\"help\": \"Enable this option when the Pipeline you are creating is a scheduled automatic pipeline, synchronized with automatic exports from Data Manager.\",\"options\": [\"True\",\"False\"]}],\"probes\": [{\"probe\": \"startup\",\"initialDelaySeconds\": \"120\",\"periodSeconds\": \"10\",\"timeoutSeconds\": \"2\",\"successThreshold\": \"1\",\"failureThreshold\": \"60\"}]}", + "settings": [ + { + "key": "package_type", + "type": "STRING", + "value": "i9" + } + ], + "maxAIFabricVersion": "24.10" +} \ No newline at end of file diff --git a/metadata/I9__34__metadata.json b/metadata/I9__34__metadata.json new file mode 100644 index 00000000..cb4f1612 --- /dev/null +++ b/metadata/I9__34__metadata.json @@ -0,0 +1,33 @@ +{ + "name": "I9", + "retrainable": true, + "gpu": 0, + "processorType": "CPU", + "description": "Machine Learning model for extracting commonly occurring data points from I9 documents, including header fields and line items. Please see more details including supported languages and link to Activities guide in the About Licensing -> Document Understanding API Key section of the UiPath Automation Cloud Guide here: https://docs.uipath.com/automation-cloud/docs/about-licensing#section-document-understanding-api-key", + "inputDescription": "ML Skills deployed using this ML Package can be integrated into RPA workflows using the Machine Learning Extractor activity in the Official feed or the MLModel activity in Connect feed. File formats accepted include pdf, tiff, jpg or png files. The activities also require an API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -> Other Services view.", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "changeLog": "", + "cpu": 1, + "inputType": "JSON", + "displayName": "I9", + "memory": 0, + "mlPackageLanguage": "PYTHON37_DU", + "projectId": "[project-id]", + "stagingUri": "[staging-uri]", + "projectName": "UiPath Document Understanding", + "projectDescription": "UiPath models to classify and extract information from images and pdfs.", + "tenantName": "UiPath", + "minAIFabricVersion": "22.10", + "languageVersion": 4, + "version": 34, + "customVersion": "24.10.3", + "imagePath": "du-semistructured:v24.10-3.27-rc02", + "parametersFileJSON": "{\"params\": [{\"name\": \"auto_retraining\",\"type\": [\"PIPELINE\"],\"value\": \"False\",\"help\": \"Enable this option when the Pipeline you are creating is a scheduled automatic pipeline, synchronized with automatic exports from Data Manager.\",\"options\": [\"True\",\"False\"]}],\"probes\": [{\"probe\": \"startup\",\"initialDelaySeconds\": \"120\",\"periodSeconds\": \"10\",\"timeoutSeconds\": \"2\",\"successThreshold\": \"1\",\"failureThreshold\": \"60\"}]}", + "settings": [ + { + "key": "package_type", + "type": "STRING", + "value": "i9" + } + ] +} diff --git a/metadata/I9__35__metadata.json b/metadata/I9__35__metadata.json new file mode 100644 index 00000000..b33b9d58 --- /dev/null +++ b/metadata/I9__35__metadata.json @@ -0,0 +1,34 @@ +{ + "name": "I9", + "retrainable": true, + "gpu": 0, + "processorType": "CPU", + "description": "Machine Learning model for extracting commonly occurring data points from I9 documents, including header fields and line items. Please see more details including supported languages and link to Activities guide in the About Licensing -> Document Understanding API Key section of the UiPath Automation Cloud Guide here: https://docs.uipath.com/automation-cloud/docs/about-licensing#section-document-understanding-api-key", + "inputDescription": "ML Skills deployed using this ML Package can be integrated into RPA workflows using the Machine Learning Extractor activity in the Official feed or the MLModel activity in Connect feed. File formats accepted include pdf, tiff, jpg or png files. The activities also require an API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -> Other Services view.", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "changeLog": "", + "cpu": 1, + "inputType": "JSON", + "displayName": "I9", + "memory": 0, + "mlPackageLanguage": "PYTHON37_DU", + "projectId": "[project-id]", + "stagingUri": "[staging-uri]", + "projectName": "UiPath Document Understanding", + "projectDescription": "UiPath models to classify and extract information from images and pdfs.", + "tenantName": "UiPath", + "minAIFabricVersion": "22.10", + "languageVersion": 4, + "version": 35, + "customVersion": "23.10.9", + "imagePath": "du-semistructured:v23.10-03.14-rc02", + "parametersFileJSON": "{\"params\": [{\"name\": \"auto_retraining\",\"type\": [\"PIPELINE\"],\"value\": \"False\",\"help\": \"Enable this option when the Pipeline you are creating is a scheduled automatic pipeline, synchronized with automatic exports from Data Manager.\",\"options\": [\"True\",\"False\"]}],\"probes\": [{\"probe\": \"startup\",\"initialDelaySeconds\": \"120\",\"periodSeconds\": \"10\",\"timeoutSeconds\": \"2\",\"successThreshold\": \"1\",\"failureThreshold\": \"60\"}]}", + "settings": [ + { + "key": "package_type", + "type": "STRING", + "value": "i9" + } + ], + "maxAIFabricVersion": "24.10" +} diff --git a/metadata/I9__36__metadata.json b/metadata/I9__36__metadata.json new file mode 100644 index 00000000..7159f87a --- /dev/null +++ b/metadata/I9__36__metadata.json @@ -0,0 +1,34 @@ +{ + "name": "I9", + "retrainable": true, + "gpu": 0, + "processorType": "CPU", + "description": "Machine Learning model for extracting commonly occurring data points from I9 documents, including header fields and line items. Please see more details including supported languages and link to Activities guide in the About Licensing -> Document Understanding API Key section of the UiPath Automation Cloud Guide here: https://docs.uipath.com/automation-cloud/docs/about-licensing#section-document-understanding-api-key", + "inputDescription": "ML Skills deployed using this ML Package can be integrated into RPA workflows using the Machine Learning Extractor activity in the Official feed or the MLModel activity in Connect feed. File formats accepted include pdf, tiff, jpg or png files. The activities also require an API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -> Other Services view.", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "changeLog": "", + "cpu": 1, + "inputType": "JSON", + "displayName": "I9", + "memory": 0, + "mlPackageLanguage": "PYTHON37_DU", + "projectId": "[project-id]", + "stagingUri": "[staging-uri]", + "projectName": "UiPath Document Understanding", + "projectDescription": "UiPath models to classify and extract information from images and pdfs.", + "tenantName": "UiPath", + "minAIFabricVersion": "22.10", + "languageVersion": 4, + "version": 36, + "customVersion": "23.4.12", + "imagePath": "du-semistructured:v23.4-03.12-rc02", + "parametersFileJSON": "{\"params\": [{\"name\": \"auto_retraining\",\"type\": [\"PIPELINE\"],\"value\": \"False\",\"help\": \"Enable this option when the Pipeline you are creating is a scheduled automatic pipeline, synchronized with automatic exports from Data Manager.\",\"options\": [\"True\",\"False\"]}],\"probes\": [{\"probe\": \"startup\",\"initialDelaySeconds\": \"120\",\"periodSeconds\": \"10\",\"timeoutSeconds\": \"2\",\"successThreshold\": \"1\",\"failureThreshold\": \"60\"}]}", + "settings": [ + { + "key": "package_type", + "type": "STRING", + "value": "i9" + } + ], + "maxAIFabricVersion": "23.10" +} diff --git a/metadata/I9__37__metadata.json b/metadata/I9__37__metadata.json new file mode 100644 index 00000000..c0e8b776 --- /dev/null +++ b/metadata/I9__37__metadata.json @@ -0,0 +1,33 @@ +{ + "name": "I9", + "retrainable": true, + "gpu": 0, + "processorType": "CPU", + "description": "Machine Learning model for extracting commonly occurring data points from I9 documents, including header fields and line items. Please see more details including supported languages and link to Activities guide in the About Licensing -> Document Understanding API Key section of the UiPath Automation Cloud Guide here: https://docs.uipath.com/automation-cloud/docs/about-licensing#section-document-understanding-api-key", + "inputDescription": "ML Skills deployed using this ML Package can be integrated into RPA workflows using the Machine Learning Extractor activity in the Official feed or the MLModel activity in Connect feed. File formats accepted include pdf, tiff, jpg or png files. The activities also require an API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -> Other Services view.", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "changeLog": "", + "cpu": 1, + "inputType": "JSON", + "displayName": "I9", + "memory": 0, + "mlPackageLanguage": "PYTHON37_DU", + "projectId": "[project-id]", + "stagingUri": "[staging-uri]", + "projectName": "UiPath Document Understanding", + "projectDescription": "UiPath models to classify and extract information from images and pdfs.", + "tenantName": "UiPath", + "minAIFabricVersion": "22.10", + "languageVersion": 4, + "version": 37, + "customVersion": "24.10.4", + "imagePath": "du-semistructured:v24.10-6.16-rc04", + "parametersFileJSON": "{\"params\": [{\"name\": \"auto_retraining\",\"type\": [\"PIPELINE\"],\"value\": \"False\",\"help\": \"Enable this option when the Pipeline you are creating is a scheduled automatic pipeline, synchronized with automatic exports from Data Manager.\",\"options\": [\"True\",\"False\"]}],\"probes\": [{\"probe\": \"startup\",\"initialDelaySeconds\": \"120\",\"periodSeconds\": \"10\",\"timeoutSeconds\": \"2\",\"successThreshold\": \"1\",\"failureThreshold\": \"60\"}]}", + "settings": [ + { + "key": "package_type", + "type": "STRING", + "value": "i9" + } + ] +} diff --git a/metadata/I9__38__metadata.json b/metadata/I9__38__metadata.json new file mode 100644 index 00000000..48a594ac --- /dev/null +++ b/metadata/I9__38__metadata.json @@ -0,0 +1,34 @@ +{ + "name": "I9", + "retrainable": true, + "gpu": 0, + "processorType": "CPU", + "description": "Machine Learning model for extracting commonly occurring data points from I9 documents, including header fields and line items. Please see more details including supported languages and link to Activities guide in the About Licensing -> Document Understanding API Key section of the UiPath Automation Cloud Guide here: https://docs.uipath.com/automation-cloud/docs/about-licensing#section-document-understanding-api-key", + "inputDescription": "ML Skills deployed using this ML Package can be integrated into RPA workflows using the Machine Learning Extractor activity in the Official feed or the MLModel activity in Connect feed. File formats accepted include pdf, tiff, jpg or png files. 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The activities also require an API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -> Other Services view.", + "inputType": "JSON", + "memory": 0, + "mlPackageLanguage": "PYTHON37_DU", + "name": "IDCards", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "processorType": "CPU", + "projectId": "[project-id]", + "retrainable": true, + "stagingUri": "[staging-uri]", + "projectName": "UiPath Document Understanding", + "projectDescription": "UiPath models to classify and extract information from images and pdfs.", + "tenantName": "UiPath", + "minAIFabricVersion": "22.10", + "languageVersion": 4, + "version": 38, + "customVersion": "23.4.12", + "imagePath": "du-semistructured:v23.4-03.12-rc02", + "parametersFileJSON": "{\"params\": [{\"name\": \"auto_retraining\",\"type\": [\"PIPELINE\"],\"value\": \"False\",\"help\": \"Enable this option when the Pipeline you are creating is a scheduled automatic pipeline, synchronized with automatic exports from Data Manager.\",\"options\": [\"True\",\"False\"]}],\"probes\": [{\"probe\": \"startup\",\"initialDelaySeconds\": \"120\",\"periodSeconds\": \"10\",\"timeoutSeconds\": \"2\",\"successThreshold\": \"1\",\"failureThreshold\": \"60\"}]}", + "settings": [ + { + "key": "package_type", + "type": "STRING", + "value": "id_cards" + } + ], + "maxAIFabricVersion": "23.10" +} diff --git a/metadata/IDCards__39__metadata.json b/metadata/IDCards__39__metadata.json new file mode 100644 index 00000000..8ae06dd5 --- /dev/null +++ b/metadata/IDCards__39__metadata.json @@ -0,0 +1,33 @@ +{ + "changeLog": "", + "cpu": 1, + "description": "Machine Learning model for extracting commonly occurring data points from ID-Cards, including header fields and line items. Please see more details including supported languages in the ML Packages section of the Document Understanding Guide: https://docs.uipath.com/document-understanding/docs/ml-packages", + "displayName": "IDCards", + "gpu": 0, + "inputDescription": "ML Skills deployed using this ML Package can be integrated into RPA workflows using the Machine Learning Extractor activity in the Official feed or the MLModel activity in Connect feed. File formats accepted include pdf, tiff, jpg or png files. The activities also require an API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -> Other Services view.", + "inputType": "JSON", + "memory": 0, + "mlPackageLanguage": "PYTHON37_DU", + "name": "IDCards", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "processorType": "CPU", + "projectId": "[project-id]", + "retrainable": true, + "stagingUri": "[staging-uri]", + "projectName": "UiPath Document Understanding", + "projectDescription": "UiPath models to classify and extract information from images and pdfs.", + "tenantName": "UiPath", + "minAIFabricVersion": "22.10", + "languageVersion": 4, + "version": 39, + "customVersion": "24.10.4", + "imagePath": "du-semistructured:v24.10-6.16-rc04", + "parametersFileJSON": "{\"params\": [{\"name\": \"auto_retraining\",\"type\": [\"PIPELINE\"],\"value\": \"False\",\"help\": \"Enable this option when the Pipeline you are creating is a scheduled automatic pipeline, synchronized with automatic exports from Data Manager.\",\"options\": [\"True\",\"False\"]}],\"probes\": [{\"probe\": \"startup\",\"initialDelaySeconds\": \"120\",\"periodSeconds\": \"10\",\"timeoutSeconds\": \"2\",\"successThreshold\": \"1\",\"failureThreshold\": \"60\"}]}", + "settings": [ + { + "key": "package_type", + "type": "STRING", + "value": "id_cards" + } + ] +} diff --git a/metadata/IDCards__40__metadata.json b/metadata/IDCards__40__metadata.json new file mode 100644 index 00000000..a856ff71 --- /dev/null +++ b/metadata/IDCards__40__metadata.json @@ -0,0 +1,34 @@ +{ + "changeLog": "", + "cpu": 1, + "description": "Machine Learning model for extracting commonly occurring data points from ID-Cards, including header fields and line items. Please see more details including supported languages in the ML Packages section of the Document Understanding Guide: https://docs.uipath.com/document-understanding/docs/ml-packages", + "displayName": "IDCards", + "gpu": 0, + "inputDescription": "ML Skills deployed using this ML Package can be integrated into RPA workflows using the Machine Learning Extractor activity in the Official feed or the MLModel activity in Connect feed. File formats accepted include pdf, tiff, jpg or png files. The activities also require an API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -> Other Services view.", + "inputType": "JSON", + "memory": 0, + "mlPackageLanguage": "PYTHON37_DU", + "name": "IDCards", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "processorType": "CPU", + "projectId": "[project-id]", + "retrainable": true, + "stagingUri": "[staging-uri]", + "projectName": "UiPath Document Understanding", + "projectDescription": "UiPath models to classify and extract information from images and pdfs.", + "tenantName": "UiPath", + "minAIFabricVersion": "22.10", + "languageVersion": 4, + "version": 40, + "customVersion": "23.10.10", + "imagePath": "du-semistructured:v23.10-06.05-rc05", + "parametersFileJSON": "{\"params\": [{\"name\": \"auto_retraining\",\"type\": [\"PIPELINE\"],\"value\": \"False\",\"help\": \"Enable this option when the Pipeline you are creating is a scheduled automatic pipeline, synchronized with automatic exports from Data Manager.\",\"options\": [\"True\",\"False\"]}],\"probes\": [{\"probe\": \"startup\",\"initialDelaySeconds\": \"120\",\"periodSeconds\": \"10\",\"timeoutSeconds\": \"2\",\"successThreshold\": \"1\",\"failureThreshold\": \"60\"}]}", + "settings": [ + { + "key": "package_type", + "type": "STRING", + "value": "id_cards" + } + ], + "maxAIFabricVersion": "24.10" +} diff --git a/metadata/IDCards__41__metadata.json b/metadata/IDCards__41__metadata.json new file mode 100644 index 00000000..81dadd99 --- /dev/null +++ b/metadata/IDCards__41__metadata.json @@ -0,0 +1,33 @@ +{ + "changeLog": "", + "cpu": 1, + "description": "Machine Learning model for extracting commonly occurring data points from ID-Cards, including header fields and line items. Please see more details including supported languages in the ML Packages section of the Document Understanding Guide: https://docs.uipath.com/document-understanding/docs/ml-packages", + "displayName": "IDCards", + "gpu": 0, + "inputDescription": "ML Skills deployed using this ML Package can be integrated into RPA workflows using the Machine Learning Extractor activity in the Official feed or the MLModel activity in Connect feed. File formats accepted include pdf, tiff, jpg or png files. The activities also require an API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -> Other Services view.", + "inputType": "JSON", + "memory": 0, + "mlPackageLanguage": "PYTHON37_DU", + "name": "IDCards", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "processorType": "CPU", + "projectId": "[project-id]", + "retrainable": true, + "stagingUri": "[staging-uri]", + "projectName": "UiPath Document Understanding", + "projectDescription": "UiPath models to classify and extract information from images and pdfs.", + "tenantName": "UiPath", + "minAIFabricVersion": "22.10", + "languageVersion": 4, + "version": 41, + "customVersion": "24.10.5", + "imagePath": "du-semistructured:v24.10-8.25-rc02", + "parametersFileJSON": "{\"params\": [{\"name\": \"auto_retraining\",\"type\": [\"PIPELINE\"],\"value\": \"False\",\"help\": \"Enable this option when the Pipeline you are creating is a scheduled automatic pipeline, synchronized with automatic exports from Data Manager.\",\"options\": [\"True\",\"False\"]}],\"probes\": [{\"probe\": \"startup\",\"initialDelaySeconds\": \"120\",\"periodSeconds\": \"10\",\"timeoutSeconds\": \"2\",\"successThreshold\": \"1\",\"failureThreshold\": \"60\"}]}", + "settings": [ + { + "key": "package_type", + "type": "STRING", + "value": "id_cards" + } + ] +} diff --git a/metadata/IDCards__42__metadata.json b/metadata/IDCards__42__metadata.json new file mode 100644 index 00000000..2cd9fae2 --- /dev/null +++ b/metadata/IDCards__42__metadata.json @@ -0,0 +1,34 @@ +{ + "changeLog": "", + "cpu": 1, + "description": "Machine Learning model for extracting commonly occurring data points from ID-Cards, including header fields and line items. Please see more details including supported languages in the ML Packages section of the Document Understanding Guide: https://docs.uipath.com/document-understanding/docs/ml-packages", + "displayName": "IDCards", + "gpu": 0, + "inputDescription": "ML Skills deployed using this ML Package can be integrated into RPA workflows using the Machine Learning Extractor activity in the Official feed or the MLModel activity in Connect feed. File formats accepted include pdf, tiff, jpg or png files. The activities also require an API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -> Other Services view.", + "inputType": "JSON", + "memory": 0, + "mlPackageLanguage": "PYTHON37_DU", + "name": "IDCards", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "processorType": "CPU", + "projectId": "[project-id]", + "retrainable": true, + "stagingUri": "[staging-uri]", + "projectName": "UiPath Document Understanding", + "projectDescription": "UiPath models to classify and extract information from images and pdfs.", + "tenantName": "UiPath", + "minAIFabricVersion": "22.10", + "languageVersion": 4, + "version": 42, + "customVersion": "23.10.11", + "imagePath": "du-semistructured:v23.10-08.25-rc02", + "parametersFileJSON": "{\"params\": [{\"name\": \"auto_retraining\",\"type\": [\"PIPELINE\"],\"value\": \"False\",\"help\": \"Enable this option when the Pipeline you are creating is a scheduled automatic pipeline, synchronized with automatic exports from Data Manager.\",\"options\": [\"True\",\"False\"]}],\"probes\": [{\"probe\": \"startup\",\"initialDelaySeconds\": \"120\",\"periodSeconds\": \"10\",\"timeoutSeconds\": \"2\",\"successThreshold\": \"1\",\"failureThreshold\": \"60\"}]}", + "settings": [ + { + "key": "package_type", + "type": "STRING", + "value": "id_cards" + } + ], + "maxAIFabricVersion": "24.10" +} diff --git a/metadata/IDCards__43__metadata.json b/metadata/IDCards__43__metadata.json new file mode 100644 index 00000000..47752a62 --- /dev/null +++ b/metadata/IDCards__43__metadata.json @@ -0,0 +1,33 @@ +{ + "changeLog": "", + "cpu": 1, + "description": "Machine Learning model for extracting commonly occurring data points from ID-Cards, including header fields and line items. Please see more details including supported languages in the ML Packages section of the Document Understanding Guide: https://docs.uipath.com/document-understanding/docs/ml-packages", + "displayName": "IDCards", + "gpu": 0, + "inputDescription": "ML Skills deployed using this ML Package can be integrated into RPA workflows using the Machine Learning Extractor activity in the Official feed or the MLModel activity in Connect feed. File formats accepted include pdf, tiff, jpg or png files. The activities also require an API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -> Other Services view.", + "inputType": "JSON", + "memory": 0, + "mlPackageLanguage": "PYTHON37_DU", + "name": "IDCards", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "processorType": "CPU", + "projectId": "[project-id]", + "retrainable": true, + "stagingUri": "[staging-uri]", + "projectName": "UiPath Document Understanding", + "projectDescription": "UiPath models to classify and extract information from images and pdfs.", + "tenantName": "UiPath", + "minAIFabricVersion": "22.10", + "languageVersion": 4, + "version": 43, + "customVersion": "25.10.0", + "imagePath": "du-semistructured:v25.10-10.17-rc04", + "parametersFileJSON": "{\"params\": [{\"name\": \"auto_retraining\",\"type\": [\"PIPELINE\"],\"value\": \"False\",\"help\": \"Enable this option when the Pipeline you are creating is a scheduled automatic pipeline, synchronized with automatic exports from Data Manager.\",\"options\": [\"True\",\"False\"]}],\"probes\": [{\"probe\": \"startup\",\"initialDelaySeconds\": \"120\",\"periodSeconds\": \"10\",\"timeoutSeconds\": \"2\",\"successThreshold\": \"1\",\"failureThreshold\": \"60\"}]}", + "settings": [ + { + "key": "package_type", + "type": "STRING", + "value": "id_cards" + } + ] +} diff --git a/metadata/Invoices__2__metadata.json b/metadata/IDCards__4__metadata.json similarity index 64% rename from metadata/Invoices__2__metadata.json rename to metadata/IDCards__4__metadata.json index 16c671e9..ba9e6228 100644 --- a/metadata/Invoices__2__metadata.json +++ b/metadata/IDCards__4__metadata.json @@ -1,21 +1,26 @@ { - "changeLog": "Release v2020.7", - "cpu": 0, - "description": "Machine Learning model for extracting commonly occurring data points from Invoices, including header fields and line items. Please see more details including supported languages and link to Activities guide in the About Licensing -> Document Understanding API Key section of the UiPath Automation Cloud Guide here: https://docs.uipath.com/automation-cloud/docs/about-licensing#section-document-understanding-api-key.", - "displayName": "Invoices", + "changeLog": "", + "cpu": 1, + "description": "Machine Learning model for extracting commonly occurring data points from ID-Cards, including header fields and line items. Please see more details including supported languages in the ML Packages section of the Document Understanding Guide: https://docs.uipath.com/document-understanding/docs/ml-packages", + "displayName": "IDCards", "gpu": 0, "inputDescription": "ML Skills deployed using this ML Package can be integrated into RPA workflows using the Machine Learning Extractor activity in the Official feed or the MLModel activity in Connect feed. File formats accepted include pdf, tiff, jpg or png files. The activities also require an API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -> Other Services view.", "inputType": "JSON", "memory": 0, "mlPackageLanguage": "PYTHON37_DU", - "name": "Invoices", + "name": "IDCards", "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", "processorType": "CPU", - "projectId": "[project-id]", + "projectId": "[project-id]", "retrainable": true, "stagingUri": "[staging-uri]", "projectName": "UiPath Document Understanding", "projectDescription": "UiPath models to classify and extract information from images and pdfs.", "tenantName": "UiPath", - "imagePath": "registry.replicated.com/aif-core/invoices:2" -} + "minAIFabricVersion": "22.4", + "languageVersion": 4, + "version": 4, + "customVersion": "22.4.1", + "contentUri": "https:///publicmodels/AIC/IDCards/22.4.1/id_cards_package.zip", + "maxAIFabricVersion": "22.4" +} \ No newline at end of file diff --git a/metadata/ImageModeration__1__metadata.json b/metadata/ImageModeration__1__metadata.json new file mode 100644 index 00000000..f3e796d9 --- /dev/null +++ b/metadata/ImageModeration__1__metadata.json @@ -0,0 +1,24 @@ +{ + "name": "ImageModeration", + "retrainable": false, + "gpu": 0, + "processorType": "CPU", + "description": "This is a model for image content moderation based on a deep learning architecture commonly referred to as Inception V3. Given an image, the model will output one of four classes \"explicit\", \"explicit-drawing\", \"neutral\", and \"pornographic\" together with a normalized confidence score for each class probability. The original is based on the paper ''Rethinking the Inception Architecture for Computer Vision'' by Szegedy et al which was open-sourced by Google.", + "inputDescription": "The path to an image file.", + "outputDescription": "A json object where the keys are the classes: \"explicit\", \"explicit-drawing\", \"neutral\", \"pornographic\" and the values are normalized (sum to one) probabilities of the image belonging to that class.", + "changeLog": "Move to Python 3.8", + "cpu": 1, + "inputType": "FILE", + "displayName": "ImageModeration", + "memory": 0, + "mlPackageLanguage": "PYTHON38", + "projectId": "[project-id]", + "stagingUri": "[staging-uri]", + "projectName": "Image Analysis", + "projectDescription": "Models for analyzing images including image classification and image moderation.", + "tenantName": "Open-Source Packages", + "minAIFabricVersion": "v21.10", + "version": 1, + "customVersion": "22.12.0", + "contentUri": "https:///publicmodels/AIC/ImageModeration/22.12/ImageModeration.zip" +} \ No newline at end of file diff --git a/metadata/AustralianInvoices__2__metadata.json b/metadata/IndianInvoices__10__metadata.json similarity index 63% rename from metadata/AustralianInvoices__2__metadata.json rename to metadata/IndianInvoices__10__metadata.json index fca4117d..776770ab 100644 --- a/metadata/AustralianInvoices__2__metadata.json +++ b/metadata/IndianInvoices__10__metadata.json @@ -1,21 +1,26 @@ { - "changeLog": "Release v2020.8", - "cpu": 0, - "description": "Machine Learning model for extracting commonly occurring data points from Australian Invoices, including header fields and line items. Please see more details including supported languages and link to Activities guide in the About Licensing -> Document Understanding API Key section of the UiPath Automation Cloud Guide here: https://docs.uipath.com/automation-cloud/docs/about-licensing#section-document-understanding-api-key", - "displayName": "AustralianInvoices", + "name": "IndianInvoices", + "retrainable": true, "gpu": 0, + "processorType": "CPU", + "description": "Machine Learning model for extracting commonly occurring data points from Invoices from India, including header fields and line items. Please see more details including supported languages in the ML Packages section of the Document Understanding Guide: https://docs.uipath.com/document-understanding/docs/ml-packages", "inputDescription": "ML Skills deployed using this ML Package can be integrated into RPA workflows using the Machine Learning Extractor activity in the Official feed or the MLModel activity in Connect feed. File formats accepted include pdf, tiff, jpg or png files. The activities also require an API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -> Other Services view.", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "changeLog": "", + "cpu": 1, "inputType": "JSON", + "displayName": "IndianInvoices", "memory": 0, "mlPackageLanguage": "PYTHON37_DU", - "name": "AustralianInvoices", - "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", - "processorType": "GPU", "projectId": "[project-id]", - "retrainable": true, "stagingUri": "[staging-uri]", "projectName": "UiPath Document Understanding", "projectDescription": "UiPath models to classify and extract information from images and pdfs.", "tenantName": "UiPath", - "imagePath": "registry.replicated.com/aif-core/australianinvoices:2" -} + "minAIFabricVersion": "22.4", + "languageVersion": 4, + "version": 10, + "customVersion": "22.4.1", + "contentUri": "https:///publicmodels/AIC/IndianInvoices/22.4.1/invoices_india_package.zip", + "maxAIFabricVersion": "22.4" +} \ No newline at end of file diff --git a/metadata/IndianInvoices__33__metadata.json b/metadata/IndianInvoices__33__metadata.json new file mode 100644 index 00000000..6d18d51c --- /dev/null +++ b/metadata/IndianInvoices__33__metadata.json @@ -0,0 +1,34 @@ +{ + "name": "IndianInvoices", + "retrainable": true, + "gpu": 0, + "processorType": "CPU", + "description": "Machine Learning model for extracting commonly occurring data points from Invoices from India, including header fields and line items. Please see more details including supported languages in the ML Packages section of the Document Understanding Guide: https://docs.uipath.com/document-understanding/docs/ml-packages", + "inputDescription": "ML Skills deployed using this ML Package can be integrated into RPA workflows using the Machine Learning Extractor activity in the Official feed or the MLModel activity in Connect feed. File formats accepted include pdf, tiff, jpg or png files. The activities also require an API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -> Other Services view.", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "changeLog": "", + "cpu": 1, + "inputType": "JSON", + "displayName": "IndianInvoices", + "memory": 0, + "mlPackageLanguage": "PYTHON37_DU", + "projectId": "[project-id]", + "stagingUri": "[staging-uri]", + "projectName": "UiPath Document Understanding", + "projectDescription": "UiPath models to classify and extract information from images and pdfs.", + "tenantName": "UiPath", + "minAIFabricVersion": "22.10", + "languageVersion": 4, + "version": 33, + "customVersion": "22.10.14", + "imagePath": "du-semistructured:v22.10-10.10-rc01", + "parametersFileJSON": "{\"params\": [{\"name\": \"auto_retraining\",\"type\": [\"PIPELINE\"],\"value\": \"False\",\"help\": \"Enable this option when the Pipeline you are creating is a scheduled automatic pipeline, synchronized with automatic exports from Data Manager.\",\"options\": [\"True\",\"False\"]}],\"probes\": [{\"probe\": \"startup\",\"initialDelaySeconds\": \"120\",\"periodSeconds\": \"10\",\"timeoutSeconds\": \"2\",\"successThreshold\": \"1\",\"failureThreshold\": \"60\"}]}", + "settings": [ + { + "key": "package_type", + "type": "STRING", + "value": "invoices_india" + } + ], + "maxAIFabricVersion": "23.4" +} \ No newline at end of file diff --git a/metadata/IndianInvoices__39__metadata.json b/metadata/IndianInvoices__39__metadata.json new file mode 100644 index 00000000..cb71e618 --- /dev/null +++ b/metadata/IndianInvoices__39__metadata.json @@ -0,0 +1,33 @@ +{ + "name": "IndianInvoices", + "retrainable": true, + "gpu": 0, + "processorType": "CPU", + "description": "Machine Learning model for extracting commonly occurring data points from Invoices from India, including header fields and line items. Please see more details including supported languages in the ML Packages section of the Document Understanding Guide: https://docs.uipath.com/document-understanding/docs/ml-packages", + "inputDescription": "ML Skills deployed using this ML Package can be integrated into RPA workflows using the Machine Learning Extractor activity in the Official feed or the MLModel activity in Connect feed. File formats accepted include pdf, tiff, jpg or png files. The activities also require an API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -> Other Services view.", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "changeLog": "", + "cpu": 1, + "inputType": "JSON", + "displayName": "IndianInvoices", + "memory": 0, + "mlPackageLanguage": "PYTHON37_DU", + "projectId": "[project-id]", + "stagingUri": "[staging-uri]", + "projectName": "UiPath Document Understanding", + "projectDescription": "UiPath models to classify and extract information from images and pdfs.", + "tenantName": "UiPath", + "minAIFabricVersion": "22.10", + "languageVersion": 4, + "version": 39, + "customVersion": "24.10.2", + "imagePath": "du-semistructured:v24.10-1.27-rc02", + "parametersFileJSON": "{\"params\": [{\"name\": \"auto_retraining\",\"type\": [\"PIPELINE\"],\"value\": \"False\",\"help\": \"Enable this option when the Pipeline you are creating is a scheduled automatic pipeline, synchronized with automatic exports from Data Manager.\",\"options\": [\"True\",\"False\"]}],\"probes\": [{\"probe\": \"startup\",\"initialDelaySeconds\": \"120\",\"periodSeconds\": \"10\",\"timeoutSeconds\": \"2\",\"successThreshold\": \"1\",\"failureThreshold\": \"60\"}]}", + "settings": [ + { + "key": "package_type", + "type": "STRING", + "value": "invoices_india" + } + ] +} \ No newline at end of file diff --git a/metadata/IndianInvoices__3__metadata.json b/metadata/IndianInvoices__3__metadata.json deleted file mode 100644 index c81ed4e6..00000000 --- a/metadata/IndianInvoices__3__metadata.json +++ /dev/null @@ -1,21 +0,0 @@ -{ - "name": "IndianInvoices", - "retrainable": true, - "gpu": 1, - "processorType": "GPU", - "description": "Machine Learning model(available in Preview) for extracting commonly occurring data points from Invoices from India, including header fields and line items. Please see more details including supported languages and link to Activities guide in the About Licensing -\u003e Document Understanding API Key section of the UiPath Automation Cloud Guide here: https://docs.uipath.com/automation-cloud/docs/about-licensing#section-document-understanding-api-key", - "inputDescription": "ML Skills deployed using this ML Package can be integrated into RPA workflows using the Machine Learning Extractor activity in the Official feed or the MLModel activity in Connect feed. File formats accepted include pdf, tiff, jpg or png files. The activities also require an API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -\u003e Other Services view.", - "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", - "changeLog": "Model: 20.10.4", - "cpu": 0, - "inputType": "JSON", - "displayName": "IndianInvoices", - "memory": 0, - "mlPackageLanguage": "PYTHON37_DU", - "projectId": "[project-id]", - "stagingUri": "[staging-uri]", - "projectName": "UiPath Document Understanding", - "projectDescription": "UiPath models to classify and extract information from images and pdfs.", - "tenantName": "UiPath", - "imagePath": "registry.replicated.com/aif-core/indianinvoices:3" -} diff --git a/metadata/IndianInvoices__40__metadata.json b/metadata/IndianInvoices__40__metadata.json new file mode 100644 index 00000000..d5fa706e --- /dev/null +++ b/metadata/IndianInvoices__40__metadata.json @@ -0,0 +1,34 @@ +{ + "name": "IndianInvoices", + "retrainable": true, + "gpu": 0, + "processorType": "CPU", + "description": "Machine Learning model for extracting commonly occurring data points from Invoices from India, including header fields and line items. Please see more details including supported languages in the ML Packages section of the Document Understanding Guide: https://docs.uipath.com/document-understanding/docs/ml-packages", + "inputDescription": "ML Skills deployed using this ML Package can be integrated into RPA workflows using the Machine Learning Extractor activity in the Official feed or the MLModel activity in Connect feed. File formats accepted include pdf, tiff, jpg or png files. The activities also require an API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -> Other Services view.", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "changeLog": "", + "cpu": 1, + "inputType": "JSON", + "displayName": "IndianInvoices", + "memory": 0, + "mlPackageLanguage": "PYTHON37_DU", + "projectId": "[project-id]", + "stagingUri": "[staging-uri]", + "projectName": "UiPath Document Understanding", + "projectDescription": "UiPath models to classify and extract information from images and pdfs.", + "tenantName": "UiPath", + "minAIFabricVersion": "22.10", + "languageVersion": 4, + "version": 40, + "customVersion": "23.4.11", + "imagePath": "du-semistructured:v23.4-01.27-rc01", + "parametersFileJSON": "{\"params\": [{\"name\": \"auto_retraining\",\"type\": [\"PIPELINE\"],\"value\": \"False\",\"help\": \"Enable this option when the Pipeline you are creating is a scheduled automatic pipeline, synchronized with automatic exports from Data Manager.\",\"options\": [\"True\",\"False\"]}],\"probes\": [{\"probe\": \"startup\",\"initialDelaySeconds\": \"120\",\"periodSeconds\": \"10\",\"timeoutSeconds\": \"2\",\"successThreshold\": \"1\",\"failureThreshold\": \"60\"}]}", + "settings": [ + { + "key": "package_type", + "type": "STRING", + "value": "invoices_india" + } + ], + "maxAIFabricVersion": "23.10" +} \ No newline at end of file diff --git a/metadata/IndianInvoices__41__metadata.json b/metadata/IndianInvoices__41__metadata.json new file mode 100644 index 00000000..b9bc1a7f --- /dev/null +++ b/metadata/IndianInvoices__41__metadata.json @@ -0,0 +1,34 @@ +{ + "name": "IndianInvoices", + "retrainable": true, + "gpu": 0, + "processorType": "CPU", + "description": "Machine Learning model for extracting commonly occurring data points from Invoices from India, including header fields and line items. Please see more details including supported languages in the ML Packages section of the Document Understanding Guide: https://docs.uipath.com/document-understanding/docs/ml-packages", + "inputDescription": "ML Skills deployed using this ML Package can be integrated into RPA workflows using the Machine Learning Extractor activity in the Official feed or the MLModel activity in Connect feed. File formats accepted include pdf, tiff, jpg or png files. The activities also require an API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -> Other Services view.", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "changeLog": "", + "cpu": 1, + "inputType": "JSON", + "displayName": "IndianInvoices", + "memory": 0, + "mlPackageLanguage": "PYTHON37_DU", + "projectId": "[project-id]", + "stagingUri": "[staging-uri]", + "projectName": "UiPath Document Understanding", + "projectDescription": "UiPath models to classify and extract information from images and pdfs.", + "tenantName": "UiPath", + "minAIFabricVersion": "22.10", + "languageVersion": 4, + "version": 41, + "customVersion": "23.10.8", + "imagePath": "du-semistructured:v23.10-01.28-rc07", + "parametersFileJSON": "{\"params\": [{\"name\": \"auto_retraining\",\"type\": [\"PIPELINE\"],\"value\": \"False\",\"help\": \"Enable this option when the Pipeline you are creating is a scheduled automatic pipeline, synchronized with automatic exports from Data Manager.\",\"options\": [\"True\",\"False\"]}],\"probes\": [{\"probe\": \"startup\",\"initialDelaySeconds\": \"120\",\"periodSeconds\": \"10\",\"timeoutSeconds\": \"2\",\"successThreshold\": \"1\",\"failureThreshold\": \"60\"}]}", + "settings": [ + { + "key": "package_type", + "type": "STRING", + "value": "invoices_india" + } + ], + "maxAIFabricVersion": "24.10" +} \ No newline at end of file diff --git a/metadata/IndianInvoices__42__metadata.json b/metadata/IndianInvoices__42__metadata.json new file mode 100644 index 00000000..44850359 --- /dev/null +++ b/metadata/IndianInvoices__42__metadata.json @@ -0,0 +1,33 @@ +{ + "name": "IndianInvoices", + "retrainable": true, + "gpu": 0, + "processorType": "CPU", + "description": "Machine Learning model for extracting commonly occurring data points from Invoices from India, including header fields and line items. Please see more details including supported languages in the ML Packages section of the Document Understanding Guide: https://docs.uipath.com/document-understanding/docs/ml-packages", + "inputDescription": "ML Skills deployed using this ML Package can be integrated into RPA workflows using the Machine Learning Extractor activity in the Official feed or the MLModel activity in Connect feed. File formats accepted include pdf, tiff, jpg or png files. The activities also require an API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -> Other Services view.", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "changeLog": "", + "cpu": 1, + "inputType": "JSON", + "displayName": "IndianInvoices", + "memory": 0, + "mlPackageLanguage": "PYTHON37_DU", + "projectId": "[project-id]", + "stagingUri": "[staging-uri]", + "projectName": "UiPath Document Understanding", + "projectDescription": "UiPath models to classify and extract information from images and pdfs.", + "tenantName": "UiPath", + "minAIFabricVersion": "22.10", + "languageVersion": 4, + "version": 42, + "customVersion": "24.10.3", + "imagePath": "du-semistructured:v24.10-3.27-rc02", + "parametersFileJSON": "{\"params\": [{\"name\": \"auto_retraining\",\"type\": [\"PIPELINE\"],\"value\": \"False\",\"help\": \"Enable this option when the Pipeline you are creating is a scheduled automatic pipeline, synchronized with automatic exports from Data Manager.\",\"options\": [\"True\",\"False\"]}],\"probes\": [{\"probe\": \"startup\",\"initialDelaySeconds\": \"120\",\"periodSeconds\": \"10\",\"timeoutSeconds\": \"2\",\"successThreshold\": \"1\",\"failureThreshold\": \"60\"}]}", + "settings": [ + { + "key": "package_type", + "type": "STRING", + "value": "invoices_india" + } + ] +} diff --git a/metadata/IndianInvoices__43__metadata.json b/metadata/IndianInvoices__43__metadata.json new file mode 100644 index 00000000..45f62ba4 --- /dev/null +++ b/metadata/IndianInvoices__43__metadata.json @@ -0,0 +1,34 @@ +{ + "name": "IndianInvoices", + "retrainable": true, + "gpu": 0, + "processorType": "CPU", + "description": "Machine Learning model for extracting commonly occurring data points from Invoices from India, including header fields and line items. Please see more details including supported languages in the ML Packages section of the Document Understanding Guide: https://docs.uipath.com/document-understanding/docs/ml-packages", + "inputDescription": "ML Skills deployed using this ML Package can be integrated into RPA workflows using the Machine Learning Extractor activity in the Official feed or the MLModel activity in Connect feed. File formats accepted include pdf, tiff, jpg or png files. The activities also require an API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -> Other Services view.", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "changeLog": "", + "cpu": 1, + "inputType": "JSON", + "displayName": "IndianInvoices", + "memory": 0, + "mlPackageLanguage": "PYTHON37_DU", + "projectId": "[project-id]", + "stagingUri": "[staging-uri]", + "projectName": "UiPath Document Understanding", + "projectDescription": "UiPath models to classify and extract information from images and pdfs.", + "tenantName": "UiPath", + "minAIFabricVersion": "22.10", + "languageVersion": 4, + "version": 43, + "customVersion": "23.10.9", + "imagePath": "du-semistructured:v23.10-03.14-rc02", + "parametersFileJSON": "{\"params\": [{\"name\": \"auto_retraining\",\"type\": [\"PIPELINE\"],\"value\": \"False\",\"help\": \"Enable this option when the Pipeline you are creating is a scheduled automatic pipeline, synchronized with automatic exports from Data Manager.\",\"options\": [\"True\",\"False\"]}],\"probes\": [{\"probe\": \"startup\",\"initialDelaySeconds\": \"120\",\"periodSeconds\": \"10\",\"timeoutSeconds\": \"2\",\"successThreshold\": \"1\",\"failureThreshold\": \"60\"}]}", + "settings": [ + { + "key": "package_type", + "type": "STRING", + "value": "invoices_india" + } + ], + "maxAIFabricVersion": "24.10" +} diff --git a/metadata/IndianInvoices__44__metadata.json b/metadata/IndianInvoices__44__metadata.json new file mode 100644 index 00000000..d06dd4e5 --- /dev/null +++ b/metadata/IndianInvoices__44__metadata.json @@ -0,0 +1,34 @@ +{ + "name": "IndianInvoices", + "retrainable": true, + "gpu": 0, + "processorType": "CPU", + "description": "Machine Learning model for extracting commonly occurring data points from Invoices from India, including header fields and line items. Please see more details including supported languages in the ML Packages section of the Document Understanding Guide: https://docs.uipath.com/document-understanding/docs/ml-packages", + "inputDescription": "ML Skills deployed using this ML Package can be integrated into RPA workflows using the Machine Learning Extractor activity in the Official feed or the MLModel activity in Connect feed. File formats accepted include pdf, tiff, jpg or png files. The activities also require an API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -> Other Services view.", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "changeLog": "", + "cpu": 1, + "inputType": "JSON", + "displayName": "IndianInvoices", + "memory": 0, + "mlPackageLanguage": "PYTHON37_DU", + "projectId": "[project-id]", + "stagingUri": "[staging-uri]", + "projectName": "UiPath Document Understanding", + "projectDescription": "UiPath models to classify and extract information from images and pdfs.", + "tenantName": "UiPath", + "minAIFabricVersion": "22.10", + "languageVersion": 4, + "version": 44, + "customVersion": "23.4.12", + "imagePath": "du-semistructured:v23.4-03.12-rc02", + "parametersFileJSON": "{\"params\": [{\"name\": \"auto_retraining\",\"type\": [\"PIPELINE\"],\"value\": \"False\",\"help\": \"Enable this option when the Pipeline you are creating is a scheduled automatic pipeline, synchronized with automatic exports from Data Manager.\",\"options\": [\"True\",\"False\"]}],\"probes\": [{\"probe\": \"startup\",\"initialDelaySeconds\": \"120\",\"periodSeconds\": \"10\",\"timeoutSeconds\": \"2\",\"successThreshold\": \"1\",\"failureThreshold\": \"60\"}]}", + "settings": [ + { + "key": "package_type", + "type": "STRING", + "value": "invoices_india" + } + ], + "maxAIFabricVersion": "23.10" +} diff --git a/metadata/IndianInvoices__45__metadata.json b/metadata/IndianInvoices__45__metadata.json new file mode 100644 index 00000000..58f6cf31 --- /dev/null +++ b/metadata/IndianInvoices__45__metadata.json @@ -0,0 +1,33 @@ +{ + "name": "IndianInvoices", + "retrainable": true, + "gpu": 0, + "processorType": "CPU", + "description": "Machine Learning model for extracting commonly occurring data points from Invoices from India, including header fields and line items. Please see more details including supported languages in the ML Packages section of the Document Understanding Guide: https://docs.uipath.com/document-understanding/docs/ml-packages", + "inputDescription": "ML Skills deployed using this ML Package can be integrated into RPA workflows using the Machine Learning Extractor activity in the Official feed or the MLModel activity in Connect feed. File formats accepted include pdf, tiff, jpg or png files. The activities also require an API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -> Other Services view.", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "changeLog": "", + "cpu": 1, + "inputType": "JSON", + "displayName": "IndianInvoices", + "memory": 0, + "mlPackageLanguage": "PYTHON37_DU", + "projectId": "[project-id]", + "stagingUri": "[staging-uri]", + "projectName": "UiPath Document Understanding", + "projectDescription": "UiPath models to classify and extract information from images and pdfs.", + "tenantName": "UiPath", + "minAIFabricVersion": "22.10", + "languageVersion": 4, + "version": 45, + "customVersion": "24.10.4", + "imagePath": "du-semistructured:v24.10-6.16-rc04", + "parametersFileJSON": "{\"params\": [{\"name\": \"auto_retraining\",\"type\": [\"PIPELINE\"],\"value\": \"False\",\"help\": \"Enable this option when the Pipeline you are creating is a scheduled automatic pipeline, synchronized with automatic exports from Data Manager.\",\"options\": [\"True\",\"False\"]}],\"probes\": [{\"probe\": \"startup\",\"initialDelaySeconds\": \"120\",\"periodSeconds\": \"10\",\"timeoutSeconds\": \"2\",\"successThreshold\": \"1\",\"failureThreshold\": \"60\"}]}", + "settings": [ + { + "key": "package_type", + "type": "STRING", + "value": "invoices_india" + } + ] +} diff --git a/metadata/IndianInvoices__46__metadata.json b/metadata/IndianInvoices__46__metadata.json new file mode 100644 index 00000000..21e5222a --- /dev/null +++ b/metadata/IndianInvoices__46__metadata.json @@ -0,0 +1,34 @@ +{ + "name": "IndianInvoices", + "retrainable": true, + "gpu": 0, + "processorType": "CPU", + "description": "Machine Learning model for extracting commonly occurring data points from Invoices from India, including header fields and line items. Please see more details including supported languages in the ML Packages section of the Document Understanding Guide: https://docs.uipath.com/document-understanding/docs/ml-packages", + "inputDescription": "ML Skills deployed using this ML Package can be integrated into RPA workflows using the Machine Learning Extractor activity in the Official feed or the MLModel activity in Connect feed. File formats accepted include pdf, tiff, jpg or png files. The activities also require an API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -> Other Services view.", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "changeLog": "", + "cpu": 1, + "inputType": "JSON", + "displayName": "IndianInvoices", + "memory": 0, + "mlPackageLanguage": "PYTHON37_DU", + "projectId": "[project-id]", + "stagingUri": "[staging-uri]", + "projectName": "UiPath Document Understanding", + "projectDescription": "UiPath models to classify and extract information from images and pdfs.", + "tenantName": "UiPath", + "minAIFabricVersion": "22.10", + "languageVersion": 4, + "version": 46, + "customVersion": "23.10.10", + "imagePath": "du-semistructured:v23.10-06.05-rc05", + "parametersFileJSON": "{\"params\": [{\"name\": \"auto_retraining\",\"type\": [\"PIPELINE\"],\"value\": \"False\",\"help\": \"Enable this option when the Pipeline you are creating is a scheduled automatic pipeline, synchronized with automatic exports from Data Manager.\",\"options\": [\"True\",\"False\"]}],\"probes\": [{\"probe\": \"startup\",\"initialDelaySeconds\": \"120\",\"periodSeconds\": \"10\",\"timeoutSeconds\": \"2\",\"successThreshold\": \"1\",\"failureThreshold\": \"60\"}]}", + "settings": [ + { + "key": "package_type", + "type": "STRING", + "value": "invoices_india" + } + ], + "maxAIFabricVersion": "24.10" +} diff --git a/metadata/IndianInvoices__47__metadata.json b/metadata/IndianInvoices__47__metadata.json new file mode 100644 index 00000000..8b2ab353 --- /dev/null +++ b/metadata/IndianInvoices__47__metadata.json @@ -0,0 +1,33 @@ +{ + "name": "IndianInvoices", + "retrainable": true, + "gpu": 0, + "processorType": "CPU", + "description": "Machine Learning model for extracting commonly occurring data points from Invoices from India, including header fields and line items. Please see more details including supported languages in the ML Packages section of the Document Understanding Guide: https://docs.uipath.com/document-understanding/docs/ml-packages", + "inputDescription": "ML Skills deployed using this ML Package can be integrated into RPA workflows using the Machine Learning Extractor activity in the Official feed or the MLModel activity in Connect feed. File formats accepted include pdf, tiff, jpg or png files. The activities also require an API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -> Other Services view.", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "changeLog": "", + "cpu": 1, + "inputType": "JSON", + "displayName": "IndianInvoices", + "memory": 0, + "mlPackageLanguage": "PYTHON37_DU", + "projectId": "[project-id]", + "stagingUri": "[staging-uri]", + "projectName": "UiPath Document Understanding", + "projectDescription": "UiPath models to classify and extract information from images and pdfs.", + "tenantName": "UiPath", + "minAIFabricVersion": "22.10", + "languageVersion": 4, + "version": 47, + "customVersion": "24.10.5", + "imagePath": "du-semistructured:v24.10-8.25-rc02", + "parametersFileJSON": "{\"params\": [{\"name\": \"auto_retraining\",\"type\": [\"PIPELINE\"],\"value\": \"False\",\"help\": \"Enable this option when the Pipeline you are creating is a scheduled automatic pipeline, synchronized with automatic exports from Data Manager.\",\"options\": [\"True\",\"False\"]}],\"probes\": [{\"probe\": \"startup\",\"initialDelaySeconds\": \"120\",\"periodSeconds\": \"10\",\"timeoutSeconds\": \"2\",\"successThreshold\": \"1\",\"failureThreshold\": \"60\"}]}", + "settings": [ + { + "key": "package_type", + "type": "STRING", + "value": "invoices_india" + } + ] +} diff --git a/metadata/IndianInvoices__48__metadata.json b/metadata/IndianInvoices__48__metadata.json new file mode 100644 index 00000000..065d576f --- /dev/null +++ b/metadata/IndianInvoices__48__metadata.json @@ -0,0 +1,34 @@ +{ + "name": "IndianInvoices", + "retrainable": true, + "gpu": 0, + "processorType": "CPU", + "description": "Machine Learning model for extracting commonly occurring data points from Invoices from India, including header fields and line items. Please see more details including supported languages in the ML Packages section of the Document Understanding Guide: https://docs.uipath.com/document-understanding/docs/ml-packages", + "inputDescription": "ML Skills deployed using this ML Package can be integrated into RPA workflows using the Machine Learning Extractor activity in the Official feed or the MLModel activity in Connect feed. File formats accepted include pdf, tiff, jpg or png files. The activities also require an API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -> Other Services view.", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "changeLog": "", + "cpu": 1, + "inputType": "JSON", + "displayName": "IndianInvoices", + "memory": 0, + "mlPackageLanguage": "PYTHON37_DU", + "projectId": "[project-id]", + "stagingUri": "[staging-uri]", + "projectName": "UiPath Document Understanding", + "projectDescription": "UiPath models to classify and extract information from images and pdfs.", + "tenantName": "UiPath", + "minAIFabricVersion": "22.10", + "languageVersion": 4, + "version": 48, + "customVersion": "23.10.11", + "imagePath": "du-semistructured:v23.10-08.25-rc02", + "parametersFileJSON": "{\"params\": [{\"name\": \"auto_retraining\",\"type\": [\"PIPELINE\"],\"value\": \"False\",\"help\": \"Enable this option when the Pipeline you are creating is a scheduled automatic pipeline, synchronized with automatic exports from Data Manager.\",\"options\": [\"True\",\"False\"]}],\"probes\": [{\"probe\": \"startup\",\"initialDelaySeconds\": \"120\",\"periodSeconds\": \"10\",\"timeoutSeconds\": \"2\",\"successThreshold\": \"1\",\"failureThreshold\": \"60\"}]}", + "settings": [ + { + "key": "package_type", + "type": "STRING", + "value": "invoices_india" + } + ], + "maxAIFabricVersion": "24.10" +} diff --git a/metadata/IndianInvoices__49__metadata.json b/metadata/IndianInvoices__49__metadata.json new file mode 100644 index 00000000..842eceb0 --- /dev/null +++ b/metadata/IndianInvoices__49__metadata.json @@ -0,0 +1,33 @@ +{ + "name": "IndianInvoices", + "retrainable": true, + "gpu": 0, + "processorType": "CPU", + "description": "Machine Learning model for extracting commonly occurring data points from Invoices from India, including header fields and line items. Please see more details including supported languages in the ML Packages section of the Document Understanding Guide: https://docs.uipath.com/document-understanding/docs/ml-packages", + "inputDescription": "ML Skills deployed using this ML Package can be integrated into RPA workflows using the Machine Learning Extractor activity in the Official feed or the MLModel activity in Connect feed. File formats accepted include pdf, tiff, jpg or png files. The activities also require an API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -> Other Services view.", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "changeLog": "", + "cpu": 1, + "inputType": "JSON", + "displayName": "IndianInvoices", + "memory": 0, + "mlPackageLanguage": "PYTHON37_DU", + "projectId": "[project-id]", + "stagingUri": "[staging-uri]", + "projectName": "UiPath Document Understanding", + "projectDescription": "UiPath models to classify and extract information from images and pdfs.", + "tenantName": "UiPath", + "minAIFabricVersion": "22.10", + "languageVersion": 4, + "version": 49, + "customVersion": "25.10.0", + "imagePath": "du-semistructured:v25.10-10.17-rc04", + "parametersFileJSON": "{\"params\": [{\"name\": \"auto_retraining\",\"type\": [\"PIPELINE\"],\"value\": \"False\",\"help\": \"Enable this option when the Pipeline you are creating is a scheduled automatic pipeline, synchronized with automatic exports from Data Manager.\",\"options\": [\"True\",\"False\"]}],\"probes\": [{\"probe\": \"startup\",\"initialDelaySeconds\": \"120\",\"periodSeconds\": \"10\",\"timeoutSeconds\": \"2\",\"successThreshold\": \"1\",\"failureThreshold\": \"60\"}]}", + "settings": [ + { + "key": "package_type", + "type": "STRING", + "value": "invoices_india" + } + ] +} diff --git a/metadata/IntelligentFormExtractor__1__metadata.json b/metadata/IntelligentFormExtractor__1__metadata.json deleted file mode 100644 index b1c728c0..00000000 --- a/metadata/IntelligentFormExtractor__1__metadata.json +++ /dev/null @@ -1,21 +0,0 @@ -{ - "changeLog": "Release v2020.8", - "cpu": 0, - "description": "This Package provides the Endpoint required by the Intelligent Form Extractor activity. Please see more details in the Form Extractor activity documentation here: https://docs.uipath.com/activities/docs/intelligent-form-extractor. For instructions about the steps required to correctly configure and deploy this Package, see the Out-of-the-box Packages documentation for AI Fabric here: https://docs.uipath.com/ai-fabric/docs/uipath-document-understanding", - "displayName": "IntelligentFormExtractor", - "gpu": 0, - "inputDescription": "ML Skills deployed using this package are queried directly by Intelligent Form Extractor Activity. For document types, languages supported and other information about the Intelligent Form Extractor product please refer to the documentation here: https://docs.uipath.com/activities/docs/intelligent-form-extractor.", - "inputType": "JSON", - "memory": 0, - "mlPackageLanguage": "PYTHON37_DU", - "name": "IntelligentFormExtractor", - "outputDescription": "Please refer to the documentation of the Intelligent Form Extractor Activity.", - "processorType": "CPU", - "projectId": "[project-id]", - "retrainable": false, - "stagingUri": "[staging-uri]", - "projectName": "UiPath Document Understanding", - "projectDescription": "UiPath models to classify and extract information from images and pdfs.", - "tenantName": "UiPath", - "imagePath": "registry.replicated.com/aif-core/intelligentformextractor:1" -} diff --git a/metadata/IntelligentKeywordClassifier__1__metadata.json b/metadata/IntelligentKeywordClassifier__1__metadata.json index c2f18671..b86f2b82 100644 --- a/metadata/IntelligentKeywordClassifier__1__metadata.json +++ b/metadata/IntelligentKeywordClassifier__1__metadata.json @@ -1,5 +1,5 @@ { - "changeLog": "Release v2020.9", + "changeLog": "v21-7-18", "cpu": 0, "description": "This Package provides the Endpoint required by the Intelligent Keyword Classifier activity. Please see more details in the Intelligent Keyword Classifier activity documentation here: https://docs.uipath.com/activities/docs/intelligent-keyword-classifier", "displayName": "IntelligentKeywordClassifier", @@ -17,5 +17,8 @@ "projectName": "UiPath Document Understanding", "projectDescription": "UiPath models to classify and extract information from images and pdfs.", "tenantName": "UiPath", - "imagePath": "registry.replicated.com/aif-core/intelligentkeywordclassifier:1" + "minAIFabricVersion": "v21.10", + "languageVersion": 3, + "version": 1, + "contentUri": "https:///publicmodels/AIC/IntelligentKeywordClassifier/1/IKC-du-svc-v21-7-18-main-4a0e8c.zip" } diff --git a/metadata/IntelligentKeywordClassifier__2__metadata.json b/metadata/IntelligentKeywordClassifier__2__metadata.json new file mode 100644 index 00000000..dd887692 --- /dev/null +++ b/metadata/IntelligentKeywordClassifier__2__metadata.json @@ -0,0 +1,24 @@ +{ + "changeLog": "v21-7-18", + "cpu": 0, + "description": "This Package provides the Endpoint required by the Intelligent Keyword Classifier activity. Please see more details in the Intelligent Keyword Classifier activity documentation here: https://docs.uipath.com/activities/docs/intelligent-keyword-classifier", + "displayName": "IntelligentKeywordClassifier", + "gpu": 0, + "inputDescription": "ML Skills deployed using this package are queried for the Intelligent Keyword Classifier Activity. To learn how to use the Intelligent Keyword Classifier, visit its documentation: https://docs.uipath.com/activities/docs/intelligent-keyword-classifier .", + "inputType": "JSON", + "memory": 0, + "mlPackageLanguage": "PYTHON37_DU", + "name": "IntelligentKeywordClassifier", + "outputDescription": "Please refer to the documentation of the Intelligent Keyword Classifier Activity.", + "processorType": "CPU", + "projectId": "[project-id]", + "retrainable": false, + "stagingUri": "[staging-uri]", + "projectName": "UiPath Document Understanding", + "projectDescription": "UiPath models to classify and extract information from images and pdfs.", + "tenantName": "UiPath", + "minAIFabricVersion": "v21.10", + "languageVersion": 3, + "version": 2, + "contentUri": "https:///publicmodels/AIC/IntelligentKeywordClassifier/2/IKC-du-svc-v21-7-18-main-4a0e8c.zip" +} diff --git a/metadata/InvoicesChina__26__metadata.json b/metadata/InvoicesChina__26__metadata.json new file mode 100644 index 00000000..fa42b4fa --- /dev/null +++ b/metadata/InvoicesChina__26__metadata.json @@ -0,0 +1,34 @@ +{ + "name": "InvoicesChina", + "retrainable": true, + "gpu": 0, + "processorType": "CPU", + "description": "Machine Learning model for extracting commonly occurring data points from Chinese Invoices, including header fields and line items. Please see more details including supported languages and link to Activities guide in the About Licensing -> Document Understanding API Key section of the UiPath Automation Cloud Guide here: https://docs.uipath.com/automation-cloud/docs/about-licensing#section-document-understanding-api-key", + "inputDescription": "ML Skills deployed using this ML Package can be integrated into RPA workflows using the Machine Learning Extractor activity in the Official feed or the MLModel activity in Connect feed. File formats accepted include pdf, tiff, jpg or png files. 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The activities also require an API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -> Other Services view.", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "changeLog": "", + "cpu": 1, + "inputType": "JSON", + "displayName": "InvoicesChina", + "memory": 0, + "mlPackageLanguage": "PYTHON37_DU", + "projectId": "[project-id]", + "stagingUri": "[staging-uri]", + "projectName": "UiPath Document Understanding", + "projectDescription": "UiPath models to classify and extract information from images and pdfs.", + "tenantName": "UiPath", + "minAIFabricVersion": "22.10", + "languageVersion": 4, + "version": 40, + "customVersion": "24.10.5", + "imagePath": "du-semistructured:v24.10-8.25-rc02", + "parametersFileJSON": "{\"params\": [{\"name\": \"auto_retraining\",\"type\": [\"PIPELINE\"],\"value\": \"False\",\"help\": \"Enable this option when the Pipeline you are creating is a scheduled automatic pipeline, synchronized with automatic exports from Data Manager.\",\"options\": [\"True\",\"False\"]}],\"probes\": [{\"probe\": \"startup\",\"initialDelaySeconds\": \"120\",\"periodSeconds\": \"10\",\"timeoutSeconds\": \"2\",\"successThreshold\": \"1\",\"failureThreshold\": \"60\"}]}", + "settings": [ + { + "key": "package_type", + "type": "STRING", + "value": "invoices_china" + } + ] +} diff --git a/metadata/InvoicesChina__41__metadata.json b/metadata/InvoicesChina__41__metadata.json new file mode 100644 index 00000000..570d077b --- /dev/null +++ b/metadata/InvoicesChina__41__metadata.json @@ -0,0 +1,34 @@ +{ + "name": "InvoicesChina", + "retrainable": true, + "gpu": 0, + "processorType": "CPU", + "description": "Machine Learning model for extracting commonly occurring data points from Chinese Invoices, including header fields and line items. 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The activities also require an API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -> Other Services view.", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "changeLog": "", + "cpu": 1, + "inputType": "JSON", + "displayName": "InvoicesChina", + "memory": 0, + "mlPackageLanguage": "PYTHON37_DU", + "projectId": "[project-id]", + "stagingUri": "[staging-uri]", + "projectName": "UiPath Document Understanding", + "projectDescription": "UiPath models to classify and extract information from images and pdfs.", + "tenantName": "UiPath", + "minAIFabricVersion": "22.10", + "languageVersion": 4, + "version": 41, + "customVersion": "23.10.11", + "imagePath": "du-semistructured:v23.10-08.25-rc02", + "parametersFileJSON": "{\"params\": [{\"name\": \"auto_retraining\",\"type\": [\"PIPELINE\"],\"value\": \"False\",\"help\": \"Enable this option when the Pipeline you are creating is a scheduled automatic pipeline, synchronized with automatic exports from Data Manager.\",\"options\": [\"True\",\"False\"]}],\"probes\": [{\"probe\": \"startup\",\"initialDelaySeconds\": \"120\",\"periodSeconds\": \"10\",\"timeoutSeconds\": \"2\",\"successThreshold\": \"1\",\"failureThreshold\": \"60\"}]}", + "settings": [ + { + "key": "package_type", + "type": "STRING", + "value": "invoices_china" + } + ], + "maxAIFabricVersion": "24.10" +} diff --git a/metadata/InvoicesChina__42__metadata.json b/metadata/InvoicesChina__42__metadata.json new file mode 100644 index 00000000..4ec5018f --- /dev/null +++ b/metadata/InvoicesChina__42__metadata.json @@ -0,0 +1,33 @@ +{ + "name": "InvoicesChina", + "retrainable": true, + "gpu": 0, + "processorType": "CPU", + "description": "Machine Learning model for extracting commonly occurring data points from Chinese Invoices, including header fields and line items. Please see more details including supported languages and link to Activities guide in the About Licensing -> Document Understanding API Key section of the UiPath Automation Cloud Guide here: https://docs.uipath.com/automation-cloud/docs/about-licensing#section-document-understanding-api-key", + "inputDescription": "ML Skills deployed using this ML Package can be integrated into RPA workflows using the Machine Learning Extractor activity in the Official feed or the MLModel activity in Connect feed. File formats accepted include pdf, tiff, jpg or png files. The activities also require an API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -> Other Services view.", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "changeLog": "", + "cpu": 1, + "inputType": "JSON", + "displayName": "InvoicesChina", + "memory": 0, + "mlPackageLanguage": "PYTHON37_DU", + "projectId": "[project-id]", + "stagingUri": "[staging-uri]", + "projectName": "UiPath Document Understanding", + "projectDescription": "UiPath models to classify and extract information from images and pdfs.", + "tenantName": "UiPath", + "minAIFabricVersion": "22.10", + "languageVersion": 4, + "version": 42, + "customVersion": "25.10.0", + "imagePath": "du-semistructured:v25.10-10.17-rc04", + "parametersFileJSON": "{\"params\": [{\"name\": \"auto_retraining\",\"type\": [\"PIPELINE\"],\"value\": \"False\",\"help\": \"Enable this option when the Pipeline you are creating is a scheduled automatic pipeline, synchronized with automatic exports from Data Manager.\",\"options\": [\"True\",\"False\"]}],\"probes\": [{\"probe\": \"startup\",\"initialDelaySeconds\": \"120\",\"periodSeconds\": \"10\",\"timeoutSeconds\": \"2\",\"successThreshold\": \"1\",\"failureThreshold\": \"60\"}]}", + "settings": [ + { + "key": "package_type", + "type": "STRING", + "value": "invoices_china" + } + ] +} diff --git a/metadata/InvoicesHebrew__3__metadata.json b/metadata/InvoicesHebrew__3__metadata.json new file mode 100644 index 00000000..fa3bff67 --- /dev/null +++ b/metadata/InvoicesHebrew__3__metadata.json @@ -0,0 +1,33 @@ +{ + "name": "InvoicesHebrew", + "retrainable": true, + "gpu": 0, + "processorType": "CPU", + "description": "Machine Learning model (available in Preview) for extracting commonly occurring data points from Hebrew Invoices documents, including header fields and line items. Please see more details including supported languages and link to Activities guide in the About Licensing -> Document Understanding API Key section of the UiPath Automation Cloud Guide here: https://docs.uipath.com/automation-cloud/docs/about-licensing#section-document-understanding-api-key", + "inputDescription": "ML Skills deployed using this ML Package can be integrated into RPA workflows using the Machine Learning Extractor activity in the Official feed or the MLModel activity in Connect feed. File formats accepted include pdf, tiff, jpg or png files. The activities also require an API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -> Other Services view.", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "changeLog": "", + "cpu": 1, + "inputType": "JSON", + "displayName": "InvoicesHebrew", + "memory": 0, + "mlPackageLanguage": "PYTHON37_DU", + "projectId": "[project-id]", + "stagingUri": "[staging-uri]", + "projectName": "UiPath Document Understanding", + "projectDescription": "UiPath models to classify and extract information from images and pdfs.", + "tenantName": "UiPath", + "minAIFabricVersion": "22.10", + "languageVersion": 4, + "version": 3, + "customVersion": "24.10.2", + "imagePath": "du-semistructured:v24.10-1.27-rc02", + "parametersFileJSON": "{\"params\": [{\"name\": \"auto_retraining\",\"type\": [\"PIPELINE\"],\"value\": \"False\",\"help\": \"Enable this option when the Pipeline you are creating is a scheduled automatic pipeline, synchronized with automatic exports from Data Manager.\",\"options\": [\"True\",\"False\"]}],\"probes\": [{\"probe\": \"startup\",\"initialDelaySeconds\": \"120\",\"periodSeconds\": \"10\",\"timeoutSeconds\": \"2\",\"successThreshold\": \"1\",\"failureThreshold\": \"60\"}]}", + "settings": [ + { + "key": "package_type", + "type": "STRING", + "value": "invoices_hebrew" + } + ] +} \ No newline at end of file diff --git a/metadata/InvoicesHebrew__4__metadata.json b/metadata/InvoicesHebrew__4__metadata.json new file mode 100644 index 00000000..10d5117a --- /dev/null +++ b/metadata/InvoicesHebrew__4__metadata.json @@ -0,0 +1,33 @@ +{ + "name": "InvoicesHebrew", + "retrainable": true, + "gpu": 0, + "processorType": "CPU", + "description": "Machine Learning model (available in Preview) for extracting commonly occurring data points from Hebrew Invoices documents, including header fields and line items. Please see more details including supported languages and link to Activities guide in the About Licensing -> Document Understanding API Key section of the UiPath Automation Cloud Guide here: https://docs.uipath.com/automation-cloud/docs/about-licensing#section-document-understanding-api-key", + "inputDescription": "ML Skills deployed using this ML Package can be integrated into RPA workflows using the Machine Learning Extractor activity in the Official feed or the MLModel activity in Connect feed. File formats accepted include pdf, tiff, jpg or png files. The activities also require an API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -> Other Services view.", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "changeLog": "", + "cpu": 1, + "inputType": "JSON", + "displayName": "InvoicesHebrew", + "memory": 0, + "mlPackageLanguage": "PYTHON37_DU", + "projectId": "[project-id]", + "stagingUri": "[staging-uri]", + "projectName": "UiPath Document Understanding", + "projectDescription": "UiPath models to classify and extract information from images and pdfs.", + "tenantName": "UiPath", + "minAIFabricVersion": "22.10", + "languageVersion": 4, + "version": 4, + "customVersion": "24.10.3", + "imagePath": "du-semistructured:v24.10-3.27-rc02", + "parametersFileJSON": "{\"params\": [{\"name\": \"auto_retraining\",\"type\": [\"PIPELINE\"],\"value\": \"False\",\"help\": \"Enable this option when the Pipeline you are creating is a scheduled automatic pipeline, synchronized with automatic exports from Data Manager.\",\"options\": [\"True\",\"False\"]}],\"probes\": [{\"probe\": \"startup\",\"initialDelaySeconds\": \"120\",\"periodSeconds\": \"10\",\"timeoutSeconds\": \"2\",\"successThreshold\": \"1\",\"failureThreshold\": \"60\"}]}", + "settings": [ + { + "key": "package_type", + "type": "STRING", + "value": "invoices_hebrew" + } + ] +} diff --git a/metadata/InvoicesHebrew__5__metadata.json b/metadata/InvoicesHebrew__5__metadata.json new file mode 100644 index 00000000..8c595fb6 --- /dev/null +++ b/metadata/InvoicesHebrew__5__metadata.json @@ -0,0 +1,33 @@ +{ + "name": "InvoicesHebrew", + "retrainable": true, + "gpu": 0, + "processorType": "CPU", + "description": "Machine Learning model (available in Preview) for extracting commonly occurring data points from Hebrew Invoices documents, including header fields and line items. Please see more details including supported languages and link to Activities guide in the About Licensing -> Document Understanding API Key section of the UiPath Automation Cloud Guide here: https://docs.uipath.com/automation-cloud/docs/about-licensing#section-document-understanding-api-key", + "inputDescription": "ML Skills deployed using this ML Package can be integrated into RPA workflows using the Machine Learning Extractor activity in the Official feed or the MLModel activity in Connect feed. File formats accepted include pdf, tiff, jpg or png files. The activities also require an API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -> Other Services view.", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "changeLog": "", + "cpu": 1, + "inputType": "JSON", + "displayName": "InvoicesHebrew", + "memory": 0, + "mlPackageLanguage": "PYTHON37_DU", + "projectId": "[project-id]", + "stagingUri": "[staging-uri]", + "projectName": "UiPath Document Understanding", + "projectDescription": "UiPath models to classify and extract information from images and pdfs.", + "tenantName": "UiPath", + "minAIFabricVersion": "22.10", + "languageVersion": 4, + "version": 5, + "customVersion": "24.10.4", + "imagePath": "du-semistructured:v24.10-6.16-rc04", + "parametersFileJSON": "{\"params\": [{\"name\": \"auto_retraining\",\"type\": [\"PIPELINE\"],\"value\": \"False\",\"help\": \"Enable this option when the Pipeline you are creating is a scheduled automatic pipeline, synchronized with automatic exports from Data Manager.\",\"options\": [\"True\",\"False\"]}],\"probes\": [{\"probe\": \"startup\",\"initialDelaySeconds\": \"120\",\"periodSeconds\": \"10\",\"timeoutSeconds\": \"2\",\"successThreshold\": \"1\",\"failureThreshold\": \"60\"}]}", + "settings": [ + { + "key": "package_type", + "type": "STRING", + "value": "invoices_hebrew" + } + ] +} diff --git a/metadata/InvoicesHebrew__6__metadata.json b/metadata/InvoicesHebrew__6__metadata.json new file mode 100644 index 00000000..e20f53a4 --- /dev/null +++ b/metadata/InvoicesHebrew__6__metadata.json @@ -0,0 +1,33 @@ +{ + "name": "InvoicesHebrew", + "retrainable": true, + "gpu": 0, + "processorType": "CPU", + "description": "Machine Learning model (available in Preview) for extracting commonly occurring data points from Hebrew Invoices documents, including header fields and line items. 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The activities also require an API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -> Other Services view.", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "changeLog": "", + "cpu": 1, + "inputType": "JSON", + "displayName": "InvoicesHebrew", + "memory": 0, + "mlPackageLanguage": "PYTHON37_DU", + "projectId": "[project-id]", + "stagingUri": "[staging-uri]", + "projectName": "UiPath Document Understanding", + "projectDescription": "UiPath models to classify and extract information from images and pdfs.", + "tenantName": "UiPath", + "minAIFabricVersion": "22.10", + "languageVersion": 4, + "version": 6, + "customVersion": "24.10.5", + "imagePath": "du-semistructured:v24.10-8.25-rc02", + "parametersFileJSON": "{\"params\": [{\"name\": \"auto_retraining\",\"type\": [\"PIPELINE\"],\"value\": \"False\",\"help\": \"Enable this option when the Pipeline you are creating is a scheduled automatic pipeline, synchronized with automatic exports from Data Manager.\",\"options\": [\"True\",\"False\"]}],\"probes\": [{\"probe\": \"startup\",\"initialDelaySeconds\": \"120\",\"periodSeconds\": \"10\",\"timeoutSeconds\": \"2\",\"successThreshold\": \"1\",\"failureThreshold\": \"60\"}]}", + "settings": [ + { + "key": "package_type", + "type": "STRING", + "value": "invoices_hebrew" + } + ] +} diff --git a/metadata/InvoicesHebrew__7__metadata.json b/metadata/InvoicesHebrew__7__metadata.json new file mode 100644 index 00000000..316ab555 --- /dev/null +++ b/metadata/InvoicesHebrew__7__metadata.json @@ -0,0 +1,33 @@ +{ + "name": "InvoicesHebrew", + "retrainable": true, + "gpu": 0, + "processorType": "CPU", + "description": "Machine Learning model (available in Preview) for extracting commonly occurring data points from Hebrew Invoices documents, including header fields and line items. 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The activities also require an API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -> Other Services view.", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "changeLog": "", + "cpu": 1, + "inputType": "JSON", + "displayName": "InvoicesShipping", + "memory": 0, + "mlPackageLanguage": "PYTHON37_DU", + "projectId": "[project-id]", + "stagingUri": "[staging-uri]", + "projectName": "UiPath Document Understanding", + "projectDescription": "UiPath models to classify and extract information from images and pdfs.", + "tenantName": "UiPath", + "minAIFabricVersion": "22.10", + "languageVersion": 4, + "version": 21, + "customVersion": "24.10.3", + "imagePath": "du-semistructured:v24.10-3.27-rc02", + "parametersFileJSON": "{\"params\": [{\"name\": \"auto_retraining\",\"type\": [\"PIPELINE\"],\"value\": \"False\",\"help\": \"Enable this option when the Pipeline you are creating is a scheduled automatic pipeline, synchronized with automatic exports from Data Manager.\",\"options\": [\"True\",\"False\"]}],\"probes\": [{\"probe\": \"startup\",\"initialDelaySeconds\": \"120\",\"periodSeconds\": \"10\",\"timeoutSeconds\": \"2\",\"successThreshold\": \"1\",\"failureThreshold\": \"60\"}]}", + "settings": [ + { + "key": "package_type", + "type": "STRING", + "value": "invoices_shipping" + } + ] +} diff --git a/metadata/InvoicesShipping__22__metadata.json b/metadata/InvoicesShipping__22__metadata.json new file mode 100644 index 00000000..e3b9c7ab --- /dev/null +++ b/metadata/InvoicesShipping__22__metadata.json @@ -0,0 +1,34 @@ +{ + "name": "InvoicesShipping", + "retrainable": true, + "gpu": 0, + "processorType": "CPU", + "description": "Machine Learning model for extracting commonly occurring data points from Invoices Shipping. See the documentation page for this model for more details https://docs.uipath.com/document-understanding/automation-cloud/latest/user-guide/invoices-shipping-ml-package", + "inputDescription": "ML Skills deployed using this ML Package can be integrated into RPA workflows using the Machine Learning Extractor activity in the Official feed or the MLModel activity in Connect feed. Accepted file formats: pdf, tiff, jpg or png files. The activities also require an API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -> Other Services view.", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "changeLog": "", + "cpu": 1, + "inputType": "JSON", + "displayName": "InvoicesShipping", + "memory": 0, + "mlPackageLanguage": "PYTHON37_DU", + "projectId": "[project-id]", + "stagingUri": "[staging-uri]", + "projectName": "UiPath Document Understanding", + "projectDescription": "UiPath models to classify and extract information from images and pdfs.", + "tenantName": "UiPath", + "minAIFabricVersion": "22.10", + "languageVersion": 4, + "version": 22, + "customVersion": "23.10.9", + "imagePath": "du-semistructured:v23.10-03.14-rc02", + "parametersFileJSON": "{\"params\": [{\"name\": \"auto_retraining\",\"type\": [\"PIPELINE\"],\"value\": \"False\",\"help\": \"Enable this option when the Pipeline you are creating is a scheduled automatic pipeline, synchronized with automatic exports from Data Manager.\",\"options\": [\"True\",\"False\"]}],\"probes\": [{\"probe\": \"startup\",\"initialDelaySeconds\": \"120\",\"periodSeconds\": \"10\",\"timeoutSeconds\": \"2\",\"successThreshold\": \"1\",\"failureThreshold\": \"60\"}]}", + "settings": [ + { + "key": "package_type", + "type": "STRING", + "value": "invoices_shipping" + } + ], + "maxAIFabricVersion": "24.10" +} diff --git a/metadata/InvoicesShipping__23__metadata.json b/metadata/InvoicesShipping__23__metadata.json new file mode 100644 index 00000000..27005817 --- /dev/null +++ b/metadata/InvoicesShipping__23__metadata.json @@ -0,0 +1,34 @@ +{ + "name": "InvoicesShipping", + "retrainable": true, + "gpu": 0, + "processorType": "CPU", + "description": "Machine Learning model for extracting commonly occurring data points from Invoices Shipping. See the documentation page for this model for more details https://docs.uipath.com/document-understanding/automation-cloud/latest/user-guide/invoices-shipping-ml-package", + "inputDescription": "ML Skills deployed using this ML Package can be integrated into RPA workflows using the Machine Learning Extractor activity in the Official feed or the MLModel activity in Connect feed. Accepted file formats: pdf, tiff, jpg or png files. The activities also require an API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -> Other Services view.", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "changeLog": "", + "cpu": 1, + "inputType": "JSON", + "displayName": "InvoicesShipping", + "memory": 0, + "mlPackageLanguage": "PYTHON37_DU", + "projectId": "[project-id]", + "stagingUri": "[staging-uri]", + "projectName": "UiPath Document Understanding", + "projectDescription": "UiPath models to classify and extract information from images and pdfs.", + "tenantName": "UiPath", + "minAIFabricVersion": "22.10", + "languageVersion": 4, + "version": 23, + "customVersion": "23.4.12", + "imagePath": "du-semistructured:v23.4-03.12-rc02", + "parametersFileJSON": "{\"params\": [{\"name\": \"auto_retraining\",\"type\": [\"PIPELINE\"],\"value\": \"False\",\"help\": \"Enable this option when the Pipeline you are creating is a scheduled automatic pipeline, synchronized with automatic exports from Data Manager.\",\"options\": [\"True\",\"False\"]}],\"probes\": [{\"probe\": \"startup\",\"initialDelaySeconds\": \"120\",\"periodSeconds\": \"10\",\"timeoutSeconds\": \"2\",\"successThreshold\": \"1\",\"failureThreshold\": \"60\"}]}", + "settings": [ + { + "key": "package_type", + "type": "STRING", + "value": "invoices_shipping" + } + ], + "maxAIFabricVersion": "23.10" +} diff --git a/metadata/InvoicesShipping__24__metadata.json b/metadata/InvoicesShipping__24__metadata.json new file mode 100644 index 00000000..5c94ea5d --- /dev/null +++ b/metadata/InvoicesShipping__24__metadata.json @@ -0,0 +1,33 @@ +{ + "name": "InvoicesShipping", + "retrainable": true, + "gpu": 0, + "processorType": "CPU", + "description": "Machine Learning model for extracting commonly occurring data points from Invoices Shipping. See the documentation page for this model for more details https://docs.uipath.com/document-understanding/automation-cloud/latest/user-guide/invoices-shipping-ml-package", + "inputDescription": "ML Skills deployed using this ML Package can be integrated into RPA workflows using the Machine Learning Extractor activity in the Official feed or the MLModel activity in Connect feed. Accepted file formats: pdf, tiff, jpg or png files. The activities also require an API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -> Other Services view.", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "changeLog": "", + "cpu": 1, + "inputType": "JSON", + "displayName": "InvoicesShipping", + "memory": 0, + "mlPackageLanguage": "PYTHON37_DU", + "projectId": "[project-id]", + "stagingUri": "[staging-uri]", + "projectName": "UiPath Document Understanding", + "projectDescription": "UiPath models to classify and extract information from images and pdfs.", + "tenantName": "UiPath", + "minAIFabricVersion": "22.10", + "languageVersion": 4, + "version": 24, + "customVersion": "24.10.4", + "imagePath": "du-semistructured:v24.10-6.16-rc04", + "parametersFileJSON": "{\"params\": [{\"name\": \"auto_retraining\",\"type\": [\"PIPELINE\"],\"value\": \"False\",\"help\": \"Enable this option when the Pipeline you are creating is a scheduled automatic pipeline, synchronized with automatic exports from Data Manager.\",\"options\": [\"True\",\"False\"]}],\"probes\": [{\"probe\": \"startup\",\"initialDelaySeconds\": \"120\",\"periodSeconds\": \"10\",\"timeoutSeconds\": \"2\",\"successThreshold\": \"1\",\"failureThreshold\": \"60\"}]}", + "settings": [ + { + "key": "package_type", + "type": "STRING", + "value": "invoices_shipping" + } + ] +} diff --git a/metadata/InvoicesShipping__25__metadata.json b/metadata/InvoicesShipping__25__metadata.json new file mode 100644 index 00000000..74000308 --- /dev/null +++ b/metadata/InvoicesShipping__25__metadata.json @@ -0,0 +1,34 @@ +{ + "name": "InvoicesShipping", + "retrainable": true, + "gpu": 0, + "processorType": "CPU", + "description": "Machine Learning model for extracting commonly occurring data points from Invoices Shipping. See the documentation page for this model for more details https://docs.uipath.com/document-understanding/automation-cloud/latest/user-guide/invoices-shipping-ml-package", + "inputDescription": "ML Skills deployed using this ML Package can be integrated into RPA workflows using the Machine Learning Extractor activity in the Official feed or the MLModel activity in Connect feed. Accepted file formats: pdf, tiff, jpg or png files. The activities also require an API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -> Other Services view.", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "changeLog": "", + "cpu": 1, + "inputType": "JSON", + "displayName": "InvoicesShipping", + "memory": 0, + "mlPackageLanguage": "PYTHON37_DU", + "projectId": "[project-id]", + "stagingUri": "[staging-uri]", + "projectName": "UiPath Document Understanding", + "projectDescription": "UiPath models to classify and extract information from images and pdfs.", + "tenantName": "UiPath", + "minAIFabricVersion": "22.10", + "languageVersion": 4, + "version": 25, + "customVersion": "23.10.10", + "imagePath": "du-semistructured:v23.10-06.05-rc05", + "parametersFileJSON": "{\"params\": [{\"name\": \"auto_retraining\",\"type\": [\"PIPELINE\"],\"value\": \"False\",\"help\": \"Enable this option when the Pipeline you are creating is a scheduled automatic pipeline, synchronized with automatic exports from Data Manager.\",\"options\": [\"True\",\"False\"]}],\"probes\": [{\"probe\": \"startup\",\"initialDelaySeconds\": \"120\",\"periodSeconds\": \"10\",\"timeoutSeconds\": \"2\",\"successThreshold\": \"1\",\"failureThreshold\": \"60\"}]}", + "settings": [ + { + "key": "package_type", + "type": "STRING", + "value": "invoices_shipping" + } + ], + "maxAIFabricVersion": "24.10" +} diff --git a/metadata/InvoicesShipping__26__metadata.json b/metadata/InvoicesShipping__26__metadata.json new file mode 100644 index 00000000..a40fd60d --- /dev/null +++ b/metadata/InvoicesShipping__26__metadata.json @@ -0,0 +1,33 @@ +{ + "name": "InvoicesShipping", + "retrainable": true, + "gpu": 0, + "processorType": "CPU", + "description": "Machine Learning model for extracting commonly occurring data points from Invoices Shipping. See the documentation page for this model for more details https://docs.uipath.com/document-understanding/automation-cloud/latest/user-guide/invoices-shipping-ml-package", + "inputDescription": "ML Skills deployed using this ML Package can be integrated into RPA workflows using the Machine Learning Extractor activity in the Official feed or the MLModel activity in Connect feed. Accepted file formats: pdf, tiff, jpg or png files. The activities also require an API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -> Other Services view.", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "changeLog": "", + "cpu": 1, + "inputType": "JSON", + "displayName": "InvoicesShipping", + "memory": 0, + "mlPackageLanguage": "PYTHON37_DU", + "projectId": "[project-id]", + "stagingUri": "[staging-uri]", + "projectName": "UiPath Document Understanding", + "projectDescription": "UiPath models to classify and extract information from images and pdfs.", + "tenantName": "UiPath", + "minAIFabricVersion": "22.10", + "languageVersion": 4, + "version": 26, + "customVersion": "24.10.5", + "imagePath": "du-semistructured:v24.10-8.25-rc02", + "parametersFileJSON": "{\"params\": [{\"name\": \"auto_retraining\",\"type\": [\"PIPELINE\"],\"value\": \"False\",\"help\": \"Enable this option when the Pipeline you are creating is a scheduled automatic pipeline, synchronized with automatic exports from Data Manager.\",\"options\": [\"True\",\"False\"]}],\"probes\": [{\"probe\": \"startup\",\"initialDelaySeconds\": \"120\",\"periodSeconds\": \"10\",\"timeoutSeconds\": \"2\",\"successThreshold\": \"1\",\"failureThreshold\": \"60\"}]}", + "settings": [ + { + "key": "package_type", + "type": "STRING", + "value": "invoices_shipping" + } + ] +} diff --git a/metadata/InvoicesShipping__27__metadata.json b/metadata/InvoicesShipping__27__metadata.json new file mode 100644 index 00000000..3f6a82e4 --- /dev/null +++ b/metadata/InvoicesShipping__27__metadata.json @@ -0,0 +1,34 @@ +{ + "name": "InvoicesShipping", + "retrainable": true, + "gpu": 0, + "processorType": "CPU", + "description": "Machine Learning model for extracting commonly occurring data points from Invoices Shipping. See the documentation page for this model for more details https://docs.uipath.com/document-understanding/automation-cloud/latest/user-guide/invoices-shipping-ml-package", + "inputDescription": "ML Skills deployed using this ML Package can be integrated into RPA workflows using the Machine Learning Extractor activity in the Official feed or the MLModel activity in Connect feed. Accepted file formats: pdf, tiff, jpg or png files. The activities also require an API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -> Other Services view.", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "changeLog": "", + "cpu": 1, + "inputType": "JSON", + "displayName": "InvoicesShipping", + "memory": 0, + "mlPackageLanguage": "PYTHON37_DU", + "projectId": "[project-id]", + "stagingUri": "[staging-uri]", + "projectName": "UiPath Document Understanding", + "projectDescription": "UiPath models to classify and extract information from images and pdfs.", + "tenantName": "UiPath", + "minAIFabricVersion": "22.10", + "languageVersion": 4, + "version": 27, + "customVersion": "23.10.11", + "imagePath": "du-semistructured:v23.10-08.25-rc02", + "parametersFileJSON": "{\"params\": [{\"name\": \"auto_retraining\",\"type\": [\"PIPELINE\"],\"value\": \"False\",\"help\": \"Enable this option when the Pipeline you are creating is a scheduled automatic pipeline, synchronized with automatic exports from Data Manager.\",\"options\": [\"True\",\"False\"]}],\"probes\": [{\"probe\": \"startup\",\"initialDelaySeconds\": \"120\",\"periodSeconds\": \"10\",\"timeoutSeconds\": \"2\",\"successThreshold\": \"1\",\"failureThreshold\": \"60\"}]}", + "settings": [ + { + "key": "package_type", + "type": "STRING", + "value": "invoices_shipping" + } + ], + "maxAIFabricVersion": "24.10" +} diff --git a/metadata/InvoicesShipping__28__metadata.json b/metadata/InvoicesShipping__28__metadata.json new file mode 100644 index 00000000..84f2bf16 --- /dev/null +++ b/metadata/InvoicesShipping__28__metadata.json @@ -0,0 +1,33 @@ +{ + "name": "InvoicesShipping", + "retrainable": true, + "gpu": 0, + "processorType": "CPU", + "description": "Machine Learning model for extracting commonly occurring data points from Invoices Shipping. See the documentation page for this model for more details https://docs.uipath.com/document-understanding/automation-cloud/latest/user-guide/invoices-shipping-ml-package", + "inputDescription": "ML Skills deployed using this ML Package can be integrated into RPA workflows using the Machine Learning Extractor activity in the Official feed or the MLModel activity in Connect feed. Accepted file formats: pdf, tiff, jpg or png files. The activities also require an API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -> Other Services view.", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "changeLog": "", + "cpu": 1, + "inputType": "JSON", + "displayName": "InvoicesShipping", + "memory": 0, + "mlPackageLanguage": "PYTHON37_DU", + "projectId": "[project-id]", + "stagingUri": "[staging-uri]", + "projectName": "UiPath Document Understanding", + "projectDescription": "UiPath models to classify and extract information from images and pdfs.", + "tenantName": "UiPath", + "minAIFabricVersion": "22.10", + "languageVersion": 4, + "version": 28, + "customVersion": "25.10.0", + "imagePath": "du-semistructured:v25.10-10.17-rc04", + "parametersFileJSON": "{\"params\": [{\"name\": \"auto_retraining\",\"type\": [\"PIPELINE\"],\"value\": \"False\",\"help\": \"Enable this option when the Pipeline you are creating is a scheduled automatic pipeline, synchronized with automatic exports from Data Manager.\",\"options\": [\"True\",\"False\"]}],\"probes\": [{\"probe\": \"startup\",\"initialDelaySeconds\": \"120\",\"periodSeconds\": \"10\",\"timeoutSeconds\": \"2\",\"successThreshold\": \"1\",\"failureThreshold\": \"60\"}]}", + "settings": [ + { + "key": "package_type", + "type": "STRING", + "value": "invoices_shipping" + } + ] +} diff --git a/metadata/Invoices__3__metadata.json b/metadata/Invoices__11__metadata.json similarity index 72% rename from metadata/Invoices__3__metadata.json rename to metadata/Invoices__11__metadata.json index 79853dad..12459d89 100644 --- a/metadata/Invoices__3__metadata.json +++ b/metadata/Invoices__11__metadata.json @@ -1,21 +1,26 @@ { - "changeLog": "Release v2020.8", - "cpu": 0, - "description": "Machine Learning model for extracting commonly occurring data points from Invoices, including header fields and line items. Please see more details including supported languages and link to Activities guide in the About Licensing -> Document Understanding API Key section of the UiPath Automation Cloud Guide here: https://docs.uipath.com/automation-cloud/docs/about-licensing#section-document-understanding-api-key.", - "displayName": "Invoices", + "name": "Invoices", + "retrainable": true, "gpu": 0, + "processorType": "CPU", + "description": "Machine Learning model for extracting commonly occurring data points from Invoices, including header fields and line items. Please see more details including supported languages in the ML Packages section of the Document Understanding Guide: https://docs.uipath.com/document-understanding/docs/ml-packages", "inputDescription": "ML Skills deployed using this ML Package can be integrated into RPA workflows using the Machine Learning Extractor activity in the Official feed or the MLModel activity in Connect feed. File formats accepted include pdf, tiff, jpg or png files. The activities also require an API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -> Other Services view.", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "changeLog": "", + "cpu": 1, "inputType": "JSON", + "displayName": "Invoices", "memory": 0, "mlPackageLanguage": "PYTHON37_DU", - "name": "Invoices", - "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", - "processorType": "CPU", - "projectId": "[project-id]", - "retrainable": true, + "projectId": "[project-id]", "stagingUri": "[staging-uri]", "projectName": "UiPath Document Understanding", "projectDescription": "UiPath models to classify and extract information from images and pdfs.", "tenantName": "UiPath", - "imagePath": "registry.replicated.com/aif-core/invoices:3" -} + "minAIFabricVersion": "22.4", + "languageVersion": 4, + "version": 11, + "customVersion": "22.4.1", + "contentUri": "https:///publicmodels/AIC/Invoices/22.4.1/invoices_package.zip", + "maxAIFabricVersion": "22.4" +} \ No newline at end of file diff --git a/metadata/Invoices__34__metadata.json b/metadata/Invoices__34__metadata.json new file mode 100644 index 00000000..99435f38 --- /dev/null +++ b/metadata/Invoices__34__metadata.json @@ -0,0 +1,33 @@ +{ + "name": "Invoices", + "retrainable": true, + "gpu": 0, + "processorType": "CPU", + "description": "Machine Learning model for extracting commonly occurring data points from Invoices, including header fields and line items. Please see more details including supported languages in the ML Packages section of the Document Understanding Guide: https://docs.uipath.com/document-understanding/docs/ml-packages", + "inputDescription": "ML Skills deployed using this ML Package can be integrated into RPA workflows using the Machine Learning Extractor activity in the Official feed or the MLModel activity in Connect feed. File formats accepted include pdf, tiff, jpg or png files. The activities also require an API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -> Other Services view.", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "cpu": 1, + "inputType": "JSON", + "displayName": "Invoices", + "memory": 0, + "mlPackageLanguage": "PYTHON37_DU", + "projectId": "[project-id]", + "stagingUri": "[staging-uri]", + "projectName": "UiPath Document Understanding", + "projectDescription": "UiPath models to classify and extract information from images and pdfs.", + "tenantName": "UiPath", + "minAIFabricVersion": "22.10", + "languageVersion": 4, + "version": 34, + "customVersion": "22.10.14", + "imagePath": "du-semistructured:v22.10-10.10-rc01", + "parametersFileJSON": "{\"params\": [{\"name\": \"auto_retraining\",\"type\": [\"PIPELINE\"],\"value\": \"False\",\"help\": \"Enable this option when the Pipeline you are creating is a scheduled automatic pipeline, synchronized with automatic exports from Data Manager.\",\"options\": [\"True\",\"False\"]}],\"probes\": [{\"probe\": \"startup\",\"initialDelaySeconds\": \"120\",\"periodSeconds\": \"10\",\"timeoutSeconds\": \"2\",\"successThreshold\": \"1\",\"failureThreshold\": \"60\"}]}", + "settings": [ + { + "key": "package_type", + "type": "STRING", + "value": "invoices" + } + ], + "maxAIFabricVersion": "23.4" +} \ No newline at end of file diff --git a/metadata/Invoices__40__metadata.json b/metadata/Invoices__40__metadata.json new file mode 100644 index 00000000..5cf2169e --- /dev/null +++ b/metadata/Invoices__40__metadata.json @@ -0,0 +1,32 @@ +{ + "name": "Invoices", + "retrainable": true, + "gpu": 0, + "processorType": "CPU", + "description": "Machine Learning model for extracting commonly occurring data points from Invoices, including header fields and line items. Please see more details including supported languages in the ML Packages section of the Document Understanding Guide: https://docs.uipath.com/document-understanding/docs/ml-packages", + "inputDescription": "ML Skills deployed using this ML Package can be integrated into RPA workflows using the Machine Learning Extractor activity in the Official feed or the MLModel activity in Connect feed. File formats accepted include pdf, tiff, jpg or png files. The activities also require an API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -> Other Services view.", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "cpu": 1, + "inputType": "JSON", + "displayName": "Invoices", + "memory": 0, + "mlPackageLanguage": "PYTHON37_DU", + "projectId": "[project-id]", + "stagingUri": "[staging-uri]", + "projectName": "UiPath Document Understanding", + "projectDescription": "UiPath models to classify and extract information from images and pdfs.", + "tenantName": "UiPath", + "minAIFabricVersion": "22.10", + "languageVersion": 4, + "version": 40, + "customVersion": "24.10.2", + "imagePath": "du-semistructured:v24.10-1.27-rc02", + "parametersFileJSON": "{\"params\": [{\"name\": \"auto_retraining\",\"type\": [\"PIPELINE\"],\"value\": \"False\",\"help\": \"Enable this option when the Pipeline you are creating is a scheduled automatic pipeline, synchronized with automatic exports from Data Manager.\",\"options\": [\"True\",\"False\"]}],\"probes\": [{\"probe\": \"startup\",\"initialDelaySeconds\": \"120\",\"periodSeconds\": \"10\",\"timeoutSeconds\": \"2\",\"successThreshold\": \"1\",\"failureThreshold\": \"60\"}]}", + "settings": [ + { + "key": "package_type", + "type": "STRING", + "value": "invoices" + } + ] +} \ No newline at end of file diff --git a/metadata/Invoices__41__metadata.json b/metadata/Invoices__41__metadata.json new file mode 100644 index 00000000..e9829a6d --- /dev/null +++ b/metadata/Invoices__41__metadata.json @@ -0,0 +1,33 @@ +{ + "name": "Invoices", + "retrainable": true, + "gpu": 0, + "processorType": "CPU", + "description": "Machine Learning model for extracting commonly occurring data points from Invoices, including header fields and line items. Please see more details including supported languages in the ML Packages section of the Document Understanding Guide: https://docs.uipath.com/document-understanding/docs/ml-packages", + "inputDescription": "ML Skills deployed using this ML Package can be integrated into RPA workflows using the Machine Learning Extractor activity in the Official feed or the MLModel activity in Connect feed. File formats accepted include pdf, tiff, jpg or png files. The activities also require an API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -> Other Services view.", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "cpu": 1, + "inputType": "JSON", + "displayName": "Invoices", + "memory": 0, + "mlPackageLanguage": "PYTHON37_DU", + "projectId": "[project-id]", + "stagingUri": "[staging-uri]", + "projectName": "UiPath Document Understanding", + "projectDescription": "UiPath models to classify and extract information from images and pdfs.", + "tenantName": "UiPath", + "minAIFabricVersion": "22.10", + "languageVersion": 4, + "version": 41, + "customVersion": "23.4.11", + "imagePath": "du-semistructured:v23.4-01.27-rc01", + "parametersFileJSON": "{\"params\": [{\"name\": \"auto_retraining\",\"type\": [\"PIPELINE\"],\"value\": \"False\",\"help\": \"Enable this option when the Pipeline you are creating is a scheduled automatic pipeline, synchronized with automatic exports from Data Manager.\",\"options\": [\"True\",\"False\"]}],\"probes\": [{\"probe\": \"startup\",\"initialDelaySeconds\": \"120\",\"periodSeconds\": \"10\",\"timeoutSeconds\": \"2\",\"successThreshold\": \"1\",\"failureThreshold\": \"60\"}]}", + "settings": [ + { + "key": "package_type", + "type": "STRING", + "value": "invoices" + } + ], + "maxAIFabricVersion": "23.10" +} \ No newline at end of file diff --git a/metadata/Invoices__42__metadata.json b/metadata/Invoices__42__metadata.json new file mode 100644 index 00000000..7ceac111 --- /dev/null +++ b/metadata/Invoices__42__metadata.json @@ -0,0 +1,33 @@ +{ + "name": "Invoices", + "retrainable": true, + "gpu": 0, + "processorType": "CPU", + "description": "Machine Learning model for extracting commonly occurring data points from Invoices, including header fields and line items. Please see more details including supported languages in the ML Packages section of the Document Understanding Guide: https://docs.uipath.com/document-understanding/docs/ml-packages", + "inputDescription": "ML Skills deployed using this ML Package can be integrated into RPA workflows using the Machine Learning Extractor activity in the Official feed or the MLModel activity in Connect feed. File formats accepted include pdf, tiff, jpg or png files. The activities also require an API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -> Other Services view.", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "cpu": 1, + "inputType": "JSON", + "displayName": "Invoices", + "memory": 0, + "mlPackageLanguage": "PYTHON37_DU", + "projectId": "[project-id]", + "stagingUri": "[staging-uri]", + "projectName": "UiPath Document Understanding", + "projectDescription": "UiPath models to classify and extract information from images and pdfs.", + "tenantName": "UiPath", + "minAIFabricVersion": "22.10", + "languageVersion": 4, + "version": 42, + "customVersion": "23.10.8", + "imagePath": "du-semistructured:v23.10-01.28-rc07", + "parametersFileJSON": "{\"params\": [{\"name\": \"auto_retraining\",\"type\": [\"PIPELINE\"],\"value\": \"False\",\"help\": \"Enable this option when the Pipeline you are creating is a scheduled automatic pipeline, synchronized with automatic exports from Data Manager.\",\"options\": [\"True\",\"False\"]}],\"probes\": [{\"probe\": \"startup\",\"initialDelaySeconds\": \"120\",\"periodSeconds\": \"10\",\"timeoutSeconds\": \"2\",\"successThreshold\": \"1\",\"failureThreshold\": \"60\"}]}", + "settings": [ + { + "key": "package_type", + "type": "STRING", + "value": "invoices" + } + ], + "maxAIFabricVersion": "24.10" +} \ No newline at end of file diff --git a/metadata/Invoices__1__metadata.json b/metadata/Invoices__43__metadata.json similarity index 52% rename from metadata/Invoices__1__metadata.json rename to metadata/Invoices__43__metadata.json index 2ae45a6c..8788c944 100644 --- a/metadata/Invoices__1__metadata.json +++ b/metadata/Invoices__43__metadata.json @@ -1,21 +1,32 @@ { - "changeLog": "", - "cpu": 0, - "description": "Machine Learning model for extracting commonly occurring data points from Invoices, including header fields and line items. Please see more details including supported languages and link to Activities guide in the About Licensing -> Document Understanding API Key section of the UiPath Automation Cloud Guide here: https://docs.uipath.com/automation-cloud/docs/about-licensing#section-document-understanding-api-key.", - "displayName": "Invoices", + "name": "Invoices", + "retrainable": true, "gpu": 0, + "processorType": "CPU", + "description": "Machine Learning model for extracting commonly occurring data points from Invoices, including header fields and line items. Please see more details including supported languages in the ML Packages section of the Document Understanding Guide: https://docs.uipath.com/document-understanding/docs/ml-packages", "inputDescription": "ML Skills deployed using this ML Package can be integrated into RPA workflows using the Machine Learning Extractor activity in the Official feed or the MLModel activity in Connect feed. File formats accepted include pdf, tiff, jpg or png files. The activities also require an API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -> Other Services view.", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "cpu": 1, "inputType": "JSON", + "displayName": "Invoices", "memory": 0, - "mlPackageLanguage": "PYTHON36_DU", - "name": "Invoices", - "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", - "processorType": "CPU", - "projectId": "[project-id]", - "retrainable": true, + "mlPackageLanguage": "PYTHON37_DU", + "projectId": "[project-id]", "stagingUri": "[staging-uri]", "projectName": "UiPath Document Understanding", "projectDescription": "UiPath models to classify and extract information from images and pdfs.", "tenantName": "UiPath", - "imagePath": "registry.replicated.com/aif-core/invoices:1" + "minAIFabricVersion": "22.10", + "languageVersion": 4, + "version": 43, + "customVersion": "24.10.3", + "imagePath": "du-semistructured:v24.10-3.27-rc02", + "parametersFileJSON": "{\"params\": [{\"name\": \"auto_retraining\",\"type\": [\"PIPELINE\"],\"value\": \"False\",\"help\": \"Enable this option when the Pipeline you are creating is a scheduled automatic pipeline, synchronized with automatic exports from Data Manager.\",\"options\": [\"True\",\"False\"]}],\"probes\": [{\"probe\": \"startup\",\"initialDelaySeconds\": \"120\",\"periodSeconds\": \"10\",\"timeoutSeconds\": \"2\",\"successThreshold\": \"1\",\"failureThreshold\": \"60\"}]}", + "settings": [ + { + "key": "package_type", + "type": "STRING", + "value": "invoices" + } + ] } diff --git a/metadata/Invoices__44__metadata.json b/metadata/Invoices__44__metadata.json new file mode 100644 index 00000000..ec0ec8a5 --- /dev/null +++ b/metadata/Invoices__44__metadata.json @@ -0,0 +1,33 @@ +{ + "name": "Invoices", + "retrainable": true, + "gpu": 0, + "processorType": "CPU", + "description": "Machine Learning model for extracting commonly occurring data points from Invoices, including header fields and line items. Please see more details including supported languages in the ML Packages section of the Document Understanding Guide: https://docs.uipath.com/document-understanding/docs/ml-packages", + "inputDescription": "ML Skills deployed using this ML Package can be integrated into RPA workflows using the Machine Learning Extractor activity in the Official feed or the MLModel activity in Connect feed. File formats accepted include pdf, tiff, jpg or png files. The activities also require an API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -> Other Services view.", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "cpu": 1, + "inputType": "JSON", + "displayName": "Invoices", + "memory": 0, + "mlPackageLanguage": "PYTHON37_DU", + "projectId": "[project-id]", + "stagingUri": "[staging-uri]", + "projectName": "UiPath Document Understanding", + "projectDescription": "UiPath models to classify and extract information from images and pdfs.", + "tenantName": "UiPath", + "minAIFabricVersion": "22.10", + "languageVersion": 4, + "version": 44, + "customVersion": "23.10.9", + "imagePath": "du-semistructured:v23.10-03.14-rc02", + "parametersFileJSON": "{\"params\": [{\"name\": \"auto_retraining\",\"type\": [\"PIPELINE\"],\"value\": \"False\",\"help\": \"Enable this option when the Pipeline you are creating is a scheduled automatic pipeline, synchronized with automatic exports from Data Manager.\",\"options\": [\"True\",\"False\"]}],\"probes\": [{\"probe\": \"startup\",\"initialDelaySeconds\": \"120\",\"periodSeconds\": \"10\",\"timeoutSeconds\": \"2\",\"successThreshold\": \"1\",\"failureThreshold\": \"60\"}]}", + "settings": [ + { + "key": "package_type", + "type": "STRING", + "value": "invoices" + } + ], + "maxAIFabricVersion": "24.10" +} diff --git a/metadata/Invoices__45__metadata.json b/metadata/Invoices__45__metadata.json new file mode 100644 index 00000000..5cc0a48c --- /dev/null +++ b/metadata/Invoices__45__metadata.json @@ -0,0 +1,33 @@ +{ + "name": "Invoices", + "retrainable": true, + "gpu": 0, + "processorType": "CPU", + "description": "Machine Learning model for extracting commonly occurring data points from Invoices, including header fields and line items. Please see more details including supported languages in the ML Packages section of the Document Understanding Guide: https://docs.uipath.com/document-understanding/docs/ml-packages", + "inputDescription": "ML Skills deployed using this ML Package can be integrated into RPA workflows using the Machine Learning Extractor activity in the Official feed or the MLModel activity in Connect feed. File formats accepted include pdf, tiff, jpg or png files. The activities also require an API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -> Other Services view.", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "cpu": 1, + "inputType": "JSON", + "displayName": "Invoices", + "memory": 0, + "mlPackageLanguage": "PYTHON37_DU", + "projectId": "[project-id]", + "stagingUri": "[staging-uri]", + "projectName": "UiPath Document Understanding", + "projectDescription": "UiPath models to classify and extract information from images and pdfs.", + "tenantName": "UiPath", + "minAIFabricVersion": "22.10", + "languageVersion": 4, + "version": 45, + "customVersion": "23.4.12", + "imagePath": "du-semistructured:v23.4-03.12-rc02", + "parametersFileJSON": "{\"params\": [{\"name\": \"auto_retraining\",\"type\": [\"PIPELINE\"],\"value\": \"False\",\"help\": \"Enable this option when the Pipeline you are creating is a scheduled automatic pipeline, synchronized with automatic exports from Data Manager.\",\"options\": [\"True\",\"False\"]}],\"probes\": [{\"probe\": \"startup\",\"initialDelaySeconds\": \"120\",\"periodSeconds\": \"10\",\"timeoutSeconds\": \"2\",\"successThreshold\": \"1\",\"failureThreshold\": \"60\"}]}", + "settings": [ + { + "key": "package_type", + "type": "STRING", + "value": "invoices" + } + ], + "maxAIFabricVersion": "23.10" +} diff --git a/metadata/Invoices__46__metadata.json b/metadata/Invoices__46__metadata.json new file mode 100644 index 00000000..2a1fbe15 --- /dev/null +++ b/metadata/Invoices__46__metadata.json @@ -0,0 +1,32 @@ +{ + "name": "Invoices", + "retrainable": true, + "gpu": 0, + "processorType": "CPU", + "description": "Machine Learning model for extracting commonly occurring data points from Invoices, including header fields and line items. Please see more details including supported languages in the ML Packages section of the Document Understanding Guide: https://docs.uipath.com/document-understanding/docs/ml-packages", + "inputDescription": "ML Skills deployed using this ML Package can be integrated into RPA workflows using the Machine Learning Extractor activity in the Official feed or the MLModel activity in Connect feed. File formats accepted include pdf, tiff, jpg or png files. The activities also require an API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -> Other Services view.", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "cpu": 1, + "inputType": "JSON", + "displayName": "Invoices", + "memory": 0, + "mlPackageLanguage": "PYTHON37_DU", + "projectId": "[project-id]", + "stagingUri": "[staging-uri]", + "projectName": "UiPath Document Understanding", + "projectDescription": "UiPath models to classify and extract information from images and pdfs.", + "tenantName": "UiPath", + "minAIFabricVersion": "22.10", + "languageVersion": 4, + "version": 46, + "customVersion": "24.10.4", + "imagePath": "du-semistructured:v24.10-6.16-rc04", + "parametersFileJSON": "{\"params\": [{\"name\": \"auto_retraining\",\"type\": [\"PIPELINE\"],\"value\": \"False\",\"help\": \"Enable this option when the Pipeline you are creating is a scheduled automatic pipeline, synchronized with automatic exports from Data Manager.\",\"options\": [\"True\",\"False\"]}],\"probes\": [{\"probe\": \"startup\",\"initialDelaySeconds\": \"120\",\"periodSeconds\": \"10\",\"timeoutSeconds\": \"2\",\"successThreshold\": \"1\",\"failureThreshold\": \"60\"}]}", + "settings": [ + { + "key": "package_type", + "type": "STRING", + "value": "invoices" + } + ] +} diff --git a/metadata/Invoices__47__metadata.json b/metadata/Invoices__47__metadata.json new file mode 100644 index 00000000..73b1c09e --- /dev/null +++ b/metadata/Invoices__47__metadata.json @@ -0,0 +1,33 @@ +{ + "name": "Invoices", + "retrainable": true, + "gpu": 0, + "processorType": "CPU", + "description": "Machine Learning model for extracting commonly occurring data points from Invoices, including header fields and line items. Please see more details including supported languages in the ML Packages section of the Document Understanding Guide: https://docs.uipath.com/document-understanding/docs/ml-packages", + "inputDescription": "ML Skills deployed using this ML Package can be integrated into RPA workflows using the Machine Learning Extractor activity in the Official feed or the MLModel activity in Connect feed. File formats accepted include pdf, tiff, jpg or png files. The activities also require an API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -> Other Services view.", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "cpu": 1, + "inputType": "JSON", + "displayName": "Invoices", + "memory": 0, + "mlPackageLanguage": "PYTHON37_DU", + "projectId": "[project-id]", + "stagingUri": "[staging-uri]", + "projectName": "UiPath Document Understanding", + "projectDescription": "UiPath models to classify and extract information from images and pdfs.", + "tenantName": "UiPath", + "minAIFabricVersion": "22.10", + "languageVersion": 4, + "version": 47, + "customVersion": "23.10.10", + "imagePath": "du-semistructured:v23.10-06.05-rc05", + "parametersFileJSON": "{\"params\": [{\"name\": \"auto_retraining\",\"type\": [\"PIPELINE\"],\"value\": \"False\",\"help\": \"Enable this option when the Pipeline you are creating is a scheduled automatic pipeline, synchronized with automatic exports from Data Manager.\",\"options\": [\"True\",\"False\"]}],\"probes\": [{\"probe\": \"startup\",\"initialDelaySeconds\": \"120\",\"periodSeconds\": \"10\",\"timeoutSeconds\": \"2\",\"successThreshold\": \"1\",\"failureThreshold\": \"60\"}]}", + "settings": [ + { + "key": "package_type", + "type": "STRING", + "value": "invoices" + } + ], + "maxAIFabricVersion": "24.10" +} diff --git a/metadata/Invoices__48__metadata.json b/metadata/Invoices__48__metadata.json new file mode 100644 index 00000000..a469d00d --- /dev/null +++ b/metadata/Invoices__48__metadata.json @@ -0,0 +1,32 @@ +{ + "name": "Invoices", + "retrainable": true, + "gpu": 0, + "processorType": "CPU", + "description": "Machine Learning model for extracting commonly occurring data points from Invoices, including header fields and line items. Please see more details including supported languages in the ML Packages section of the Document Understanding Guide: https://docs.uipath.com/document-understanding/docs/ml-packages", + "inputDescription": "ML Skills deployed using this ML Package can be integrated into RPA workflows using the Machine Learning Extractor activity in the Official feed or the MLModel activity in Connect feed. File formats accepted include pdf, tiff, jpg or png files. The activities also require an API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -> Other Services view.", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "cpu": 1, + "inputType": "JSON", + "displayName": "Invoices", + "memory": 0, + "mlPackageLanguage": "PYTHON37_DU", + "projectId": "[project-id]", + "stagingUri": "[staging-uri]", + "projectName": "UiPath Document Understanding", + "projectDescription": "UiPath models to classify and extract information from images and pdfs.", + "tenantName": "UiPath", + "minAIFabricVersion": "22.10", + "languageVersion": 4, + "version": 48, + "customVersion": "24.10.5", + "imagePath": "du-semistructured:v24.10-8.25-rc02", + "parametersFileJSON": "{\"params\": [{\"name\": \"auto_retraining\",\"type\": [\"PIPELINE\"],\"value\": \"False\",\"help\": \"Enable this option when the Pipeline you are creating is a scheduled automatic pipeline, synchronized with automatic exports from Data Manager.\",\"options\": [\"True\",\"False\"]}],\"probes\": [{\"probe\": \"startup\",\"initialDelaySeconds\": \"120\",\"periodSeconds\": \"10\",\"timeoutSeconds\": \"2\",\"successThreshold\": \"1\",\"failureThreshold\": \"60\"}]}", + "settings": [ + { + "key": "package_type", + "type": "STRING", + "value": "invoices" + } + ] +} diff --git a/metadata/Invoices__49__metadata.json b/metadata/Invoices__49__metadata.json new file mode 100644 index 00000000..449aff5f --- /dev/null +++ b/metadata/Invoices__49__metadata.json @@ -0,0 +1,33 @@ +{ + "name": "Invoices", + "retrainable": true, + "gpu": 0, + "processorType": "CPU", + "description": "Machine Learning model for extracting commonly occurring data points from Invoices, including header fields and line items. Please see more details including supported languages in the ML Packages section of the Document Understanding Guide: https://docs.uipath.com/document-understanding/docs/ml-packages", + "inputDescription": "ML Skills deployed using this ML Package can be integrated into RPA workflows using the Machine Learning Extractor activity in the Official feed or the MLModel activity in Connect feed. File formats accepted include pdf, tiff, jpg or png files. The activities also require an API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -> Other Services view.", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "cpu": 1, + "inputType": "JSON", + "displayName": "Invoices", + "memory": 0, + "mlPackageLanguage": "PYTHON37_DU", + "projectId": "[project-id]", + "stagingUri": "[staging-uri]", + "projectName": "UiPath Document Understanding", + "projectDescription": "UiPath models to classify and extract information from images and pdfs.", + "tenantName": "UiPath", + "minAIFabricVersion": "22.10", + "languageVersion": 4, + "version": 49, + "customVersion": "23.10.11", + "imagePath": "du-semistructured:v23.10-08.25-rc02", + "parametersFileJSON": "{\"params\": [{\"name\": \"auto_retraining\",\"type\": [\"PIPELINE\"],\"value\": \"False\",\"help\": \"Enable this option when the Pipeline you are creating is a scheduled automatic pipeline, synchronized with automatic exports from Data Manager.\",\"options\": [\"True\",\"False\"]}],\"probes\": [{\"probe\": \"startup\",\"initialDelaySeconds\": \"120\",\"periodSeconds\": \"10\",\"timeoutSeconds\": \"2\",\"successThreshold\": \"1\",\"failureThreshold\": \"60\"}]}", + "settings": [ + { + "key": "package_type", + "type": "STRING", + "value": "invoices" + } + ], + "maxAIFabricVersion": "24.10" +} diff --git a/metadata/Invoices__4__metadata.json b/metadata/Invoices__4__metadata.json deleted file mode 100644 index b1b5103d..00000000 --- a/metadata/Invoices__4__metadata.json +++ /dev/null @@ -1,21 +0,0 @@ -{ - "name": "Invoices", - "retrainable": true, - "gpu": 1, - "processorType": "CPU", - "description": "Machine Learning model for extracting commonly occurring data points from Invoices, including header fields and line items. Please see more details including supported languages and link to Activities guide in the About Licensing -\u003e Document Understanding API Key section of the UiPath Automation Cloud Guide here: https://docs.uipath.com/automation-cloud/docs/about-licensing#section-document-understanding-api-key", - "inputDescription": "ML Skills deployed using this ML Package can be integrated into RPA workflows using the Machine Learning Extractor activity in the Official feed or the MLModel activity in Connect feed. File formats accepted include pdf, tiff, jpg or png files. The activities also require an API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -\u003e Other Services view.", - "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", - "changeLog": "Model: 20.10.4", - "cpu": 0, - "inputType": "JSON", - "displayName": "Invoices", - "memory": 0, - "mlPackageLanguage": "PYTHON37_DU", - "projectId": "[project-id]", - "stagingUri": "[staging-uri]", - "projectName": "UiPath Document Understanding", - "projectDescription": "UiPath models to classify and extract information from images and pdfs.", - "tenantName": "UiPath", - "imagePath": "registry.replicated.com/aif-core/invoices:4" -} diff --git a/metadata/Invoices__50__metadata.json b/metadata/Invoices__50__metadata.json new file mode 100644 index 00000000..a91df2ae --- /dev/null +++ b/metadata/Invoices__50__metadata.json @@ -0,0 +1,32 @@ +{ + "name": "Invoices", + "retrainable": true, + "gpu": 0, + "processorType": "CPU", + "description": "Machine Learning model for extracting commonly occurring data points from Invoices, including header fields and line items. Please see more details including supported languages in the ML Packages section of the Document Understanding Guide: https://docs.uipath.com/document-understanding/docs/ml-packages", + "inputDescription": "ML Skills deployed using this ML Package can be integrated into RPA workflows using the Machine Learning Extractor activity in the Official feed or the MLModel activity in Connect feed. File formats accepted include pdf, tiff, jpg or png files. The activities also require an API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -> Other Services view.", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "cpu": 1, + "inputType": "JSON", + "displayName": "Invoices", + "memory": 0, + "mlPackageLanguage": "PYTHON37_DU", + "projectId": "[project-id]", + "stagingUri": "[staging-uri]", + "projectName": "UiPath Document Understanding", + "projectDescription": "UiPath models to classify and extract information from images and pdfs.", + "tenantName": "UiPath", + "minAIFabricVersion": "22.10", + "languageVersion": 4, + "version": 50, + "customVersion": "25.10.0", + "imagePath": "du-semistructured:v25.10-10.17-rc04", + "parametersFileJSON": "{\"params\": [{\"name\": \"auto_retraining\",\"type\": [\"PIPELINE\"],\"value\": \"False\",\"help\": \"Enable this option when the Pipeline you are creating is a scheduled automatic pipeline, synchronized with automatic exports from Data Manager.\",\"options\": [\"True\",\"False\"]}],\"probes\": [{\"probe\": \"startup\",\"initialDelaySeconds\": \"120\",\"periodSeconds\": \"10\",\"timeoutSeconds\": \"2\",\"successThreshold\": \"1\",\"failureThreshold\": \"60\"}]}", + "settings": [ + { + "key": "package_type", + "type": "STRING", + "value": "invoices" + } + ] +} diff --git a/metadata/JapaneseInvoices__1__metadata.json b/metadata/JapaneseInvoices__1__metadata.json deleted file mode 100644 index 9c2a76e3..00000000 --- a/metadata/JapaneseInvoices__1__metadata.json +++ /dev/null @@ -1,21 +0,0 @@ -{ - "name": "JapaneseInvoices", - "retrainable": true, - "gpu": 1, - "processorType": "GPU", - "description": "Machine Learning model(available in Preview) for extracting commonly occurring data points from Invoices from Japan, including header fields and line items. Please see more details including supported languages and link to Activities guide in the About Licensing -\u003e Document Understanding API Key section of the UiPath Automation Cloud Guide here: https://docs.uipath.com/automation-cloud/docs/about-licensing#section-document-understanding-api-key", - "inputDescription": "ML Skills deployed using this ML Package can be integrated into RPA workflows using the Machine Learning Extractor activity in the Official feed or the MLModel activity in Connect feed. File formats accepted include pdf, tiff, jpg or png files. Only Google Cloud Vision OCR is supported for training or serving this model. The activities also require an API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -\u003e Other Services view.", - "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", - "changeLog": "Model: 20.10.4", - "cpu": 0, - "inputType": "JSON", - "displayName": "JapaneseInvoices", - "memory": 0, - "mlPackageLanguage": "PYTHON37_DU", - "projectId": "[project-id]", - "stagingUri": "[staging-uri]", - "projectName": "UiPath Document Understanding", - "projectDescription": "UiPath models to classify and extract information from images and pdfs.", - "tenantName": "UiPath", - "imagePath": "registry.replicated.com/aif-core/japaneseinvoices:1" -} diff --git a/metadata/JapaneseInvoices__31__metadata.json b/metadata/JapaneseInvoices__31__metadata.json new file mode 100644 index 00000000..5f5cb620 --- /dev/null +++ b/metadata/JapaneseInvoices__31__metadata.json @@ -0,0 +1,34 @@ +{ + "name": "JapaneseInvoices", + "retrainable": true, + "gpu": 0, + "processorType": "CPU", + "description": "Machine Learning model for extracting commonly occurring data points from Invoices from Japan, including header fields and line items. Please see more details including supported languages in the ML Packages section of the Document Understanding Guide: https://docs.uipath.com/document-understanding/docs/ml-packages", + "inputDescription": "ML Skills deployed using this ML Package can be integrated into RPA workflows using the Machine Learning Extractor activity in the Official feed or the MLModel activity in Connect feed. File formats accepted include pdf, tiff, jpg or png files. Only Google Cloud Vision OCR is supported for training or serving this model. The activities also require an API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -> Other Services view.", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "changeLog": "", + "cpu": 1, + "inputType": "JSON", + "displayName": "JapaneseInvoices", + "memory": 0, + "mlPackageLanguage": "PYTHON37_DU", + "projectId": "[project-id]", + "stagingUri": "[staging-uri]", + "projectName": "UiPath Document Understanding", + "projectDescription": "UiPath models to classify and extract information from images and pdfs.", + "tenantName": "UiPath", + "minAIFabricVersion": "22.10", + "languageVersion": 4, + "version": 31, + "customVersion": "22.10.14", + "imagePath": "du-semistructured:v22.10-10.10-rc01", + "parametersFileJSON": "{\"params\": [{\"name\": \"auto_retraining\",\"type\": [\"PIPELINE\"],\"value\": \"False\",\"help\": \"Enable this option when the Pipeline you are creating is a scheduled automatic pipeline, synchronized with automatic exports from Data Manager.\",\"options\": [\"True\",\"False\"]}],\"probes\": [{\"probe\": \"startup\",\"initialDelaySeconds\": \"120\",\"periodSeconds\": \"10\",\"timeoutSeconds\": \"2\",\"successThreshold\": \"1\",\"failureThreshold\": \"60\"}]}", + "settings": [ + { + "key": "package_type", + "type": "STRING", + "value": "invoices_japan" + } + ], + "maxAIFabricVersion": "23.4" +} \ No newline at end of file diff --git a/metadata/JapaneseInvoices__37__metadata.json b/metadata/JapaneseInvoices__37__metadata.json new file mode 100644 index 00000000..feedda11 --- /dev/null +++ b/metadata/JapaneseInvoices__37__metadata.json @@ -0,0 +1,33 @@ +{ + "name": "JapaneseInvoices", + "retrainable": true, + "gpu": 0, + "processorType": "CPU", + "description": "Machine Learning model for extracting commonly occurring data points from Invoices from Japan, including header fields and line items. Please see more details including supported languages in the ML Packages section of the Document Understanding Guide: https://docs.uipath.com/document-understanding/docs/ml-packages", + "inputDescription": "ML Skills deployed using this ML Package can be integrated into RPA workflows using the Machine Learning Extractor activity in the Official feed or the MLModel activity in Connect feed. File formats accepted include pdf, tiff, jpg or png files. Only Google Cloud Vision OCR is supported for training or serving this model. The activities also require an API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -> Other Services view.", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "changeLog": "", + "cpu": 1, + "inputType": "JSON", + "displayName": "JapaneseInvoices", + "memory": 0, + "mlPackageLanguage": "PYTHON37_DU", + "projectId": "[project-id]", + "stagingUri": "[staging-uri]", + "projectName": "UiPath Document Understanding", + "projectDescription": "UiPath models to classify and extract information from images and pdfs.", + "tenantName": "UiPath", + "minAIFabricVersion": "22.10", + "languageVersion": 4, + "version": 37, + "customVersion": "24.10.2", + "imagePath": "du-semistructured:v24.10-1.27-rc02", + "parametersFileJSON": "{\"params\": [{\"name\": \"auto_retraining\",\"type\": [\"PIPELINE\"],\"value\": \"False\",\"help\": \"Enable this option when the Pipeline you are creating is a scheduled automatic pipeline, synchronized with automatic exports from Data Manager.\",\"options\": [\"True\",\"False\"]}],\"probes\": [{\"probe\": \"startup\",\"initialDelaySeconds\": \"120\",\"periodSeconds\": \"10\",\"timeoutSeconds\": \"2\",\"successThreshold\": \"1\",\"failureThreshold\": \"60\"}]}", + "settings": [ + { + "key": "package_type", + "type": "STRING", + "value": "invoices_japan" + } + ] +} \ No newline at end of file diff --git a/metadata/JapaneseInvoices__38__metadata.json b/metadata/JapaneseInvoices__38__metadata.json new file mode 100644 index 00000000..0c98b5c3 --- /dev/null +++ b/metadata/JapaneseInvoices__38__metadata.json @@ -0,0 +1,34 @@ +{ + "name": "JapaneseInvoices", + "retrainable": true, + "gpu": 0, + "processorType": "CPU", + "description": "Machine Learning model for extracting commonly occurring data points from Invoices from Japan, including header fields and line items. Please see more details including supported languages in the ML Packages section of the Document Understanding Guide: https://docs.uipath.com/document-understanding/docs/ml-packages", + "inputDescription": "ML Skills deployed using this ML Package can be integrated into RPA workflows using the Machine Learning Extractor activity in the Official feed or the MLModel activity in Connect feed. File formats accepted include pdf, tiff, jpg or png files. Only Google Cloud Vision OCR is supported for training or serving this model. The activities also require an API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -> Other Services view.", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "changeLog": "", + "cpu": 1, + "inputType": "JSON", + "displayName": "JapaneseInvoices", + "memory": 0, + "mlPackageLanguage": "PYTHON37_DU", + "projectId": "[project-id]", + "stagingUri": "[staging-uri]", + "projectName": "UiPath Document Understanding", + "projectDescription": "UiPath models to classify and extract information from images and pdfs.", + "tenantName": "UiPath", + "minAIFabricVersion": "22.10", + "languageVersion": 4, + "version": 38, + "customVersion": "23.4.11", + "imagePath": "du-semistructured:v23.4-01.27-rc01", + "parametersFileJSON": "{\"params\": [{\"name\": \"auto_retraining\",\"type\": [\"PIPELINE\"],\"value\": \"False\",\"help\": \"Enable this option when the Pipeline you are creating is a scheduled automatic pipeline, synchronized with automatic exports from Data Manager.\",\"options\": [\"True\",\"False\"]}],\"probes\": [{\"probe\": \"startup\",\"initialDelaySeconds\": \"120\",\"periodSeconds\": \"10\",\"timeoutSeconds\": \"2\",\"successThreshold\": \"1\",\"failureThreshold\": \"60\"}]}", + "settings": [ + { + "key": "package_type", + "type": "STRING", + "value": "invoices_japan" + } + ], + "maxAIFabricVersion": "23.10" +} \ No newline at end of file diff --git a/metadata/JapaneseInvoices__39__metadata.json b/metadata/JapaneseInvoices__39__metadata.json new file mode 100644 index 00000000..a08fc4ad --- /dev/null +++ b/metadata/JapaneseInvoices__39__metadata.json @@ -0,0 +1,34 @@ +{ + "name": "JapaneseInvoices", + "retrainable": true, + "gpu": 0, + "processorType": "CPU", + "description": "Machine Learning model for extracting commonly occurring data points from Invoices from Japan, including header fields and line items. Please see more details including supported languages in the ML Packages section of the Document Understanding Guide: https://docs.uipath.com/document-understanding/docs/ml-packages", + "inputDescription": "ML Skills deployed using this ML Package can be integrated into RPA workflows using the Machine Learning Extractor activity in the Official feed or the MLModel activity in Connect feed. File formats accepted include pdf, tiff, jpg or png files. Only Google Cloud Vision OCR is supported for training or serving this model. The activities also require an API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -> Other Services view.", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "changeLog": "", + "cpu": 1, + "inputType": "JSON", + "displayName": "JapaneseInvoices", + "memory": 0, + "mlPackageLanguage": "PYTHON37_DU", + "projectId": "[project-id]", + "stagingUri": "[staging-uri]", + "projectName": "UiPath Document Understanding", + "projectDescription": "UiPath models to classify and extract information from images and pdfs.", + "tenantName": "UiPath", + "minAIFabricVersion": "22.10", + "languageVersion": 4, + "version": 39, + "customVersion": "23.10.8", + "imagePath": "du-semistructured:v23.10-01.28-rc07", + "parametersFileJSON": "{\"params\": [{\"name\": \"auto_retraining\",\"type\": [\"PIPELINE\"],\"value\": \"False\",\"help\": \"Enable this option when the Pipeline you are creating is a scheduled automatic pipeline, synchronized with automatic exports from Data Manager.\",\"options\": [\"True\",\"False\"]}],\"probes\": [{\"probe\": \"startup\",\"initialDelaySeconds\": \"120\",\"periodSeconds\": \"10\",\"timeoutSeconds\": \"2\",\"successThreshold\": \"1\",\"failureThreshold\": \"60\"}]}", + "settings": [ + { + "key": "package_type", + "type": "STRING", + "value": "invoices_japan" + } + ], + "maxAIFabricVersion": "24.10" +} \ No newline at end of file diff --git a/metadata/JapaneseInvoices__40__metadata.json b/metadata/JapaneseInvoices__40__metadata.json new file mode 100644 index 00000000..dbf656ce --- /dev/null +++ b/metadata/JapaneseInvoices__40__metadata.json @@ -0,0 +1,33 @@ +{ + "name": "JapaneseInvoices", + "retrainable": true, + "gpu": 0, + "processorType": "CPU", + "description": "Machine Learning model for extracting commonly occurring data points from Invoices from Japan, including header fields and line items. Please see more details including supported languages in the ML Packages section of the Document Understanding Guide: https://docs.uipath.com/document-understanding/docs/ml-packages", + "inputDescription": "ML Skills deployed using this ML Package can be integrated into RPA workflows using the Machine Learning Extractor activity in the Official feed or the MLModel activity in Connect feed. File formats accepted include pdf, tiff, jpg or png files. Only Google Cloud Vision OCR is supported for training or serving this model. The activities also require an API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -> Other Services view.", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "changeLog": "", + "cpu": 1, + "inputType": "JSON", + "displayName": "JapaneseInvoices", + "memory": 0, + "mlPackageLanguage": "PYTHON37_DU", + "projectId": "[project-id]", + "stagingUri": "[staging-uri]", + "projectName": "UiPath Document Understanding", + "projectDescription": "UiPath models to classify and extract information from images and pdfs.", + "tenantName": "UiPath", + "minAIFabricVersion": "22.10", + "languageVersion": 4, + "version": 40, + "customVersion": "24.10.3", + "imagePath": "du-semistructured:v24.10-3.27-rc02", + "parametersFileJSON": "{\"params\": [{\"name\": \"auto_retraining\",\"type\": [\"PIPELINE\"],\"value\": \"False\",\"help\": \"Enable this option when the Pipeline you are creating is a scheduled automatic pipeline, synchronized with automatic exports from Data Manager.\",\"options\": [\"True\",\"False\"]}],\"probes\": [{\"probe\": \"startup\",\"initialDelaySeconds\": \"120\",\"periodSeconds\": \"10\",\"timeoutSeconds\": \"2\",\"successThreshold\": \"1\",\"failureThreshold\": \"60\"}]}", + "settings": [ + { + "key": "package_type", + "type": "STRING", + "value": "invoices_japan" + } + ] +} diff --git a/metadata/JapaneseInvoices__41__metadata.json b/metadata/JapaneseInvoices__41__metadata.json new file mode 100644 index 00000000..9b28a59c --- /dev/null +++ b/metadata/JapaneseInvoices__41__metadata.json @@ -0,0 +1,34 @@ +{ + "name": "JapaneseInvoices", + "retrainable": true, + "gpu": 0, + "processorType": "CPU", + "description": "Machine Learning model for extracting commonly occurring data points from Invoices from Japan, including header fields and line items. Please see more details including supported languages in the ML Packages section of the Document Understanding Guide: https://docs.uipath.com/document-understanding/docs/ml-packages", + "inputDescription": "ML Skills deployed using this ML Package can be integrated into RPA workflows using the Machine Learning Extractor activity in the Official feed or the MLModel activity in Connect feed. File formats accepted include pdf, tiff, jpg or png files. Only Google Cloud Vision OCR is supported for training or serving this model. The activities also require an API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -> Other Services view.", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "changeLog": "", + "cpu": 1, + "inputType": "JSON", + "displayName": "JapaneseInvoices", + "memory": 0, + "mlPackageLanguage": "PYTHON37_DU", + "projectId": "[project-id]", + "stagingUri": "[staging-uri]", + "projectName": "UiPath Document Understanding", + "projectDescription": "UiPath models to classify and extract information from images and pdfs.", + "tenantName": "UiPath", + "minAIFabricVersion": "22.10", + "languageVersion": 4, + "version": 41, + "customVersion": "23.10.9", + "imagePath": "du-semistructured:v23.10-03.14-rc02", + "parametersFileJSON": "{\"params\": [{\"name\": \"auto_retraining\",\"type\": [\"PIPELINE\"],\"value\": \"False\",\"help\": \"Enable this option when the Pipeline you are creating is a scheduled automatic pipeline, synchronized with automatic exports from Data Manager.\",\"options\": [\"True\",\"False\"]}],\"probes\": [{\"probe\": \"startup\",\"initialDelaySeconds\": \"120\",\"periodSeconds\": \"10\",\"timeoutSeconds\": \"2\",\"successThreshold\": \"1\",\"failureThreshold\": \"60\"}]}", + "settings": [ + { + "key": "package_type", + "type": "STRING", + "value": "invoices_japan" + } + ], + "maxAIFabricVersion": "24.10" +} diff --git a/metadata/JapaneseInvoices__42__metadata.json b/metadata/JapaneseInvoices__42__metadata.json new file mode 100644 index 00000000..3490804a --- /dev/null +++ b/metadata/JapaneseInvoices__42__metadata.json @@ -0,0 +1,34 @@ +{ + "name": "JapaneseInvoices", + "retrainable": true, + "gpu": 0, + "processorType": "CPU", + "description": "Machine Learning model for extracting commonly occurring data points from Invoices from Japan, including header fields and line items. Please see more details including supported languages in the ML Packages section of the Document Understanding Guide: https://docs.uipath.com/document-understanding/docs/ml-packages", + "inputDescription": "ML Skills deployed using this ML Package can be integrated into RPA workflows using the Machine Learning Extractor activity in the Official feed or the MLModel activity in Connect feed. File formats accepted include pdf, tiff, jpg or png files. Only Google Cloud Vision OCR is supported for training or serving this model. The activities also require an API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -> Other Services view.", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "changeLog": "", + "cpu": 1, + "inputType": "JSON", + "displayName": "JapaneseInvoices", + "memory": 0, + "mlPackageLanguage": "PYTHON37_DU", + "projectId": "[project-id]", + "stagingUri": "[staging-uri]", + "projectName": "UiPath Document Understanding", + "projectDescription": "UiPath models to classify and extract information from images and pdfs.", + "tenantName": "UiPath", + "minAIFabricVersion": "22.10", + "languageVersion": 4, + "version": 42, + "customVersion": "23.4.12", + "imagePath": "du-semistructured:v23.4-03.12-rc02", + "parametersFileJSON": "{\"params\": [{\"name\": \"auto_retraining\",\"type\": [\"PIPELINE\"],\"value\": \"False\",\"help\": \"Enable this option when the Pipeline you are creating is a scheduled automatic pipeline, synchronized with automatic exports from Data Manager.\",\"options\": [\"True\",\"False\"]}],\"probes\": [{\"probe\": \"startup\",\"initialDelaySeconds\": \"120\",\"periodSeconds\": \"10\",\"timeoutSeconds\": \"2\",\"successThreshold\": \"1\",\"failureThreshold\": \"60\"}]}", + "settings": [ + { + "key": "package_type", + "type": "STRING", + "value": "invoices_japan" + } + ], + "maxAIFabricVersion": "23.10" +} diff --git a/metadata/JapaneseInvoices__43__metadata.json b/metadata/JapaneseInvoices__43__metadata.json new file mode 100644 index 00000000..0f2248f6 --- /dev/null +++ b/metadata/JapaneseInvoices__43__metadata.json @@ -0,0 +1,33 @@ +{ + "name": "JapaneseInvoices", + "retrainable": true, + "gpu": 0, + "processorType": "CPU", + "description": "Machine Learning model for extracting commonly occurring data points from Invoices from Japan, including header fields and line items. Please see more details including supported languages in the ML Packages section of the Document Understanding Guide: https://docs.uipath.com/document-understanding/docs/ml-packages", + "inputDescription": "ML Skills deployed using this ML Package can be integrated into RPA workflows using the Machine Learning Extractor activity in the Official feed or the MLModel activity in Connect feed. File formats accepted include pdf, tiff, jpg or png files. Only Google Cloud Vision OCR is supported for training or serving this model. The activities also require an API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -> Other Services view.", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "changeLog": "", + "cpu": 1, + "inputType": "JSON", + "displayName": "JapaneseInvoices", + "memory": 0, + "mlPackageLanguage": "PYTHON37_DU", + "projectId": "[project-id]", + "stagingUri": "[staging-uri]", + "projectName": "UiPath Document Understanding", + "projectDescription": "UiPath models to classify and extract information from images and pdfs.", + "tenantName": "UiPath", + "minAIFabricVersion": "22.10", + "languageVersion": 4, + "version": 43, + "customVersion": "24.10.4", + "imagePath": "du-semistructured:v24.10-6.16-rc04", + "parametersFileJSON": "{\"params\": [{\"name\": \"auto_retraining\",\"type\": [\"PIPELINE\"],\"value\": \"False\",\"help\": \"Enable this option when the Pipeline you are creating is a scheduled automatic pipeline, synchronized with automatic exports from Data Manager.\",\"options\": [\"True\",\"False\"]}],\"probes\": [{\"probe\": \"startup\",\"initialDelaySeconds\": \"120\",\"periodSeconds\": \"10\",\"timeoutSeconds\": \"2\",\"successThreshold\": \"1\",\"failureThreshold\": \"60\"}]}", + "settings": [ + { + "key": "package_type", + "type": "STRING", + "value": "invoices_japan" + } + ] +} diff --git a/metadata/JapaneseInvoices__44__metadata.json b/metadata/JapaneseInvoices__44__metadata.json new file mode 100644 index 00000000..94459c67 --- /dev/null +++ b/metadata/JapaneseInvoices__44__metadata.json @@ -0,0 +1,34 @@ +{ + "name": "JapaneseInvoices", + "retrainable": true, + "gpu": 0, + "processorType": "CPU", + "description": "Machine Learning model for extracting commonly occurring data points from Invoices from Japan, including header fields and line items. Please see more details including supported languages in the ML Packages section of the Document Understanding Guide: https://docs.uipath.com/document-understanding/docs/ml-packages", + "inputDescription": "ML Skills deployed using this ML Package can be integrated into RPA workflows using the Machine Learning Extractor activity in the Official feed or the MLModel activity in Connect feed. File formats accepted include pdf, tiff, jpg or png files. Only Google Cloud Vision OCR is supported for training or serving this model. The activities also require an API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -> Other Services view.", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "changeLog": "", + "cpu": 1, + "inputType": "JSON", + "displayName": "JapaneseInvoices", + "memory": 0, + "mlPackageLanguage": "PYTHON37_DU", + "projectId": "[project-id]", + "stagingUri": "[staging-uri]", + "projectName": "UiPath Document Understanding", + "projectDescription": "UiPath models to classify and extract information from images and pdfs.", + "tenantName": "UiPath", + "minAIFabricVersion": "22.10", + "languageVersion": 4, + "version": 44, + "customVersion": "23.10.10", + "imagePath": "du-semistructured:v23.10-06.05-rc05", + "parametersFileJSON": "{\"params\": [{\"name\": \"auto_retraining\",\"type\": [\"PIPELINE\"],\"value\": \"False\",\"help\": \"Enable this option when the Pipeline you are creating is a scheduled automatic pipeline, synchronized with automatic exports from Data Manager.\",\"options\": [\"True\",\"False\"]}],\"probes\": [{\"probe\": \"startup\",\"initialDelaySeconds\": \"120\",\"periodSeconds\": \"10\",\"timeoutSeconds\": \"2\",\"successThreshold\": \"1\",\"failureThreshold\": \"60\"}]}", + "settings": [ + { + "key": "package_type", + "type": "STRING", + "value": "invoices_japan" + } + ], + "maxAIFabricVersion": "24.10" +} diff --git a/metadata/JapaneseInvoices__45__metadata.json b/metadata/JapaneseInvoices__45__metadata.json new file mode 100644 index 00000000..ddb7b070 --- /dev/null +++ b/metadata/JapaneseInvoices__45__metadata.json @@ -0,0 +1,33 @@ +{ + "name": "JapaneseInvoices", + "retrainable": true, + "gpu": 0, + "processorType": "CPU", + "description": "Machine Learning model for extracting commonly occurring data points from Invoices from Japan, including header fields and line items. Please see more details including supported languages in the ML Packages section of the Document Understanding Guide: https://docs.uipath.com/document-understanding/docs/ml-packages", + "inputDescription": "ML Skills deployed using this ML Package can be integrated into RPA workflows using the Machine Learning Extractor activity in the Official feed or the MLModel activity in Connect feed. File formats accepted include pdf, tiff, jpg or png files. Only Google Cloud Vision OCR is supported for training or serving this model. The activities also require an API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -> Other Services view.", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "changeLog": "", + "cpu": 1, + "inputType": "JSON", + "displayName": "JapaneseInvoices", + "memory": 0, + "mlPackageLanguage": "PYTHON37_DU", + "projectId": "[project-id]", + "stagingUri": "[staging-uri]", + "projectName": "UiPath Document Understanding", + "projectDescription": "UiPath models to classify and extract information from images and pdfs.", + "tenantName": "UiPath", + "minAIFabricVersion": "22.10", + "languageVersion": 4, + "version": 45, + "customVersion": "24.10.5", + "imagePath": "du-semistructured:v24.10-8.25-rc02", + "parametersFileJSON": "{\"params\": [{\"name\": \"auto_retraining\",\"type\": [\"PIPELINE\"],\"value\": \"False\",\"help\": \"Enable this option when the Pipeline you are creating is a scheduled automatic pipeline, synchronized with automatic exports from Data Manager.\",\"options\": [\"True\",\"False\"]}],\"probes\": [{\"probe\": \"startup\",\"initialDelaySeconds\": \"120\",\"periodSeconds\": \"10\",\"timeoutSeconds\": \"2\",\"successThreshold\": \"1\",\"failureThreshold\": \"60\"}]}", + "settings": [ + { + "key": "package_type", + "type": "STRING", + "value": "invoices_japan" + } + ] +} diff --git a/metadata/JapaneseInvoices__46__metadata.json b/metadata/JapaneseInvoices__46__metadata.json new file mode 100644 index 00000000..0c41c60b --- /dev/null +++ b/metadata/JapaneseInvoices__46__metadata.json @@ -0,0 +1,34 @@ +{ + "name": "JapaneseInvoices", + "retrainable": true, + "gpu": 0, + "processorType": "CPU", + "description": "Machine Learning model for extracting commonly occurring data points from Invoices from Japan, including header fields and line items. Please see more details including supported languages in the ML Packages section of the Document Understanding Guide: https://docs.uipath.com/document-understanding/docs/ml-packages", + "inputDescription": "ML Skills deployed using this ML Package can be integrated into RPA workflows using the Machine Learning Extractor activity in the Official feed or the MLModel activity in Connect feed. File formats accepted include pdf, tiff, jpg or png files. Only Google Cloud Vision OCR is supported for training or serving this model. The activities also require an API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -> Other Services view.", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "changeLog": "", + "cpu": 1, + "inputType": "JSON", + "displayName": "JapaneseInvoices", + "memory": 0, + "mlPackageLanguage": "PYTHON37_DU", + "projectId": "[project-id]", + "stagingUri": "[staging-uri]", + "projectName": "UiPath Document Understanding", + "projectDescription": "UiPath models to classify and extract information from images and pdfs.", + "tenantName": "UiPath", + "minAIFabricVersion": "22.10", + "languageVersion": 4, + "version": 46, + "customVersion": "23.10.11", + "imagePath": "du-semistructured:v23.10-08.25-rc02", + "parametersFileJSON": "{\"params\": [{\"name\": \"auto_retraining\",\"type\": [\"PIPELINE\"],\"value\": \"False\",\"help\": \"Enable this option when the Pipeline you are creating is a scheduled automatic pipeline, synchronized with automatic exports from Data Manager.\",\"options\": [\"True\",\"False\"]}],\"probes\": [{\"probe\": \"startup\",\"initialDelaySeconds\": \"120\",\"periodSeconds\": \"10\",\"timeoutSeconds\": \"2\",\"successThreshold\": \"1\",\"failureThreshold\": \"60\"}]}", + "settings": [ + { + "key": "package_type", + "type": "STRING", + "value": "invoices_japan" + } + ], + "maxAIFabricVersion": "24.10" +} diff --git a/metadata/JapaneseInvoices__47__metadata.json b/metadata/JapaneseInvoices__47__metadata.json new file mode 100644 index 00000000..159f8db0 --- /dev/null +++ b/metadata/JapaneseInvoices__47__metadata.json @@ -0,0 +1,33 @@ +{ + "name": "JapaneseInvoices", + "retrainable": true, + "gpu": 0, + "processorType": "CPU", + "description": "Machine Learning model for extracting commonly occurring data points from Invoices from Japan, including header fields and line items. Please see more details including supported languages in the ML Packages section of the Document Understanding Guide: https://docs.uipath.com/document-understanding/docs/ml-packages", + "inputDescription": "ML Skills deployed using this ML Package can be integrated into RPA workflows using the Machine Learning Extractor activity in the Official feed or the MLModel activity in Connect feed. File formats accepted include pdf, tiff, jpg or png files. Only Google Cloud Vision OCR is supported for training or serving this model. The activities also require an API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -> Other Services view.", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "changeLog": "", + "cpu": 1, + "inputType": "JSON", + "displayName": "JapaneseInvoices", + "memory": 0, + "mlPackageLanguage": "PYTHON37_DU", + "projectId": "[project-id]", + "stagingUri": "[staging-uri]", + "projectName": "UiPath Document Understanding", + "projectDescription": "UiPath models to classify and extract information from images and pdfs.", + "tenantName": "UiPath", + "minAIFabricVersion": "22.10", + "languageVersion": 4, + "version": 47, + "customVersion": "25.10.0", + "imagePath": "du-semistructured:v25.10-10.17-rc04", + "parametersFileJSON": "{\"params\": [{\"name\": \"auto_retraining\",\"type\": [\"PIPELINE\"],\"value\": \"False\",\"help\": \"Enable this option when the Pipeline you are creating is a scheduled automatic pipeline, synchronized with automatic exports from Data Manager.\",\"options\": [\"True\",\"False\"]}],\"probes\": [{\"probe\": \"startup\",\"initialDelaySeconds\": \"120\",\"periodSeconds\": \"10\",\"timeoutSeconds\": \"2\",\"successThreshold\": \"1\",\"failureThreshold\": \"60\"}]}", + "settings": [ + { + "key": "package_type", + "type": "STRING", + "value": "invoices_japan" + } + ] +} diff --git a/metadata/JapaneseInvoices__8__metadata.json b/metadata/JapaneseInvoices__8__metadata.json new file mode 100644 index 00000000..de4ad69e --- /dev/null +++ b/metadata/JapaneseInvoices__8__metadata.json @@ -0,0 +1,26 @@ +{ + "name": "JapaneseInvoices", + "retrainable": true, + "gpu": 0, + "processorType": "CPU", + "description": "Machine Learning model for extracting commonly occurring data points from Invoices from Japan, including header fields and line items. Please see more details including supported languages in the ML Packages section of the Document Understanding Guide: https://docs.uipath.com/document-understanding/docs/ml-packages", + "inputDescription": "ML Skills deployed using this ML Package can be integrated into RPA workflows using the Machine Learning Extractor activity in the Official feed or the MLModel activity in Connect feed. File formats accepted include pdf, tiff, jpg or png files. Only Google Cloud Vision OCR is supported for training or serving this model. The activities also require an API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -> Other Services view.", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "changeLog": "", + "cpu": 1, + "inputType": "JSON", + "displayName": "JapaneseInvoices", + "memory": 0, + "mlPackageLanguage": "PYTHON37_DU", + "projectId": "[project-id]", + "stagingUri": "[staging-uri]", + "projectName": "UiPath Document Understanding", + "projectDescription": "UiPath models to classify and extract information from images and pdfs.", + "tenantName": "UiPath", + "minAIFabricVersion": "22.4", + "languageVersion": 4, + "version": 8, + "customVersion": "22.4.1", + "contentUri": "https:///publicmodels/AIC/JapaneseInvoices/22.4.1/invoices_japan_package.zip", + "maxAIFabricVersion": "22.4" +} \ No newline at end of file diff --git a/metadata/LanguageDetection__1__metadata.json b/metadata/LanguageDetection__1__metadata.json new file mode 100644 index 00000000..2ed0d8b8 --- /dev/null +++ b/metadata/LanguageDetection__1__metadata.json @@ -0,0 +1,24 @@ +{ + "name": "LanguageDetection", + "retrainable": false, + "gpu": 0, + "processorType": "CPU", + "description": "This model was open sourced by Facebook Research. It predicts the language of a text input. Possible predictions are one of 176 Languages. See the Documentation for AI Center for a list of all languages. A common use case is to route unstructured language content (e.g. emails) to an appropriate responder based on the language of the text. The Licence for this model is the Creative Commons Attribution-Share-Alike License 3.0 found here: https://creativecommons.org/licenses/by-sa/3.0/legalcode", + "inputDescription": "Text to be analyzed.For example:\"Necesito ayuda con mi pedido.\"", + "outputDescription": "JSON with prediction of the language. Output will be a prediction for the input''s language along with a confidence of that prediction. {\"language\": \"Spanish\", \"confidence\": 0.97}", + "changeLog": "Use Python 3.8", + "cpu": 1, + "inputType": "JSON", + "displayName": "LanguageDetection", + "memory": 0, + "mlPackageLanguage": "PYTHON38", + "projectId": "[project-id]", + "stagingUri": "[staging-uri]", + "projectName": "Language Analysis", + "projectDescription": "Models for analyzing text including language detection, sentiment analysis, and named-entity recognition.", + "tenantName": "Open-Source Packages", + "minAIFabricVersion": "v21.10", + "version": 1, + "customVersion": "22.12.0", + "contentUri": "https:///publicmodels/AIC/LanguageDetection/22.12/LanguageDetection.zip" +} diff --git a/metadata/LightTextClassification__3__metadata.json b/metadata/LightTextClassification__3__metadata.json new file mode 100644 index 00000000..ca0644ff --- /dev/null +++ b/metadata/LightTextClassification__3__metadata.json @@ -0,0 +1,23 @@ +{ + "name": "LightTextClassification", + "retrainable": true, + "gpu": 0, + "processorType": "CPU", + "description": "This is a generic, retrainable model for text classification. It supports all languages based on Latin characters, such as English, French, Spanish, and others. This ML Package must be trained, and if deployed without training first, the deployment will fail with an error stating that the model is not trained. This model operates on bag of Words and supports two variations of algorithms, one based on Logistic Regression and the other one based on Random Forest. You can switch between these two algorithms using environment variables.", + "inputDescription": "Text that would be classified.", + "outputDescription": "A JSON with predicted class name, associated confidence on that class prediction (between 0-1) and ngrams in the input that affected the prediction (optional)", + "changeLog": "model 22.4.0", + "cpu": 1, + "inputType": "JSON", + "displayName": "LightTextClassification", + "memory": 0, + "mlPackageLanguage": "PYTHON38", + "projectId": "[project-id]", + "stagingUri": "[staging-uri]", + "projectName": "UiPath Language Analysis", + "projectDescription": "Curated Models from UiPath to analyze language in Emails, Documents, Web Pages, Text Messages, Portals and more", + "tenantName": "UiPath", + "minAIFabricVersion": "v22.4", + "version": 3, + "contentUri": "https:///publicmodels/AIC/LightTextClassification/3/bow_text_classifier_package.zip" +} diff --git a/metadata/LightTextClassification__4__metadata.json b/metadata/LightTextClassification__4__metadata.json new file mode 100644 index 00000000..aec74827 --- /dev/null +++ b/metadata/LightTextClassification__4__metadata.json @@ -0,0 +1,23 @@ +{ + "name": "LightTextClassification", + "retrainable": true, + "gpu": 0, + "processorType": "CPU", + "description": "This is a generic, retrainable model for text classification. It supports all languages based on Latin characters, such as English, French, Spanish, and others. This ML Package must be trained, and if deployed without training first, the deployment will fail with an error stating that the model is not trained. This model operates on bag of Words and supports two variations of algorithms, one based on Logistic Regression and the other one based on Random Forest. You can switch between these two algorithms using environment variables.", + "inputDescription": "Text that would be classified.", + "outputDescription": "A JSON with predicted class name, associated confidence on that class prediction (between 0-1) and ngrams in the input that affected the prediction (optional)", + "changeLog": "model 22.6.0", + "cpu": 1, + "inputType": "JSON", + "displayName": "LightTextClassification", + "memory": 0, + "mlPackageLanguage": "PYTHON38", + "projectId": "[project-id]", + "stagingUri": "[staging-uri]", + "projectName": "UiPath Language Analysis", + "projectDescription": "Curated Models from UiPath to analyze language in Emails, Documents, Web Pages, Text Messages, Portals and more", + "tenantName": "UiPath", + "minAIFabricVersion": "v22.4", + "version": 4, + "contentUri": "https:///publicmodels/AIC/LightTextClassification/4/bow_text_classifier_package.zip" +} diff --git a/metadata/LightTextClassification__5__metadata.json b/metadata/LightTextClassification__5__metadata.json new file mode 100644 index 00000000..32c70e60 --- /dev/null +++ b/metadata/LightTextClassification__5__metadata.json @@ -0,0 +1,24 @@ +{ + "name": "LightTextClassification", + "retrainable": true, + "gpu": 0, + "processorType": "CPU", + "description": "This is the preview version of a generic, retrainable model for text classification. It supports all languages based on Latin characters, such as English, French, Spanish, and others. This ML Package must be trained, and if deployed without training first, the deployment will fail with an error stating that the model is not trained. This model operates on bag of Words and supports two variations of algorithms, one based on Logistic Regression and the other one based on Random Forest. You can switch between these two algorithms using environment variables.", + "inputDescription": "Text that would be classified.", + "outputDescription": "A JSON with predicted class name, associated confidence on that class prediction (between 0-1) and ngrams in the input that affected the prediction (optional)", + "changeLog": "model 22.6.0", + "cpu": 1, + "inputType": "JSON", + "displayName": "LightTextClassification", + "memory": 0, + "mlPackageLanguage": "PYTHON39", + "projectId": "[project-id]", + "stagingUri": "[staging-uri]", + "projectName": "UiPath Language Analysis", + "projectDescription": "Curated Models from UiPath to analyze language in Emails, Documents, Web Pages, Text Messages, Portals and more", + "tenantName": "UiPath", + "minAIFabricVersion": "v22.4", + "version": 4, + "customVersion": "23.10.3", + "contentUri": "https:///publicmodels/AIC/LightTextClassification/23.10.3/bow_text_classifier_package.zip" +} diff --git a/metadata/LightTextClassification__7__metadata.json b/metadata/LightTextClassification__7__metadata.json new file mode 100644 index 00000000..135b9455 --- /dev/null +++ b/metadata/LightTextClassification__7__metadata.json @@ -0,0 +1,24 @@ +{ + "name": "LightTextClassification", + "retrainable": true, + "gpu": 0, + "processorType": "CPU", + "description": "This is a generic, retrainable model for text classification. It supports all languages based on Latin characters, such as English, French, Spanish, and others. This ML Package must be trained, and if deployed without training first, the deployment will fail with an error stating that the model is not trained. This model operates on bag of Words and supports two variations of algorithms, one based on Logistic Regression and the other one based on Random Forest. You can switch between these two algorithms using environment variables.", + "inputDescription": "Text that would be classified.", + "outputDescription": "A JSON with predicted class name, associated confidence on that class prediction (between 0-1) and ngrams in the input that affected the prediction (optional)", + "changeLog": "Fix dependency bug and move to Python39", + "cpu": 1, + "inputType": "JSON", + "displayName": "LightTextClassification", + "memory": 0, + "mlPackageLanguage": "PYTHON39", + "projectId": "[project-id]", + "stagingUri": "[staging-uri]", + "projectName": "UiPath Language Analysis", + "projectDescription": "Curated Models from UiPath to analyze language in Emails, Documents, Web Pages, Text Messages, Portals and more", + "tenantName": "UiPath", + "minAIFabricVersion": "v22.4", + "version": 7, + "customVersion": "23.10.3", + "contentUri": "https:///publicmodels/AIC/LightTextClassification/23.10.3/bow_text_classifier_package.zip" +} diff --git a/metadata/LightTextClassification__8__metadata.json b/metadata/LightTextClassification__8__metadata.json new file mode 100644 index 00000000..63ffe2d2 --- /dev/null +++ b/metadata/LightTextClassification__8__metadata.json @@ -0,0 +1,24 @@ +{ + "name": "LightTextClassification", + "retrainable": true, + "gpu": 0, + "processorType": "CPU", + "description": "This is a generic, retrainable model for text classification. It supports all languages based on Latin characters, such as English, French, Spanish, and others. This ML Package must be trained, and if deployed without training first, the deployment will fail with an error stating that the model is not trained. This model operates on bag of Words and supports two variations of algorithms, one based on Logistic Regression and the other one based on Random Forest. You can switch between these two algorithms using environment variables.", + "inputDescription": "Text that would be classified.", + "outputDescription": "A JSON with predicted class name, associated confidence on that class prediction (between 0-1) and ngrams in the input that affected the prediction (optional)", + "changeLog": "Move to Python3.12", + "cpu": 1, + "inputType": "JSON", + "displayName": "LightTextClassification", + "memory": 0, + "mlPackageLanguage": "PYTHON312", + "projectId": "[project-id]", + "stagingUri": "[staging-uri]", + "projectName": "UiPath Language Analysis", + "projectDescription": "Curated Models from UiPath to analyze language in Emails, Documents, Web Pages, Text Messages, Portals and more", + "tenantName": "UiPath", + "minAIFabricVersion": "v23.10", + "version": 8, + "customVersion": "25.9.0", + "contentUri": "https:///publicmodels/AIC/LightTextClassification/25.9.0/bow_text_classifier_package.zip" +} diff --git a/metadata/MultiLingualTextClassification__3__metadata.json b/metadata/MultiLingualTextClassification__3__metadata.json new file mode 100644 index 00000000..fc9d0574 --- /dev/null +++ b/metadata/MultiLingualTextClassification__3__metadata.json @@ -0,0 +1,23 @@ +{ + "name": "MultiLingualTextClassification", + "retrainable": true, + "gpu": 0, + "processorType": "CPU", + "description": "This is a generic, retrainable model for text classification. It supports the top 100 Wikipedia languages listed here (https://docs.uipath.com/ai-fabric/v0/docs/multi-lingual-text-classification#languages). This ML Package must be trained, and if deployed without training first, the deployment will fail with an error stating that the model is not trained. It is based on BERT, a self-supervised method for pretraining natural language processing systems. A GPU is recommended especially during training. A GPU delivers ~5-10x improvement in speed.", + "inputDescription": "Text that would be classified", + "outputDescription": "A JSON with predicted class name, associated confidence on that class prediction (between 0-1).", + "changeLog": "model 22.4.0", + "cpu": 1, + "inputType": "JSON", + "displayName": "MultiLingualTextClassification", + "memory": 0, + "mlPackageLanguage": "PYTHON38", + "projectId": "[project-id]", + "stagingUri": "[staging-uri]", + "projectName": "UiPath Language Analysis", + "projectDescription": "Curated Models from UiPath to analyze language in Emails, Documents, Web Pages, Text Messages, Portals and more", + "tenantName": "UiPath", + "minAIFabricVersion": "v22.40", + "version": 3, + "contentUri": "https:///publicmodels/AIC/MultiLingualTextClassification/3/bert_text_classifier_package.zip" +} \ No newline at end of file diff --git a/metadata/MultiLingualTextClassification__4__metadata.json b/metadata/MultiLingualTextClassification__4__metadata.json new file mode 100644 index 00000000..a477981c --- /dev/null +++ b/metadata/MultiLingualTextClassification__4__metadata.json @@ -0,0 +1,23 @@ +{ + "name": "MultiLingualTextClassification", + "retrainable": true, + "gpu": 0, + "processorType": "CPU", + "description": "This is a generic, retrainable model for text classification. It supports the top 100 Wikipedia languages listed here (https://docs.uipath.com/ai-fabric/v0/docs/multi-lingual-text-classification#languages). This ML Package must be trained, and if deployed without training first, the deployment will fail with an error stating that the model is not trained. It is based on BERT, a self-supervised method for pretraining natural language processing systems. A GPU is recommended especially during training. A GPU delivers ~5-10x improvement in speed.", + "inputDescription": "Text that would be classified", + "outputDescription": "A JSON with predicted class name, associated confidence on that class prediction (between 0-1).", + "changeLog": "model 22.6.0", + "cpu": 1, + "inputType": "JSON", + "displayName": "MultiLingualTextClassification", + "memory": 0, + "mlPackageLanguage": "PYTHON38", + "projectId": "[project-id]", + "stagingUri": "[staging-uri]", + "projectName": "UiPath Language Analysis", + "projectDescription": "Curated Models from UiPath to analyze language in Emails, Documents, Web Pages, Text Messages, Portals and more", + "tenantName": "UiPath", + "minAIFabricVersion": "v22.40", + "version": 4, + "contentUri": "https:///publicmodels/AIC/MultiLingualTextClassification/4/bert_text_classifier_package.zip" +} \ No newline at end of file diff --git a/metadata/MultiLingualTextClassification__5__metadata.json b/metadata/MultiLingualTextClassification__5__metadata.json new file mode 100644 index 00000000..4056e485 --- /dev/null +++ b/metadata/MultiLingualTextClassification__5__metadata.json @@ -0,0 +1,23 @@ +{ + "name": "MultiLingualTextClassification", + "retrainable": true, + "gpu": 0, + "processorType": "CPU", + "description": "This is a generic, retrainable model for text classification. It supports the top 100 Wikipedia languages listed here (https://docs.uipath.com/ai-fabric/v0/docs/multi-lingual-text-classification#languages). This ML Package must be trained, and if deployed without training first, the deployment will fail with an error stating that the model is not trained. It is based on BERT, a self-supervised method for pretraining natural language processing systems. A GPU is recommended especially during training. A GPU delivers ~5-10x improvement in speed.", + "inputDescription": "Text that would be classified", + "outputDescription": "A JSON with predicted class name, associated confidence on that class prediction (between 0-1).", + "changeLog": "model 22.6.0", + "cpu": 1, + "inputType": "JSON", + "displayName": "MultiLingualTextClassification", + "memory": 0, + "mlPackageLanguage": "PYTHON39", + "projectId": "[project-id]", + "stagingUri": "[staging-uri]", + "projectName": "UiPath Language Analysis", + "projectDescription": "Curated Models from UiPath to analyze language in Emails, Documents, Web Pages, Text Messages, Portals and more", + "tenantName": "UiPath", + "minAIFabricVersion": "v22.40", + "version": 5, + "contentUri": "https:///publicmodels/AIC/MultiLingualTextClassification/5/bert_text_classifier_package.zip" +} \ No newline at end of file diff --git a/metadata/MultiLingualTextClassification__6__metadata.json b/metadata/MultiLingualTextClassification__6__metadata.json new file mode 100644 index 00000000..862ea6fa --- /dev/null +++ b/metadata/MultiLingualTextClassification__6__metadata.json @@ -0,0 +1,24 @@ +{ + "name": "MultiLingualTextClassification", + "retrainable": true, + "gpu": 0, + "processorType": "CPU", + "description": "This is a generic, retrainable model for text classification. It supports the top 100 Wikipedia languages listed here (https://docs.uipath.com/ai-fabric/v0/docs/multi-lingual-text-classification#languages). This ML Package must be trained, and if deployed without training first, the deployment will fail with an error stating that the model is not trained. It is based on BERT, a self-supervised method for pretraining natural language processing systems. A GPU is recommended especially during training. A GPU delivers ~5-10x improvement in speed.", + "inputDescription": "Text that would be classified", + "outputDescription": "A JSON with predicted class name, associated confidence on that class prediction (between 0-1).", + "changeLog": "Move to Python3.12", + "cpu": 1, + "inputType": "JSON", + "displayName": "MultiLingualTextClassification", + "memory": 0, + "mlPackageLanguage": "PYTHON312", + "projectId": "[project-id]", + "stagingUri": "[staging-uri]", + "projectName": "UiPath Language Analysis", + "projectDescription": "Curated Models from UiPath to analyze language in Emails, Documents, Web Pages, Text Messages, Portals and more", + "tenantName": "UiPath", + "minAIFabricVersion": "v23.10", + "version": 6, + "customVersion": "25.9.0", + "contentUri": "https:///publicmodels/AIC/MultiLingualTextClassification/25.9.0/bert_text_classifier_package.zip" +} \ No newline at end of file diff --git a/metadata/MultilingualTranslator__1__metadata.json b/metadata/MultilingualTranslator__1__metadata.json new file mode 100644 index 00000000..285b8543 --- /dev/null +++ b/metadata/MultilingualTranslator__1__metadata.json @@ -0,0 +1,24 @@ +{ + "name": "MultilingualTranslator", + "retrainable": false, + "gpu": 0, + "processorType": "CPU", + "description": "This is the HuggingFace integration of No Language Left Behind model open sourced by Meta AI Research. It delivers translations directly between any pair of 200+ languages. You can find the full list of languages and the corresponding code to use for each of them here: https://github.com/facebookresearch/flores/blob/main/flores200/README.md#languages-in-flores-200. The model was published under the following license https://github.com/facebookresearch/fairseq/blob/nllb/LICENSE.", + "inputDescription": "The model input is json string with three keys text: the text to be translated, from_lang: Language code of the text, to_lang: Language code of the targeted text. Example: {\"text\" : \"UN Chief says there is no military solution in Syria\", \"from_lang\" : \"eng_Latn\", \"to_lang\" : \"fra_Latn\" }\"", + "outputDescription": "The model will return translated text in targeted language: \"Le chef de l''ONU dit qu''il n''y a pas de solution militaire en Syrie\" ", + "changeLog": "Use Python 3.8", + "cpu": 1, + "inputType": "JSON", + "displayName": "MultilingualTranslator", + "memory": 0, + "mlPackageLanguage": "PYTHON38", + "projectId": "[project-id]", + "stagingUri": "[staging-uri]", + "projectName": "Language Translation", + "projectDescription": "Models that use Neural Machine Translation to translate text from one language to another.", + "tenantName": "Open-Source Packages", + "minAIFabricVersion": "v21.10", + "version": 1, + "customVersion": "22.11.2", + "contentUri": "https:///publicmodels/AIC/MultilingualTranslator/22.11.2/languageTranslation.zip" +} \ No newline at end of file diff --git a/metadata/NamedEntityRecognition__1__metadata.json b/metadata/NamedEntityRecognition__1__metadata.json new file mode 100644 index 00000000..c88c7cf0 --- /dev/null +++ b/metadata/NamedEntityRecognition__1__metadata.json @@ -0,0 +1,24 @@ +{ + "name": "NamedEntityRecognition", + "retrainable": false, + "gpu": 0, + "processorType": "CPU", + "description": "This model returns a list of entities recognized in text. The 18 types of named entities recognized use the same output class as in [OntoNotes5|https://catalog.ldc.upenn.edu/LDC2013T19] which is commonly used for benchamrking this task in academia. Please see AI Center documentation for detailed explanation of these 18 output classes. The model is based on the paper ''Approaching nested named entity recognition with parallel LSTM-CRFs'' by Borchmann et al, 2018. It achieves an F1 score of 93.18 on the dataset Conll-2003. The implementation is Open-Sourced by the following license: https://github.com/flairNLP/flair/blob/master/LICENSE", + "inputDescription": "Text in English from which entities will be extracted.", + "outputDescription": "List of named entities in the text. Each elemnt in the list has the text that was recognized, the starting and ending positions (character-wise) of the text, the type of named entity and the confidence in that prediction. For example: [ {\"text\": \"George Washington\",\"start_pos\": 0,\"end_pos\": 17,\"type\": \"PERSON\",\"confidence\": 0.96469810605049133 }, {\"text\": \"New York City\",\"start_pos\": 26,\"end_pos\": 39,\"type\": \"LOC\",\"confidence\": 0.944470226764679 }, {\"text\": \"Congress\",\"start_pos\": 51,\"end_pos\": 59,\"type\": \"ORG\",\"confidence\": 0.99666249752044678 }]", + "changeLog": "Use Python 3.8", + "cpu": 1, + "inputType": "JSON", + "displayName": "NamedEntityRecognition", + "memory": 0, + "mlPackageLanguage": "PYTHON38", + "projectId": "[project-id]", + "stagingUri": "[staging-uri]", + "projectName": "Language Analysis", + "projectDescription": "Models for analyzing text including language detection, sentiment analysis, and named-entity recognition.", + "tenantName": "Open-Source Packages", + "minAIFabricVersion": "v21.10", + "version": 1, + "customVersion": "22.12.0", + "contentUri": "https:///publicmodels/AIC/NamedEntityRecognition/22.12/NER.zip" +} \ No newline at end of file diff --git a/metadata/ObjectDetection__1__metadata.json b/metadata/ObjectDetection__1__metadata.json new file mode 100644 index 00000000..dba8edda --- /dev/null +++ b/metadata/ObjectDetection__1__metadata.json @@ -0,0 +1,25 @@ +{ + "name": "ObjectDetection", + "retrainable": true, + "gpu": 1, + "processorType": "GPU", + "description": "This is a generic, retrainable deep learning model to perform Object Detection. This ML Package is pretrained on COCO Dataset so you can directly create an ML Skill which can be used for identifying 80 classes of COCO Dataset. You can also train it on your own data and create an ML Skill and use for performing object detection where it will now work on your data. This deep learning model uses You only look once (YOLO) which a state-of-the-art and one of the most effective object detection algorithms that also encompasses many of the most innovative ideas evolving from the field of computer vision.", + "inputDescription": "Path to the image to be analyzed.", + "outputDescription": "JSON with identified object''s class byte array representation (allows you to see box around objects), identified object''s class - name, score (between 0-1) and bounding_boxes coordinates. Example: {\"Predicted ByteArray\": \"/9j/4AAQSkZJRgABAQAAAQABAAD/2wBDAAIBAQEBAQIBAQECAgICAgQDAgI…TD\", \"Predicted Class\": [{\"class\": \"book\", \"score\": \" 0.65\", \"bounding_box\": [38.348690032958984, 274.14703369140625, 116.80537414550781, 306.26788330078125]}, {\"class\": \"book\", \"score\": \" 0.62\", \"bounding_box\": [34.108882904052734, 236.6508331298828, 118.49102783203125, 272.1687316894531]}]}", + "changeLog": "Move to Python 3.8 and support training on GPU.", + "cpu": 1, + "inputType": "FILE", + "displayName": "ObjectDetection", + "memory": 0, + "mlPackageLanguage": "PYTHON38_OPENCV", + "projectId": "[project-id]", + "stagingUri": "[staging-uri]", + "projectName": "Image Analysis", + "projectDescription": "Models for analyzing images including image classification and image moderation.", + "tenantName": "Open-Source Packages", + "minAIFabricVersion": "v22.4", + "languageVersion": 1, + "version": 1, + "customVersion": "22.12.0", + "contentUri": "https:///publicmodels/AIC/ObjectDetection/22.12.0/ObjectDetection_Yolo_airgapped.zip" +} \ No newline at end of file diff --git a/metadata/PackingLists__23__metadata.json b/metadata/PackingLists__23__metadata.json new file mode 100644 index 00000000..575e732d --- /dev/null +++ b/metadata/PackingLists__23__metadata.json @@ -0,0 +1,33 @@ +{ + "name": "PackingLists", + "retrainable": true, + "gpu": 0, + "processorType": "CPU", + "description": "Machine Learning model for extracting commonly occurring data points from Packing Lists, including header fields and line items. Please see more details including supported languages in the ML Packages section of the Document Understanding Guide: https://docs.uipath.com/document-understanding/docs/ml-packages", + "inputDescription": "ML Skills deployed using this ML Package can be integrated into RPA workflows using the Machine Learning Extractor activity in the Official feed or the MLModel activity in Connect feed. File formats accepted include pdf, tiff, jpg or png files. The activities also require an API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -> Other Services view.", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "cpu": 1, + "inputType": "JSON", + "displayName": "PackingLists", + "memory": 0, + "mlPackageLanguage": "PYTHON37_DU", + "projectId": "[project-id]", + "stagingUri": "[staging-uri]", + "projectName": "UiPath Document Understanding", + "projectDescription": "UiPath models to classify and extract information from images and pdfs.", + "tenantName": "UiPath", + "minAIFabricVersion": "22.10", + "languageVersion": 4, + "version": 23, + "customVersion": "22.10.14", + "imagePath": "du-semistructured:v22.10-10.10-rc01", + "parametersFileJSON": "{\"params\": [{\"name\": \"auto_retraining\",\"type\": [\"PIPELINE\"],\"value\": \"False\",\"help\": \"Enable this option when the Pipeline you are creating is a scheduled automatic pipeline, synchronized with automatic exports from Data Manager.\",\"options\": [\"True\",\"False\"]}],\"probes\": [{\"probe\": \"startup\",\"initialDelaySeconds\": \"120\",\"periodSeconds\": \"10\",\"timeoutSeconds\": \"2\",\"successThreshold\": \"1\",\"failureThreshold\": \"60\"}]}", + "settings": [ + { + "key": "package_type", + "type": "STRING", + "value": "packing_lists" + } + ], + "maxAIFabricVersion": "23.4" +} \ No newline at end of file diff --git a/metadata/PackingLists__29__metadata.json b/metadata/PackingLists__29__metadata.json new file mode 100644 index 00000000..70e7f860 --- /dev/null +++ b/metadata/PackingLists__29__metadata.json @@ -0,0 +1,32 @@ +{ + "name": "PackingLists", + "retrainable": true, + "gpu": 0, + "processorType": "CPU", + "description": "Machine Learning model for extracting commonly occurring data points from Packing Lists, including header fields and line items. Please see more details including supported languages in the ML Packages section of the Document Understanding Guide: https://docs.uipath.com/document-understanding/docs/ml-packages", + "inputDescription": "ML Skills deployed using this ML Package can be integrated into RPA workflows using the Machine Learning Extractor activity in the Official feed or the MLModel activity in Connect feed. File formats accepted include pdf, tiff, jpg or png files. The activities also require an API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -> Other Services view.", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "cpu": 1, + "inputType": "JSON", + "displayName": "PackingLists", + "memory": 0, + "mlPackageLanguage": "PYTHON37_DU", + "projectId": "[project-id]", + "stagingUri": "[staging-uri]", + "projectName": "UiPath Document Understanding", + "projectDescription": "UiPath models to classify and extract information from images and pdfs.", + "tenantName": "UiPath", + "minAIFabricVersion": "22.10", + "languageVersion": 4, + "version": 29, + "customVersion": "24.10.2", + "imagePath": "du-semistructured:v24.10-1.27-rc02", + "parametersFileJSON": "{\"params\": [{\"name\": \"auto_retraining\",\"type\": [\"PIPELINE\"],\"value\": \"False\",\"help\": \"Enable this option when the Pipeline you are creating is a scheduled automatic pipeline, synchronized with automatic exports from Data Manager.\",\"options\": [\"True\",\"False\"]}],\"probes\": [{\"probe\": \"startup\",\"initialDelaySeconds\": \"120\",\"periodSeconds\": \"10\",\"timeoutSeconds\": \"2\",\"successThreshold\": \"1\",\"failureThreshold\": \"60\"}]}", + "settings": [ + { + "key": "package_type", + "type": "STRING", + "value": "packing_lists" + } + ] +} \ No newline at end of file diff --git a/metadata/PackingLists__30__metadata.json b/metadata/PackingLists__30__metadata.json new file mode 100644 index 00000000..307f6a15 --- /dev/null +++ b/metadata/PackingLists__30__metadata.json @@ -0,0 +1,33 @@ +{ + "name": "PackingLists", + "retrainable": true, + "gpu": 0, + "processorType": "CPU", + "description": "Machine Learning model for extracting commonly occurring data points from Packing Lists, including header fields and line items. Please see more details including supported languages in the ML Packages section of the Document Understanding Guide: https://docs.uipath.com/document-understanding/docs/ml-packages", + "inputDescription": "ML Skills deployed using this ML Package can be integrated into RPA workflows using the Machine Learning Extractor activity in the Official feed or the MLModel activity in Connect feed. File formats accepted include pdf, tiff, jpg or png files. The activities also require an API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -> Other Services view.", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "cpu": 1, + "inputType": "JSON", + "displayName": "PackingLists", + "memory": 0, + "mlPackageLanguage": "PYTHON37_DU", + "projectId": "[project-id]", + "stagingUri": "[staging-uri]", + "projectName": "UiPath Document Understanding", + "projectDescription": "UiPath models to classify and extract information from images and pdfs.", + "tenantName": "UiPath", + "minAIFabricVersion": "22.10", + "languageVersion": 4, + "version": 30, + "customVersion": "23.4.11", + "imagePath": "du-semistructured:v23.4-01.27-rc01", + "parametersFileJSON": "{\"params\": [{\"name\": \"auto_retraining\",\"type\": [\"PIPELINE\"],\"value\": \"False\",\"help\": \"Enable this option when the Pipeline you are creating is a scheduled automatic pipeline, synchronized with automatic exports from Data Manager.\",\"options\": [\"True\",\"False\"]}],\"probes\": [{\"probe\": \"startup\",\"initialDelaySeconds\": \"120\",\"periodSeconds\": \"10\",\"timeoutSeconds\": \"2\",\"successThreshold\": \"1\",\"failureThreshold\": \"60\"}]}", + "settings": [ + { + "key": "package_type", + "type": "STRING", + "value": "packing_lists" + } + ], + "maxAIFabricVersion": "23.10" +} \ No newline at end of file diff --git a/metadata/PackingLists__31__metadata.json b/metadata/PackingLists__31__metadata.json new file mode 100644 index 00000000..1ec19708 --- /dev/null +++ b/metadata/PackingLists__31__metadata.json @@ -0,0 +1,33 @@ +{ + "name": "PackingLists", + "retrainable": true, + "gpu": 0, + "processorType": "CPU", + "description": "Machine Learning model for extracting commonly occurring data points from Packing Lists, including header fields and line items. Please see more details including supported languages in the ML Packages section of the Document Understanding Guide: https://docs.uipath.com/document-understanding/docs/ml-packages", + "inputDescription": "ML Skills deployed using this ML Package can be integrated into RPA workflows using the Machine Learning Extractor activity in the Official feed or the MLModel activity in Connect feed. File formats accepted include pdf, tiff, jpg or png files. The activities also require an API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -> Other Services view.", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "cpu": 1, + "inputType": "JSON", + "displayName": "PackingLists", + "memory": 0, + "mlPackageLanguage": "PYTHON37_DU", + "projectId": "[project-id]", + "stagingUri": "[staging-uri]", + "projectName": "UiPath Document Understanding", + "projectDescription": "UiPath models to classify and extract information from images and pdfs.", + "tenantName": "UiPath", + "minAIFabricVersion": "22.10", + "languageVersion": 4, + "version": 31, + "customVersion": "23.10.8", + "imagePath": "du-semistructured:v23.10-01.28-rc07", + "parametersFileJSON": "{\"params\": [{\"name\": \"auto_retraining\",\"type\": [\"PIPELINE\"],\"value\": \"False\",\"help\": \"Enable this option when the Pipeline you are creating is a scheduled automatic pipeline, synchronized with automatic exports from Data Manager.\",\"options\": [\"True\",\"False\"]}],\"probes\": [{\"probe\": \"startup\",\"initialDelaySeconds\": \"120\",\"periodSeconds\": \"10\",\"timeoutSeconds\": \"2\",\"successThreshold\": \"1\",\"failureThreshold\": \"60\"}]}", + "settings": [ + { + "key": "package_type", + "type": "STRING", + "value": "packing_lists" + } + ], + "maxAIFabricVersion": "24.10" +} \ No newline at end of file diff --git a/metadata/PackingLists__32__metadata.json b/metadata/PackingLists__32__metadata.json new file mode 100644 index 00000000..7d619304 --- /dev/null +++ b/metadata/PackingLists__32__metadata.json @@ -0,0 +1,32 @@ +{ + "name": "PackingLists", + "retrainable": true, + "gpu": 0, + "processorType": "CPU", + "description": "Machine Learning model for extracting commonly occurring data points from Packing Lists, including header fields and line items. Please see more details including supported languages in the ML Packages section of the Document Understanding Guide: https://docs.uipath.com/document-understanding/docs/ml-packages", + "inputDescription": "ML Skills deployed using this ML Package can be integrated into RPA workflows using the Machine Learning Extractor activity in the Official feed or the MLModel activity in Connect feed. File formats accepted include pdf, tiff, jpg or png files. The activities also require an API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -> Other Services view.", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "cpu": 1, + "inputType": "JSON", + "displayName": "PackingLists", + "memory": 0, + "mlPackageLanguage": "PYTHON37_DU", + "projectId": "[project-id]", + "stagingUri": "[staging-uri]", + "projectName": "UiPath Document Understanding", + "projectDescription": "UiPath models to classify and extract information from images and pdfs.", + "tenantName": "UiPath", + "minAIFabricVersion": "22.10", + "languageVersion": 4, + "version": 32, + "customVersion": "24.10.3", + "imagePath": "du-semistructured:v24.10-3.27-rc02", + "parametersFileJSON": "{\"params\": [{\"name\": \"auto_retraining\",\"type\": [\"PIPELINE\"],\"value\": \"False\",\"help\": \"Enable this option when the Pipeline you are creating is a scheduled automatic pipeline, synchronized with automatic exports from Data Manager.\",\"options\": [\"True\",\"False\"]}],\"probes\": [{\"probe\": \"startup\",\"initialDelaySeconds\": \"120\",\"periodSeconds\": \"10\",\"timeoutSeconds\": \"2\",\"successThreshold\": \"1\",\"failureThreshold\": \"60\"}]}", + "settings": [ + { + "key": "package_type", + "type": "STRING", + "value": "packing_lists" + } + ] +} diff --git a/metadata/PackingLists__33__metadata.json b/metadata/PackingLists__33__metadata.json new file mode 100644 index 00000000..0439aac4 --- /dev/null +++ b/metadata/PackingLists__33__metadata.json @@ -0,0 +1,33 @@ +{ + "name": "PackingLists", + "retrainable": true, + "gpu": 0, + "processorType": "CPU", + "description": "Machine Learning model for extracting commonly occurring data points from Packing Lists, including header fields and line items. Please see more details including supported languages in the ML Packages section of the Document Understanding Guide: https://docs.uipath.com/document-understanding/docs/ml-packages", + "inputDescription": "ML Skills deployed using this ML Package can be integrated into RPA workflows using the Machine Learning Extractor activity in the Official feed or the MLModel activity in Connect feed. File formats accepted include pdf, tiff, jpg or png files. The activities also require an API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -> Other Services view.", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "cpu": 1, + "inputType": "JSON", + "displayName": "PackingLists", + "memory": 0, + "mlPackageLanguage": "PYTHON37_DU", + "projectId": "[project-id]", + "stagingUri": "[staging-uri]", + "projectName": "UiPath Document Understanding", + "projectDescription": "UiPath models to classify and extract information from images and pdfs.", + "tenantName": "UiPath", + "minAIFabricVersion": "22.10", + "languageVersion": 4, + "version": 33, + "customVersion": "23.10.9", + "imagePath": "du-semistructured:v23.10-03.14-rc02", + "parametersFileJSON": "{\"params\": [{\"name\": \"auto_retraining\",\"type\": [\"PIPELINE\"],\"value\": \"False\",\"help\": \"Enable this option when the Pipeline you are creating is a scheduled automatic pipeline, synchronized with automatic exports from Data Manager.\",\"options\": [\"True\",\"False\"]}],\"probes\": [{\"probe\": \"startup\",\"initialDelaySeconds\": \"120\",\"periodSeconds\": \"10\",\"timeoutSeconds\": \"2\",\"successThreshold\": \"1\",\"failureThreshold\": \"60\"}]}", + "settings": [ + { + "key": "package_type", + "type": "STRING", + "value": "packing_lists" + } + ], + "maxAIFabricVersion": "24.10" +} diff --git a/metadata/PackingLists__34__metadata.json b/metadata/PackingLists__34__metadata.json new file mode 100644 index 00000000..43582795 --- /dev/null +++ b/metadata/PackingLists__34__metadata.json @@ -0,0 +1,33 @@ +{ + "name": "PackingLists", + "retrainable": true, + "gpu": 0, + "processorType": "CPU", + "description": "Machine Learning model for extracting commonly occurring data points from Packing Lists, including header fields and line items. Please see more details including supported languages in the ML Packages section of the Document Understanding Guide: https://docs.uipath.com/document-understanding/docs/ml-packages", + "inputDescription": "ML Skills deployed using this ML Package can be integrated into RPA workflows using the Machine Learning Extractor activity in the Official feed or the MLModel activity in Connect feed. File formats accepted include pdf, tiff, jpg or png files. The activities also require an API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -> Other Services view.", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "cpu": 1, + "inputType": "JSON", + "displayName": "PackingLists", + "memory": 0, + "mlPackageLanguage": "PYTHON37_DU", + "projectId": "[project-id]", + "stagingUri": "[staging-uri]", + "projectName": "UiPath Document Understanding", + "projectDescription": "UiPath models to classify and extract information from images and pdfs.", + "tenantName": "UiPath", + "minAIFabricVersion": "22.10", + "languageVersion": 4, + "version": 34, + "customVersion": "23.4.12", + "imagePath": "du-semistructured:v23.4-03.12-rc02", + "parametersFileJSON": "{\"params\": [{\"name\": \"auto_retraining\",\"type\": [\"PIPELINE\"],\"value\": \"False\",\"help\": \"Enable this option when the Pipeline you are creating is a scheduled automatic pipeline, synchronized with automatic exports from Data Manager.\",\"options\": [\"True\",\"False\"]}],\"probes\": [{\"probe\": \"startup\",\"initialDelaySeconds\": \"120\",\"periodSeconds\": \"10\",\"timeoutSeconds\": \"2\",\"successThreshold\": \"1\",\"failureThreshold\": \"60\"}]}", + "settings": [ + { + "key": "package_type", + "type": "STRING", + "value": "packing_lists" + } + ], + "maxAIFabricVersion": "23.10" +} diff --git a/metadata/PackingLists__35__metadata.json b/metadata/PackingLists__35__metadata.json new file mode 100644 index 00000000..5cd0889f --- /dev/null +++ b/metadata/PackingLists__35__metadata.json @@ -0,0 +1,32 @@ +{ + "name": "PackingLists", + "retrainable": true, + "gpu": 0, + "processorType": "CPU", + "description": "Machine Learning model for extracting commonly occurring data points from Packing Lists, including header fields and line items. Please see more details including supported languages in the ML Packages section of the Document Understanding Guide: https://docs.uipath.com/document-understanding/docs/ml-packages", + "inputDescription": "ML Skills deployed using this ML Package can be integrated into RPA workflows using the Machine Learning Extractor activity in the Official feed or the MLModel activity in Connect feed. File formats accepted include pdf, tiff, jpg or png files. The activities also require an API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -> Other Services view.", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "cpu": 1, + "inputType": "JSON", + "displayName": "PackingLists", + "memory": 0, + "mlPackageLanguage": "PYTHON37_DU", + "projectId": "[project-id]", + "stagingUri": "[staging-uri]", + "projectName": "UiPath Document Understanding", + "projectDescription": "UiPath models to classify and extract information from images and pdfs.", + "tenantName": "UiPath", + "minAIFabricVersion": "22.10", + "languageVersion": 4, + "version": 35, + "customVersion": "24.10.4", + "imagePath": "du-semistructured:v24.10-6.16-rc04", + "parametersFileJSON": "{\"params\": [{\"name\": \"auto_retraining\",\"type\": [\"PIPELINE\"],\"value\": \"False\",\"help\": \"Enable this option when the Pipeline you are creating is a scheduled automatic pipeline, synchronized with automatic exports from Data Manager.\",\"options\": [\"True\",\"False\"]}],\"probes\": [{\"probe\": \"startup\",\"initialDelaySeconds\": \"120\",\"periodSeconds\": \"10\",\"timeoutSeconds\": \"2\",\"successThreshold\": \"1\",\"failureThreshold\": \"60\"}]}", + "settings": [ + { + "key": "package_type", + "type": "STRING", + "value": "packing_lists" + } + ] +} diff --git a/metadata/PackingLists__36__metadata.json b/metadata/PackingLists__36__metadata.json new file mode 100644 index 00000000..785bf20a --- /dev/null +++ b/metadata/PackingLists__36__metadata.json @@ -0,0 +1,33 @@ +{ + "name": "PackingLists", + "retrainable": true, + "gpu": 0, + "processorType": "CPU", + "description": "Machine Learning model for extracting commonly occurring data points from Packing Lists, including header fields and line items. Please see more details including supported languages in the ML Packages section of the Document Understanding Guide: https://docs.uipath.com/document-understanding/docs/ml-packages", + "inputDescription": "ML Skills deployed using this ML Package can be integrated into RPA workflows using the Machine Learning Extractor activity in the Official feed or the MLModel activity in Connect feed. File formats accepted include pdf, tiff, jpg or png files. The activities also require an API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -> Other Services view.", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "cpu": 1, + "inputType": "JSON", + "displayName": "PackingLists", + "memory": 0, + "mlPackageLanguage": "PYTHON37_DU", + "projectId": "[project-id]", + "stagingUri": "[staging-uri]", + "projectName": "UiPath Document Understanding", + "projectDescription": "UiPath models to classify and extract information from images and pdfs.", + "tenantName": "UiPath", + "minAIFabricVersion": "22.10", + "languageVersion": 4, + "version": 36, + "customVersion": "23.10.10", + "imagePath": "du-semistructured:v23.10-06.05-rc05", + "parametersFileJSON": "{\"params\": [{\"name\": \"auto_retraining\",\"type\": [\"PIPELINE\"],\"value\": \"False\",\"help\": \"Enable this option when the Pipeline you are creating is a scheduled automatic pipeline, synchronized with automatic exports from Data Manager.\",\"options\": [\"True\",\"False\"]}],\"probes\": [{\"probe\": \"startup\",\"initialDelaySeconds\": \"120\",\"periodSeconds\": \"10\",\"timeoutSeconds\": \"2\",\"successThreshold\": \"1\",\"failureThreshold\": \"60\"}]}", + "settings": [ + { + "key": "package_type", + "type": "STRING", + "value": "packing_lists" + } + ], + "maxAIFabricVersion": "24.10" +} diff --git a/metadata/PackingLists__37__metadata.json b/metadata/PackingLists__37__metadata.json new file mode 100644 index 00000000..233b1259 --- /dev/null +++ b/metadata/PackingLists__37__metadata.json @@ -0,0 +1,32 @@ +{ + "name": "PackingLists", + "retrainable": true, + "gpu": 0, + "processorType": "CPU", + "description": "Machine Learning model for extracting commonly occurring data points from Packing Lists, including header fields and line items. Please see more details including supported languages in the ML Packages section of the Document Understanding Guide: https://docs.uipath.com/document-understanding/docs/ml-packages", + "inputDescription": "ML Skills deployed using this ML Package can be integrated into RPA workflows using the Machine Learning Extractor activity in the Official feed or the MLModel activity in Connect feed. File formats accepted include pdf, tiff, jpg or png files. The activities also require an API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -> Other Services view.", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "cpu": 1, + "inputType": "JSON", + "displayName": "PackingLists", + "memory": 0, + "mlPackageLanguage": "PYTHON37_DU", + "projectId": "[project-id]", + "stagingUri": "[staging-uri]", + "projectName": "UiPath Document Understanding", + "projectDescription": "UiPath models to classify and extract information from images and pdfs.", + "tenantName": "UiPath", + "minAIFabricVersion": "22.10", + "languageVersion": 4, + "version": 37, + "customVersion": "24.10.5", + "imagePath": "du-semistructured:v24.10-8.25-rc02", + "parametersFileJSON": "{\"params\": [{\"name\": \"auto_retraining\",\"type\": [\"PIPELINE\"],\"value\": \"False\",\"help\": \"Enable this option when the Pipeline you are creating is a scheduled automatic pipeline, synchronized with automatic exports from Data Manager.\",\"options\": [\"True\",\"False\"]}],\"probes\": [{\"probe\": \"startup\",\"initialDelaySeconds\": \"120\",\"periodSeconds\": \"10\",\"timeoutSeconds\": \"2\",\"successThreshold\": \"1\",\"failureThreshold\": \"60\"}]}", + "settings": [ + { + "key": "package_type", + "type": "STRING", + "value": "packing_lists" + } + ] +} diff --git a/metadata/PackingLists__38__metadata.json b/metadata/PackingLists__38__metadata.json new file mode 100644 index 00000000..298b30b0 --- /dev/null +++ b/metadata/PackingLists__38__metadata.json @@ -0,0 +1,33 @@ +{ + "name": "PackingLists", + "retrainable": true, + "gpu": 0, + "processorType": "CPU", + "description": "Machine Learning model for extracting commonly occurring data points from Packing Lists, including header fields and line items. Please see more details including supported languages in the ML Packages section of the Document Understanding Guide: https://docs.uipath.com/document-understanding/docs/ml-packages", + "inputDescription": "ML Skills deployed using this ML Package can be integrated into RPA workflows using the Machine Learning Extractor activity in the Official feed or the MLModel activity in Connect feed. File formats accepted include pdf, tiff, jpg or png files. The activities also require an API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -> Other Services view.", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "cpu": 1, + "inputType": "JSON", + "displayName": "PackingLists", + "memory": 0, + "mlPackageLanguage": "PYTHON37_DU", + "projectId": "[project-id]", + "stagingUri": "[staging-uri]", + "projectName": "UiPath Document Understanding", + "projectDescription": "UiPath models to classify and extract information from images and pdfs.", + "tenantName": "UiPath", + "minAIFabricVersion": "22.10", + "languageVersion": 4, + "version": 38, + "customVersion": "23.10.11", + "imagePath": "du-semistructured:v23.10-08.25-rc02", + "parametersFileJSON": "{\"params\": [{\"name\": \"auto_retraining\",\"type\": [\"PIPELINE\"],\"value\": \"False\",\"help\": \"Enable this option when the Pipeline you are creating is a scheduled automatic pipeline, synchronized with automatic exports from Data Manager.\",\"options\": [\"True\",\"False\"]}],\"probes\": [{\"probe\": \"startup\",\"initialDelaySeconds\": \"120\",\"periodSeconds\": \"10\",\"timeoutSeconds\": \"2\",\"successThreshold\": \"1\",\"failureThreshold\": \"60\"}]}", + "settings": [ + { + "key": "package_type", + "type": "STRING", + "value": "packing_lists" + } + ], + "maxAIFabricVersion": "24.10" +} diff --git a/metadata/PackingLists__39__metadata.json b/metadata/PackingLists__39__metadata.json new file mode 100644 index 00000000..928104c1 --- /dev/null +++ b/metadata/PackingLists__39__metadata.json @@ -0,0 +1,32 @@ +{ + "name": "PackingLists", + "retrainable": true, + "gpu": 0, + "processorType": "CPU", + "description": "Machine Learning model for extracting commonly occurring data points from Packing Lists, including header fields and line items. Please see more details including supported languages in the ML Packages section of the Document Understanding Guide: https://docs.uipath.com/document-understanding/docs/ml-packages", + "inputDescription": "ML Skills deployed using this ML Package can be integrated into RPA workflows using the Machine Learning Extractor activity in the Official feed or the MLModel activity in Connect feed. File formats accepted include pdf, tiff, jpg or png files. 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The activities also require an API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -> Other Services view.", + "inputType": "JSON", + "memory": 0, + "mlPackageLanguage": "PYTHON37_DU", + "name": "Passports", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "processorType": "GPU", + "projectId": "[project-id]", + "retrainable": true, + "stagingUri": "[staging-uri]", + "projectName": "UiPath Document Understanding", + "projectDescription": "UiPath models to classify and extract information from images and pdfs.", + "tenantName": "UiPath", + "minAIFabricVersion": "22.10", + "languageVersion": 4, + "version": 42, + "customVersion": "23.10.11", + "imagePath": "du-semistructured:v23.10-08.25-rc02", + "parametersFileJSON": "{\"params\": [{\"name\": \"auto_retraining\",\"type\": [\"PIPELINE\"],\"value\": \"False\",\"help\": \"Enable this option when the Pipeline you are creating is a scheduled automatic pipeline, synchronized with automatic exports from Data Manager.\",\"options\": [\"True\",\"False\"]}],\"probes\": [{\"probe\": \"startup\",\"initialDelaySeconds\": \"120\",\"periodSeconds\": \"10\",\"timeoutSeconds\": \"2\",\"successThreshold\": \"1\",\"failureThreshold\": \"60\"}]}", + "settings": [ + { + "key": "package_type", + "type": "STRING", + "value": "passports" + } + ], + "maxAIFabricVersion": "24.10" +} diff --git a/metadata/Passports__43__metadata.json b/metadata/Passports__43__metadata.json new file mode 100644 index 00000000..93a8994a --- /dev/null +++ b/metadata/Passports__43__metadata.json @@ -0,0 +1,33 @@ +{ + "changeLog": "", + "cpu": 1, + "description": "Machine Learning model for extracting commonly occurring data points from Passports, including header fields and line items. 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The activities also require an API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -> Other Services view.", + "inputType": "JSON", + "memory": 0, + "mlPackageLanguage": "PYTHON37_DU", + "name": "Passports", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "processorType": "GPU", + "projectId": "[project-id]", + "retrainable": true, + "stagingUri": "[staging-uri]", + "projectName": "UiPath Document Understanding", + "projectDescription": "UiPath models to classify and extract information from images and pdfs.", + "tenantName": "UiPath", + "minAIFabricVersion": "22.10", + "languageVersion": 4, + "version": 43, + "customVersion": "25.10.0", + "imagePath": "du-semistructured:v25.10-10.17-rc04", + "parametersFileJSON": "{\"params\": [{\"name\": \"auto_retraining\",\"type\": [\"PIPELINE\"],\"value\": \"False\",\"help\": \"Enable this option when the Pipeline you are creating is a scheduled automatic pipeline, synchronized with automatic exports from Data Manager.\",\"options\": [\"True\",\"False\"]}],\"probes\": [{\"probe\": \"startup\",\"initialDelaySeconds\": \"120\",\"periodSeconds\": \"10\",\"timeoutSeconds\": \"2\",\"successThreshold\": \"1\",\"failureThreshold\": \"60\"}]}", + "settings": [ + { + "key": "package_type", + "type": "STRING", + "value": "passports" + } + ] +} diff --git a/metadata/IndianInvoices__2__metadata.json b/metadata/Passports__4__metadata.json similarity index 60% rename from metadata/IndianInvoices__2__metadata.json rename to metadata/Passports__4__metadata.json index 39165072..8d64c6d7 100644 --- a/metadata/IndianInvoices__2__metadata.json +++ b/metadata/Passports__4__metadata.json @@ -1,21 +1,26 @@ { - "changeLog": "Release v2020.8", - "cpu": 0, - "description": "Machine Learning model for extracting commonly occurring data points from Indian Invoices, including header fields and line items. Please see more details including supported languages and link to Activities guide in the About Licensing -> Document Understanding API Key section of the UiPath Automation Cloud Guide here: https://docs.uipath.com/automation-cloud/docs/about-licensing#section-document-understanding-api-key.", - "displayName": "IndianInvoices", + "changeLog": "", + "cpu": 1, + "description": "Machine Learning model for extracting commonly occurring data points from Passports, including header fields and line items. Please see more details including supported languages in the ML Packages section of the Document Understanding Guide: https://docs.uipath.com/document-understanding/docs/ml-packages", + "displayName": "Passports", "gpu": 0, "inputDescription": "ML Skills deployed using this ML Package can be integrated into RPA workflows using the Machine Learning Extractor activity in the Official feed or the MLModel activity in Connect feed. File formats accepted include pdf, tiff, jpg or png files. The activities also require an API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -> Other Services view.", "inputType": "JSON", "memory": 0, "mlPackageLanguage": "PYTHON37_DU", - "name": "IndianInvoices", + "name": "Passports", "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", "processorType": "GPU", - "projectId": "[project-id]", + "projectId": "[project-id]", "retrainable": true, "stagingUri": "[staging-uri]", "projectName": "UiPath Document Understanding", "projectDescription": "UiPath models to classify and extract information from images and pdfs.", "tenantName": "UiPath", - "imagePath": "registry.replicated.com/aif-core/indianinvoices:2" -} + "minAIFabricVersion": "22.4", + "languageVersion": 4, + "version": 4, + "customVersion": "22.4.1", + "contentUri": "https:///publicmodels/AIC/Passports/22.4.1/passports_package.zip", + "maxAIFabricVersion": "22.4" +} \ No newline at end of file diff --git a/metadata/Payslips__18__metadata.json b/metadata/Payslips__18__metadata.json new file mode 100644 index 00000000..bef36b34 --- /dev/null +++ b/metadata/Payslips__18__metadata.json @@ -0,0 +1,33 @@ +{ + "name": "Payslips", + "retrainable": true, + "gpu": 0, + "processorType": "CPU", + "description": "Machine Learning model for extracting commonly occurring data points from Payslips. See the documentation page for this model for more details https://docs.uipath.com/document-understanding/automation-cloud/latest/user-guide/payslips-ml-package", + "inputDescription": "ML Skills deployed using this ML Package can be integrated into RPA workflows using the Machine Learning Extractor activity in the Official feed or the MLModel activity in Connect feed. Accepted file formats: pdf, tiff, jpg or png files. The activities also require an API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -> Other Services view.", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "changeLog": "", + "cpu": 1, + "inputType": "JSON", + "displayName": "Payslips", + "memory": 0, + "mlPackageLanguage": "PYTHON37_DU", + "projectId": "[project-id]", + "stagingUri": "[staging-uri]", + "projectName": "UiPath Document Understanding", + "projectDescription": "UiPath models to classify and extract information from images and pdfs.", + "tenantName": "UiPath", + "minAIFabricVersion": "22.10", + "languageVersion": 4, + "version": 18, + "customVersion": "24.10.2", + "imagePath": "du-semistructured:v24.10-1.27-rc02", + "parametersFileJSON": "{\"params\": [{\"name\": \"auto_retraining\",\"type\": [\"PIPELINE\"],\"value\": \"False\",\"help\": \"Enable this option when the Pipeline you are creating is a scheduled automatic pipeline, synchronized with automatic exports from Data Manager.\",\"options\": [\"True\",\"False\"]}],\"probes\": [{\"probe\": \"startup\",\"initialDelaySeconds\": \"120\",\"periodSeconds\": \"10\",\"timeoutSeconds\": \"2\",\"successThreshold\": \"1\",\"failureThreshold\": \"60\"}]}", + "settings": [ + { + "key": "package_type", + "type": "STRING", + "value": "payslips" + } + ] +} \ No newline at end of file diff --git a/metadata/Payslips__19__metadata.json b/metadata/Payslips__19__metadata.json new file mode 100644 index 00000000..12d7b968 --- /dev/null +++ b/metadata/Payslips__19__metadata.json @@ -0,0 +1,34 @@ +{ + "name": "Payslips", + "retrainable": true, + "gpu": 0, + "processorType": "CPU", + "description": "Machine Learning model for extracting commonly occurring data points from Payslips. See the documentation page for this model for more details https://docs.uipath.com/document-understanding/automation-cloud/latest/user-guide/payslips-ml-package", + "inputDescription": "ML Skills deployed using this ML Package can be integrated into RPA workflows using the Machine Learning Extractor activity in the Official feed or the MLModel activity in Connect feed. Accepted file formats: pdf, tiff, jpg or png files. The activities also require an API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -> Other Services view.", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "changeLog": "", + "cpu": 1, + "inputType": "JSON", + "displayName": "Payslips", + "memory": 0, + "mlPackageLanguage": "PYTHON37_DU", + "projectId": "[project-id]", + "stagingUri": "[staging-uri]", + "projectName": "UiPath Document Understanding", + "projectDescription": "UiPath models to classify and extract information from images and pdfs.", + "tenantName": "UiPath", + "minAIFabricVersion": "22.10", + "languageVersion": 4, + "version": 19, + "customVersion": "23.4.11", + "imagePath": "du-semistructured:v23.4-01.27-rc01", + "parametersFileJSON": "{\"params\": [{\"name\": \"auto_retraining\",\"type\": [\"PIPELINE\"],\"value\": \"False\",\"help\": \"Enable this option when the Pipeline you are creating is a scheduled automatic pipeline, synchronized with automatic exports from Data Manager.\",\"options\": [\"True\",\"False\"]}],\"probes\": [{\"probe\": \"startup\",\"initialDelaySeconds\": \"120\",\"periodSeconds\": \"10\",\"timeoutSeconds\": \"2\",\"successThreshold\": \"1\",\"failureThreshold\": \"60\"}]}", + "settings": [ + { + "key": "package_type", + "type": "STRING", + "value": "payslips" + } + ], + "maxAIFabricVersion": "23.10" +} \ No newline at end of file diff --git a/metadata/Payslips__20__metadata.json b/metadata/Payslips__20__metadata.json new file mode 100644 index 00000000..9cc9bc5b --- /dev/null +++ b/metadata/Payslips__20__metadata.json @@ -0,0 +1,34 @@ +{ + "name": "Payslips", + "retrainable": true, + "gpu": 0, + "processorType": "CPU", + "description": "Machine Learning model for extracting commonly occurring data points from Payslips. See the documentation page for this model for more details https://docs.uipath.com/document-understanding/automation-cloud/latest/user-guide/payslips-ml-package", + "inputDescription": "ML Skills deployed using this ML Package can be integrated into RPA workflows using the Machine Learning Extractor activity in the Official feed or the MLModel activity in Connect feed. Accepted file formats: pdf, tiff, jpg or png files. The activities also require an API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -> Other Services view.", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "changeLog": "", + "cpu": 1, + "inputType": "JSON", + "displayName": "Payslips", + "memory": 0, + "mlPackageLanguage": "PYTHON37_DU", + "projectId": "[project-id]", + "stagingUri": "[staging-uri]", + "projectName": "UiPath Document Understanding", + "projectDescription": "UiPath models to classify and extract information from images and pdfs.", + "tenantName": "UiPath", + "minAIFabricVersion": "22.10", + "languageVersion": 4, + "version": 20, + "customVersion": "23.10.8", + "imagePath": "du-semistructured:v23.10-01.28-rc07", + "parametersFileJSON": "{\"params\": [{\"name\": \"auto_retraining\",\"type\": [\"PIPELINE\"],\"value\": \"False\",\"help\": \"Enable this option when the Pipeline you are creating is a scheduled automatic pipeline, synchronized with automatic exports from Data Manager.\",\"options\": [\"True\",\"False\"]}],\"probes\": [{\"probe\": \"startup\",\"initialDelaySeconds\": \"120\",\"periodSeconds\": \"10\",\"timeoutSeconds\": \"2\",\"successThreshold\": \"1\",\"failureThreshold\": \"60\"}]}", + "settings": [ + { + "key": "package_type", + "type": "STRING", + "value": "payslips" + } + ], + "maxAIFabricVersion": "24.10" +} \ No newline at end of file diff --git a/metadata/Payslips__21__metadata.json b/metadata/Payslips__21__metadata.json new file mode 100644 index 00000000..781e3d5e --- /dev/null +++ b/metadata/Payslips__21__metadata.json @@ -0,0 +1,33 @@ +{ + "name": "Payslips", + "retrainable": true, + "gpu": 0, + "processorType": "CPU", + "description": "Machine Learning model for extracting commonly occurring data points from Payslips. See the documentation page for this model for more details https://docs.uipath.com/document-understanding/automation-cloud/latest/user-guide/payslips-ml-package", + "inputDescription": "ML Skills deployed using this ML Package can be integrated into RPA workflows using the Machine Learning Extractor activity in the Official feed or the MLModel activity in Connect feed. Accepted file formats: pdf, tiff, jpg or png files. The activities also require an API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -> Other Services view.", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "changeLog": "", + "cpu": 1, + "inputType": "JSON", + "displayName": "Payslips", + "memory": 0, + "mlPackageLanguage": "PYTHON37_DU", + "projectId": "[project-id]", + "stagingUri": "[staging-uri]", + "projectName": "UiPath Document Understanding", + "projectDescription": "UiPath models to classify and extract information from images and pdfs.", + "tenantName": "UiPath", + "minAIFabricVersion": "22.10", + "languageVersion": 4, + "version": 21, + "customVersion": "24.10.3", + "imagePath": "du-semistructured:v24.10-3.27-rc02", + "parametersFileJSON": "{\"params\": [{\"name\": \"auto_retraining\",\"type\": [\"PIPELINE\"],\"value\": \"False\",\"help\": \"Enable this option when the Pipeline you are creating is a scheduled automatic pipeline, synchronized with automatic exports from Data Manager.\",\"options\": [\"True\",\"False\"]}],\"probes\": [{\"probe\": \"startup\",\"initialDelaySeconds\": \"120\",\"periodSeconds\": \"10\",\"timeoutSeconds\": \"2\",\"successThreshold\": \"1\",\"failureThreshold\": \"60\"}]}", + "settings": [ + { + "key": "package_type", + "type": "STRING", + "value": "payslips" + } + ] +} diff --git a/metadata/Payslips__22__metadata.json b/metadata/Payslips__22__metadata.json new file mode 100644 index 00000000..53c4f68c --- /dev/null +++ b/metadata/Payslips__22__metadata.json @@ -0,0 +1,34 @@ +{ + "name": "Payslips", + "retrainable": true, + "gpu": 0, + "processorType": "CPU", + "description": "Machine Learning model for extracting commonly occurring data points from Payslips. See the documentation page for this model for more details https://docs.uipath.com/document-understanding/automation-cloud/latest/user-guide/payslips-ml-package", + "inputDescription": "ML Skills deployed using this ML Package can be integrated into RPA workflows using the Machine Learning Extractor activity in the Official feed or the MLModel activity in Connect feed. Accepted file formats: pdf, tiff, jpg or png files. The activities also require an API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -> Other Services view.", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "changeLog": "", + "cpu": 1, + "inputType": "JSON", + "displayName": "Payslips", + "memory": 0, + "mlPackageLanguage": "PYTHON37_DU", + "projectId": "[project-id]", + "stagingUri": "[staging-uri]", + "projectName": "UiPath Document Understanding", + "projectDescription": "UiPath models to classify and extract information from images and pdfs.", + "tenantName": "UiPath", + "minAIFabricVersion": "22.10", + "languageVersion": 4, + "version": 22, + "customVersion": "23.10.9", + "imagePath": "du-semistructured:v23.10-03.14-rc02", + "parametersFileJSON": "{\"params\": [{\"name\": \"auto_retraining\",\"type\": [\"PIPELINE\"],\"value\": \"False\",\"help\": \"Enable this option when the Pipeline you are creating is a scheduled automatic pipeline, synchronized with automatic exports from Data Manager.\",\"options\": [\"True\",\"False\"]}],\"probes\": [{\"probe\": \"startup\",\"initialDelaySeconds\": \"120\",\"periodSeconds\": \"10\",\"timeoutSeconds\": \"2\",\"successThreshold\": \"1\",\"failureThreshold\": \"60\"}]}", + "settings": [ + { + "key": "package_type", + "type": "STRING", + "value": "payslips" + } + ], + "maxAIFabricVersion": "24.10" +} diff --git a/metadata/Payslips__23__metadata.json b/metadata/Payslips__23__metadata.json new file mode 100644 index 00000000..3a167a3d --- /dev/null +++ b/metadata/Payslips__23__metadata.json @@ -0,0 +1,34 @@ +{ + "name": "Payslips", + "retrainable": true, + "gpu": 0, + "processorType": "CPU", + "description": "Machine Learning model for extracting commonly occurring data points from Payslips. See the documentation page for this model for more details https://docs.uipath.com/document-understanding/automation-cloud/latest/user-guide/payslips-ml-package", + "inputDescription": "ML Skills deployed using this ML Package can be integrated into RPA workflows using the Machine Learning Extractor activity in the Official feed or the MLModel activity in Connect feed. Accepted file formats: pdf, tiff, jpg or png files. The activities also require an API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -> Other Services view.", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "changeLog": "", + "cpu": 1, + "inputType": "JSON", + "displayName": "Payslips", + "memory": 0, + "mlPackageLanguage": "PYTHON37_DU", + "projectId": "[project-id]", + "stagingUri": "[staging-uri]", + "projectName": "UiPath Document Understanding", + "projectDescription": "UiPath models to classify and extract information from images and pdfs.", + "tenantName": "UiPath", + "minAIFabricVersion": "22.10", + "languageVersion": 4, + "version": 23, + "customVersion": "23.4.12", + "imagePath": "du-semistructured:v23.4-03.12-rc02", + "parametersFileJSON": "{\"params\": [{\"name\": \"auto_retraining\",\"type\": [\"PIPELINE\"],\"value\": \"False\",\"help\": \"Enable this option when the Pipeline you are creating is a scheduled automatic pipeline, synchronized with automatic exports from Data Manager.\",\"options\": [\"True\",\"False\"]}],\"probes\": [{\"probe\": \"startup\",\"initialDelaySeconds\": \"120\",\"periodSeconds\": \"10\",\"timeoutSeconds\": \"2\",\"successThreshold\": \"1\",\"failureThreshold\": \"60\"}]}", + "settings": [ + { + "key": "package_type", + "type": "STRING", + "value": "payslips" + } + ], + "maxAIFabricVersion": "23.10" +} diff --git a/metadata/Payslips__24__metadata.json b/metadata/Payslips__24__metadata.json new file mode 100644 index 00000000..6a56a30b --- /dev/null +++ b/metadata/Payslips__24__metadata.json @@ -0,0 +1,33 @@ +{ + "name": "Payslips", + "retrainable": true, + "gpu": 0, + "processorType": "CPU", + "description": "Machine Learning model for extracting commonly occurring data points from Payslips. See the documentation page for this model for more details https://docs.uipath.com/document-understanding/automation-cloud/latest/user-guide/payslips-ml-package", + "inputDescription": "ML Skills deployed using this ML Package can be integrated into RPA workflows using the Machine Learning Extractor activity in the Official feed or the MLModel activity in Connect feed. Accepted file formats: pdf, tiff, jpg or png files. The activities also require an API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -> Other Services view.", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "changeLog": "", + "cpu": 1, + "inputType": "JSON", + "displayName": "Payslips", + "memory": 0, + "mlPackageLanguage": "PYTHON37_DU", + "projectId": "[project-id]", + "stagingUri": "[staging-uri]", + "projectName": "UiPath Document Understanding", + "projectDescription": "UiPath models to classify and extract information from images and pdfs.", + "tenantName": "UiPath", + "minAIFabricVersion": "22.10", + "languageVersion": 4, + "version": 24, + "customVersion": "24.10.4", + "imagePath": "du-semistructured:v24.10-6.16-rc04", + "parametersFileJSON": "{\"params\": [{\"name\": \"auto_retraining\",\"type\": [\"PIPELINE\"],\"value\": \"False\",\"help\": \"Enable this option when the Pipeline you are creating is a scheduled automatic pipeline, synchronized with automatic exports from Data Manager.\",\"options\": [\"True\",\"False\"]}],\"probes\": [{\"probe\": \"startup\",\"initialDelaySeconds\": \"120\",\"periodSeconds\": \"10\",\"timeoutSeconds\": \"2\",\"successThreshold\": \"1\",\"failureThreshold\": \"60\"}]}", + "settings": [ + { + "key": "package_type", + "type": "STRING", + "value": "payslips" + } + ] +} diff --git a/metadata/Payslips__25__metadata.json b/metadata/Payslips__25__metadata.json new file mode 100644 index 00000000..e75a9a32 --- /dev/null +++ b/metadata/Payslips__25__metadata.json @@ -0,0 +1,34 @@ +{ + "name": "Payslips", + "retrainable": true, + "gpu": 0, + "processorType": "CPU", + "description": "Machine Learning model for extracting commonly occurring data points from Payslips. See the documentation page for this model for more details https://docs.uipath.com/document-understanding/automation-cloud/latest/user-guide/payslips-ml-package", + "inputDescription": "ML Skills deployed using this ML Package can be integrated into RPA workflows using the Machine Learning Extractor activity in the Official feed or the MLModel activity in Connect feed. Accepted file formats: pdf, tiff, jpg or png files. The activities also require an API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -> Other Services view.", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "changeLog": "", + "cpu": 1, + "inputType": "JSON", + "displayName": "Payslips", + "memory": 0, + "mlPackageLanguage": "PYTHON37_DU", + "projectId": "[project-id]", + "stagingUri": "[staging-uri]", + "projectName": "UiPath Document Understanding", + "projectDescription": "UiPath models to classify and extract information from images and pdfs.", + "tenantName": "UiPath", + "minAIFabricVersion": "22.10", + "languageVersion": 4, + "version": 25, + "customVersion": "23.10.10", + "imagePath": "du-semistructured:v23.10-06.05-rc05", + "parametersFileJSON": "{\"params\": [{\"name\": \"auto_retraining\",\"type\": [\"PIPELINE\"],\"value\": \"False\",\"help\": \"Enable this option when the Pipeline you are creating is a scheduled automatic pipeline, synchronized with automatic exports from Data Manager.\",\"options\": [\"True\",\"False\"]}],\"probes\": [{\"probe\": \"startup\",\"initialDelaySeconds\": \"120\",\"periodSeconds\": \"10\",\"timeoutSeconds\": \"2\",\"successThreshold\": \"1\",\"failureThreshold\": \"60\"}]}", + "settings": [ + { + "key": "package_type", + "type": "STRING", + "value": "payslips" + } + ], + "maxAIFabricVersion": "24.10" +} diff --git a/metadata/Payslips__26__metadata.json b/metadata/Payslips__26__metadata.json new file mode 100644 index 00000000..7a09e449 --- /dev/null +++ b/metadata/Payslips__26__metadata.json @@ -0,0 +1,33 @@ +{ + "name": "Payslips", + "retrainable": true, + "gpu": 0, + "processorType": "CPU", + "description": "Machine Learning model for extracting commonly occurring data points from Payslips. See the documentation page for this model for more details https://docs.uipath.com/document-understanding/automation-cloud/latest/user-guide/payslips-ml-package", + "inputDescription": "ML Skills deployed using this ML Package can be integrated into RPA workflows using the Machine Learning Extractor activity in the Official feed or the MLModel activity in Connect feed. Accepted file formats: pdf, tiff, jpg or png files. The activities also require an API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -> Other Services view.", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "changeLog": "", + "cpu": 1, + "inputType": "JSON", + "displayName": "Payslips", + "memory": 0, + "mlPackageLanguage": "PYTHON37_DU", + "projectId": "[project-id]", + "stagingUri": "[staging-uri]", + "projectName": "UiPath Document Understanding", + "projectDescription": "UiPath models to classify and extract information from images and pdfs.", + "tenantName": "UiPath", + "minAIFabricVersion": "22.10", + "languageVersion": 4, + "version": 26, + "customVersion": "24.10.5", + "imagePath": "du-semistructured:v24.10-8.25-rc02", + "parametersFileJSON": "{\"params\": [{\"name\": \"auto_retraining\",\"type\": [\"PIPELINE\"],\"value\": \"False\",\"help\": \"Enable this option when the Pipeline you are creating is a scheduled automatic pipeline, synchronized with automatic exports from Data Manager.\",\"options\": [\"True\",\"False\"]}],\"probes\": [{\"probe\": \"startup\",\"initialDelaySeconds\": \"120\",\"periodSeconds\": \"10\",\"timeoutSeconds\": \"2\",\"successThreshold\": \"1\",\"failureThreshold\": \"60\"}]}", + "settings": [ + { + "key": "package_type", + "type": "STRING", + "value": "payslips" + } + ] +} diff --git a/metadata/Payslips__27__metadata.json b/metadata/Payslips__27__metadata.json new file mode 100644 index 00000000..4e73d4e3 --- /dev/null +++ b/metadata/Payslips__27__metadata.json @@ -0,0 +1,34 @@ +{ + "name": "Payslips", + "retrainable": true, + "gpu": 0, + "processorType": "CPU", + "description": "Machine Learning model for extracting commonly occurring data points from Payslips. See the documentation page for this model for more details https://docs.uipath.com/document-understanding/automation-cloud/latest/user-guide/payslips-ml-package", + "inputDescription": "ML Skills deployed using this ML Package can be integrated into RPA workflows using the Machine Learning Extractor activity in the Official feed or the MLModel activity in Connect feed. Accepted file formats: pdf, tiff, jpg or png files. The activities also require an API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -> Other Services view.", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "changeLog": "", + "cpu": 1, + "inputType": "JSON", + "displayName": "Payslips", + "memory": 0, + "mlPackageLanguage": "PYTHON37_DU", + "projectId": "[project-id]", + "stagingUri": "[staging-uri]", + "projectName": "UiPath Document Understanding", + "projectDescription": "UiPath models to classify and extract information from images and pdfs.", + "tenantName": "UiPath", + "minAIFabricVersion": "22.10", + "languageVersion": 4, + "version": 27, + "customVersion": "23.10.11", + "imagePath": "du-semistructured:v23.10-08.25-rc02", + "parametersFileJSON": "{\"params\": [{\"name\": \"auto_retraining\",\"type\": [\"PIPELINE\"],\"value\": \"False\",\"help\": \"Enable this option when the Pipeline you are creating is a scheduled automatic pipeline, synchronized with automatic exports from Data Manager.\",\"options\": [\"True\",\"False\"]}],\"probes\": [{\"probe\": \"startup\",\"initialDelaySeconds\": \"120\",\"periodSeconds\": \"10\",\"timeoutSeconds\": \"2\",\"successThreshold\": \"1\",\"failureThreshold\": \"60\"}]}", + "settings": [ + { + "key": "package_type", + "type": "STRING", + "value": "payslips" + } + ], + "maxAIFabricVersion": "24.10" +} diff --git a/metadata/Payslips__28__metadata.json b/metadata/Payslips__28__metadata.json new file mode 100644 index 00000000..0e89e081 --- /dev/null +++ b/metadata/Payslips__28__metadata.json @@ -0,0 +1,33 @@ +{ + "name": "Payslips", + "retrainable": true, + "gpu": 0, + "processorType": "CPU", + "description": "Machine Learning model for extracting commonly occurring data points from Payslips. See the documentation page for this model for more details https://docs.uipath.com/document-understanding/automation-cloud/latest/user-guide/payslips-ml-package", + "inputDescription": "ML Skills deployed using this ML Package can be integrated into RPA workflows using the Machine Learning Extractor activity in the Official feed or the MLModel activity in Connect feed. Accepted file formats: pdf, tiff, jpg or png files. The activities also require an API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -> Other Services view.", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "changeLog": "", + "cpu": 1, + "inputType": "JSON", + "displayName": "Payslips", + "memory": 0, + "mlPackageLanguage": "PYTHON37_DU", + "projectId": "[project-id]", + "stagingUri": "[staging-uri]", + "projectName": "UiPath Document Understanding", + "projectDescription": "UiPath models to classify and extract information from images and pdfs.", + "tenantName": "UiPath", + "minAIFabricVersion": "22.10", + "languageVersion": 4, + "version": 28, + "customVersion": "25.10.0", + "imagePath": "du-semistructured:v25.10-10.17-rc04", + "parametersFileJSON": "{\"params\": [{\"name\": \"auto_retraining\",\"type\": [\"PIPELINE\"],\"value\": \"False\",\"help\": \"Enable this option when the Pipeline you are creating is a scheduled automatic pipeline, synchronized with automatic exports from Data Manager.\",\"options\": [\"True\",\"False\"]}],\"probes\": [{\"probe\": \"startup\",\"initialDelaySeconds\": \"120\",\"periodSeconds\": \"10\",\"timeoutSeconds\": \"2\",\"successThreshold\": \"1\",\"failureThreshold\": \"60\"}]}", + "settings": [ + { + "key": "package_type", + "type": "STRING", + "value": "payslips" + } + ] +} diff --git a/metadata/PurchaseOrders__1__metadata.json b/metadata/PurchaseOrders__10__metadata.json similarity index 68% rename from metadata/PurchaseOrders__1__metadata.json rename to metadata/PurchaseOrders__10__metadata.json index c318662b..0d35cd33 100644 --- a/metadata/PurchaseOrders__1__metadata.json +++ b/metadata/PurchaseOrders__10__metadata.json @@ -1,21 +1,26 @@ { - "changeLog": "", - "cpu": 0, - "description": "Machine Learning model for extracting commonly occurring data points from Purchase Orders, including header fields and line items. Please see more details including supported languages and link to Activities guide in the About Licensing -> Document Understanding API Key section of the UiPath Automation Cloud Guide here: https://docs.uipath.com/automation-cloud/docs/about-licensing#section-document-understanding-api-key", - "displayName": "PurchaseOrders", + "name": "PurchaseOrders", + "retrainable": true, "gpu": 0, + "processorType": "CPU", + "description": "Machine Learning model for extracting commonly occurring data points from Purchase Orders, including header fields and line items. Please see more details including supported languages in the ML Packages section of the Document Understanding Guide: https://docs.uipath.com/document-understanding/docs/ml-packages", "inputDescription": "ML Skills deployed using this ML Package can be integrated into RPA workflows using the Machine Learning Extractor activity in the Official feed or the MLModel activity in Connect feed. File formats accepted include pdf, tiff, jpg or png files. The activities also require an API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -> Other Services view.", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "changeLog": "", + "cpu": 1, "inputType": "JSON", + "displayName": "PurchaseOrders", "memory": 0, - "mlPackageLanguage": "PYTHON36_DU", - "name": "PurchaseOrders", - "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", - "processorType": "GPU", - "projectId": "[project-id]", - "retrainable": true, + "mlPackageLanguage": "PYTHON37_DU", + "projectId": "[project-id]", "stagingUri": "[staging-uri]", "projectName": "UiPath Document Understanding", "projectDescription": "UiPath models to classify and extract information from images and pdfs.", "tenantName": "UiPath", - "imagePath": "registry.replicated.com/aif-core/purchaseorders:1" -} + "minAIFabricVersion": "22.4", + "languageVersion": 4, + "version": 10, + "customVersion": "22.4.1", + "contentUri": "https:///publicmodels/AIC/PurchaseOrders/22.4.1/purchase_orders_package.zip", + "maxAIFabricVersion": "22.4" +} \ No newline at end of file diff --git a/metadata/PurchaseOrders__33__metadata.json b/metadata/PurchaseOrders__33__metadata.json new file mode 100644 index 00000000..aaa71d58 --- /dev/null +++ b/metadata/PurchaseOrders__33__metadata.json @@ -0,0 +1,34 @@ +{ + "name": "PurchaseOrders", + "retrainable": true, + "gpu": 0, + "processorType": "CPU", + "description": "Machine Learning model for extracting commonly occurring data points from Purchase Orders, including header fields and line items. Please see more details including supported languages in the ML Packages section of the Document Understanding Guide: https://docs.uipath.com/document-understanding/docs/ml-packages", + "inputDescription": "ML Skills deployed using this ML Package can be integrated into RPA workflows using the Machine Learning Extractor activity in the Official feed or the MLModel activity in Connect feed. File formats accepted include pdf, tiff, jpg or png files. The activities also require an API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -> Other Services view.", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "changeLog": "", + "cpu": 1, + "inputType": "JSON", + "displayName": "PurchaseOrders", + "memory": 0, + "mlPackageLanguage": "PYTHON37_DU", + "projectId": "[project-id]", + "stagingUri": "[staging-uri]", + "projectName": "UiPath Document Understanding", + "projectDescription": "UiPath models to classify and extract information from images and pdfs.", + "tenantName": "UiPath", + "minAIFabricVersion": "22.10", + "languageVersion": 4, + "version": 33, + "customVersion": "22.10.14", + "imagePath": "du-semistructured:v22.10-10.10-rc01", + "parametersFileJSON": "{\"params\": [{\"name\": \"auto_retraining\",\"type\": [\"PIPELINE\"],\"value\": \"False\",\"help\": \"Enable this option when the Pipeline you are creating is a scheduled automatic pipeline, synchronized with automatic exports from Data Manager.\",\"options\": [\"True\",\"False\"]}],\"probes\": [{\"probe\": \"startup\",\"initialDelaySeconds\": \"120\",\"periodSeconds\": \"10\",\"timeoutSeconds\": \"2\",\"successThreshold\": \"1\",\"failureThreshold\": \"60\"}]}", + "settings": [ + { + "key": "package_type", + "type": "STRING", + "value": "purchase_orders" + } + ], + "maxAIFabricVersion": "23.4" +} \ No newline at end of file diff --git a/metadata/PurchaseOrders__39__metadata.json b/metadata/PurchaseOrders__39__metadata.json new file mode 100644 index 00000000..33bb37a1 --- /dev/null +++ b/metadata/PurchaseOrders__39__metadata.json @@ -0,0 +1,33 @@ +{ + "name": "PurchaseOrders", + "retrainable": true, + "gpu": 0, + "processorType": "CPU", + "description": "Machine Learning model for extracting commonly occurring data points from Purchase Orders, including header fields and line items. Please see more details including supported languages in the ML Packages section of the Document Understanding Guide: https://docs.uipath.com/document-understanding/docs/ml-packages", + "inputDescription": "ML Skills deployed using this ML Package can be integrated into RPA workflows using the Machine Learning Extractor activity in the Official feed or the MLModel activity in Connect feed. File formats accepted include pdf, tiff, jpg or png files. The activities also require an API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -> Other Services view.", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "changeLog": "", + "cpu": 1, + "inputType": "JSON", + "displayName": "PurchaseOrders", + "memory": 0, + "mlPackageLanguage": "PYTHON37_DU", + "projectId": "[project-id]", + "stagingUri": "[staging-uri]", + "projectName": "UiPath Document Understanding", + "projectDescription": "UiPath models to classify and extract information from images and pdfs.", + "tenantName": "UiPath", + "minAIFabricVersion": "22.10", + "languageVersion": 4, + "version": 39, + "customVersion": "24.10.2", + "imagePath": "du-semistructured:v24.10-1.27-rc02", + "parametersFileJSON": "{\"params\": [{\"name\": \"auto_retraining\",\"type\": [\"PIPELINE\"],\"value\": \"False\",\"help\": \"Enable this option when the Pipeline you are creating is a scheduled automatic pipeline, synchronized with automatic exports from Data Manager.\",\"options\": [\"True\",\"False\"]}],\"probes\": [{\"probe\": \"startup\",\"initialDelaySeconds\": \"120\",\"periodSeconds\": \"10\",\"timeoutSeconds\": \"2\",\"successThreshold\": \"1\",\"failureThreshold\": \"60\"}]}", + "settings": [ + { + "key": "package_type", + "type": "STRING", + "value": "purchase_orders" + } + ] +} \ No newline at end of file diff --git a/metadata/PurchaseOrders__3__metadata.json b/metadata/PurchaseOrders__3__metadata.json deleted file mode 100644 index 61fa5b70..00000000 --- a/metadata/PurchaseOrders__3__metadata.json +++ /dev/null @@ -1,21 +0,0 @@ -{ - "name": "PurchaseOrders", - "retrainable": true, - "gpu": 1, - "processorType": "GPU", - "description": "Machine Learning model(available in Preview) for extracting commonly occurring data points from Purchase Orders, including header fields and line items. Please see more details including supported languages and link to Activities guide in the About Licensing -\u003e Document Understanding API Key section of the UiPath Automation Cloud Guide here: https://docs.uipath.com/automation-cloud/docs/about-licensing#section-document-understanding-api-key", - "inputDescription": "ML Skills deployed using this ML Package can be integrated into RPA workflows using the Machine Learning Extractor activity in the Official feed or the MLModel activity in Connect feed. File formats accepted include pdf, tiff, jpg or png files. The activities also require an API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -\u003e Other Services view.", - "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", - "changeLog": "Model: 20.10.4", - "cpu": 0, - "inputType": "JSON", - "displayName": "PurchaseOrders", - "memory": 0, - "mlPackageLanguage": "PYTHON37_DU", - "projectId": "[project-id]", - "stagingUri": "[staging-uri]", - "projectName": "UiPath Document Understanding", - "projectDescription": "UiPath models to classify and extract information from images and pdfs.", - "tenantName": "UiPath", - "imagePath": "registry.replicated.com/aif-core/purchaseorders:3" -} diff --git a/metadata/PurchaseOrders__40__metadata.json b/metadata/PurchaseOrders__40__metadata.json new file mode 100644 index 00000000..be119b82 --- /dev/null +++ b/metadata/PurchaseOrders__40__metadata.json @@ -0,0 +1,34 @@ +{ + "name": "PurchaseOrders", + "retrainable": true, + "gpu": 0, + "processorType": "CPU", + "description": "Machine Learning model for extracting commonly occurring data points from Purchase Orders, including header fields and line items. Please see more details including supported languages in the ML Packages section of the Document Understanding Guide: https://docs.uipath.com/document-understanding/docs/ml-packages", + "inputDescription": "ML Skills deployed using this ML Package can be integrated into RPA workflows using the Machine Learning Extractor activity in the Official feed or the MLModel activity in Connect feed. File formats accepted include pdf, tiff, jpg or png files. The activities also require an API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -> Other Services view.", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "changeLog": "", + "cpu": 1, + "inputType": "JSON", + "displayName": "PurchaseOrders", + "memory": 0, + "mlPackageLanguage": "PYTHON37_DU", + "projectId": "[project-id]", + "stagingUri": "[staging-uri]", + "projectName": "UiPath Document Understanding", + "projectDescription": "UiPath models to classify and extract information from images and pdfs.", + "tenantName": "UiPath", + "minAIFabricVersion": "22.10", + "languageVersion": 4, + "version": 40, + "customVersion": "23.4.11", + "imagePath": "du-semistructured:v23.4-01.27-rc01", + "parametersFileJSON": "{\"params\": [{\"name\": \"auto_retraining\",\"type\": [\"PIPELINE\"],\"value\": \"False\",\"help\": \"Enable this option when the Pipeline you are creating is a scheduled automatic pipeline, synchronized with automatic exports from Data Manager.\",\"options\": [\"True\",\"False\"]}],\"probes\": [{\"probe\": \"startup\",\"initialDelaySeconds\": \"120\",\"periodSeconds\": \"10\",\"timeoutSeconds\": \"2\",\"successThreshold\": \"1\",\"failureThreshold\": \"60\"}]}", + "settings": [ + { + "key": "package_type", + "type": "STRING", + "value": "purchase_orders" + } + ], + "maxAIFabricVersion": "23.10" +} \ No newline at end of file diff --git a/metadata/PurchaseOrders__41__metadata.json b/metadata/PurchaseOrders__41__metadata.json new file mode 100644 index 00000000..51a58ade --- /dev/null +++ b/metadata/PurchaseOrders__41__metadata.json @@ -0,0 +1,34 @@ +{ + "name": "PurchaseOrders", + "retrainable": true, + "gpu": 0, + "processorType": "CPU", + "description": "Machine Learning model for extracting commonly occurring data points from Purchase Orders, including header fields and line items. Please see more details including supported languages in the ML Packages section of the Document Understanding Guide: https://docs.uipath.com/document-understanding/docs/ml-packages", + "inputDescription": "ML Skills deployed using this ML Package can be integrated into RPA workflows using the Machine Learning Extractor activity in the Official feed or the MLModel activity in Connect feed. File formats accepted include pdf, tiff, jpg or png files. The activities also require an API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -> Other Services view.", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "changeLog": "", + "cpu": 1, + "inputType": "JSON", + "displayName": "PurchaseOrders", + "memory": 0, + "mlPackageLanguage": "PYTHON37_DU", + "projectId": "[project-id]", + "stagingUri": "[staging-uri]", + "projectName": "UiPath Document Understanding", + "projectDescription": "UiPath models to classify and extract information from images and pdfs.", + "tenantName": "UiPath", + "minAIFabricVersion": "22.10", + "languageVersion": 4, + "version": 41, + "customVersion": "23.10.8", + "imagePath": "du-semistructured:v23.10-01.28-rc07", + "parametersFileJSON": "{\"params\": [{\"name\": \"auto_retraining\",\"type\": [\"PIPELINE\"],\"value\": \"False\",\"help\": \"Enable this option when the Pipeline you are creating is a scheduled automatic pipeline, synchronized with automatic exports from Data Manager.\",\"options\": [\"True\",\"False\"]}],\"probes\": [{\"probe\": \"startup\",\"initialDelaySeconds\": \"120\",\"periodSeconds\": \"10\",\"timeoutSeconds\": \"2\",\"successThreshold\": \"1\",\"failureThreshold\": \"60\"}]}", + "settings": [ + { + "key": "package_type", + "type": "STRING", + "value": "purchase_orders" + } + ], + "maxAIFabricVersion": "24.10" +} \ No newline at end of file diff --git a/metadata/PurchaseOrders__42__metadata.json b/metadata/PurchaseOrders__42__metadata.json new file mode 100644 index 00000000..394fd38a --- /dev/null +++ b/metadata/PurchaseOrders__42__metadata.json @@ -0,0 +1,33 @@ +{ + "name": "PurchaseOrders", + "retrainable": true, + "gpu": 0, + "processorType": "CPU", + "description": "Machine Learning model for extracting commonly occurring data points from Purchase Orders, including header fields and line items. Please see more details including supported languages in the ML Packages section of the Document Understanding Guide: https://docs.uipath.com/document-understanding/docs/ml-packages", + "inputDescription": "ML Skills deployed using this ML Package can be integrated into RPA workflows using the Machine Learning Extractor activity in the Official feed or the MLModel activity in Connect feed. File formats accepted include pdf, tiff, jpg or png files. The activities also require an API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -> Other Services view.", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "changeLog": "", + "cpu": 1, + "inputType": "JSON", + "displayName": "PurchaseOrders", + "memory": 0, + "mlPackageLanguage": "PYTHON37_DU", + "projectId": "[project-id]", + "stagingUri": "[staging-uri]", + "projectName": "UiPath Document Understanding", + "projectDescription": "UiPath models to classify and extract information from images and pdfs.", + "tenantName": "UiPath", + "minAIFabricVersion": "22.10", + "languageVersion": 4, + "version": 42, + "customVersion": "24.10.3", + "imagePath": "du-semistructured:v24.10-3.27-rc02", + "parametersFileJSON": "{\"params\": [{\"name\": \"auto_retraining\",\"type\": [\"PIPELINE\"],\"value\": \"False\",\"help\": \"Enable this option when the Pipeline you are creating is a scheduled automatic pipeline, synchronized with automatic exports from Data Manager.\",\"options\": [\"True\",\"False\"]}],\"probes\": [{\"probe\": \"startup\",\"initialDelaySeconds\": \"120\",\"periodSeconds\": \"10\",\"timeoutSeconds\": \"2\",\"successThreshold\": \"1\",\"failureThreshold\": \"60\"}]}", + "settings": [ + { + "key": "package_type", + "type": "STRING", + "value": "purchase_orders" + } + ] +} diff --git a/metadata/PurchaseOrders__43__metadata.json b/metadata/PurchaseOrders__43__metadata.json new file mode 100644 index 00000000..a8cc5904 --- /dev/null +++ b/metadata/PurchaseOrders__43__metadata.json @@ -0,0 +1,34 @@ +{ + "name": "PurchaseOrders", + "retrainable": true, + "gpu": 0, + "processorType": "CPU", + "description": "Machine Learning model for extracting commonly occurring data points from Purchase Orders, including header fields and line items. Please see more details including supported languages in the ML Packages section of the Document Understanding Guide: https://docs.uipath.com/document-understanding/docs/ml-packages", + "inputDescription": "ML Skills deployed using this ML Package can be integrated into RPA workflows using the Machine Learning Extractor activity in the Official feed or the MLModel activity in Connect feed. File formats accepted include pdf, tiff, jpg or png files. The activities also require an API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -> Other Services view.", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "changeLog": "", + "cpu": 1, + "inputType": "JSON", + "displayName": "PurchaseOrders", + "memory": 0, + "mlPackageLanguage": "PYTHON37_DU", + "projectId": "[project-id]", + "stagingUri": "[staging-uri]", + "projectName": "UiPath Document Understanding", + "projectDescription": "UiPath models to classify and extract information from images and pdfs.", + "tenantName": "UiPath", + "minAIFabricVersion": "22.10", + "languageVersion": 4, + "version": 43, + "customVersion": "23.10.9", + "imagePath": "du-semistructured:v23.10-03.14-rc02", + "parametersFileJSON": "{\"params\": [{\"name\": \"auto_retraining\",\"type\": [\"PIPELINE\"],\"value\": \"False\",\"help\": \"Enable this option when the Pipeline you are creating is a scheduled automatic pipeline, synchronized with automatic exports from Data Manager.\",\"options\": [\"True\",\"False\"]}],\"probes\": [{\"probe\": \"startup\",\"initialDelaySeconds\": \"120\",\"periodSeconds\": \"10\",\"timeoutSeconds\": \"2\",\"successThreshold\": \"1\",\"failureThreshold\": \"60\"}]}", + "settings": [ + { + "key": "package_type", + "type": "STRING", + "value": "purchase_orders" + } + ], + "maxAIFabricVersion": "24.10" +} diff --git a/metadata/PurchaseOrders__44__metadata.json b/metadata/PurchaseOrders__44__metadata.json new file mode 100644 index 00000000..6e17bbb4 --- /dev/null +++ b/metadata/PurchaseOrders__44__metadata.json @@ -0,0 +1,34 @@ +{ + "name": "PurchaseOrders", + "retrainable": true, + "gpu": 0, + "processorType": "CPU", + "description": "Machine Learning model for extracting commonly occurring data points from Purchase Orders, including header fields and line items. Please see more details including supported languages in the ML Packages section of the Document Understanding Guide: https://docs.uipath.com/document-understanding/docs/ml-packages", + "inputDescription": "ML Skills deployed using this ML Package can be integrated into RPA workflows using the Machine Learning Extractor activity in the Official feed or the MLModel activity in Connect feed. File formats accepted include pdf, tiff, jpg or png files. The activities also require an API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -> Other Services view.", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "changeLog": "", + "cpu": 1, + "inputType": "JSON", + "displayName": "PurchaseOrders", + "memory": 0, + "mlPackageLanguage": "PYTHON37_DU", + "projectId": "[project-id]", + "stagingUri": "[staging-uri]", + "projectName": "UiPath Document Understanding", + "projectDescription": "UiPath models to classify and extract information from images and pdfs.", + "tenantName": "UiPath", + "minAIFabricVersion": "22.10", + "languageVersion": 4, + "version": 44, + "customVersion": "23.4.12", + "imagePath": "du-semistructured:v23.4-03.12-rc02", + "parametersFileJSON": "{\"params\": [{\"name\": \"auto_retraining\",\"type\": [\"PIPELINE\"],\"value\": \"False\",\"help\": \"Enable this option when the Pipeline you are creating is a scheduled automatic pipeline, synchronized with automatic exports from Data Manager.\",\"options\": [\"True\",\"False\"]}],\"probes\": [{\"probe\": \"startup\",\"initialDelaySeconds\": \"120\",\"periodSeconds\": \"10\",\"timeoutSeconds\": \"2\",\"successThreshold\": \"1\",\"failureThreshold\": \"60\"}]}", + "settings": [ + { + "key": "package_type", + "type": "STRING", + "value": "purchase_orders" + } + ], + "maxAIFabricVersion": "23.10" +} diff --git a/metadata/PurchaseOrders__45__metadata.json b/metadata/PurchaseOrders__45__metadata.json new file mode 100644 index 00000000..0868c83f --- /dev/null +++ b/metadata/PurchaseOrders__45__metadata.json @@ -0,0 +1,33 @@ +{ + "name": "PurchaseOrders", + "retrainable": true, + "gpu": 0, + "processorType": "CPU", + "description": "Machine Learning model for extracting commonly occurring data points from Purchase Orders, including header fields and line items. Please see more details including supported languages in the ML Packages section of the Document Understanding Guide: https://docs.uipath.com/document-understanding/docs/ml-packages", + "inputDescription": "ML Skills deployed using this ML Package can be integrated into RPA workflows using the Machine Learning Extractor activity in the Official feed or the MLModel activity in Connect feed. File formats accepted include pdf, tiff, jpg or png files. The activities also require an API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -> Other Services view.", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "changeLog": "", + "cpu": 1, + "inputType": "JSON", + "displayName": "PurchaseOrders", + "memory": 0, + "mlPackageLanguage": "PYTHON37_DU", + "projectId": "[project-id]", + "stagingUri": "[staging-uri]", + "projectName": "UiPath Document Understanding", + "projectDescription": "UiPath models to classify and extract information from images and pdfs.", + "tenantName": "UiPath", + "minAIFabricVersion": "22.10", + "languageVersion": 4, + "version": 45, + "customVersion": "24.10.4", + "imagePath": "du-semistructured:v24.10-6.16-rc04", + "parametersFileJSON": "{\"params\": [{\"name\": \"auto_retraining\",\"type\": [\"PIPELINE\"],\"value\": \"False\",\"help\": \"Enable this option when the Pipeline you are creating is a scheduled automatic pipeline, synchronized with automatic exports from Data Manager.\",\"options\": [\"True\",\"False\"]}],\"probes\": [{\"probe\": \"startup\",\"initialDelaySeconds\": \"120\",\"periodSeconds\": \"10\",\"timeoutSeconds\": \"2\",\"successThreshold\": \"1\",\"failureThreshold\": \"60\"}]}", + "settings": [ + { + "key": "package_type", + "type": "STRING", + "value": "purchase_orders" + } + ] +} diff --git a/metadata/PurchaseOrders__46__metadata.json b/metadata/PurchaseOrders__46__metadata.json new file mode 100644 index 00000000..3734442c --- /dev/null +++ b/metadata/PurchaseOrders__46__metadata.json @@ -0,0 +1,34 @@ +{ + "name": "PurchaseOrders", + "retrainable": true, + "gpu": 0, + "processorType": "CPU", + "description": "Machine Learning model for extracting commonly occurring data points from Purchase Orders, including header fields and line items. Please see more details including supported languages in the ML Packages section of the Document Understanding Guide: https://docs.uipath.com/document-understanding/docs/ml-packages", + "inputDescription": "ML Skills deployed using this ML Package can be integrated into RPA workflows using the Machine Learning Extractor activity in the Official feed or the MLModel activity in Connect feed. File formats accepted include pdf, tiff, jpg or png files. The activities also require an API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -> Other Services view.", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "changeLog": "", + "cpu": 1, + "inputType": "JSON", + "displayName": "PurchaseOrders", + "memory": 0, + "mlPackageLanguage": "PYTHON37_DU", + "projectId": "[project-id]", + "stagingUri": "[staging-uri]", + "projectName": "UiPath Document Understanding", + "projectDescription": "UiPath models to classify and extract information from images and pdfs.", + "tenantName": "UiPath", + "minAIFabricVersion": "22.10", + "languageVersion": 4, + "version": 46, + "customVersion": "23.10.10", + "imagePath": "du-semistructured:v23.10-06.05-rc05", + "parametersFileJSON": "{\"params\": [{\"name\": \"auto_retraining\",\"type\": [\"PIPELINE\"],\"value\": \"False\",\"help\": \"Enable this option when the Pipeline you are creating is a scheduled automatic pipeline, synchronized with automatic exports from Data Manager.\",\"options\": [\"True\",\"False\"]}],\"probes\": [{\"probe\": \"startup\",\"initialDelaySeconds\": \"120\",\"periodSeconds\": \"10\",\"timeoutSeconds\": \"2\",\"successThreshold\": \"1\",\"failureThreshold\": \"60\"}]}", + "settings": [ + { + "key": "package_type", + "type": "STRING", + "value": "purchase_orders" + } + ], + "maxAIFabricVersion": "24.10" +} diff --git a/metadata/PurchaseOrders__47__metadata.json b/metadata/PurchaseOrders__47__metadata.json new file mode 100644 index 00000000..3f66e989 --- /dev/null +++ b/metadata/PurchaseOrders__47__metadata.json @@ -0,0 +1,33 @@ +{ + "name": "PurchaseOrders", + "retrainable": true, + "gpu": 0, + "processorType": "CPU", + "description": "Machine Learning model for extracting commonly occurring data points from Purchase Orders, including header fields and line items. Please see more details including supported languages in the ML Packages section of the Document Understanding Guide: https://docs.uipath.com/document-understanding/docs/ml-packages", + "inputDescription": "ML Skills deployed using this ML Package can be integrated into RPA workflows using the Machine Learning Extractor activity in the Official feed or the MLModel activity in Connect feed. File formats accepted include pdf, tiff, jpg or png files. The activities also require an API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -> Other Services view.", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "changeLog": "", + "cpu": 1, + "inputType": "JSON", + "displayName": "PurchaseOrders", + "memory": 0, + "mlPackageLanguage": "PYTHON37_DU", + "projectId": "[project-id]", + "stagingUri": "[staging-uri]", + "projectName": "UiPath Document Understanding", + "projectDescription": "UiPath models to classify and extract information from images and pdfs.", + "tenantName": "UiPath", + "minAIFabricVersion": "22.10", + "languageVersion": 4, + "version": 47, + "customVersion": "24.10.5", + "imagePath": "du-semistructured:v24.10-8.25-rc02", + "parametersFileJSON": "{\"params\": [{\"name\": \"auto_retraining\",\"type\": [\"PIPELINE\"],\"value\": \"False\",\"help\": \"Enable this option when the Pipeline you are creating is a scheduled automatic pipeline, synchronized with automatic exports from Data Manager.\",\"options\": [\"True\",\"False\"]}],\"probes\": [{\"probe\": \"startup\",\"initialDelaySeconds\": \"120\",\"periodSeconds\": \"10\",\"timeoutSeconds\": \"2\",\"successThreshold\": \"1\",\"failureThreshold\": \"60\"}]}", + "settings": [ + { + "key": "package_type", + "type": "STRING", + "value": "purchase_orders" + } + ] +} diff --git a/metadata/PurchaseOrders__48__metadata.json b/metadata/PurchaseOrders__48__metadata.json new file mode 100644 index 00000000..cc44c0c4 --- /dev/null +++ b/metadata/PurchaseOrders__48__metadata.json @@ -0,0 +1,34 @@ +{ + "name": "PurchaseOrders", + "retrainable": true, + "gpu": 0, + "processorType": "CPU", + "description": "Machine Learning model for extracting commonly occurring data points from Purchase Orders, including header fields and line items. Please see more details including supported languages in the ML Packages section of the Document Understanding Guide: https://docs.uipath.com/document-understanding/docs/ml-packages", + "inputDescription": "ML Skills deployed using this ML Package can be integrated into RPA workflows using the Machine Learning Extractor activity in the Official feed or the MLModel activity in Connect feed. File formats accepted include pdf, tiff, jpg or png files. The activities also require an API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -> Other Services view.", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "changeLog": "", + "cpu": 1, + "inputType": "JSON", + "displayName": "PurchaseOrders", + "memory": 0, + "mlPackageLanguage": "PYTHON37_DU", + "projectId": "[project-id]", + "stagingUri": "[staging-uri]", + "projectName": "UiPath Document Understanding", + "projectDescription": "UiPath models to classify and extract information from images and pdfs.", + "tenantName": "UiPath", + "minAIFabricVersion": "22.10", + "languageVersion": 4, + "version": 48, + "customVersion": "23.10.11", + "imagePath": "du-semistructured:v23.10-08.25-rc02", + "parametersFileJSON": "{\"params\": [{\"name\": \"auto_retraining\",\"type\": [\"PIPELINE\"],\"value\": \"False\",\"help\": \"Enable this option when the Pipeline you are creating is a scheduled automatic pipeline, synchronized with automatic exports from Data Manager.\",\"options\": [\"True\",\"False\"]}],\"probes\": [{\"probe\": \"startup\",\"initialDelaySeconds\": \"120\",\"periodSeconds\": \"10\",\"timeoutSeconds\": \"2\",\"successThreshold\": \"1\",\"failureThreshold\": \"60\"}]}", + "settings": [ + { + "key": "package_type", + "type": "STRING", + "value": "purchase_orders" + } + ], + "maxAIFabricVersion": "24.10" +} diff --git a/metadata/PurchaseOrders__49__metadata.json b/metadata/PurchaseOrders__49__metadata.json new file mode 100644 index 00000000..36402c0c --- /dev/null +++ b/metadata/PurchaseOrders__49__metadata.json @@ -0,0 +1,33 @@ +{ + "name": "PurchaseOrders", + "retrainable": true, + "gpu": 0, + "processorType": "CPU", + "description": "Machine Learning model for extracting commonly occurring data points from Purchase Orders, including header fields and line items. Please see more details including supported languages in the ML Packages section of the Document Understanding Guide: https://docs.uipath.com/document-understanding/docs/ml-packages", + "inputDescription": "ML Skills deployed using this ML Package can be integrated into RPA workflows using the Machine Learning Extractor activity in the Official feed or the MLModel activity in Connect feed. File formats accepted include pdf, tiff, jpg or png files. The activities also require an API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -> Other Services view.", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "changeLog": "", + "cpu": 1, + "inputType": "JSON", + "displayName": "PurchaseOrders", + "memory": 0, + "mlPackageLanguage": "PYTHON37_DU", + "projectId": "[project-id]", + "stagingUri": "[staging-uri]", + "projectName": "UiPath Document Understanding", + "projectDescription": "UiPath models to classify and extract information from images and pdfs.", + "tenantName": "UiPath", + "minAIFabricVersion": "22.10", + "languageVersion": 4, + "version": 49, + "customVersion": "25.10.0", + "imagePath": "du-semistructured:v25.10-10.17-rc04", + "parametersFileJSON": "{\"params\": [{\"name\": \"auto_retraining\",\"type\": [\"PIPELINE\"],\"value\": \"False\",\"help\": \"Enable this option when the Pipeline you are creating is a scheduled automatic pipeline, synchronized with automatic exports from Data Manager.\",\"options\": [\"True\",\"False\"]}],\"probes\": [{\"probe\": \"startup\",\"initialDelaySeconds\": \"120\",\"periodSeconds\": \"10\",\"timeoutSeconds\": \"2\",\"successThreshold\": \"1\",\"failureThreshold\": \"60\"}]}", + "settings": [ + { + "key": "package_type", + "type": "STRING", + "value": "purchase_orders" + } + ] +} diff --git a/metadata/QuestionAnswering__1__metadata.json b/metadata/QuestionAnswering__1__metadata.json index 5b482686..a48831bb 100644 --- a/metadata/QuestionAnswering__1__metadata.json +++ b/metadata/QuestionAnswering__1__metadata.json @@ -8,14 +8,17 @@ "inputType": "JSON", "memory": 0, "mlPackageLanguage": "PYTHON36", + "languageVersion": 0, "name": "QuestionAnswering", "outputDescription": "Answer to the questions asked in input mapped to ids\n```\n{\n \"answer\": \"1865\"\n}\n```", - "processorType": "GPU", + "processorType": "CPU", "projectId": "[project-id]", "retrainable": false, "stagingUri": "[staging-uri]", "projectName": "Language Comprehension", + "minAIFabricVersion": "v21.10", "projectDescription": "Models performing cognitively challenging tasks such as text summarization and question answering.", "tenantName": "Open-Source Packages", - "imagePath": "registry.replicated.com/aif-core/questionanswering:1" + "version": 1, + "contentUri": "https:///download/AIC/QuestionAnswering/1/Model.zip" } diff --git a/metadata/Receipts__3__metadata.json b/metadata/Receipts__11__metadata.json similarity index 72% rename from metadata/Receipts__3__metadata.json rename to metadata/Receipts__11__metadata.json index e9ae610a..422d8aa3 100644 --- a/metadata/Receipts__3__metadata.json +++ b/metadata/Receipts__11__metadata.json @@ -1,21 +1,26 @@ { - "changeLog": "Release v2020.8", - "cpu": 0, - "description": "Machine Learning model for extracting commonly occurring data points from Receipts, including header fields and line items. Please see more details including supported languages and link to Activities guide in the About Licensing -> Document Understanding API Key section of the UiPath Automation Cloud Guide here: https://docs.uipath.com/automation-cloud/docs/about-licensing#section-document-understanding-api-key.", - "displayName": "Receipts", + "name": "Receipts", + "retrainable": true, "gpu": 0, + "processorType": "CPU", + "description": "Machine Learning model for extracting commonly occurring data points from Receipts, including header fields and line items. Please see more details including supported languages in the ML Packages section of the Document Understanding Guide: https://docs.uipath.com/document-understanding/docs/ml-packages", "inputDescription": "ML Skills deployed using this ML Package can be integrated into RPA workflows using the Machine Learning Extractor activity in the Official feed or the MLModel activity in Connect feed. File formats accepted include pdf, tiff, jpg or png files. The activities also require an API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -> Other Services view.", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "changeLog": "", + "cpu": 1, "inputType": "JSON", + "displayName": "Receipts", "memory": 0, "mlPackageLanguage": "PYTHON37_DU", - "name": "Receipts", - "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", - "processorType": "CPU", - "projectId": "[project-id]", - "retrainable": true, + "projectId": "[project-id]", "stagingUri": "[staging-uri]", "projectName": "UiPath Document Understanding", "projectDescription": "UiPath models to classify and extract information from images and pdfs.", "tenantName": "UiPath", - "imagePath": "registry.replicated.com/aif-core/receipts:3" -} + "minAIFabricVersion": "22.4", + "languageVersion": 4, + "version": 11, + "customVersion": "22.4.1", + "contentUri": "https:///publicmodels/AIC/Receipts/22.4.1/receipts_package.zip", + "maxAIFabricVersion": "22.4" +} \ No newline at end of file diff --git a/metadata/Receipts__34__metadata.json b/metadata/Receipts__34__metadata.json new file mode 100644 index 00000000..64dbc917 --- /dev/null +++ b/metadata/Receipts__34__metadata.json @@ -0,0 +1,34 @@ +{ + "name": "Receipts", + "retrainable": true, + "gpu": 0, + "processorType": "CPU", + "description": "Machine Learning model for extracting commonly occurring data points from Receipts, including header fields and line items. Please see more details including supported languages in the ML Packages section of the Document Understanding Guide: https://docs.uipath.com/document-understanding/docs/ml-packages", + "inputDescription": "ML Skills deployed using this ML Package can be integrated into RPA workflows using the Machine Learning Extractor activity in the Official feed or the MLModel activity in Connect feed. File formats accepted include pdf, tiff, jpg or png files. The activities also require an API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -> Other Services view.", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "changeLog": "", + "cpu": 1, + "inputType": "JSON", + "displayName": "Receipts", + "memory": 0, + "mlPackageLanguage": "PYTHON37_DU", + "projectId": "[project-id]", + "stagingUri": "[staging-uri]", + "projectName": "UiPath Document Understanding", + "projectDescription": "UiPath models to classify and extract information from images and pdfs.", + "tenantName": "UiPath", + "minAIFabricVersion": "22.10", + "languageVersion": 4, + "version": 34, + "customVersion": "22.10.14", + "imagePath": "du-semistructured:v22.10-10.10-rc01", + "parametersFileJSON": "{\"params\": [{\"name\": \"auto_retraining\",\"type\": [\"PIPELINE\"],\"value\": \"False\",\"help\": \"Enable this option when the Pipeline you are creating is a scheduled automatic pipeline, synchronized with automatic exports from Data Manager.\",\"options\": [\"True\",\"False\"]}],\"probes\": [{\"probe\": \"startup\",\"initialDelaySeconds\": \"120\",\"periodSeconds\": \"10\",\"timeoutSeconds\": \"2\",\"successThreshold\": \"1\",\"failureThreshold\": \"60\"}]}", + "settings": [ + { + "key": "package_type", + "type": "STRING", + "value": "receipts" + } + ], + "maxAIFabricVersion": "23.4" +} \ No newline at end of file diff --git a/metadata/Receipts__40__metadata.json b/metadata/Receipts__40__metadata.json new file mode 100644 index 00000000..e4684d35 --- /dev/null +++ b/metadata/Receipts__40__metadata.json @@ -0,0 +1,33 @@ +{ + "name": "Receipts", + "retrainable": true, + "gpu": 0, + "processorType": "CPU", + "description": "Machine Learning model for extracting commonly occurring data points from Receipts, including header fields and line items. Please see more details including supported languages in the ML Packages section of the Document Understanding Guide: https://docs.uipath.com/document-understanding/docs/ml-packages", + "inputDescription": "ML Skills deployed using this ML Package can be integrated into RPA workflows using the Machine Learning Extractor activity in the Official feed or the MLModel activity in Connect feed. File formats accepted include pdf, tiff, jpg or png files. The activities also require an API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -> Other Services view.", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "changeLog": "", + "cpu": 1, + "inputType": "JSON", + "displayName": "Receipts", + "memory": 0, + "mlPackageLanguage": "PYTHON37_DU", + "projectId": "[project-id]", + "stagingUri": "[staging-uri]", + "projectName": "UiPath Document Understanding", + "projectDescription": "UiPath models to classify and extract information from images and pdfs.", + "tenantName": "UiPath", + "minAIFabricVersion": "22.10", + "languageVersion": 4, + "version": 40, + "customVersion": "24.10.2", + "imagePath": "du-semistructured:v24.10-1.27-rc02", + "parametersFileJSON": "{\"params\": [{\"name\": \"auto_retraining\",\"type\": [\"PIPELINE\"],\"value\": \"False\",\"help\": \"Enable this option when the Pipeline you are creating is a scheduled automatic pipeline, synchronized with automatic exports from Data Manager.\",\"options\": [\"True\",\"False\"]}],\"probes\": [{\"probe\": \"startup\",\"initialDelaySeconds\": \"120\",\"periodSeconds\": \"10\",\"timeoutSeconds\": \"2\",\"successThreshold\": \"1\",\"failureThreshold\": \"60\"}]}", + "settings": [ + { + "key": "package_type", + "type": "STRING", + "value": "receipts" + } + ] +} \ No newline at end of file diff --git a/metadata/Receipts__41__metadata.json b/metadata/Receipts__41__metadata.json new file mode 100644 index 00000000..5aebf4a3 --- /dev/null +++ b/metadata/Receipts__41__metadata.json @@ -0,0 +1,34 @@ +{ + "name": "Receipts", + "retrainable": true, + "gpu": 0, + "processorType": "CPU", + "description": "Machine Learning model for extracting commonly occurring data points from Receipts, including header fields and line items. Please see more details including supported languages in the ML Packages section of the Document Understanding Guide: https://docs.uipath.com/document-understanding/docs/ml-packages", + "inputDescription": "ML Skills deployed using this ML Package can be integrated into RPA workflows using the Machine Learning Extractor activity in the Official feed or the MLModel activity in Connect feed. File formats accepted include pdf, tiff, jpg or png files. The activities also require an API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -> Other Services view.", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "changeLog": "", + "cpu": 1, + "inputType": "JSON", + "displayName": "Receipts", + "memory": 0, + "mlPackageLanguage": "PYTHON37_DU", + "projectId": "[project-id]", + "stagingUri": "[staging-uri]", + "projectName": "UiPath Document Understanding", + "projectDescription": "UiPath models to classify and extract information from images and pdfs.", + "tenantName": "UiPath", + "minAIFabricVersion": "22.10", + "languageVersion": 4, + "version": 41, + "customVersion": "23.4.11", + "imagePath": "du-semistructured:v23.4-01.27-rc01", + "parametersFileJSON": "{\"params\": [{\"name\": \"auto_retraining\",\"type\": [\"PIPELINE\"],\"value\": \"False\",\"help\": \"Enable this option when the Pipeline you are creating is a scheduled automatic pipeline, synchronized with automatic exports from Data Manager.\",\"options\": [\"True\",\"False\"]}],\"probes\": [{\"probe\": \"startup\",\"initialDelaySeconds\": \"120\",\"periodSeconds\": \"10\",\"timeoutSeconds\": \"2\",\"successThreshold\": \"1\",\"failureThreshold\": \"60\"}]}", + "settings": [ + { + "key": "package_type", + "type": "STRING", + "value": "receipts" + } + ], + "maxAIFabricVersion": "23.10" +} \ No newline at end of file diff --git a/metadata/Receipts__42__metadata.json b/metadata/Receipts__42__metadata.json new file mode 100644 index 00000000..1e6599b9 --- /dev/null +++ b/metadata/Receipts__42__metadata.json @@ -0,0 +1,34 @@ +{ + "name": "Receipts", + "retrainable": true, + "gpu": 0, + "processorType": "CPU", + "description": "Machine Learning model for extracting commonly occurring data points from Receipts, including header fields and line items. Please see more details including supported languages in the ML Packages section of the Document Understanding Guide: https://docs.uipath.com/document-understanding/docs/ml-packages", + "inputDescription": "ML Skills deployed using this ML Package can be integrated into RPA workflows using the Machine Learning Extractor activity in the Official feed or the MLModel activity in Connect feed. File formats accepted include pdf, tiff, jpg or png files. The activities also require an API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -> Other Services view.", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "changeLog": "", + "cpu": 1, + "inputType": "JSON", + "displayName": "Receipts", + "memory": 0, + "mlPackageLanguage": "PYTHON37_DU", + "projectId": "[project-id]", + "stagingUri": "[staging-uri]", + "projectName": "UiPath Document Understanding", + "projectDescription": "UiPath models to classify and extract information from images and pdfs.", + "tenantName": "UiPath", + "minAIFabricVersion": "22.10", + "languageVersion": 4, + "version": 42, + "customVersion": "23.10.8", + "imagePath": "du-semistructured:v23.10-01.28-rc07", + "parametersFileJSON": "{\"params\": [{\"name\": \"auto_retraining\",\"type\": [\"PIPELINE\"],\"value\": \"False\",\"help\": \"Enable this option when the Pipeline you are creating is a scheduled automatic pipeline, synchronized with automatic exports from Data Manager.\",\"options\": [\"True\",\"False\"]}],\"probes\": [{\"probe\": \"startup\",\"initialDelaySeconds\": \"120\",\"periodSeconds\": \"10\",\"timeoutSeconds\": \"2\",\"successThreshold\": \"1\",\"failureThreshold\": \"60\"}]}", + "settings": [ + { + "key": "package_type", + "type": "STRING", + "value": "receipts" + } + ], + "maxAIFabricVersion": "24.10" +} \ No newline at end of file diff --git a/metadata/Receipts__1__metadata.json b/metadata/Receipts__43__metadata.json similarity index 52% rename from metadata/Receipts__1__metadata.json rename to metadata/Receipts__43__metadata.json index 7a71fdaa..9fc50409 100644 --- a/metadata/Receipts__1__metadata.json +++ b/metadata/Receipts__43__metadata.json @@ -1,21 +1,33 @@ { - "changeLog": "", - "cpu": 0, - "description": "Machine Learning model for extracting commonly occurring data points from Receipts, including header fields and line items. Please see more details including supported languages and link to Activities guide in the About Licensing -> Document Understanding API Key section of the UiPath Automation Cloud Guide here: https://docs.uipath.com/automation-cloud/docs/about-licensing#section-document-understanding-api-key.", - "displayName": "Receipts", + "name": "Receipts", + "retrainable": true, "gpu": 0, + "processorType": "CPU", + "description": "Machine Learning model for extracting commonly occurring data points from Receipts, including header fields and line items. Please see more details including supported languages in the ML Packages section of the Document Understanding Guide: https://docs.uipath.com/document-understanding/docs/ml-packages", "inputDescription": "ML Skills deployed using this ML Package can be integrated into RPA workflows using the Machine Learning Extractor activity in the Official feed or the MLModel activity in Connect feed. File formats accepted include pdf, tiff, jpg or png files. The activities also require an API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -> Other Services view.", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "changeLog": "", + "cpu": 1, "inputType": "JSON", + "displayName": "Receipts", "memory": 0, - "mlPackageLanguage": "PYTHON36_DU", - "name": "Receipts", - "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", - "processorType": "CPU", - "projectId": "[project-id]", - "retrainable": true, + "mlPackageLanguage": "PYTHON37_DU", + "projectId": "[project-id]", "stagingUri": "[staging-uri]", "projectName": "UiPath Document Understanding", "projectDescription": "UiPath models to classify and extract information from images and pdfs.", "tenantName": "UiPath", - "imagePath": "registry.replicated.com/aif-core/receipts:1" + "minAIFabricVersion": "22.10", + "languageVersion": 4, + "version": 43, + "customVersion": "24.10.3", + "imagePath": "du-semistructured:v24.10-3.27-rc02", + "parametersFileJSON": "{\"params\": [{\"name\": \"auto_retraining\",\"type\": [\"PIPELINE\"],\"value\": \"False\",\"help\": \"Enable this option when the Pipeline you are creating is a scheduled automatic pipeline, synchronized with automatic exports from Data Manager.\",\"options\": [\"True\",\"False\"]}],\"probes\": [{\"probe\": \"startup\",\"initialDelaySeconds\": \"120\",\"periodSeconds\": \"10\",\"timeoutSeconds\": \"2\",\"successThreshold\": \"1\",\"failureThreshold\": \"60\"}]}", + "settings": [ + { + "key": "package_type", + "type": "STRING", + "value": "receipts" + } + ] } diff --git a/metadata/Receipts__44__metadata.json b/metadata/Receipts__44__metadata.json new file mode 100644 index 00000000..85aae7bc --- /dev/null +++ b/metadata/Receipts__44__metadata.json @@ -0,0 +1,34 @@ +{ + "name": "Receipts", + "retrainable": true, + "gpu": 0, + "processorType": "CPU", + "description": "Machine Learning model for extracting commonly occurring data points from Receipts, including header fields and line items. 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Please see more details including supported languages and link to Activities guide in the About Licensing -> Document Understanding API Key section of the UiPath Automation Cloud Guide here: https://docs.uipath.com/automation-cloud/docs/about-licensing#section-document-understanding-api-key", + "inputDescription": "ML Skills deployed using this ML Package can be integrated into RPA workflows using the Machine Learning Extractor activity in the Official feed or the MLModel activity in Connect feed. File formats accepted include pdf, tiff, jpg or png files. The activities also require an API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -> Other Services view.", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "changeLog": "", + "cpu": 1, + "inputType": "JSON", + "displayName": "RemittanceAdvices", + "memory": 0, + "mlPackageLanguage": "PYTHON37_DU", + "projectId": "[project-id]", + "stagingUri": "[staging-uri]", + "projectName": "UiPath Document Understanding", + "projectDescription": "UiPath models to classify and extract information from images and pdfs.", + "tenantName": "UiPath", + "minAIFabricVersion": "22.10", + "languageVersion": 4, + "version": 34, + "customVersion": "23.10.8", + "imagePath": "du-semistructured:v23.10-01.28-rc07", + "parametersFileJSON": "{\"params\": [{\"name\": \"auto_retraining\",\"type\": [\"PIPELINE\"],\"value\": \"False\",\"help\": \"Enable this option when the Pipeline you are creating is a scheduled automatic pipeline, synchronized with automatic exports from Data Manager.\",\"options\": [\"True\",\"False\"]}],\"probes\": [{\"probe\": \"startup\",\"initialDelaySeconds\": \"120\",\"periodSeconds\": \"10\",\"timeoutSeconds\": \"2\",\"successThreshold\": \"1\",\"failureThreshold\": \"60\"}]}", + "settings": [ + { + "key": "package_type", + "type": "STRING", + "value": "remittance_advices" + } + ], + "maxAIFabricVersion": "24.10" +} \ No newline at end of file diff --git a/metadata/RemittanceAdvices__35__metadata.json b/metadata/RemittanceAdvices__35__metadata.json new file mode 100644 index 00000000..21adbd72 --- /dev/null +++ b/metadata/RemittanceAdvices__35__metadata.json @@ -0,0 +1,33 @@ +{ + "name": "RemittanceAdvices", + "retrainable": true, + "gpu": 0, + "processorType": "CPU", + "description": "Machine Learning model for extracting commonly occurring data points from Remittance Advices, including header fields and line items. Please see more details including supported languages and link to Activities guide in the About Licensing -> Document Understanding API Key section of the UiPath Automation Cloud Guide here: https://docs.uipath.com/automation-cloud/docs/about-licensing#section-document-understanding-api-key", + "inputDescription": "ML Skills deployed using this ML Package can be integrated into RPA workflows using the Machine Learning Extractor activity in the Official feed or the MLModel activity in Connect feed. File formats accepted include pdf, tiff, jpg or png files. The activities also require an API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -> Other Services view.", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "changeLog": "", + "cpu": 1, + "inputType": "JSON", + "displayName": "RemittanceAdvices", + "memory": 0, + "mlPackageLanguage": "PYTHON37_DU", + "projectId": "[project-id]", + "stagingUri": "[staging-uri]", + "projectName": "UiPath Document Understanding", + "projectDescription": "UiPath models to classify and extract information from images and pdfs.", + "tenantName": "UiPath", + "minAIFabricVersion": "22.10", + "languageVersion": 4, + "version": 35, + "customVersion": "24.10.3", + "imagePath": "du-semistructured:v24.10-3.27-rc02", + "parametersFileJSON": "{\"params\": [{\"name\": \"auto_retraining\",\"type\": [\"PIPELINE\"],\"value\": \"False\",\"help\": \"Enable this option when the Pipeline you are creating is a scheduled automatic pipeline, synchronized with automatic exports from Data Manager.\",\"options\": [\"True\",\"False\"]}],\"probes\": [{\"probe\": \"startup\",\"initialDelaySeconds\": \"120\",\"periodSeconds\": \"10\",\"timeoutSeconds\": \"2\",\"successThreshold\": \"1\",\"failureThreshold\": \"60\"}]}", + "settings": [ + { + "key": "package_type", + "type": "STRING", + "value": "remittance_advices" + } + ] +} diff --git a/metadata/RemittanceAdvices__36__metadata.json b/metadata/RemittanceAdvices__36__metadata.json new file mode 100644 index 00000000..83461fc7 --- /dev/null +++ b/metadata/RemittanceAdvices__36__metadata.json @@ -0,0 +1,34 @@ +{ + "name": "RemittanceAdvices", + "retrainable": true, + "gpu": 0, + "processorType": "CPU", + "description": "Machine Learning model for extracting commonly occurring data points from Remittance Advices, including header fields and line items. Please see more details including supported languages and link to Activities guide in the About Licensing -> Document Understanding API Key section of the UiPath Automation Cloud Guide here: https://docs.uipath.com/automation-cloud/docs/about-licensing#section-document-understanding-api-key", + "inputDescription": "ML Skills deployed using this ML Package can be integrated into RPA workflows using the Machine Learning Extractor activity in the Official feed or the MLModel activity in Connect feed. File formats accepted include pdf, tiff, jpg or png files. The activities also require an API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -> Other Services view.", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "changeLog": "", + "cpu": 1, + "inputType": "JSON", + "displayName": "RemittanceAdvices", + "memory": 0, + "mlPackageLanguage": "PYTHON37_DU", + "projectId": "[project-id]", + "stagingUri": "[staging-uri]", + "projectName": "UiPath Document Understanding", + "projectDescription": "UiPath models to classify and extract information from images and pdfs.", + "tenantName": "UiPath", + "minAIFabricVersion": "22.10", + "languageVersion": 4, + "version": 36, + "customVersion": "23.10.9", + "imagePath": "du-semistructured:v23.10-03.14-rc02", + "parametersFileJSON": "{\"params\": [{\"name\": \"auto_retraining\",\"type\": [\"PIPELINE\"],\"value\": \"False\",\"help\": \"Enable this option when the Pipeline you are creating is a scheduled automatic pipeline, synchronized with automatic exports from Data Manager.\",\"options\": [\"True\",\"False\"]}],\"probes\": [{\"probe\": \"startup\",\"initialDelaySeconds\": \"120\",\"periodSeconds\": \"10\",\"timeoutSeconds\": \"2\",\"successThreshold\": \"1\",\"failureThreshold\": \"60\"}]}", + "settings": [ + { + "key": "package_type", + "type": "STRING", + "value": "remittance_advices" + } + ], + "maxAIFabricVersion": "24.10" +} diff --git a/metadata/RemittanceAdvices__37__metadata.json b/metadata/RemittanceAdvices__37__metadata.json new file mode 100644 index 00000000..482aa3dc --- /dev/null +++ b/metadata/RemittanceAdvices__37__metadata.json @@ -0,0 +1,34 @@ +{ + "name": "RemittanceAdvices", + "retrainable": true, + "gpu": 0, + "processorType": "CPU", + "description": "Machine Learning model for extracting commonly occurring data points from Remittance Advices, including header fields and line items. 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The activities also require an API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -> Other Services view.", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "changeLog": "", + "cpu": 1, + "inputType": "JSON", + "displayName": "RemittanceAdvices", + "memory": 0, + "mlPackageLanguage": "PYTHON37_DU", + "projectId": "[project-id]", + "stagingUri": "[staging-uri]", + "projectName": "UiPath Document Understanding", + "projectDescription": "UiPath models to classify and extract information from images and pdfs.", + "tenantName": "UiPath", + "minAIFabricVersion": "22.10", + "languageVersion": 4, + "version": 37, + "customVersion": "23.4.12", + "imagePath": "du-semistructured:v23.4-03.12-rc02", + "parametersFileJSON": "{\"params\": [{\"name\": \"auto_retraining\",\"type\": [\"PIPELINE\"],\"value\": \"False\",\"help\": \"Enable this option when the Pipeline you are creating is a scheduled automatic pipeline, synchronized with automatic exports from Data Manager.\",\"options\": [\"True\",\"False\"]}],\"probes\": [{\"probe\": \"startup\",\"initialDelaySeconds\": \"120\",\"periodSeconds\": \"10\",\"timeoutSeconds\": \"2\",\"successThreshold\": \"1\",\"failureThreshold\": \"60\"}]}", + "settings": [ + { + "key": "package_type", + "type": "STRING", + "value": "remittance_advices" + } + ], + "maxAIFabricVersion": "23.10" +} diff --git a/metadata/RemittanceAdvices__38__metadata.json b/metadata/RemittanceAdvices__38__metadata.json new file mode 100644 index 00000000..c920166f --- /dev/null +++ b/metadata/RemittanceAdvices__38__metadata.json @@ -0,0 +1,33 @@ +{ + "name": "RemittanceAdvices", + "retrainable": true, + "gpu": 0, + "processorType": "CPU", + "description": "Machine Learning model for extracting commonly occurring data points from Remittance Advices, including header fields and line items. 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The activities also require an API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -> Other Services view.", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "changeLog": "", + "cpu": 1, + "inputType": "JSON", + "displayName": "RemittanceAdvices", + "memory": 0, + "mlPackageLanguage": "PYTHON37_DU", + "projectId": "[project-id]", + "stagingUri": "[staging-uri]", + "projectName": "UiPath Document Understanding", + "projectDescription": "UiPath models to classify and extract information from images and pdfs.", + "tenantName": "UiPath", + "minAIFabricVersion": "22.10", + "languageVersion": 4, + "version": 38, + "customVersion": "24.10.4", + "imagePath": "du-semistructured:v24.10-6.16-rc04", + "parametersFileJSON": "{\"params\": [{\"name\": \"auto_retraining\",\"type\": [\"PIPELINE\"],\"value\": \"False\",\"help\": \"Enable this option when the Pipeline you are creating is a scheduled automatic pipeline, synchronized with automatic exports from Data Manager.\",\"options\": [\"True\",\"False\"]}],\"probes\": [{\"probe\": \"startup\",\"initialDelaySeconds\": \"120\",\"periodSeconds\": \"10\",\"timeoutSeconds\": \"2\",\"successThreshold\": \"1\",\"failureThreshold\": \"60\"}]}", + "settings": [ + { + "key": "package_type", + "type": "STRING", + "value": "remittance_advices" + } + ] +} diff --git a/metadata/RemittanceAdvices__39__metadata.json b/metadata/RemittanceAdvices__39__metadata.json new file mode 100644 index 00000000..9d6ab56c --- /dev/null +++ b/metadata/RemittanceAdvices__39__metadata.json @@ -0,0 +1,34 @@ +{ + "name": "RemittanceAdvices", + "retrainable": true, + "gpu": 0, + "processorType": "CPU", + "description": "Machine Learning model for extracting commonly occurring data points from Remittance Advices, including header fields and line items. Please see more details including supported languages and link to Activities guide in the About Licensing -> Document Understanding API Key section of the UiPath Automation Cloud Guide here: https://docs.uipath.com/automation-cloud/docs/about-licensing#section-document-understanding-api-key", + "inputDescription": "ML Skills deployed using this ML Package can be integrated into RPA workflows using the Machine Learning Extractor activity in the Official feed or the MLModel activity in Connect feed. File formats accepted include pdf, tiff, jpg or png files. 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Please see more details including supported languages and link to Activities guide in the About Licensing -> Document Understanding API Key section of the UiPath Automation Cloud Guide here: https://docs.uipath.com/automation-cloud/docs/about-licensing#section-document-understanding-api-key", + "inputDescription": "ML Skills deployed using this ML Package can be integrated into RPA workflows using the Machine Learning Extractor activity in the Official feed or the MLModel activity in Connect feed. File formats accepted include pdf, tiff, jpg or png files. The activities also require an API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -> Other Services view.", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "changeLog": "", + "cpu": 1, + "inputType": "JSON", + "displayName": "RemittanceAdvices", + "memory": 0, + "mlPackageLanguage": "PYTHON37_DU", + "projectId": "[project-id]", + "stagingUri": "[staging-uri]", + "projectName": "UiPath Document Understanding", + "projectDescription": "UiPath models to classify and extract information from images and pdfs.", + "tenantName": "UiPath", + "minAIFabricVersion": "22.4", + "languageVersion": 4, + "version": 3, + "customVersion": "22.4.1", + "contentUri": "https:///publicmodels/AIC/RemittanceAdvices/22.4.1/remittance_advices_package.zip", + "maxAIFabricVersion": "22.4" +} \ No newline at end of file diff --git a/metadata/RemittanceAdvices__40__metadata.json b/metadata/RemittanceAdvices__40__metadata.json new file mode 100644 index 00000000..38d74aed --- /dev/null +++ b/metadata/RemittanceAdvices__40__metadata.json @@ -0,0 +1,33 @@ +{ + "name": "RemittanceAdvices", + "retrainable": true, + "gpu": 0, + "processorType": "CPU", + "description": "Machine Learning model for extracting commonly occurring data points from Remittance Advices, including header fields and line items. Please see more details including supported languages and link to Activities guide in the About Licensing -> Document Understanding API Key section of the UiPath Automation Cloud Guide here: https://docs.uipath.com/automation-cloud/docs/about-licensing#section-document-understanding-api-key", + "inputDescription": "ML Skills deployed using this ML Package can be integrated into RPA workflows using the Machine Learning Extractor activity in the Official feed or the MLModel activity in Connect feed. File formats accepted include pdf, tiff, jpg or png files. The activities also require an API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -> Other Services view.", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "changeLog": "", + "cpu": 1, + "inputType": "JSON", + "displayName": "RemittanceAdvices", + "memory": 0, + "mlPackageLanguage": "PYTHON37_DU", + "projectId": "[project-id]", + "stagingUri": "[staging-uri]", + "projectName": "UiPath Document Understanding", + "projectDescription": "UiPath models to classify and extract information from images and pdfs.", + "tenantName": "UiPath", + "minAIFabricVersion": "22.10", + "languageVersion": 4, + "version": 40, + "customVersion": "24.10.5", + "imagePath": "du-semistructured:v24.10-8.25-rc02", + "parametersFileJSON": "{\"params\": [{\"name\": \"auto_retraining\",\"type\": [\"PIPELINE\"],\"value\": \"False\",\"help\": \"Enable this option when the Pipeline you are creating is a scheduled automatic pipeline, synchronized with automatic exports from Data Manager.\",\"options\": [\"True\",\"False\"]}],\"probes\": [{\"probe\": \"startup\",\"initialDelaySeconds\": \"120\",\"periodSeconds\": \"10\",\"timeoutSeconds\": \"2\",\"successThreshold\": \"1\",\"failureThreshold\": \"60\"}]}", + "settings": [ + { + "key": "package_type", + "type": "STRING", + "value": "remittance_advices" + } + ] +} diff --git a/metadata/RemittanceAdvices__41__metadata.json b/metadata/RemittanceAdvices__41__metadata.json new file mode 100644 index 00000000..2cf7f9e0 --- /dev/null +++ b/metadata/RemittanceAdvices__41__metadata.json @@ -0,0 +1,34 @@ +{ + "name": "RemittanceAdvices", + "retrainable": true, + "gpu": 0, + "processorType": "CPU", + "description": "Machine Learning model for extracting commonly occurring data points from Remittance Advices, including header fields and line items. Please see more details including supported languages and link to Activities guide in the About Licensing -> Document Understanding API Key section of the UiPath Automation Cloud Guide here: https://docs.uipath.com/automation-cloud/docs/about-licensing#section-document-understanding-api-key", + "inputDescription": "ML Skills deployed using this ML Package can be integrated into RPA workflows using the Machine Learning Extractor activity in the Official feed or the MLModel activity in Connect feed. File formats accepted include pdf, tiff, jpg or png files. The activities also require an API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -> Other Services view.", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "changeLog": "", + "cpu": 1, + "inputType": "JSON", + "displayName": "RemittanceAdvices", + "memory": 0, + "mlPackageLanguage": "PYTHON37_DU", + "projectId": "[project-id]", + "stagingUri": "[staging-uri]", + "projectName": "UiPath Document Understanding", + "projectDescription": "UiPath models to classify and extract information from images and pdfs.", + "tenantName": "UiPath", + "minAIFabricVersion": "22.10", + "languageVersion": 4, + "version": 41, + "customVersion": "23.10.11", + "imagePath": "du-semistructured:v23.10-08.25-rc02", + "parametersFileJSON": "{\"params\": [{\"name\": \"auto_retraining\",\"type\": [\"PIPELINE\"],\"value\": \"False\",\"help\": \"Enable this option when the Pipeline you are creating is a scheduled automatic pipeline, synchronized with automatic exports from Data Manager.\",\"options\": [\"True\",\"False\"]}],\"probes\": [{\"probe\": \"startup\",\"initialDelaySeconds\": \"120\",\"periodSeconds\": \"10\",\"timeoutSeconds\": \"2\",\"successThreshold\": \"1\",\"failureThreshold\": \"60\"}]}", + "settings": [ + { + "key": "package_type", + "type": "STRING", + "value": "remittance_advices" + } + ], + "maxAIFabricVersion": "24.10" +} diff --git a/metadata/RemittanceAdvices__42__metadata.json b/metadata/RemittanceAdvices__42__metadata.json new file mode 100644 index 00000000..1da26ac9 --- /dev/null +++ b/metadata/RemittanceAdvices__42__metadata.json @@ -0,0 +1,33 @@ +{ + "name": "RemittanceAdvices", + "retrainable": true, + "gpu": 0, + "processorType": "CPU", + "description": "Machine Learning model for extracting commonly occurring data points from Remittance Advices, including header fields and line items. Please see more details including supported languages and link to Activities guide in the About Licensing -> Document Understanding API Key section of the UiPath Automation Cloud Guide here: https://docs.uipath.com/automation-cloud/docs/about-licensing#section-document-understanding-api-key", + "inputDescription": "ML Skills deployed using this ML Package can be integrated into RPA workflows using the Machine Learning Extractor activity in the Official feed or the MLModel activity in Connect feed. File formats accepted include pdf, tiff, jpg or png files. The activities also require an API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -> Other Services view.", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "changeLog": "", + "cpu": 1, + "inputType": "JSON", + "displayName": "RemittanceAdvices", + "memory": 0, + "mlPackageLanguage": "PYTHON37_DU", + "projectId": "[project-id]", + "stagingUri": "[staging-uri]", + "projectName": "UiPath Document Understanding", + "projectDescription": "UiPath models to classify and extract information from images and pdfs.", + "tenantName": "UiPath", + "minAIFabricVersion": "22.10", + "languageVersion": 4, + "version": 42, + "customVersion": "25.10.0", + "imagePath": "du-semistructured:v25.10-10.17-rc04", + "parametersFileJSON": "{\"params\": [{\"name\": \"auto_retraining\",\"type\": [\"PIPELINE\"],\"value\": \"False\",\"help\": \"Enable this option when the Pipeline you are creating is a scheduled automatic pipeline, synchronized with automatic exports from Data Manager.\",\"options\": [\"True\",\"False\"]}],\"probes\": [{\"probe\": \"startup\",\"initialDelaySeconds\": \"120\",\"periodSeconds\": \"10\",\"timeoutSeconds\": \"2\",\"successThreshold\": \"1\",\"failureThreshold\": \"60\"}]}", + "settings": [ + { + "key": "package_type", + "type": "STRING", + "value": "remittance_advices" + } + ] +} diff --git a/metadata/SentimentAnalysis__1__metadata.json b/metadata/SentimentAnalysis__1__metadata.json index 29de3a3a..5f54ad89 100644 --- a/metadata/SentimentAnalysis__1__metadata.json +++ b/metadata/SentimentAnalysis__1__metadata.json @@ -8,6 +8,7 @@ "inputType": "JSON", "memory": 0, "mlPackageLanguage": "PYTHON36", + "languageVersion": 0, "name": "SentimentAnalysis", "outputDescription": "JSON with class name and confidence on that class prediction (between 0-1). Class prediction can be one of: \"Very Negative\", \"Negative\", \"Neutral\", \"Positive\", \"Very Positive\". For example: {\"sentiment\": \"Very Negative\", \"confidence\": 0.97}", "processorType": "CPU", @@ -15,7 +16,9 @@ "retrainable": false, "stagingUri": "[staging-uri]", "projectName": "Language Analysis", + "minAIFabricVersion": "v21.10", "projectDescription": "Models for analyzing text including language detection, sentiment analysis, and named-entity recognition.", "tenantName": "Open-Source Packages", - "imagePath": "registry.replicated.com/aif-core/sentimentanalysis:1" + "version": 1, + "contentUri": "https:///download/AIC/SentimentAnalysis/1/Model.zip" } diff --git a/metadata/SentimentAnalysis__2__metadata.json b/metadata/SentimentAnalysis__2__metadata.json new file mode 100644 index 00000000..b83aa2c7 --- /dev/null +++ b/metadata/SentimentAnalysis__2__metadata.json @@ -0,0 +1,24 @@ +{ + "name": "SentimentAnalysis", + "retrainable": false, + "gpu": 0, + "processorType": "CPU", + "description": "This model was open sourced by Facebook Research. It predicts the sentiment of a text in English Language. Possible predictions are one of \"Very Negative\", \"Negative\", \"Neutral\", \"Positive\", \"Very Positive\". The model was trained on amazon product review data thus, the model predictions may have some unexpected results for different data distributions. A common use case is to route unstructured language content (e.g. emails) to an appropriate responder based on the sentiment of the text. The model implementation is open sourced by the license here: https://github.com/facebookresearch/fastText/blob/master/LICENSE", + "inputDescription": "Text to be analyzed.For example:\"I am dissatisfied with this service\"", + "outputDescription": "JSON with class name and confidence on that class prediction (between 0-1). Class prediction can be one of: \"Very Negative\", \"Negative\", \"Neutral\", \"Positive\", \"Very Positive\". For example: {\"sentiment\": \"Very Negative\", \"confidence\": 0.97}", + "changeLog": "Use Python 3.8", + "cpu": 1, + "inputType": "JSON", + "displayName": "SentimentAnalysis", + "memory": 0, + "mlPackageLanguage": "PYTHON38", + "projectId": "[project-id]", + "stagingUri": "[staging-uri]", + "projectName": "Language Analysis", + "projectDescription": "Models for analyzing text including language detection, sentiment analysis, and named-entity recognition.", + "tenantName": "Open-Source Packages", + "minAIFabricVersion": "v21.10", + "version": 2, + "customVersion": "22.12.0", + "contentUri": "https:///publicmodels/AIC/SentimentAnalysis/22.12.0/SentimentAnalysis.zip" +} \ No newline at end of file diff --git a/metadata/SignatureComparison__1__metadata.json b/metadata/SignatureComparison__1__metadata.json new file mode 100644 index 00000000..cc1e8495 --- /dev/null +++ b/metadata/SignatureComparison__1__metadata.json @@ -0,0 +1,24 @@ +{ + "name": "SignatureComparison", + "retrainable": false, + "gpu": 0, + "processorType": "CPU", + "description": "The UiPath signature comparison model is used to determine the similarity between a pair of signature and suggest if the signatures are from the same author.", + "inputDescription": "A pair of signature images in a list. Files formats accepted are png, and jpeg.", + "outputDescription": "Similarity score (between 0 and 1) and a preliminary determination if the signatures are from the same author or different authors based on a defined threshold in a Json format.", + "changeLog": "", + "cpu": 1, + "inputType": "FILE_LIST", + "displayName": "SignatureComparison", + "memory": 0, + "mlPackageLanguage": "PYTHON38_OPENCV", + "projectId": "[project-id]", + "stagingUri": "[staging-uri]", + "projectName": "UiPath Image Analysis", + "projectDescription": "Curated Models from UiPath to analyze images", + "tenantName": "UiPath", + "minAIFabricVersion": "v21.10", + "version": 1, + "customVersion": "22.10.0", + "contentUri": "https:///publicmodels/AIC/SignatureComparison/22.10.0/signature_comparison_package.zip" +} diff --git a/metadata/SignatureComparison__2__metadata.json b/metadata/SignatureComparison__2__metadata.json new file mode 100644 index 00000000..864fd7be --- /dev/null +++ b/metadata/SignatureComparison__2__metadata.json @@ -0,0 +1,24 @@ +{ + "name": "SignatureComparison", + "retrainable": false, + "gpu": 0, + "processorType": "CPU", + "description": "The UiPath signature comparison model is used to determine the similarity between a pair of signature and suggest if the signatures are from the same author.", + "inputDescription": "A pair of signature images in a list. Files formats accepted are png, and jpeg.", + "outputDescription": "Similarity score (between 0 and 1) and a preliminary determination if the signatures are from the same author or different authors based on a defined threshold in a Json format.", + "changeLog": "", + "cpu": 1, + "inputType": "FILE_LIST", + "displayName": "SignatureComparison", + "memory": 0, + "mlPackageLanguage": "PYTHON38_OPENCV", + "projectId": "[project-id]", + "stagingUri": "[staging-uri]", + "projectName": "UiPath Image Analysis", + "projectDescription": "Curated Models from UiPath to analyze images", + "tenantName": "UiPath", + "minAIFabricVersion": "v21.10", + "version": 2, + "customVersion": "22.10.1", + "contentUri": "https:///publicmodels/AIC/SignatureComparison/22.10.1/signature_comparison_package.zip" +} \ No newline at end of file diff --git a/metadata/TMAnalyzerModel__1__metadata.json b/metadata/TMAnalyzerModel__1__metadata.json new file mode 100644 index 00000000..3a7cb778 --- /dev/null +++ b/metadata/TMAnalyzerModel__1__metadata.json @@ -0,0 +1,24 @@ +{ + "name": "TMAnalyzerModel", + "retrainable": true, + "gpu": 1, + "processorType": "CPU", + "description": "Task Mining Model package meant to process and produce the data containing detected tasks.", + "inputDescription": "Dataset that contains files captured and uploaded by Task Mining Desktop application (or uploaded via Task Mining Uploader tool).", + "outputDescription": "Definition of the tasks that can be visualized in Task Mining Admin Console. Data will be imported into Admin Console Discovered Tasks section automatically some time after the package is finished.", + "changeLog": "Model: 1.0.21", + "cpu": 0, + "inputType": "JSON", + "displayName": "TMAnalyzerModel", + "memory": 0, + "mlPackageLanguage": "PYTHON38", + "projectId": "[project-id]", + "stagingUri": "[staging-uri]", + "projectName": "UiPath Task Mining", + "projectDescription": "UiPath Task Mining models to analyze captured data", + "tenantName": "UiPath", + "minAIFabricVersion": "v21.10", + "languageVersion": 0, + "version": 1, + "contentUri": "https:///publicmodels/AIC/TaskMining/1/task_mining.zip" +} \ No newline at end of file diff --git a/metadata/TMAnalyzerModel__21__metadata.json b/metadata/TMAnalyzerModel__21__metadata.json new file mode 100644 index 00000000..efcd4e31 --- /dev/null +++ b/metadata/TMAnalyzerModel__21__metadata.json @@ -0,0 +1,24 @@ +{ + "name": "TMAnalyzerModel", + "retrainable": true, + "gpu": 1, + "processorType": "CPU", + "description": "Task Mining Model package meant to process and produce the data containing detected tasks.", + "inputDescription": "Dataset that contains files captured and uploaded by Task Mining Desktop application (or uploaded via Task Mining Uploader tool).", + "outputDescription": "Definition of the tasks that can be visualized in Task Mining Admin Console. Data will be imported into Admin Console Discovered Tasks section automatically some time after the package is finished.", + "changeLog": "Internal Version 1.1.6", + "cpu": 0, + "inputType": "JSON", + "displayName": "TMAnalyzerModel", + "memory": 0, + "mlPackageLanguage": "PYTHON38", + "projectId": "[project-id]", + "stagingUri": "[staging-uri]", + "projectName": "UiPath Task Mining", + "projectDescription": "UiPath Task Mining models to analyze captured data", + "tenantName": "UiPath", + "minAIFabricVersion": "22.4", + "languageVersion": 0, + "version": 21, + "contentUri": "https:///publicmodels/AIC/TaskMining/21/task_mining.zip" +} diff --git a/metadata/TPOTAutoMLClassification__1__metadata.json b/metadata/TPOTAutoMLClassification__1__metadata.json index 9be292c4..2927d5c3 100644 --- a/metadata/TPOTAutoMLClassification__1__metadata.json +++ b/metadata/TPOTAutoMLClassification__1__metadata.json @@ -1,21 +1,24 @@ -{ - "changeLog": "", - "cpu": 0, - "description":"Please train this ML Package before deploying it as it will not return anything otherwise.   \n\nTPOT is a Python Automated Machine Learning tool that optimizes machine learning pipelines using genetic programming. TPOT will automate the most tedious part of machine learning by intelligently exploring thousands of possible pipelines to find the best one for your data. Once TPOT is finished searching (or you get tired of waiting), it provides you with the Python code for the best pipeline it found so you can tinker with the pipeline from there. TPOT is built on top of scikit-learn, so all the code it generates should look familiar to scikit-learn users.   \n\nThe model is based on a publication entitled \"Scaling tree-based automated machine learning to biomedical big data with a feature set selector.\" from Trang T. Le, Weixuan Fu and Jason H. Moore (2020) and \"Evaluation of a Tree-based Pipeline Optimization Tool for Automating Data Science.\" from Randal S. Olson, Nathan Bartley, Ryan J. Urbanowicz, and Jason H. Moore. ", - "displayName": "TPOTAutoMLClassification", - "gpu": 0, - "inputDescription":"Features used by the model to make predictions. For example: { \n\n“Feature1”: 12, \n\n“Feature2”: 222, \n\n. \n\n. \n\n“FeatureN”: 110 \n\n} ", - "inputType": "JSON", - "memory": 0, - "mlPackageLanguage": "PYTHON36", - "name": "TPOTAutoMLClassification", - "outputDescription":"JSON with predicted class, associated confidence on that class prediction (between 0-1) and label name. Label names are returned only if the label encoding was performed by the pipeline, within AI Fabric. Some scikit-learn models do not support confidence scores. If the output of the optimization pipeline is a scikit-learn model which does not support confidence scores the output will only contain the predicted class. Ex: { \n\n  \"predictions\": 0,  \n\n  \"confidences\": 0.6, \n\n  \"labels\": “yes” \n\n} \n\nOr if label encoding was done outside of the model: { \n\n  \"predictions\": 0,  \n\n  \"confidences\": 0.6, \n\n}   ", "processorType": "CPU", - "processorType": "CPU", - "projectId": "[project-id]", - "retrainable": true, - "stagingUri": "[staging-uri]", - "projectName": "Tabular Data", - "projectDescription": "Models for analyzing tabular data including classification and regression ML Packages", - "tenantName": "Open-Source Packages", - "imagePath": "registry.replicated.com/aif-core/tpotautomlclassification:1" +{ + "changeLog": "", + "cpu": 0, + "description":"Please train this ML Package before deploying it as it will not return anything otherwise.   \n\nTPOT is a Python Automated Machine Learning tool that optimizes machine learning pipelines using genetic programming. TPOT will automate the most tedious part of machine learning by intelligently exploring thousands of possible pipelines to find the best one for your data. Once TPOT is finished searching (or you get tired of waiting), it provides you with the Python code for the best pipeline it found so you can tinker with the pipeline from there. TPOT is built on top of scikit-learn, so all the code it generates should look familiar to scikit-learn users.   \n\nThe model is based on a publication entitled \"Scaling tree-based automated machine learning to biomedical big data with a feature set selector.\" from Trang T. Le, Weixuan Fu and Jason H. Moore (2020) and \"Evaluation of a Tree-based Pipeline Optimization Tool for Automating Data Science.\" from Randal S. Olson, Nathan Bartley, Ryan J. Urbanowicz, and Jason H. Moore. ", + "displayName": "TPOTAutoMLClassification", + "gpu": 0, + "inputDescription":"Features used by the model to make predictions. For example: { \n\n“Feature1”: 12, \n\n“Feature2”: 222, \n\n. \n\n. \n\n“FeatureN”: 110 \n\n} ", + "inputType": "JSON", + "memory": 0, + "mlPackageLanguage": "PYTHON36", + "name": "TPOTAutoMLClassification", + "outputDescription":"JSON with predicted class, associated confidence on that class prediction (between 0-1) and label name. Label names are returned only if the label encoding was performed by the pipeline, within AI Center. Some scikit-learn models do not support confidence scores. If the output of the optimization pipeline is a scikit-learn model which does not support confidence scores the output will only contain the predicted class. Ex: { \n\n  \"predictions\": 0,  \n\n  \"confidences\": 0.6, \n\n  \"labels\": “yes” \n\n} \n\nOr if label encoding was done outside of the model: { \n\n  \"predictions\": 0,  \n\n  \"confidences\": 0.6, \n\n}   ", "processorType": "CPU", + "processorType": "CPU", + "projectId": "[project-id]", + "retrainable": true, + "stagingUri": "[staging-uri]", + "projectName": "Tabular Data", + "projectDescription": "Models for analyzing tabular data including classification and regression ML Packages", + "tenantName": "Open-Source Packages", + "minAIFabricVersion": "v21.10", + "languageVersion": 0, + "version": 1, + "contentUri": "https:///publicmodels/AIC/TPOTAutoMLClassification/1/TPOTAutoMLClassification.zip" } \ No newline at end of file diff --git a/metadata/TPOTAutoMLRegression__1__metadata.json b/metadata/TPOTAutoMLRegression__1__metadata.json new file mode 100644 index 00000000..47e2b946 --- /dev/null +++ b/metadata/TPOTAutoMLRegression__1__metadata.json @@ -0,0 +1,24 @@ +{ + "name": "TPOTAutoMLRegression", + "retrainable": true, + "gpu": 0, + "processorType": "CPU", + "description": "Please train this ML Package before deploying it as it will not return anything otherwise. TPOT is a Python Automated Machine Learning tool that optimizes machine learning pipelines using genetic programming. TPOT will automate the most tedious part of machine learning by intelligently exploring thousands of possible pipelines to find the best one for your data. Once TPOT is finished searching (or you get tired of waiting), it provides you with the Python code for the best pipeline it found so you can tinker with the pipeline from there. TPOT is built on top of scikit-learn, so all the code it generates should look familiar to scikit-learn users. TPOT uses the following set of models and pre-processing steps in its optimizations process: https://github.com/EpistasisLab/tpot/blob/master/tpot/config/regressor.py", + "inputDescription": "Features used by the model to make predictions. For example: [{ \"Feature1\": 12, \"Feature2\": 222, . . \"FeatureN\": 110 }]", + "outputDescription": "String with list of predictions: \"[12, 12, 2, 354, 12, 2]\"", + "changeLog": "Move to Python 3.8", + "cpu": 1, + "inputType": "JSON", + "displayName": "TPOTAutoMLRegression", + "memory": 0, + "mlPackageLanguage": "PYTHON38", + "projectId": "[project-id]", + "stagingUri": "[staging-uri]", + "projectName": "Tabular Data", + "projectDescription": "Models for analyzing tabular data including classification and regression ML Packages", + "tenantName": "Open-Source Packages", + "minAIFabricVersion": "v21.10", + "version": 1, + "customVersion": "22.12.0", + "contentUri": "https:///publicmodels/AIC/TPOTAutoMLRegression/22.12/TPOTAutoMLRegression.zip" +} \ No newline at end of file diff --git a/metadata/TextSummarization__1__metadata.json b/metadata/TextSummarization__1__metadata.json index 8e18ed08..a8921c7e 100644 --- a/metadata/TextSummarization__1__metadata.json +++ b/metadata/TextSummarization__1__metadata.json @@ -1,21 +1,24 @@ -{ - "changeLog": "", - "cpu": 0, - "description": "This is a abstractive text summarization model open sourced by Facebook AI Research. It is a sequence-to-sequence model based on the paper `BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension` by Lewis, et al.", - "displayName": "TextSummarization", - "gpu": 0, - "inputDescription": "Text to be summarized as a String. Please note this model can be slow for long inputs.", - "inputType": "JSON", - "memory": 0, - "mlPackageLanguage": "PYTHON36", - "name": "TextSummarization", - "outputDescription": "JSON with summarized text. The resulting output will have about 20-30% the length of the input", - "processorType": "CPU", - "projectId": "[project-id]", - "retrainable": false, - "stagingUri": "[staging-uri]", - "projectName": "Language Comprehension", - "projectDescription": "Models performing cognitively challenging tasks such as text summarization and question answering", - "tenantName": "Open-Source Packages", - "imagePath": "registry.replicated.com/aif-core/textsummarization:1" +{ + "changeLog": "", + "cpu": 0, + "description": "This is a abstractive text summarization model open sourced by Facebook AI Research. It is a sequence-to-sequence model based on the paper `BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension` by Lewis, et al.", + "displayName": "TextSummarization", + "gpu": 0, + "inputDescription": "Text to be summarized as a String. Please note this model can be slow for long inputs.", + "inputType": "JSON", + "memory": 0, + "mlPackageLanguage": "PYTHON36", + "name": "TextSummarization", + "outputDescription": "JSON with summarized text. The resulting output will have about 20-30% the length of the input", + "processorType": "CPU", + "projectId": "[project-id]", + "retrainable": false, + "stagingUri": "[staging-uri]", + "projectName": "Language Comprehension", + "projectDescription": "Models performing cognitively challenging tasks such as text summarization and question answering", + "tenantName": "Open-Source Packages", + "minAIFabricVersion": "v21.10", + "languageVersion": 0, + "version": 1, + "contentUri": "https:///publicmodels/AIC/TextSummarization/1/Model.zip" } \ No newline at end of file diff --git a/metadata/UB04__10__metadata.json b/metadata/UB04__10__metadata.json new file mode 100644 index 00000000..028f1fe2 --- /dev/null +++ b/metadata/UB04__10__metadata.json @@ -0,0 +1,33 @@ +{ + "name": "UB04", + "retrainable": true, + "gpu": 0, + "processorType": "CPU", + "description": "Machine Learning model (available in Preview) for extracting commonly occurring data points from UB04. Please see more details including supported languages and sample documents in the official documentation page for Pre-trained Out of the Box models: https://docs.uipath.com/document-understanding/automation-cloud/latest/user-guide/out-of-the-box-pre-trained-ml-packages.", + "inputDescription": "ML Skills deployed using this ML Package can be integrated into RPA workflows using the Machine Learning Extractor activity in the Official feed or the MLModel activity in Connect feed. File formats accepted include pdf, tiff, jpg or png files. The activities also require an API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -> Other Services view.", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "changeLog": "", + "cpu": 1, + "inputType": "JSON", + "displayName": "UB04", + "memory": 0, + "mlPackageLanguage": "PYTHON37_DU", + "projectId": "[project-id]", + "stagingUri": "[staging-uri]", + "projectName": "UiPath Document Understanding", + "projectDescription": "UiPath models to classify and extract information from images and pdfs.", + "tenantName": "UiPath", + "minAIFabricVersion": "22.10", + "languageVersion": 4, + "version": 10, + "customVersion": "24.10.3", + "imagePath": "du-semistructured:v24.10-3.27-rc02", + "parametersFileJSON": "{\"params\": [{\"name\": \"auto_retraining\",\"type\": [\"PIPELINE\"],\"value\": \"False\",\"help\": \"Enable this option when the Pipeline you are creating is a scheduled automatic pipeline, synchronized with automatic exports from Data Manager.\",\"options\": [\"True\",\"False\"]}],\"probes\": [{\"probe\": \"startup\",\"initialDelaySeconds\": \"120\",\"periodSeconds\": \"10\",\"timeoutSeconds\": \"2\",\"successThreshold\": \"1\",\"failureThreshold\": \"60\"}]}", + "settings": [ + { + "key": "package_type", + "type": "STRING", + "value": "ub04" + } + ] +} diff --git a/metadata/UB04__11__metadata.json b/metadata/UB04__11__metadata.json new file mode 100644 index 00000000..54ddb157 --- /dev/null +++ b/metadata/UB04__11__metadata.json @@ -0,0 +1,34 @@ +{ + "name": "UB04", + "retrainable": true, + "gpu": 0, + "processorType": "CPU", + "description": "Machine Learning model (available in Preview) for extracting commonly occurring data points from UB04. Please see more details including supported languages and sample documents in the official documentation page for Pre-trained Out of the Box models: https://docs.uipath.com/document-understanding/automation-cloud/latest/user-guide/out-of-the-box-pre-trained-ml-packages.", + "inputDescription": "ML Skills deployed using this ML Package can be integrated into RPA workflows using the Machine Learning Extractor activity in the Official feed or the MLModel activity in Connect feed. File formats accepted include pdf, tiff, jpg or png files. The activities also require an API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -> Other Services view.", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "changeLog": "", + "cpu": 1, + "inputType": "JSON", + "displayName": "UB04", + "memory": 0, + "mlPackageLanguage": "PYTHON37_DU", + "projectId": "[project-id]", + "stagingUri": "[staging-uri]", + "projectName": "UiPath Document Understanding", + "projectDescription": "UiPath models to classify and extract information from images and pdfs.", + "tenantName": "UiPath", + "minAIFabricVersion": "22.10", + "languageVersion": 4, + "version": 11, + "customVersion": "23.10.9", + "imagePath": "du-semistructured:v23.10-03.14-rc02", + "parametersFileJSON": "{\"params\": [{\"name\": \"auto_retraining\",\"type\": [\"PIPELINE\"],\"value\": \"False\",\"help\": \"Enable this option when the Pipeline you are creating is a scheduled automatic pipeline, synchronized with automatic exports from Data Manager.\",\"options\": [\"True\",\"False\"]}],\"probes\": [{\"probe\": \"startup\",\"initialDelaySeconds\": \"120\",\"periodSeconds\": \"10\",\"timeoutSeconds\": \"2\",\"successThreshold\": \"1\",\"failureThreshold\": \"60\"}]}", + "settings": [ + { + "key": "package_type", + "type": "STRING", + "value": "ub04" + } + ], + "maxAIFabricVersion": "24.10" +} diff --git a/metadata/UB04__12__metadata.json b/metadata/UB04__12__metadata.json new file mode 100644 index 00000000..73844987 --- /dev/null +++ b/metadata/UB04__12__metadata.json @@ -0,0 +1,33 @@ +{ + "name": "UB04", + "retrainable": true, + "gpu": 0, + "processorType": "CPU", + "description": "Machine Learning model (available in Preview) for extracting commonly occurring data points from UB04. Please see more details including supported languages and sample documents in the official documentation page for Pre-trained Out of the Box models: https://docs.uipath.com/document-understanding/automation-cloud/latest/user-guide/out-of-the-box-pre-trained-ml-packages.", + "inputDescription": "ML Skills deployed using this ML Package can be integrated into RPA workflows using the Machine Learning Extractor activity in the Official feed or the MLModel activity in Connect feed. File formats accepted include pdf, tiff, jpg or png files. The activities also require an API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -> Other Services view.", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "changeLog": "", + "cpu": 1, + "inputType": "JSON", + "displayName": "UB04", + "memory": 0, + "mlPackageLanguage": "PYTHON37_DU", + "projectId": "[project-id]", + "stagingUri": "[staging-uri]", + "projectName": "UiPath Document Understanding", + "projectDescription": "UiPath models to classify and extract information from images and pdfs.", + "tenantName": "UiPath", + "minAIFabricVersion": "22.10", + "languageVersion": 4, + "version": 12, + "customVersion": "24.10.4", + "imagePath": "du-semistructured:v24.10-6.16-rc04", + "parametersFileJSON": "{\"params\": [{\"name\": \"auto_retraining\",\"type\": [\"PIPELINE\"],\"value\": \"False\",\"help\": \"Enable this option when the Pipeline you are creating is a scheduled automatic pipeline, synchronized with automatic exports from Data Manager.\",\"options\": [\"True\",\"False\"]}],\"probes\": [{\"probe\": \"startup\",\"initialDelaySeconds\": \"120\",\"periodSeconds\": \"10\",\"timeoutSeconds\": \"2\",\"successThreshold\": \"1\",\"failureThreshold\": \"60\"}]}", + "settings": [ + { + "key": "package_type", + "type": "STRING", + "value": "ub04" + } + ] +} diff --git a/metadata/UB04__13__metadata.json b/metadata/UB04__13__metadata.json new file mode 100644 index 00000000..e23c51d4 --- /dev/null +++ b/metadata/UB04__13__metadata.json @@ -0,0 +1,34 @@ +{ + "name": "UB04", + "retrainable": true, + "gpu": 0, + "processorType": "CPU", + "description": "Machine Learning model (available in Preview) for extracting commonly occurring data points from UB04. Please see more details including supported languages and sample documents in the official documentation page for Pre-trained Out of the Box models: https://docs.uipath.com/document-understanding/automation-cloud/latest/user-guide/out-of-the-box-pre-trained-ml-packages.", + "inputDescription": "ML Skills deployed using this ML Package can be integrated into RPA workflows using the Machine Learning Extractor activity in the Official feed or the MLModel activity in Connect feed. File formats accepted include pdf, tiff, jpg or png files. The activities also require an API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -> Other Services view.", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "changeLog": "", + "cpu": 1, + "inputType": "JSON", + "displayName": "UB04", + "memory": 0, + "mlPackageLanguage": "PYTHON37_DU", + "projectId": "[project-id]", + "stagingUri": "[staging-uri]", + "projectName": "UiPath Document Understanding", + "projectDescription": "UiPath models to classify and extract information from images and pdfs.", + "tenantName": "UiPath", + "minAIFabricVersion": "22.10", + "languageVersion": 4, + "version": 13, + "customVersion": "23.10.10", + "imagePath": "du-semistructured:v23.10-06.05-rc05", + "parametersFileJSON": "{\"params\": [{\"name\": \"auto_retraining\",\"type\": [\"PIPELINE\"],\"value\": \"False\",\"help\": \"Enable this option when the Pipeline you are creating is a scheduled automatic pipeline, synchronized with automatic exports from Data Manager.\",\"options\": [\"True\",\"False\"]}],\"probes\": [{\"probe\": \"startup\",\"initialDelaySeconds\": \"120\",\"periodSeconds\": \"10\",\"timeoutSeconds\": \"2\",\"successThreshold\": \"1\",\"failureThreshold\": \"60\"}]}", + "settings": [ + { + "key": "package_type", + "type": "STRING", + "value": "ub04" + } + ], + "maxAIFabricVersion": "24.10" +} diff --git a/metadata/UB04__14__metadata.json b/metadata/UB04__14__metadata.json new file mode 100644 index 00000000..bbd860fc --- /dev/null +++ b/metadata/UB04__14__metadata.json @@ -0,0 +1,33 @@ +{ + "name": "UB04", + "retrainable": true, + "gpu": 0, + "processorType": "CPU", + "description": "Machine Learning model (available in Preview) for extracting commonly occurring data points from UB04. 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File formats accepted include pdf, tiff, jpg or png files.", + "inputType": "JSON", + "memory": 0, + "mlPackageLanguage": "PYTHON37_DU", + "name": "UiPathDocumentOCR_CPU", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "processorType": "CPU", + "projectId": "[project-id]", + "retrainable": false, + "stagingUri": "[staging-uri]", + "projectName": "UiPath Document Understanding", + "projectDescription": "UiPath models to classify and extract information from images and pdfs.", + "tenantName": "UiPath", + "minAIFabricVersion": "22.10", + "languageVersion": 4, + "version": 29, + "customVersion": "23.10.9", + "imagePath": "du-doc-ocr-cpu:v23.10-03.14-rc02", + "parametersFileJSON": "{\"params\": [],\"probes\": [{\"probe\": \"startup\",\"initialDelaySeconds\": \"30\",\"periodSeconds\": \"10\",\"timeoutSeconds\": \"5\",\"successThreshold\": \"1\",\"failureThreshold\": \"60\"},{\"probe\": \"readiness\",\"initialDelaySeconds\": \"30\",\"periodSeconds\": \"15\",\"timeoutSeconds\": \"45\",\"successThreshold\": \"1\",\"failureThreshold\": \"2\"},{\"probe\": \"liveness\",\"initialDelaySeconds\": \"30\",\"periodSeconds\": \"60\",\"timeoutSeconds\": \"45\",\"successThreshold\": \"1\",\"failureThreshold\": \"3\"}]}", + "maxAIFabricVersion": "24.10" +} diff --git a/metadata/UiPathDocumentOCR_CPU__30__metadata.json b/metadata/UiPathDocumentOCR_CPU__30__metadata.json new file mode 100644 index 00000000..d17ad2c6 --- /dev/null +++ b/metadata/UiPathDocumentOCR_CPU__30__metadata.json @@ -0,0 +1,27 @@ +{ + "changeLog": "", + "cpu": 0, + "description": "Machine Learning model for extracting text and checkboxes/radio buttons from Documents. Please see more details including supported languages and link to Activities guide in the Document Understanding Guide here: https://docs.uipath.com/document-understanding/v2021.4/docs/ml-packages#uipath-document-ocr. For alternative deployment methods in high-volume scenarios see also this page: https://docs.uipath.com/document-understanding/v2021.4/docs/ocr-services", + "displayName": "UiPathDocumentOCR_CPU", + "gpu": 0, + "inputDescription": "ML Skills deployed using this ML Package can be integrated into RPA workflows using the UiPath Document OCR activity from OCR.Activities pack in the Official feed. File formats accepted include pdf, tiff, jpg or png files.", + "inputType": "JSON", + "memory": 0, + "mlPackageLanguage": "PYTHON37_DU", + "name": "UiPathDocumentOCR_CPU", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "processorType": "CPU", + "projectId": "[project-id]", + "retrainable": false, + "stagingUri": "[staging-uri]", + "projectName": "UiPath Document Understanding", + "projectDescription": "UiPath models to classify and extract information from images and pdfs.", + "tenantName": "UiPath", + "minAIFabricVersion": "22.10", + "languageVersion": 4, + "version": 30, + "customVersion": "23.4.12", + "imagePath": "du-doc-ocr-cpu:v23.4-03.12-rc02", + "parametersFileJSON": "{\"params\": [],\"probes\": [{\"probe\": \"startup\",\"initialDelaySeconds\": \"30\",\"periodSeconds\": \"10\",\"timeoutSeconds\": \"5\",\"successThreshold\": \"1\",\"failureThreshold\": \"60\"},{\"probe\": \"readiness\",\"initialDelaySeconds\": \"30\",\"periodSeconds\": \"15\",\"timeoutSeconds\": \"45\",\"successThreshold\": \"1\",\"failureThreshold\": \"2\"},{\"probe\": \"liveness\",\"initialDelaySeconds\": \"30\",\"periodSeconds\": \"60\",\"timeoutSeconds\": \"45\",\"successThreshold\": \"1\",\"failureThreshold\": \"3\"}]}", + "maxAIFabricVersion": "23.10" +} diff --git a/metadata/UiPathDocumentOCR_CPU__31__metadata.json b/metadata/UiPathDocumentOCR_CPU__31__metadata.json new file mode 100644 index 00000000..6c1a64cb --- /dev/null +++ b/metadata/UiPathDocumentOCR_CPU__31__metadata.json @@ -0,0 +1,26 @@ +{ + "changeLog": "", + "cpu": 0, + "description": "Machine Learning model for extracting text and checkboxes/radio buttons from Documents. Please see more details including supported languages and link to Activities guide in the Document Understanding Guide here: https://docs.uipath.com/document-understanding/v2021.4/docs/ml-packages#uipath-document-ocr. For alternative deployment methods in high-volume scenarios see also this page: https://docs.uipath.com/document-understanding/v2021.4/docs/ocr-services", + "displayName": "UiPathDocumentOCR_CPU", + "gpu": 0, + "inputDescription": "ML Skills deployed using this ML Package can be integrated into RPA workflows using the UiPath Document OCR activity from OCR.Activities pack in the Official feed. File formats accepted include pdf, tiff, jpg or png files.", + "inputType": "JSON", + "memory": 0, + "mlPackageLanguage": "PYTHON37_DU", + "name": "UiPathDocumentOCR_CPU", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "processorType": "CPU", + "projectId": "[project-id]", + "retrainable": false, + "stagingUri": "[staging-uri]", + "projectName": "UiPath Document Understanding", + "projectDescription": "UiPath models to classify and extract information from images and pdfs.", + "tenantName": "UiPath", + "minAIFabricVersion": "22.10", + "languageVersion": 4, + "version": 31, + "customVersion": "24.10.4", + "imagePath": "du-doc-ocr-cpu:v24.10-5.23-rc03", + "parametersFileJSON": "{\"params\": [],\"probes\": [{\"probe\": \"startup\",\"initialDelaySeconds\": \"30\",\"periodSeconds\": \"10\",\"timeoutSeconds\": \"5\",\"successThreshold\": \"1\",\"failureThreshold\": \"60\"},{\"probe\": \"readiness\",\"initialDelaySeconds\": \"30\",\"periodSeconds\": \"15\",\"timeoutSeconds\": \"45\",\"successThreshold\": \"1\",\"failureThreshold\": \"2\"},{\"probe\": \"liveness\",\"initialDelaySeconds\": \"30\",\"periodSeconds\": \"60\",\"timeoutSeconds\": \"45\",\"successThreshold\": \"1\",\"failureThreshold\": \"3\"}]}" +} diff --git a/metadata/UiPathDocumentOCR_CPU__32__metadata.json b/metadata/UiPathDocumentOCR_CPU__32__metadata.json new file mode 100644 index 00000000..c8ce88b2 --- /dev/null +++ b/metadata/UiPathDocumentOCR_CPU__32__metadata.json @@ -0,0 +1,27 @@ +{ + "changeLog": "", + "cpu": 0, + "description": "Machine Learning model for extracting text and checkboxes/radio buttons from Documents. Please see more details including supported languages and link to Activities guide in the Document Understanding Guide here: https://docs.uipath.com/document-understanding/v2021.4/docs/ml-packages#uipath-document-ocr. For alternative deployment methods in high-volume scenarios see also this page: https://docs.uipath.com/document-understanding/v2021.4/docs/ocr-services", + "displayName": "UiPathDocumentOCR_CPU", + "gpu": 0, + "inputDescription": "ML Skills deployed using this ML Package can be integrated into RPA workflows using the UiPath Document OCR activity from OCR.Activities pack in the Official feed. File formats accepted include pdf, tiff, jpg or png files.", + "inputType": "JSON", + "memory": 0, + "mlPackageLanguage": "PYTHON37_DU", + "name": "UiPathDocumentOCR_CPU", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "processorType": "CPU", + "projectId": "[project-id]", + "retrainable": false, + "stagingUri": "[staging-uri]", + "projectName": "UiPath Document Understanding", + "projectDescription": "UiPath models to classify and extract information from images and pdfs.", + "tenantName": "UiPath", + "minAIFabricVersion": "22.10", + "languageVersion": 4, + "version": 32, + "customVersion": "23.10.10", + "imagePath": "du-doc-ocr-cpu:v23.10-06.05-rc05", + "parametersFileJSON": "{\"params\": [],\"probes\": [{\"probe\": \"startup\",\"initialDelaySeconds\": \"30\",\"periodSeconds\": \"10\",\"timeoutSeconds\": \"5\",\"successThreshold\": \"1\",\"failureThreshold\": \"60\"},{\"probe\": \"readiness\",\"initialDelaySeconds\": \"30\",\"periodSeconds\": \"15\",\"timeoutSeconds\": \"45\",\"successThreshold\": \"1\",\"failureThreshold\": \"2\"},{\"probe\": \"liveness\",\"initialDelaySeconds\": \"30\",\"periodSeconds\": \"60\",\"timeoutSeconds\": \"45\",\"successThreshold\": \"1\",\"failureThreshold\": \"3\"}]}", + "maxAIFabricVersion": "24.10" +} diff --git a/metadata/UiPathDocumentOCR_CPU__33__metadata.json b/metadata/UiPathDocumentOCR_CPU__33__metadata.json new file mode 100644 index 00000000..57bf95f5 --- /dev/null +++ b/metadata/UiPathDocumentOCR_CPU__33__metadata.json @@ -0,0 +1,26 @@ +{ + "changeLog": "", + "cpu": 0, + "description": "Machine Learning model for extracting text and checkboxes/radio buttons from Documents. Please see more details including supported languages and link to Activities guide in the Document Understanding Guide here: https://docs.uipath.com/document-understanding/v2021.4/docs/ml-packages#uipath-document-ocr. For alternative deployment methods in high-volume scenarios see also this page: https://docs.uipath.com/document-understanding/v2021.4/docs/ocr-services", + "displayName": "UiPathDocumentOCR_CPU", + "gpu": 0, + "inputDescription": "ML Skills deployed using this ML Package can be integrated into RPA workflows using the UiPath Document OCR activity from OCR.Activities pack in the Official feed. File formats accepted include pdf, tiff, jpg or png files.", + "inputType": "JSON", + "memory": 0, + "mlPackageLanguage": "PYTHON37_DU", + "name": "UiPathDocumentOCR_CPU", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "processorType": "CPU", + "projectId": "[project-id]", + "retrainable": false, + "stagingUri": "[staging-uri]", + "projectName": "UiPath Document Understanding", + "projectDescription": "UiPath models to classify and extract information from images and pdfs.", + "tenantName": "UiPath", + "minAIFabricVersion": "22.10", + "languageVersion": 4, + "version": 33, + "customVersion": "24.10.5", + "imagePath": "du-doc-ocr-cpu:v24.10-8.25-rc02", + "parametersFileJSON": "{\"params\": [],\"probes\": [{\"probe\": \"startup\",\"initialDelaySeconds\": \"30\",\"periodSeconds\": \"10\",\"timeoutSeconds\": \"5\",\"successThreshold\": \"1\",\"failureThreshold\": \"60\"},{\"probe\": \"readiness\",\"initialDelaySeconds\": \"30\",\"periodSeconds\": \"15\",\"timeoutSeconds\": \"45\",\"successThreshold\": \"1\",\"failureThreshold\": \"2\"},{\"probe\": \"liveness\",\"initialDelaySeconds\": \"30\",\"periodSeconds\": \"60\",\"timeoutSeconds\": \"45\",\"successThreshold\": \"1\",\"failureThreshold\": \"3\"}]}" +} diff --git a/metadata/UiPathDocumentOCR_CPU__34__metadata.json b/metadata/UiPathDocumentOCR_CPU__34__metadata.json new file mode 100644 index 00000000..2dc7249a --- /dev/null +++ b/metadata/UiPathDocumentOCR_CPU__34__metadata.json @@ -0,0 +1,27 @@ +{ + "changeLog": "", + "cpu": 0, + "description": "Machine Learning model for extracting text and checkboxes/radio buttons from Documents. Please see more details including supported languages and link to Activities guide in the Document Understanding Guide here: https://docs.uipath.com/document-understanding/v2021.4/docs/ml-packages#uipath-document-ocr. For alternative deployment methods in high-volume scenarios see also this page: https://docs.uipath.com/document-understanding/v2021.4/docs/ocr-services", + "displayName": "UiPathDocumentOCR_CPU", + "gpu": 0, + "inputDescription": "ML Skills deployed using this ML Package can be integrated into RPA workflows using the UiPath Document OCR activity from OCR.Activities pack in the Official feed. File formats accepted include pdf, tiff, jpg or png files.", + "inputType": "JSON", + "memory": 0, + "mlPackageLanguage": "PYTHON37_DU", + "name": "UiPathDocumentOCR_CPU", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "processorType": "CPU", + "projectId": "[project-id]", + "retrainable": false, + "stagingUri": "[staging-uri]", + "projectName": "UiPath Document Understanding", + "projectDescription": "UiPath models to classify and extract information from images and pdfs.", + "tenantName": "UiPath", + "minAIFabricVersion": "22.10", + "languageVersion": 4, + "version": 34, + "customVersion": "23.10.11", + "imagePath": "du-doc-ocr-cpu:v23.10-08.25-rc02", + "parametersFileJSON": "{\"params\": [],\"probes\": [{\"probe\": \"startup\",\"initialDelaySeconds\": \"30\",\"periodSeconds\": \"10\",\"timeoutSeconds\": \"5\",\"successThreshold\": \"1\",\"failureThreshold\": \"60\"},{\"probe\": \"readiness\",\"initialDelaySeconds\": \"30\",\"periodSeconds\": \"15\",\"timeoutSeconds\": \"45\",\"successThreshold\": \"1\",\"failureThreshold\": \"2\"},{\"probe\": \"liveness\",\"initialDelaySeconds\": \"30\",\"periodSeconds\": \"60\",\"timeoutSeconds\": \"45\",\"successThreshold\": \"1\",\"failureThreshold\": \"3\"}]}", + "maxAIFabricVersion": "24.10" +} diff --git a/metadata/UiPathDocumentOCR_CPU__35__metadata.json b/metadata/UiPathDocumentOCR_CPU__35__metadata.json new file mode 100644 index 00000000..4b6485f2 --- /dev/null +++ b/metadata/UiPathDocumentOCR_CPU__35__metadata.json @@ -0,0 +1,26 @@ +{ + "changeLog": "", + "cpu": 0, + "description": "Machine Learning model for extracting text and checkboxes/radio buttons from Documents. Please see more details including supported languages and link to Activities guide in the Document Understanding Guide here: https://docs.uipath.com/document-understanding/v2021.4/docs/ml-packages#uipath-document-ocr. For alternative deployment methods in high-volume scenarios see also this page: https://docs.uipath.com/document-understanding/v2021.4/docs/ocr-services", + "displayName": "UiPathDocumentOCR_CPU", + "gpu": 0, + "inputDescription": "ML Skills deployed using this ML Package can be integrated into RPA workflows using the UiPath Document OCR activity from OCR.Activities pack in the Official feed. File formats accepted include pdf, tiff, jpg or png files.", + "inputType": "JSON", + "memory": 0, + "mlPackageLanguage": "PYTHON37_DU", + "name": "UiPathDocumentOCR_CPU", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "processorType": "CPU", + "projectId": "[project-id]", + "retrainable": false, + "stagingUri": "[staging-uri]", + "projectName": "UiPath Document Understanding", + "projectDescription": "UiPath models to classify and extract information from images and pdfs.", + "tenantName": "UiPath", + "minAIFabricVersion": "22.10", + "languageVersion": 4, + "version": 35, + "customVersion": "25.10.0", + "imagePath": "du-doc-ocr-cpu:v25.10-10.17-rc04", + "parametersFileJSON": "{\"params\": [],\"probes\": [{\"probe\": \"startup\",\"initialDelaySeconds\": \"30\",\"periodSeconds\": \"10\",\"timeoutSeconds\": \"5\",\"successThreshold\": \"1\",\"failureThreshold\": \"60\"},{\"probe\": \"readiness\",\"initialDelaySeconds\": \"30\",\"periodSeconds\": \"15\",\"timeoutSeconds\": \"45\",\"successThreshold\": \"1\",\"failureThreshold\": \"2\"},{\"probe\": \"liveness\",\"initialDelaySeconds\": \"30\",\"periodSeconds\": \"60\",\"timeoutSeconds\": \"45\",\"successThreshold\": \"1\",\"failureThreshold\": \"3\"}]}" +} diff --git a/metadata/UiPathDocumentOCR__1__metadata.json b/metadata/UiPathDocumentOCR__1__metadata.json deleted file mode 100644 index 811ceba1..00000000 --- a/metadata/UiPathDocumentOCR__1__metadata.json +++ /dev/null @@ -1,21 +0,0 @@ -{ - "changeLog": "Release v2020.10", - "cpu": 0, - "description": "Machine Learning model for extracting text from Documents. Please see more details including supported languages and link to Activities guide in the About Licensing -> Document Understanding API Key section of the UiPath Automation Cloud Guide here: https://docs.uipath.com/automation-cloud/docs/about-licensing#section-document-understanding-api-key.", - "displayName": "UiPathDocumentOCR", - "gpu": 0, - "inputDescription": "ML Skills deployed using this ML Package can be integrated into RPA workflows using the UiPath Document OCR activity from OCR.Activities pack in the Official feed. File formats accepted include pdf, tiff, jpg or png files. 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In non-airgapped deployments, the activity requires the Document Understanding API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -> Other Services view.", + "inputType": "JSON", + "memory": 0, + "mlPackageLanguage": "PYTHON37_DU", + "name": "UiPathDocumentOCR", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "processorType": "GPU", + "projectId": "[project-id]", + "retrainable": false, + "stagingUri": "[staging-uri]", + "projectName": "UiPath Document Understanding", + "projectDescription": "UiPath models to classify and extract information from images and pdfs.", + "tenantName": "UiPath", + "minAIFabricVersion": "22.10", + "languageVersion": 4, + "version": 32, + "customVersion": "23.4.11", + "imagePath": "du-doc-ocr:v23.4-01.27-rc01", + "parametersFileJSON": "{\"params\": [],\"probes\": [{\"probe\": \"startup\",\"initialDelaySeconds\": \"30\",\"periodSeconds\": \"10\",\"timeoutSeconds\": \"5\",\"successThreshold\": \"1\",\"failureThreshold\": \"60\"},{\"probe\": \"readiness\",\"initialDelaySeconds\": \"30\",\"periodSeconds\": \"15\",\"timeoutSeconds\": \"45\",\"successThreshold\": \"1\",\"failureThreshold\": \"2\"},{\"probe\": \"liveness\",\"initialDelaySeconds\": \"30\",\"periodSeconds\": \"60\",\"timeoutSeconds\": \"45\",\"successThreshold\": \"1\",\"failureThreshold\": \"3\"}]}", + "maxAIFabricVersion": "23.10" +} \ No newline at end of file diff --git a/metadata/UiPathDocumentOCR__33__metadata.json b/metadata/UiPathDocumentOCR__33__metadata.json new file mode 100644 index 00000000..b20a5bde --- /dev/null +++ b/metadata/UiPathDocumentOCR__33__metadata.json @@ -0,0 +1,27 @@ +{ + "changeLog": "", + "cpu": 0, + "description": "Machine Learning model for extracting text and checkboxes/radio buttons from Documents. Please see more details including supported languages and link to Activities guide in the Document Understanding Guide here: https://docs.uipath.com/document-understanding/v2021.4/docs/ml-packages#uipath-document-ocr. For alternative deployment methods in high-volume scenarios see also this page: https://docs.uipath.com/document-understanding/v2021.4/docs/ocr-services", + "displayName": "UiPathDocumentOCR", + "gpu": 0, + "inputDescription": "ML Skills deployed using this ML Package can be integrated into RPA workflows using the UiPath Document OCR activity from OCR.Activities pack in the Official feed. File formats accepted include pdf, tiff, jpg or png files. In non-airgapped deployments, the activity requires the Document Understanding API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -> Other Services view.", + "inputType": "JSON", + "memory": 0, + "mlPackageLanguage": "PYTHON37_DU", + "name": "UiPathDocumentOCR", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "processorType": "GPU", + "projectId": "[project-id]", + "retrainable": false, + "stagingUri": "[staging-uri]", + "projectName": "UiPath Document Understanding", + "projectDescription": "UiPath models to classify and extract information from images and pdfs.", + "tenantName": "UiPath", + "minAIFabricVersion": "22.10", + "languageVersion": 4, + "version": 33, + "customVersion": "23.10.8", + "imagePath": "du-doc-ocr:v23.10-01.28-rc07", + "parametersFileJSON": "{\"params\": [],\"probes\": [{\"probe\": \"startup\",\"initialDelaySeconds\": \"30\",\"periodSeconds\": \"10\",\"timeoutSeconds\": \"5\",\"successThreshold\": \"1\",\"failureThreshold\": \"60\"},{\"probe\": \"readiness\",\"initialDelaySeconds\": \"30\",\"periodSeconds\": \"15\",\"timeoutSeconds\": \"45\",\"successThreshold\": \"1\",\"failureThreshold\": \"2\"},{\"probe\": \"liveness\",\"initialDelaySeconds\": \"30\",\"periodSeconds\": \"60\",\"timeoutSeconds\": \"45\",\"successThreshold\": \"1\",\"failureThreshold\": \"3\"}]}", + "maxAIFabricVersion": "24.10" +} \ No newline at end of file diff --git a/metadata/UiPathDocumentOCR__34__metadata.json b/metadata/UiPathDocumentOCR__34__metadata.json new file mode 100644 index 00000000..08a012cc --- /dev/null +++ b/metadata/UiPathDocumentOCR__34__metadata.json @@ -0,0 +1,26 @@ +{ + "changeLog": "", + "cpu": 0, + "description": "Machine Learning model for extracting text and checkboxes/radio buttons from Documents. Please see more details including supported languages and link to Activities guide in the Document Understanding Guide here: https://docs.uipath.com/document-understanding/v2021.4/docs/ml-packages#uipath-document-ocr. For alternative deployment methods in high-volume scenarios see also this page: https://docs.uipath.com/document-understanding/v2021.4/docs/ocr-services", + "displayName": "UiPathDocumentOCR", + "gpu": 0, + "inputDescription": "ML Skills deployed using this ML Package can be integrated into RPA workflows using the UiPath Document OCR activity from OCR.Activities pack in the Official feed. File formats accepted include pdf, tiff, jpg or png files. In non-airgapped deployments, the activity requires the Document Understanding API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -> Other Services view.", + "inputType": "JSON", + "memory": 0, + "mlPackageLanguage": "PYTHON37_DU", + "name": "UiPathDocumentOCR", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "processorType": "GPU", + "projectId": "[project-id]", + "retrainable": false, + "stagingUri": "[staging-uri]", + "projectName": "UiPath Document Understanding", + "projectDescription": "UiPath models to classify and extract information from images and pdfs.", + "tenantName": "UiPath", + "minAIFabricVersion": "22.10", + "languageVersion": 4, + "version": 34, + "customVersion": "24.10.3", + "imagePath": "du-doc-ocr:v24.10-3.28-rc01", + "parametersFileJSON": "{\"params\": [],\"probes\": [{\"probe\": \"startup\",\"initialDelaySeconds\": \"30\",\"periodSeconds\": \"10\",\"timeoutSeconds\": \"5\",\"successThreshold\": \"1\",\"failureThreshold\": \"60\"},{\"probe\": \"readiness\",\"initialDelaySeconds\": \"30\",\"periodSeconds\": \"15\",\"timeoutSeconds\": \"45\",\"successThreshold\": \"1\",\"failureThreshold\": \"2\"},{\"probe\": \"liveness\",\"initialDelaySeconds\": \"30\",\"periodSeconds\": \"60\",\"timeoutSeconds\": \"45\",\"successThreshold\": \"1\",\"failureThreshold\": \"3\"}]}" +} diff --git a/metadata/UiPathDocumentOCR__35__metadata.json b/metadata/UiPathDocumentOCR__35__metadata.json new file mode 100644 index 00000000..7af091f3 --- /dev/null +++ b/metadata/UiPathDocumentOCR__35__metadata.json @@ -0,0 +1,27 @@ +{ + "changeLog": "", + "cpu": 0, + "description": "Machine Learning model for extracting text and checkboxes/radio buttons from Documents. Please see more details including supported languages and link to Activities guide in the Document Understanding Guide here: https://docs.uipath.com/document-understanding/v2021.4/docs/ml-packages#uipath-document-ocr. For alternative deployment methods in high-volume scenarios see also this page: https://docs.uipath.com/document-understanding/v2021.4/docs/ocr-services", + "displayName": "UiPathDocumentOCR", + "gpu": 0, + "inputDescription": "ML Skills deployed using this ML Package can be integrated into RPA workflows using the UiPath Document OCR activity from OCR.Activities pack in the Official feed. File formats accepted include pdf, tiff, jpg or png files. In non-airgapped deployments, the activity requires the Document Understanding API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -> Other Services view.", + "inputType": "JSON", + "memory": 0, + "mlPackageLanguage": "PYTHON37_DU", + "name": "UiPathDocumentOCR", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "processorType": "GPU", + "projectId": "[project-id]", + "retrainable": false, + "stagingUri": "[staging-uri]", + "projectName": "UiPath Document Understanding", + "projectDescription": "UiPath models to classify and extract information from images and pdfs.", + "tenantName": "UiPath", + "minAIFabricVersion": "22.10", + "languageVersion": 4, + "version": 35, + "customVersion": "23.10.9", + "imagePath": "du-doc-ocr:v23.10-03.14-rc02", + "parametersFileJSON": "{\"params\": [],\"probes\": [{\"probe\": \"startup\",\"initialDelaySeconds\": \"30\",\"periodSeconds\": \"10\",\"timeoutSeconds\": \"5\",\"successThreshold\": \"1\",\"failureThreshold\": \"60\"},{\"probe\": \"readiness\",\"initialDelaySeconds\": \"30\",\"periodSeconds\": \"15\",\"timeoutSeconds\": \"45\",\"successThreshold\": \"1\",\"failureThreshold\": \"2\"},{\"probe\": \"liveness\",\"initialDelaySeconds\": \"30\",\"periodSeconds\": \"60\",\"timeoutSeconds\": \"45\",\"successThreshold\": \"1\",\"failureThreshold\": \"3\"}]}", + "maxAIFabricVersion": "24.10" +} diff --git a/metadata/UiPathDocumentOCR__36__metadata.json b/metadata/UiPathDocumentOCR__36__metadata.json new file mode 100644 index 00000000..7cccaedf --- /dev/null +++ b/metadata/UiPathDocumentOCR__36__metadata.json @@ -0,0 +1,27 @@ +{ + "changeLog": "", + "cpu": 0, + "description": "Machine Learning model for extracting text and checkboxes/radio buttons from Documents. Please see more details including supported languages and link to Activities guide in the Document Understanding Guide here: https://docs.uipath.com/document-understanding/v2021.4/docs/ml-packages#uipath-document-ocr. For alternative deployment methods in high-volume scenarios see also this page: https://docs.uipath.com/document-understanding/v2021.4/docs/ocr-services", + "displayName": "UiPathDocumentOCR", + "gpu": 0, + "inputDescription": "ML Skills deployed using this ML Package can be integrated into RPA workflows using the UiPath Document OCR activity from OCR.Activities pack in the Official feed. File formats accepted include pdf, tiff, jpg or png files. In non-airgapped deployments, the activity requires the Document Understanding API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -> Other Services view.", + "inputType": "JSON", + "memory": 0, + "mlPackageLanguage": "PYTHON37_DU", + "name": "UiPathDocumentOCR", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "processorType": "GPU", + "projectId": "[project-id]", + "retrainable": false, + "stagingUri": "[staging-uri]", + "projectName": "UiPath Document Understanding", + "projectDescription": "UiPath models to classify and extract information from images and pdfs.", + "tenantName": "UiPath", + "minAIFabricVersion": "22.10", + "languageVersion": 4, + "version": 36, + "customVersion": "23.4.12", + "imagePath": "du-doc-ocr:v23.4-03.12-rc02", + "parametersFileJSON": "{\"params\": [],\"probes\": [{\"probe\": \"startup\",\"initialDelaySeconds\": \"30\",\"periodSeconds\": \"10\",\"timeoutSeconds\": \"5\",\"successThreshold\": \"1\",\"failureThreshold\": \"60\"},{\"probe\": \"readiness\",\"initialDelaySeconds\": \"30\",\"periodSeconds\": \"15\",\"timeoutSeconds\": \"45\",\"successThreshold\": \"1\",\"failureThreshold\": \"2\"},{\"probe\": \"liveness\",\"initialDelaySeconds\": \"30\",\"periodSeconds\": \"60\",\"timeoutSeconds\": \"45\",\"successThreshold\": \"1\",\"failureThreshold\": \"3\"}]}", + "maxAIFabricVersion": "23.10" +} diff --git a/metadata/UiPathDocumentOCR__37__metadata.json b/metadata/UiPathDocumentOCR__37__metadata.json new file mode 100644 index 00000000..bf23f253 --- /dev/null +++ b/metadata/UiPathDocumentOCR__37__metadata.json @@ -0,0 +1,26 @@ +{ + "changeLog": "", + "cpu": 0, + "description": "Machine Learning model for extracting text and checkboxes/radio buttons from Documents. Please see more details including supported languages and link to Activities guide in the Document Understanding Guide here: https://docs.uipath.com/document-understanding/v2021.4/docs/ml-packages#uipath-document-ocr. For alternative deployment methods in high-volume scenarios see also this page: https://docs.uipath.com/document-understanding/v2021.4/docs/ocr-services", + "displayName": "UiPathDocumentOCR", + "gpu": 0, + "inputDescription": "ML Skills deployed using this ML Package can be integrated into RPA workflows using the UiPath Document OCR activity from OCR.Activities pack in the Official feed. File formats accepted include pdf, tiff, jpg or png files. In non-airgapped deployments, the activity requires the Document Understanding API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -> Other Services view.", + "inputType": "JSON", + "memory": 0, + "mlPackageLanguage": "PYTHON37_DU", + "name": "UiPathDocumentOCR", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "processorType": "GPU", + "projectId": "[project-id]", + "retrainable": false, + "stagingUri": "[staging-uri]", + "projectName": "UiPath Document Understanding", + "projectDescription": "UiPath models to classify and extract information from images and pdfs.", + "tenantName": "UiPath", + "minAIFabricVersion": "22.10", + "languageVersion": 4, + "version": 37, + "customVersion": "24.10.4", + "imagePath": "du-doc-ocr:v24.10-5.23-rc03", + "parametersFileJSON": "{\"params\": [],\"probes\": [{\"probe\": \"startup\",\"initialDelaySeconds\": \"30\",\"periodSeconds\": \"10\",\"timeoutSeconds\": \"5\",\"successThreshold\": \"1\",\"failureThreshold\": \"60\"},{\"probe\": \"readiness\",\"initialDelaySeconds\": \"30\",\"periodSeconds\": \"15\",\"timeoutSeconds\": \"45\",\"successThreshold\": \"1\",\"failureThreshold\": \"2\"},{\"probe\": \"liveness\",\"initialDelaySeconds\": \"30\",\"periodSeconds\": \"60\",\"timeoutSeconds\": \"45\",\"successThreshold\": \"1\",\"failureThreshold\": \"3\"}]}" +} diff --git a/metadata/UiPathDocumentOCR__38__metadata.json b/metadata/UiPathDocumentOCR__38__metadata.json new file mode 100644 index 00000000..a2d92f0e --- /dev/null +++ b/metadata/UiPathDocumentOCR__38__metadata.json @@ -0,0 +1,27 @@ +{ + "changeLog": "", + "cpu": 0, + "description": "Machine Learning model for extracting text and checkboxes/radio buttons from Documents. Please see more details including supported languages and link to Activities guide in the Document Understanding Guide here: https://docs.uipath.com/document-understanding/v2021.4/docs/ml-packages#uipath-document-ocr. For alternative deployment methods in high-volume scenarios see also this page: https://docs.uipath.com/document-understanding/v2021.4/docs/ocr-services", + "displayName": "UiPathDocumentOCR", + "gpu": 0, + "inputDescription": "ML Skills deployed using this ML Package can be integrated into RPA workflows using the UiPath Document OCR activity from OCR.Activities pack in the Official feed. File formats accepted include pdf, tiff, jpg or png files. In non-airgapped deployments, the activity requires the Document Understanding API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -> Other Services view.", + "inputType": "JSON", + "memory": 0, + "mlPackageLanguage": "PYTHON37_DU", + "name": "UiPathDocumentOCR", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "processorType": "GPU", + "projectId": "[project-id]", + "retrainable": false, + "stagingUri": "[staging-uri]", + "projectName": "UiPath Document Understanding", + "projectDescription": "UiPath models to classify and extract information from images and pdfs.", + "tenantName": "UiPath", + "minAIFabricVersion": "22.10", + "languageVersion": 4, + "version": 38, + "customVersion": "23.10.10", + "imagePath": "du-doc-ocr:v23.10-06.05-rc05", + "parametersFileJSON": "{\"params\": [],\"probes\": [{\"probe\": \"startup\",\"initialDelaySeconds\": \"30\",\"periodSeconds\": \"10\",\"timeoutSeconds\": \"5\",\"successThreshold\": \"1\",\"failureThreshold\": \"60\"},{\"probe\": \"readiness\",\"initialDelaySeconds\": \"30\",\"periodSeconds\": \"15\",\"timeoutSeconds\": \"45\",\"successThreshold\": \"1\",\"failureThreshold\": \"2\"},{\"probe\": \"liveness\",\"initialDelaySeconds\": \"30\",\"periodSeconds\": \"60\",\"timeoutSeconds\": \"45\",\"successThreshold\": \"1\",\"failureThreshold\": \"3\"}]}", + "maxAIFabricVersion": "24.10" +} diff --git a/metadata/UiPathDocumentOCR__39__metadata.json b/metadata/UiPathDocumentOCR__39__metadata.json new file mode 100644 index 00000000..490ddc66 --- /dev/null +++ b/metadata/UiPathDocumentOCR__39__metadata.json @@ -0,0 +1,26 @@ +{ + "changeLog": "", + "cpu": 0, + "description": "Machine Learning model for extracting text and checkboxes/radio buttons from Documents. Please see more details including supported languages and link to Activities guide in the Document Understanding Guide here: https://docs.uipath.com/document-understanding/v2021.4/docs/ml-packages#uipath-document-ocr. For alternative deployment methods in high-volume scenarios see also this page: https://docs.uipath.com/document-understanding/v2021.4/docs/ocr-services", + "displayName": "UiPathDocumentOCR", + "gpu": 0, + "inputDescription": "ML Skills deployed using this ML Package can be integrated into RPA workflows using the UiPath Document OCR activity from OCR.Activities pack in the Official feed. File formats accepted include pdf, tiff, jpg or png files. In non-airgapped deployments, the activity requires the Document Understanding API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -> Other Services view.", + "inputType": "JSON", + "memory": 0, + "mlPackageLanguage": "PYTHON37_DU", + "name": "UiPathDocumentOCR", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "processorType": "GPU", + "projectId": "[project-id]", + "retrainable": false, + "stagingUri": "[staging-uri]", + "projectName": "UiPath Document Understanding", + "projectDescription": "UiPath models to classify and extract information from images and pdfs.", + "tenantName": "UiPath", + "minAIFabricVersion": "22.10", + "languageVersion": 4, + "version": 39, + "customVersion": "24.10.5", + "imagePath": "du-doc-ocr:v24.10-8.25-rc02", + "parametersFileJSON": "{\"params\": [],\"probes\": [{\"probe\": \"startup\",\"initialDelaySeconds\": \"30\",\"periodSeconds\": \"10\",\"timeoutSeconds\": \"5\",\"successThreshold\": \"1\",\"failureThreshold\": \"60\"},{\"probe\": \"readiness\",\"initialDelaySeconds\": \"30\",\"periodSeconds\": \"15\",\"timeoutSeconds\": \"45\",\"successThreshold\": \"1\",\"failureThreshold\": \"2\"},{\"probe\": \"liveness\",\"initialDelaySeconds\": \"30\",\"periodSeconds\": \"60\",\"timeoutSeconds\": \"45\",\"successThreshold\": \"1\",\"failureThreshold\": \"3\"}]}" +} diff --git a/metadata/UiPathDocumentOCR__40__metadata.json b/metadata/UiPathDocumentOCR__40__metadata.json new file mode 100644 index 00000000..1a7c2c01 --- /dev/null +++ b/metadata/UiPathDocumentOCR__40__metadata.json @@ -0,0 +1,27 @@ +{ + "changeLog": "", + "cpu": 0, + "description": "Machine Learning model for extracting text and checkboxes/radio buttons from Documents. Please see more details including supported languages and link to Activities guide in the Document Understanding Guide here: https://docs.uipath.com/document-understanding/v2021.4/docs/ml-packages#uipath-document-ocr. For alternative deployment methods in high-volume scenarios see also this page: https://docs.uipath.com/document-understanding/v2021.4/docs/ocr-services", + "displayName": "UiPathDocumentOCR", + "gpu": 0, + "inputDescription": "ML Skills deployed using this ML Package can be integrated into RPA workflows using the UiPath Document OCR activity from OCR.Activities pack in the Official feed. File formats accepted include pdf, tiff, jpg or png files. In non-airgapped deployments, the activity requires the Document Understanding API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -> Other Services view.", + "inputType": "JSON", + "memory": 0, + "mlPackageLanguage": "PYTHON37_DU", + "name": "UiPathDocumentOCR", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "processorType": "GPU", + "projectId": "[project-id]", + "retrainable": false, + "stagingUri": "[staging-uri]", + "projectName": "UiPath Document Understanding", + "projectDescription": "UiPath models to classify and extract information from images and pdfs.", + "tenantName": "UiPath", + "minAIFabricVersion": "22.10", + "languageVersion": 4, + "version": 40, + "customVersion": "23.10.11", + "imagePath": "du-doc-ocr:v23.10-08.25-rc02", + "parametersFileJSON": "{\"params\": [],\"probes\": [{\"probe\": \"startup\",\"initialDelaySeconds\": \"30\",\"periodSeconds\": \"10\",\"timeoutSeconds\": \"5\",\"successThreshold\": \"1\",\"failureThreshold\": \"60\"},{\"probe\": \"readiness\",\"initialDelaySeconds\": \"30\",\"periodSeconds\": \"15\",\"timeoutSeconds\": \"45\",\"successThreshold\": \"1\",\"failureThreshold\": \"2\"},{\"probe\": \"liveness\",\"initialDelaySeconds\": \"30\",\"periodSeconds\": \"60\",\"timeoutSeconds\": \"45\",\"successThreshold\": \"1\",\"failureThreshold\": \"3\"}]}", + "maxAIFabricVersion": "24.10" +} diff --git a/metadata/UiPathDocumentOCR__41__metadata.json b/metadata/UiPathDocumentOCR__41__metadata.json new file mode 100644 index 00000000..dc02f7e8 --- /dev/null +++ b/metadata/UiPathDocumentOCR__41__metadata.json @@ -0,0 +1,26 @@ +{ + "changeLog": "", + "cpu": 0, + "description": "Machine Learning model for extracting text and checkboxes/radio buttons from Documents. Please see more details including supported languages and link to Activities guide in the Document Understanding Guide here: https://docs.uipath.com/document-understanding/v2021.4/docs/ml-packages#uipath-document-ocr. For alternative deployment methods in high-volume scenarios see also this page: https://docs.uipath.com/document-understanding/v2021.4/docs/ocr-services", + "displayName": "UiPathDocumentOCR", + "gpu": 0, + "inputDescription": "ML Skills deployed using this ML Package can be integrated into RPA workflows using the UiPath Document OCR activity from OCR.Activities pack in the Official feed. File formats accepted include pdf, tiff, jpg or png files. In non-airgapped deployments, the activity requires the Document Understanding API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -> Other Services view.", + "inputType": "JSON", + "memory": 0, + "mlPackageLanguage": "PYTHON37_DU", + "name": "UiPathDocumentOCR", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "processorType": "GPU", + "projectId": "[project-id]", + "retrainable": false, + "stagingUri": "[staging-uri]", + "projectName": "UiPath Document Understanding", + "projectDescription": "UiPath models to classify and extract information from images and pdfs.", + "tenantName": "UiPath", + "minAIFabricVersion": "22.10", + "languageVersion": 4, + "version": 41, + "customVersion": "25.10.0", + "imagePath": "du-doc-ocr:v25.10-10.17-rc04", + "parametersFileJSON": "{\"params\": [],\"probes\": [{\"probe\": \"startup\",\"initialDelaySeconds\": \"30\",\"periodSeconds\": \"10\",\"timeoutSeconds\": \"5\",\"successThreshold\": \"1\",\"failureThreshold\": \"60\"},{\"probe\": \"readiness\",\"initialDelaySeconds\": \"30\",\"periodSeconds\": \"15\",\"timeoutSeconds\": \"45\",\"successThreshold\": \"1\",\"failureThreshold\": \"2\"},{\"probe\": \"liveness\",\"initialDelaySeconds\": \"30\",\"periodSeconds\": \"60\",\"timeoutSeconds\": \"45\",\"successThreshold\": \"1\",\"failureThreshold\": \"3\"}]}" +} diff --git a/metadata/UtilityBills__10__metadata.json b/metadata/UtilityBills__10__metadata.json new file mode 100644 index 00000000..48ccd45b --- /dev/null +++ b/metadata/UtilityBills__10__metadata.json @@ -0,0 +1,26 @@ +{ + "name": "UtilityBills", + "retrainable": true, + "gpu": 0, + "processorType": "CPU", + "description": "Machine Learning model for extracting commonly occurring data points from Utility Bills. Please see more details including supported languages in the ML Packages section of the Document Understanding Guide: https://docs.uipath.com/document-understanding/docs/ml-packages", + "inputDescription": "ML Skills deployed using this ML Package can be integrated into RPA workflows using the Machine Learning Extractor activity in the Official feed or the MLModel activity in Connect feed. File formats accepted include pdf, tiff, jpg or png files. The activities also require an API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -> Other Services view.", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "changeLog": "", + "cpu": 1, + "inputType": "JSON", + "displayName": "UtilityBills", + "memory": 0, + "mlPackageLanguage": "PYTHON37_DU", + "projectId": "[project-id]", + "stagingUri": "[staging-uri]", + "projectName": "UiPath Document Understanding", + "projectDescription": "UiPath models to classify and extract information from images and pdfs.", + "tenantName": "UiPath", + "minAIFabricVersion": "22.4", + "languageVersion": 4, + "version": 10, + "customVersion": "22.4.1", + "contentUri": "https:///publicmodels/AIC/UtilityBills/22.4.1/utility_bills_package.zip", + "maxAIFabricVersion": "22.4" +} \ No newline at end of file diff --git a/metadata/UtilityBills__1__metadata.json b/metadata/UtilityBills__1__metadata.json deleted file mode 100644 index 2a4934bb..00000000 --- a/metadata/UtilityBills__1__metadata.json +++ /dev/null @@ -1,21 +0,0 @@ -{ - "changeLog": "", - "cpu": 0, - "description": "Machine Learning model for extracting commonly occurring data points from Utility Bills, including header fields and line items. Please see more details including supported languages and link to Activities guide in the About Licensing -> Document Understanding API Key section of the UiPath Automation Cloud Guide here: https://docs.uipath.com/automation-cloud/docs/about-licensing#section-document-understanding-api-key", - "displayName": "UtilityBills", - "gpu": 0, - "inputDescription": "ML Skills deployed using this ML Package can be integrated into RPA workflows using the Machine Learning Extractor activity in the Official feed or the MLModel activity in Connect feed. File formats accepted include pdf, tiff, jpg or png files. The activities also require an API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -> Other Services view.", - "inputType": "JSON", - "memory": 0, - "mlPackageLanguage": "PYTHON36_DU", - "name": "UtilityBills", - "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", - "processorType": "GPU", - "projectId": "[project-id]", - "retrainable": true, - "stagingUri": "[staging-uri]", - "projectName": "UiPath Document Understanding", - "projectDescription": "UiPath models to classify and extract information from images and pdfs.", - "tenantName": "UiPath", - "imagePath": "registry.replicated.com/aif-core/utilitybills:1" -} diff --git a/metadata/UtilityBills__2__metadata.json b/metadata/UtilityBills__2__metadata.json deleted file mode 100644 index 861c74fa..00000000 --- a/metadata/UtilityBills__2__metadata.json +++ /dev/null @@ -1,21 +0,0 @@ -{ - "changeLog": "Release v2020.8", - "cpu": 0, - "description": "Machine Learning model for extracting commonly occurring data points from Utility Bills, including header fields and line items. Please see more details including supported languages and link to Activities guide in the About Licensing -> Document Understanding API Key section of the UiPath Automation Cloud Guide here: https://docs.uipath.com/automation-cloud/docs/about-licensing#section-document-understanding-api-key", - "displayName": "UtilityBills", - "gpu": 0, - "inputDescription": "ML Skills deployed using this ML Package can be integrated into RPA workflows using the Machine Learning Extractor activity in the Official feed or the MLModel activity in Connect feed. File formats accepted include pdf, tiff, jpg or png files. The activities also require an API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -> Other Services view.", - "inputType": "JSON", - "memory": 0, - "mlPackageLanguage": "PYTHON37_DU", - "name": "UtilityBills", - "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", - "processorType": "GPU", - "projectId": "[project-id]", - "retrainable": true, - "stagingUri": "[staging-uri]", - "projectName": "UiPath Document Understanding", - "projectDescription": "UiPath models to classify and extract information from images and pdfs.", - "tenantName": "UiPath", - "imagePath": "registry.replicated.com/aif-core/utilitybills:2" -} diff --git a/metadata/UtilityBills__33__metadata.json b/metadata/UtilityBills__33__metadata.json new file mode 100644 index 00000000..ae21c53f --- /dev/null +++ b/metadata/UtilityBills__33__metadata.json @@ -0,0 +1,34 @@ +{ + "name": "UtilityBills", + "retrainable": true, + "gpu": 0, + "processorType": "CPU", + "description": "Machine Learning model for extracting commonly occurring data points from Utility Bills. Please see more details including supported languages in the ML Packages section of the Document Understanding Guide: https://docs.uipath.com/document-understanding/docs/ml-packages", + "inputDescription": "ML Skills deployed using this ML Package can be integrated into RPA workflows using the Machine Learning Extractor activity in the Official feed or the MLModel activity in Connect feed. File formats accepted include pdf, tiff, jpg or png files. The activities also require an API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -> Other Services view.", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "changeLog": "", + "cpu": 1, + "inputType": "JSON", + "displayName": "UtilityBills", + "memory": 0, + "mlPackageLanguage": "PYTHON37_DU", + "projectId": "[project-id]", + "stagingUri": "[staging-uri]", + "projectName": "UiPath Document Understanding", + "projectDescription": "UiPath models to classify and extract information from images and pdfs.", + "tenantName": "UiPath", + "minAIFabricVersion": "22.10", + "languageVersion": 4, + "version": 33, + "customVersion": "22.10.14", + "imagePath": "du-semistructured:v22.10-10.10-rc01", + "parametersFileJSON": "{\"params\": [{\"name\": \"auto_retraining\",\"type\": [\"PIPELINE\"],\"value\": \"False\",\"help\": \"Enable this option when the Pipeline you are creating is a scheduled automatic pipeline, synchronized with automatic exports from Data Manager.\",\"options\": [\"True\",\"False\"]}],\"probes\": [{\"probe\": \"startup\",\"initialDelaySeconds\": \"120\",\"periodSeconds\": \"10\",\"timeoutSeconds\": \"2\",\"successThreshold\": \"1\",\"failureThreshold\": \"60\"}]}", + "settings": [ + { + "key": "package_type", + "type": "STRING", + "value": "utility_bills" + } + ], + "maxAIFabricVersion": "23.4" +} \ No newline at end of file diff --git a/metadata/UtilityBills__39__metadata.json b/metadata/UtilityBills__39__metadata.json new file mode 100644 index 00000000..f474d17c --- /dev/null +++ b/metadata/UtilityBills__39__metadata.json @@ -0,0 +1,33 @@ +{ + "name": "UtilityBills", + "retrainable": true, + "gpu": 0, + "processorType": "CPU", + "description": "Machine Learning model for extracting commonly occurring data points from Utility Bills. Please see more details including supported languages in the ML Packages section of the Document Understanding Guide: https://docs.uipath.com/document-understanding/docs/ml-packages", + "inputDescription": "ML Skills deployed using this ML Package can be integrated into RPA workflows using the Machine Learning Extractor activity in the Official feed or the MLModel activity in Connect feed. File formats accepted include pdf, tiff, jpg or png files. The activities also require an API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -> Other Services view.", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "changeLog": "", + "cpu": 1, + "inputType": "JSON", + "displayName": "UtilityBills", + "memory": 0, + "mlPackageLanguage": "PYTHON37_DU", + "projectId": "[project-id]", + "stagingUri": "[staging-uri]", + "projectName": "UiPath Document Understanding", + "projectDescription": "UiPath models to classify and extract information from images and pdfs.", + "tenantName": "UiPath", + "minAIFabricVersion": "22.10", + "languageVersion": 4, + "version": 39, + "customVersion": "24.10.2", + "imagePath": "du-semistructured:v24.10-1.27-rc02", + "parametersFileJSON": "{\"params\": [{\"name\": \"auto_retraining\",\"type\": [\"PIPELINE\"],\"value\": \"False\",\"help\": \"Enable this option when the Pipeline you are creating is a scheduled automatic pipeline, synchronized with automatic exports from Data Manager.\",\"options\": [\"True\",\"False\"]}],\"probes\": [{\"probe\": \"startup\",\"initialDelaySeconds\": \"120\",\"periodSeconds\": \"10\",\"timeoutSeconds\": \"2\",\"successThreshold\": \"1\",\"failureThreshold\": \"60\"}]}", + "settings": [ + { + "key": "package_type", + "type": "STRING", + "value": "utility_bills" + } + ] +} \ No newline at end of file diff --git a/metadata/UtilityBills__3__metadata.json b/metadata/UtilityBills__3__metadata.json deleted file mode 100644 index b0442dbf..00000000 --- a/metadata/UtilityBills__3__metadata.json +++ /dev/null @@ -1,21 +0,0 @@ -{ - "name": "UtilityBills", - "retrainable": true, - "gpu": 1, - "processorType": "GPU", - "description": "Machine Learning model(available in Preview) for extracting commonly occurring data points from Utility Bills. Please see more details including supported languages and link to Activities guide in the About Licensing -\u003e Document Understanding API Key section of the UiPath Automation Cloud Guide here: https://docs.uipath.com/automation-cloud/docs/about-licensing#section-document-understanding-api-key", - "inputDescription": "ML Skills deployed using this ML Package can be integrated into RPA workflows using the Machine Learning Extractor activity in the Official feed or the MLModel activity in Connect feed. File formats accepted include pdf, tiff, jpg or png files. The activities also require an API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -\u003e Other Services view.", - "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", - "changeLog": "Model: 20.10.4", - "cpu": 0, - "inputType": "JSON", - "displayName": "UtilityBills", - "memory": 0, - "mlPackageLanguage": "PYTHON37_DU", - "projectId": "[project-id]", - "stagingUri": "[staging-uri]", - "projectName": "UiPath Document Understanding", - "projectDescription": "UiPath models to classify and extract information from images and pdfs.", - "tenantName": "UiPath", - "imagePath": "registry.replicated.com/aif-core/utilitybills:3" -} diff --git a/metadata/UtilityBills__40__metadata.json b/metadata/UtilityBills__40__metadata.json new file mode 100644 index 00000000..723538fc --- /dev/null +++ b/metadata/UtilityBills__40__metadata.json @@ -0,0 +1,34 @@ +{ + "name": "UtilityBills", + "retrainable": true, + "gpu": 0, + "processorType": "CPU", + "description": "Machine Learning model for extracting commonly occurring data points from Utility Bills. Please see more details including supported languages in the ML Packages section of the Document Understanding Guide: https://docs.uipath.com/document-understanding/docs/ml-packages", + "inputDescription": "ML Skills deployed using this ML Package can be integrated into RPA workflows using the Machine Learning Extractor activity in the Official feed or the MLModel activity in Connect feed. File formats accepted include pdf, tiff, jpg or png files. The activities also require an API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -> Other Services view.", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "changeLog": "", + "cpu": 1, + "inputType": "JSON", + "displayName": "UtilityBills", + "memory": 0, + "mlPackageLanguage": "PYTHON37_DU", + "projectId": "[project-id]", + "stagingUri": "[staging-uri]", + "projectName": "UiPath Document Understanding", + "projectDescription": "UiPath models to classify and extract information from images and pdfs.", + "tenantName": "UiPath", + "minAIFabricVersion": "22.10", + "languageVersion": 4, + "version": 40, + "customVersion": "23.4.11", + "imagePath": "du-semistructured:v23.4-01.27-rc01", + "parametersFileJSON": "{\"params\": [{\"name\": \"auto_retraining\",\"type\": [\"PIPELINE\"],\"value\": \"False\",\"help\": \"Enable this option when the Pipeline you are creating is a scheduled automatic pipeline, synchronized with automatic exports from Data Manager.\",\"options\": [\"True\",\"False\"]}],\"probes\": [{\"probe\": \"startup\",\"initialDelaySeconds\": \"120\",\"periodSeconds\": \"10\",\"timeoutSeconds\": \"2\",\"successThreshold\": \"1\",\"failureThreshold\": \"60\"}]}", + "settings": [ + { + "key": "package_type", + "type": "STRING", + "value": "utility_bills" + } + ], + "maxAIFabricVersion": "23.10" +} \ No newline at end of file diff --git a/metadata/UtilityBills__41__metadata.json b/metadata/UtilityBills__41__metadata.json new file mode 100644 index 00000000..a9f09b7e --- /dev/null +++ b/metadata/UtilityBills__41__metadata.json @@ -0,0 +1,34 @@ +{ + "name": "UtilityBills", + "retrainable": true, + "gpu": 0, + "processorType": "CPU", + "description": "Machine Learning model for extracting commonly occurring data points from Utility Bills. Please see more details including supported languages in the ML Packages section of the Document Understanding Guide: https://docs.uipath.com/document-understanding/docs/ml-packages", + "inputDescription": "ML Skills deployed using this ML Package can be integrated into RPA workflows using the Machine Learning Extractor activity in the Official feed or the MLModel activity in Connect feed. File formats accepted include pdf, tiff, jpg or png files. The activities also require an API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -> Other Services view.", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "changeLog": "", + "cpu": 1, + "inputType": "JSON", + "displayName": "UtilityBills", + "memory": 0, + "mlPackageLanguage": "PYTHON37_DU", + "projectId": "[project-id]", + "stagingUri": "[staging-uri]", + "projectName": "UiPath Document Understanding", + "projectDescription": "UiPath models to classify and extract information from images and pdfs.", + "tenantName": "UiPath", + "minAIFabricVersion": "22.10", + "languageVersion": 4, + "version": 41, + "customVersion": "23.10.8", + "imagePath": "du-semistructured:v23.10-01.28-rc07", + "parametersFileJSON": "{\"params\": [{\"name\": \"auto_retraining\",\"type\": [\"PIPELINE\"],\"value\": \"False\",\"help\": \"Enable this option when the Pipeline you are creating is a scheduled automatic pipeline, synchronized with automatic exports from Data Manager.\",\"options\": [\"True\",\"False\"]}],\"probes\": [{\"probe\": \"startup\",\"initialDelaySeconds\": \"120\",\"periodSeconds\": \"10\",\"timeoutSeconds\": \"2\",\"successThreshold\": \"1\",\"failureThreshold\": \"60\"}]}", + "settings": [ + { + "key": "package_type", + "type": "STRING", + "value": "utility_bills" + } + ], + "maxAIFabricVersion": "24.10" +} \ No newline at end of file diff --git a/metadata/UtilityBills__42__metadata.json b/metadata/UtilityBills__42__metadata.json new file mode 100644 index 00000000..401f339e --- /dev/null +++ b/metadata/UtilityBills__42__metadata.json @@ -0,0 +1,33 @@ +{ + "name": "UtilityBills", + "retrainable": true, + "gpu": 0, + "processorType": "CPU", + "description": "Machine Learning model for extracting commonly occurring data points from Utility Bills. Please see more details including supported languages in the ML Packages section of the Document Understanding Guide: https://docs.uipath.com/document-understanding/docs/ml-packages", + "inputDescription": "ML Skills deployed using this ML Package can be integrated into RPA workflows using the Machine Learning Extractor activity in the Official feed or the MLModel activity in Connect feed. File formats accepted include pdf, tiff, jpg or png files. The activities also require an API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -> Other Services view.", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "changeLog": "", + "cpu": 1, + "inputType": "JSON", + "displayName": "UtilityBills", + "memory": 0, + "mlPackageLanguage": "PYTHON37_DU", + "projectId": "[project-id]", + "stagingUri": "[staging-uri]", + "projectName": "UiPath Document Understanding", + "projectDescription": "UiPath models to classify and extract information from images and pdfs.", + "tenantName": "UiPath", + "minAIFabricVersion": "22.10", + "languageVersion": 4, + "version": 42, + "customVersion": "24.10.3", + "imagePath": "du-semistructured:v24.10-3.27-rc02", + "parametersFileJSON": "{\"params\": [{\"name\": \"auto_retraining\",\"type\": [\"PIPELINE\"],\"value\": \"False\",\"help\": \"Enable this option when the Pipeline you are creating is a scheduled automatic pipeline, synchronized with automatic exports from Data Manager.\",\"options\": [\"True\",\"False\"]}],\"probes\": [{\"probe\": \"startup\",\"initialDelaySeconds\": \"120\",\"periodSeconds\": \"10\",\"timeoutSeconds\": \"2\",\"successThreshold\": \"1\",\"failureThreshold\": \"60\"}]}", + "settings": [ + { + "key": "package_type", + "type": "STRING", + "value": "utility_bills" + } + ] +} diff --git a/metadata/UtilityBills__43__metadata.json b/metadata/UtilityBills__43__metadata.json new file mode 100644 index 00000000..7e6fd92b --- /dev/null +++ b/metadata/UtilityBills__43__metadata.json @@ -0,0 +1,34 @@ +{ + "name": "UtilityBills", + "retrainable": true, + "gpu": 0, + "processorType": "CPU", + "description": "Machine Learning model for extracting commonly occurring data points from Utility Bills. Please see more details including supported languages in the ML Packages section of the Document Understanding Guide: https://docs.uipath.com/document-understanding/docs/ml-packages", + "inputDescription": "ML Skills deployed using this ML Package can be integrated into RPA workflows using the Machine Learning Extractor activity in the Official feed or the MLModel activity in Connect feed. File formats accepted include pdf, tiff, jpg or png files. The activities also require an API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -> Other Services view.", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "changeLog": "", + "cpu": 1, + "inputType": "JSON", + "displayName": "UtilityBills", + "memory": 0, + "mlPackageLanguage": "PYTHON37_DU", + "projectId": "[project-id]", + "stagingUri": "[staging-uri]", + "projectName": "UiPath Document Understanding", + "projectDescription": "UiPath models to classify and extract information from images and pdfs.", + "tenantName": "UiPath", + "minAIFabricVersion": "22.10", + "languageVersion": 4, + "version": 43, + "customVersion": "23.10.9", + "imagePath": "du-semistructured:v23.10-03.14-rc02", + "parametersFileJSON": "{\"params\": [{\"name\": \"auto_retraining\",\"type\": [\"PIPELINE\"],\"value\": \"False\",\"help\": \"Enable this option when the Pipeline you are creating is a scheduled automatic pipeline, synchronized with automatic exports from Data Manager.\",\"options\": [\"True\",\"False\"]}],\"probes\": [{\"probe\": \"startup\",\"initialDelaySeconds\": \"120\",\"periodSeconds\": \"10\",\"timeoutSeconds\": \"2\",\"successThreshold\": \"1\",\"failureThreshold\": \"60\"}]}", + "settings": [ + { + "key": "package_type", + "type": "STRING", + "value": "utility_bills" + } + ], + "maxAIFabricVersion": "24.10" +} diff --git a/metadata/UtilityBills__44__metadata.json b/metadata/UtilityBills__44__metadata.json new file mode 100644 index 00000000..f3285e6f --- /dev/null +++ b/metadata/UtilityBills__44__metadata.json @@ -0,0 +1,34 @@ +{ + "name": "UtilityBills", + "retrainable": true, + "gpu": 0, + "processorType": "CPU", + "description": "Machine Learning model for extracting commonly occurring data points from Utility Bills. Please see more details including supported languages in the ML Packages section of the Document Understanding Guide: https://docs.uipath.com/document-understanding/docs/ml-packages", + "inputDescription": "ML Skills deployed using this ML Package can be integrated into RPA workflows using the Machine Learning Extractor activity in the Official feed or the MLModel activity in Connect feed. File formats accepted include pdf, tiff, jpg or png files. The activities also require an API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -> Other Services view.", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "changeLog": "", + "cpu": 1, + "inputType": "JSON", + "displayName": "UtilityBills", + "memory": 0, + "mlPackageLanguage": "PYTHON37_DU", + "projectId": "[project-id]", + "stagingUri": "[staging-uri]", + "projectName": "UiPath Document Understanding", + "projectDescription": "UiPath models to classify and extract information from images and pdfs.", + "tenantName": "UiPath", + "minAIFabricVersion": "22.10", + "languageVersion": 4, + "version": 44, + "customVersion": "23.4.12", + "imagePath": "du-semistructured:v23.4-03.12-rc02", + "parametersFileJSON": "{\"params\": [{\"name\": \"auto_retraining\",\"type\": [\"PIPELINE\"],\"value\": \"False\",\"help\": \"Enable this option when the Pipeline you are creating is a scheduled automatic pipeline, synchronized with automatic exports from Data Manager.\",\"options\": [\"True\",\"False\"]}],\"probes\": [{\"probe\": \"startup\",\"initialDelaySeconds\": \"120\",\"periodSeconds\": \"10\",\"timeoutSeconds\": \"2\",\"successThreshold\": \"1\",\"failureThreshold\": \"60\"}]}", + "settings": [ + { + "key": "package_type", + "type": "STRING", + "value": "utility_bills" + } + ], + "maxAIFabricVersion": "23.10" +} diff --git a/metadata/UtilityBills__45__metadata.json b/metadata/UtilityBills__45__metadata.json new file mode 100644 index 00000000..520c7890 --- /dev/null +++ b/metadata/UtilityBills__45__metadata.json @@ -0,0 +1,33 @@ +{ + "name": "UtilityBills", + "retrainable": true, + "gpu": 0, + "processorType": "CPU", + "description": "Machine Learning model for extracting commonly occurring data points from Utility Bills. Please see more details including supported languages in the ML Packages section of the Document Understanding Guide: https://docs.uipath.com/document-understanding/docs/ml-packages", + "inputDescription": "ML Skills deployed using this ML Package can be integrated into RPA workflows using the Machine Learning Extractor activity in the Official feed or the MLModel activity in Connect feed. File formats accepted include pdf, tiff, jpg or png files. The activities also require an API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -> Other Services view.", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "changeLog": "", + "cpu": 1, + "inputType": "JSON", + "displayName": "UtilityBills", + "memory": 0, + "mlPackageLanguage": "PYTHON37_DU", + "projectId": "[project-id]", + "stagingUri": "[staging-uri]", + "projectName": "UiPath Document Understanding", + "projectDescription": "UiPath models to classify and extract information from images and pdfs.", + "tenantName": "UiPath", + "minAIFabricVersion": "22.10", + "languageVersion": 4, + "version": 45, + "customVersion": "24.10.4", + "imagePath": "du-semistructured:v24.10-6.16-rc04", + "parametersFileJSON": "{\"params\": [{\"name\": \"auto_retraining\",\"type\": [\"PIPELINE\"],\"value\": \"False\",\"help\": \"Enable this option when the Pipeline you are creating is a scheduled automatic pipeline, synchronized with automatic exports from Data Manager.\",\"options\": [\"True\",\"False\"]}],\"probes\": [{\"probe\": \"startup\",\"initialDelaySeconds\": \"120\",\"periodSeconds\": \"10\",\"timeoutSeconds\": \"2\",\"successThreshold\": \"1\",\"failureThreshold\": \"60\"}]}", + "settings": [ + { + "key": "package_type", + "type": "STRING", + "value": "utility_bills" + } + ] +} diff --git a/metadata/UtilityBills__46__metadata.json b/metadata/UtilityBills__46__metadata.json new file mode 100644 index 00000000..324ec132 --- /dev/null +++ b/metadata/UtilityBills__46__metadata.json @@ -0,0 +1,34 @@ +{ + "name": "UtilityBills", + "retrainable": true, + "gpu": 0, + "processorType": "CPU", + "description": "Machine Learning model for extracting commonly occurring data points from Utility Bills. Please see more details including supported languages in the ML Packages section of the Document Understanding Guide: https://docs.uipath.com/document-understanding/docs/ml-packages", + "inputDescription": "ML Skills deployed using this ML Package can be integrated into RPA workflows using the Machine Learning Extractor activity in the Official feed or the MLModel activity in Connect feed. File formats accepted include pdf, tiff, jpg or png files. The activities also require an API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -> Other Services view.", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "changeLog": "", + "cpu": 1, + "inputType": "JSON", + "displayName": "UtilityBills", + "memory": 0, + "mlPackageLanguage": "PYTHON37_DU", + "projectId": "[project-id]", + "stagingUri": "[staging-uri]", + "projectName": "UiPath Document Understanding", + "projectDescription": "UiPath models to classify and extract information from images and pdfs.", + "tenantName": "UiPath", + "minAIFabricVersion": "22.10", + "languageVersion": 4, + "version": 46, + "customVersion": "23.10.10", + "imagePath": "du-semistructured:v23.10-06.05-rc05", + "parametersFileJSON": "{\"params\": [{\"name\": \"auto_retraining\",\"type\": [\"PIPELINE\"],\"value\": \"False\",\"help\": \"Enable this option when the Pipeline you are creating is a scheduled automatic pipeline, synchronized with automatic exports from Data Manager.\",\"options\": [\"True\",\"False\"]}],\"probes\": [{\"probe\": \"startup\",\"initialDelaySeconds\": \"120\",\"periodSeconds\": \"10\",\"timeoutSeconds\": \"2\",\"successThreshold\": \"1\",\"failureThreshold\": \"60\"}]}", + "settings": [ + { + "key": "package_type", + "type": "STRING", + "value": "utility_bills" + } + ], + "maxAIFabricVersion": "24.10" +} diff --git a/metadata/UtilityBills__47__metadata.json b/metadata/UtilityBills__47__metadata.json new file mode 100644 index 00000000..667e84f8 --- /dev/null +++ b/metadata/UtilityBills__47__metadata.json @@ -0,0 +1,33 @@ +{ + "name": "UtilityBills", + "retrainable": true, + "gpu": 0, + "processorType": "CPU", + "description": "Machine Learning model for extracting commonly occurring data points from Utility Bills. Please see more details including supported languages in the ML Packages section of the Document Understanding Guide: https://docs.uipath.com/document-understanding/docs/ml-packages", + "inputDescription": "ML Skills deployed using this ML Package can be integrated into RPA workflows using the Machine Learning Extractor activity in the Official feed or the MLModel activity in Connect feed. File formats accepted include pdf, tiff, jpg or png files. The activities also require an API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -> Other Services view.", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "changeLog": "", + "cpu": 1, + "inputType": "JSON", + "displayName": "UtilityBills", + "memory": 0, + "mlPackageLanguage": "PYTHON37_DU", + "projectId": "[project-id]", + "stagingUri": "[staging-uri]", + "projectName": "UiPath Document Understanding", + "projectDescription": "UiPath models to classify and extract information from images and pdfs.", + "tenantName": "UiPath", + "minAIFabricVersion": "22.10", + "languageVersion": 4, + "version": 47, + "customVersion": "24.10.5", + "imagePath": "du-semistructured:v24.10-8.25-rc02", + "parametersFileJSON": "{\"params\": [{\"name\": \"auto_retraining\",\"type\": [\"PIPELINE\"],\"value\": \"False\",\"help\": \"Enable this option when the Pipeline you are creating is a scheduled automatic pipeline, synchronized with automatic exports from Data Manager.\",\"options\": [\"True\",\"False\"]}],\"probes\": [{\"probe\": \"startup\",\"initialDelaySeconds\": \"120\",\"periodSeconds\": \"10\",\"timeoutSeconds\": \"2\",\"successThreshold\": \"1\",\"failureThreshold\": \"60\"}]}", + "settings": [ + { + "key": "package_type", + "type": "STRING", + "value": "utility_bills" + } + ] +} diff --git a/metadata/UtilityBills__48__metadata.json b/metadata/UtilityBills__48__metadata.json new file mode 100644 index 00000000..a4d8822d --- /dev/null +++ b/metadata/UtilityBills__48__metadata.json @@ -0,0 +1,34 @@ +{ + "name": "UtilityBills", + "retrainable": true, + "gpu": 0, + "processorType": "CPU", + "description": "Machine Learning model for extracting commonly occurring data points from Utility Bills. Please see more details including supported languages in the ML Packages section of the Document Understanding Guide: https://docs.uipath.com/document-understanding/docs/ml-packages", + "inputDescription": "ML Skills deployed using this ML Package can be integrated into RPA workflows using the Machine Learning Extractor activity in the Official feed or the MLModel activity in Connect feed. File formats accepted include pdf, tiff, jpg or png files. The activities also require an API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -> Other Services view.", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "changeLog": "", + "cpu": 1, + "inputType": "JSON", + "displayName": "UtilityBills", + "memory": 0, + "mlPackageLanguage": "PYTHON37_DU", + "projectId": "[project-id]", + "stagingUri": "[staging-uri]", + "projectName": "UiPath Document Understanding", + "projectDescription": "UiPath models to classify and extract information from images and pdfs.", + "tenantName": "UiPath", + "minAIFabricVersion": "22.10", + "languageVersion": 4, + "version": 48, + "customVersion": "23.10.11", + "imagePath": "du-semistructured:v23.10-08.25-rc02", + "parametersFileJSON": "{\"params\": [{\"name\": \"auto_retraining\",\"type\": [\"PIPELINE\"],\"value\": \"False\",\"help\": \"Enable this option when the Pipeline you are creating is a scheduled automatic pipeline, synchronized with automatic exports from Data Manager.\",\"options\": [\"True\",\"False\"]}],\"probes\": [{\"probe\": \"startup\",\"initialDelaySeconds\": \"120\",\"periodSeconds\": \"10\",\"timeoutSeconds\": \"2\",\"successThreshold\": \"1\",\"failureThreshold\": \"60\"}]}", + "settings": [ + { + "key": "package_type", + "type": "STRING", + "value": "utility_bills" + } + ], + "maxAIFabricVersion": "24.10" +} diff --git a/metadata/UtilityBills__49__metadata.json b/metadata/UtilityBills__49__metadata.json new file mode 100644 index 00000000..afb2d457 --- /dev/null +++ b/metadata/UtilityBills__49__metadata.json @@ -0,0 +1,33 @@ +{ + "name": "UtilityBills", + "retrainable": true, + "gpu": 0, + "processorType": "CPU", + "description": "Machine Learning model for extracting commonly occurring data points from Utility Bills. Please see more details including supported languages in the ML Packages section of the Document Understanding Guide: https://docs.uipath.com/document-understanding/docs/ml-packages", + "inputDescription": "ML Skills deployed using this ML Package can be integrated into RPA workflows using the Machine Learning Extractor activity in the Official feed or the MLModel activity in Connect feed. File formats accepted include pdf, tiff, jpg or png files. The activities also require an API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -> Other Services view.", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "changeLog": "", + "cpu": 1, + "inputType": "JSON", + "displayName": "UtilityBills", + "memory": 0, + "mlPackageLanguage": "PYTHON37_DU", + "projectId": "[project-id]", + "stagingUri": "[staging-uri]", + "projectName": "UiPath Document Understanding", + "projectDescription": "UiPath models to classify and extract information from images and pdfs.", + "tenantName": "UiPath", + "minAIFabricVersion": "22.10", + "languageVersion": 4, + "version": 49, + "customVersion": "25.10.0", + "imagePath": "du-semistructured:v25.10-10.17-rc04", + "parametersFileJSON": "{\"params\": [{\"name\": \"auto_retraining\",\"type\": [\"PIPELINE\"],\"value\": \"False\",\"help\": \"Enable this option when the Pipeline you are creating is a scheduled automatic pipeline, synchronized with automatic exports from Data Manager.\",\"options\": [\"True\",\"False\"]}],\"probes\": [{\"probe\": \"startup\",\"initialDelaySeconds\": \"120\",\"periodSeconds\": \"10\",\"timeoutSeconds\": \"2\",\"successThreshold\": \"1\",\"failureThreshold\": \"60\"}]}", + "settings": [ + { + "key": "package_type", + "type": "STRING", + "value": "utility_bills" + } + ] +} diff --git a/metadata/VehicleTitles__23__metadata.json b/metadata/VehicleTitles__23__metadata.json new file mode 100644 index 00000000..e2b83a8a --- /dev/null +++ b/metadata/VehicleTitles__23__metadata.json @@ -0,0 +1,33 @@ +{ + "name": "VehicleTitles", + "retrainable": true, + "gpu": 0, + "processorType": "CPU", + "description": "Machine Learning model for extracting commonly occurring data points from Vehicle Titles, including header fields and line items. Please see more details including supported languages in the ML Packages section of the Document Understanding Guide: https://docs.uipath.com/document-understanding/docs/ml-packages", + "inputDescription": "ML Skills deployed using this ML Package can be integrated into RPA workflows using the Machine Learning Extractor activity in the Official feed or the MLModel activity in Connect feed. File formats accepted include pdf, tiff, jpg or png files. The activities also require an API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -> Other Services view.", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "cpu": 1, + "inputType": "JSON", + "displayName": "VehicleTitles", + "memory": 0, + "mlPackageLanguage": "PYTHON37_DU", + "projectId": "[project-id]", + "stagingUri": "[staging-uri]", + "projectName": "UiPath Document Understanding", + "projectDescription": "UiPath models to classify and extract information from images and pdfs.", + "tenantName": "UiPath", + "minAIFabricVersion": "22.10", + "languageVersion": 4, + "version": 23, + "customVersion": "22.10.14", + "imagePath": "du-semistructured:v22.10-10.10-rc01", + "parametersFileJSON": "{\"params\": [{\"name\": \"auto_retraining\",\"type\": [\"PIPELINE\"],\"value\": \"False\",\"help\": \"Enable this option when the Pipeline you are creating is a scheduled automatic pipeline, synchronized with automatic exports from Data Manager.\",\"options\": [\"True\",\"False\"]}],\"probes\": [{\"probe\": \"startup\",\"initialDelaySeconds\": \"120\",\"periodSeconds\": \"10\",\"timeoutSeconds\": \"2\",\"successThreshold\": \"1\",\"failureThreshold\": \"60\"}]}", + "settings": [ + { + "key": "package_type", + "type": "STRING", + "value": "vehicle_titles" + } + ], + "maxAIFabricVersion": "23.4" +} \ No newline at end of file diff --git a/metadata/VehicleTitles__29__metadata.json b/metadata/VehicleTitles__29__metadata.json new file mode 100644 index 00000000..70207bc3 --- /dev/null +++ b/metadata/VehicleTitles__29__metadata.json @@ -0,0 +1,32 @@ +{ + "name": "VehicleTitles", + "retrainable": true, + "gpu": 0, + "processorType": "CPU", + "description": "Machine Learning model for extracting commonly occurring data points from Vehicle Titles, including header fields and line items. Please see more details including supported languages in the ML Packages section of the Document Understanding Guide: https://docs.uipath.com/document-understanding/docs/ml-packages", + "inputDescription": "ML Skills deployed using this ML Package can be integrated into RPA workflows using the Machine Learning Extractor activity in the Official feed or the MLModel activity in Connect feed. File formats accepted include pdf, tiff, jpg or png files. The activities also require an API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -> Other Services view.", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "cpu": 1, + "inputType": "JSON", + "displayName": "VehicleTitles", + "memory": 0, + "mlPackageLanguage": "PYTHON37_DU", + "projectId": "[project-id]", + "stagingUri": "[staging-uri]", + "projectName": "UiPath Document Understanding", + "projectDescription": "UiPath models to classify and extract information from images and pdfs.", + "tenantName": "UiPath", + "minAIFabricVersion": "22.10", + "languageVersion": 4, + "version": 29, + "customVersion": "24.10.2", + "imagePath": "du-semistructured:v24.10-1.27-rc02", + "parametersFileJSON": "{\"params\": [{\"name\": \"auto_retraining\",\"type\": [\"PIPELINE\"],\"value\": \"False\",\"help\": \"Enable this option when the Pipeline you are creating is a scheduled automatic pipeline, synchronized with automatic exports from Data Manager.\",\"options\": [\"True\",\"False\"]}],\"probes\": [{\"probe\": \"startup\",\"initialDelaySeconds\": \"120\",\"periodSeconds\": \"10\",\"timeoutSeconds\": \"2\",\"successThreshold\": \"1\",\"failureThreshold\": \"60\"}]}", + "settings": [ + { + "key": "package_type", + "type": "STRING", + "value": "vehicle_titles" + } + ] +} \ No newline at end of file diff --git a/metadata/VehicleTitles__30__metadata.json b/metadata/VehicleTitles__30__metadata.json new file mode 100644 index 00000000..69d21a38 --- /dev/null +++ b/metadata/VehicleTitles__30__metadata.json @@ -0,0 +1,33 @@ +{ + "name": "VehicleTitles", + "retrainable": true, + "gpu": 0, + "processorType": "CPU", + "description": "Machine Learning model for extracting commonly occurring data points from Vehicle Titles, including header fields and line items. Please see more details including supported languages in the ML Packages section of the Document Understanding Guide: https://docs.uipath.com/document-understanding/docs/ml-packages", + "inputDescription": "ML Skills deployed using this ML Package can be integrated into RPA workflows using the Machine Learning Extractor activity in the Official feed or the MLModel activity in Connect feed. File formats accepted include pdf, tiff, jpg or png files. The activities also require an API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -> Other Services view.", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "cpu": 1, + "inputType": "JSON", + "displayName": "VehicleTitles", + "memory": 0, + "mlPackageLanguage": "PYTHON37_DU", + "projectId": "[project-id]", + "stagingUri": "[staging-uri]", + "projectName": "UiPath Document Understanding", + "projectDescription": "UiPath models to classify and extract information from images and pdfs.", + "tenantName": "UiPath", + "minAIFabricVersion": "22.10", + "languageVersion": 4, + "version": 30, + "customVersion": "23.4.11", + "imagePath": "du-semistructured:v23.4-01.27-rc01", + "parametersFileJSON": "{\"params\": [{\"name\": \"auto_retraining\",\"type\": [\"PIPELINE\"],\"value\": \"False\",\"help\": \"Enable this option when the Pipeline you are creating is a scheduled automatic pipeline, synchronized with automatic exports from Data Manager.\",\"options\": [\"True\",\"False\"]}],\"probes\": [{\"probe\": \"startup\",\"initialDelaySeconds\": \"120\",\"periodSeconds\": \"10\",\"timeoutSeconds\": \"2\",\"successThreshold\": \"1\",\"failureThreshold\": \"60\"}]}", + "settings": [ + { + "key": "package_type", + "type": "STRING", + "value": "vehicle_titles" + } + ], + "maxAIFabricVersion": "23.10" +} \ No newline at end of file diff --git a/metadata/VehicleTitles__31__metadata.json b/metadata/VehicleTitles__31__metadata.json new file mode 100644 index 00000000..797cc6fd --- /dev/null +++ b/metadata/VehicleTitles__31__metadata.json @@ -0,0 +1,33 @@ +{ + "name": "VehicleTitles", + "retrainable": true, + "gpu": 0, + "processorType": "CPU", + "description": "Machine Learning model for extracting commonly occurring data points from Vehicle Titles, including header fields and line items. Please see more details including supported languages in the ML Packages section of the Document Understanding Guide: https://docs.uipath.com/document-understanding/docs/ml-packages", + "inputDescription": "ML Skills deployed using this ML Package can be integrated into RPA workflows using the Machine Learning Extractor activity in the Official feed or the MLModel activity in Connect feed. File formats accepted include pdf, tiff, jpg or png files. The activities also require an API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -> Other Services view.", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "cpu": 1, + "inputType": "JSON", + "displayName": "VehicleTitles", + "memory": 0, + "mlPackageLanguage": "PYTHON37_DU", + "projectId": "[project-id]", + "stagingUri": "[staging-uri]", + "projectName": "UiPath Document Understanding", + "projectDescription": "UiPath models to classify and extract information from images and pdfs.", + "tenantName": "UiPath", + "minAIFabricVersion": "22.10", + "languageVersion": 4, + "version": 31, + "customVersion": "23.10.8", + "imagePath": "du-semistructured:v23.10-01.28-rc07", + "parametersFileJSON": "{\"params\": [{\"name\": \"auto_retraining\",\"type\": [\"PIPELINE\"],\"value\": \"False\",\"help\": \"Enable this option when the Pipeline you are creating is a scheduled automatic pipeline, synchronized with automatic exports from Data Manager.\",\"options\": [\"True\",\"False\"]}],\"probes\": [{\"probe\": \"startup\",\"initialDelaySeconds\": \"120\",\"periodSeconds\": \"10\",\"timeoutSeconds\": \"2\",\"successThreshold\": \"1\",\"failureThreshold\": \"60\"}]}", + "settings": [ + { + "key": "package_type", + "type": "STRING", + "value": "vehicle_titles" + } + ], + "maxAIFabricVersion": "24.10" +} \ No newline at end of file diff --git a/metadata/VehicleTitles__32__metadata.json b/metadata/VehicleTitles__32__metadata.json new file mode 100644 index 00000000..15a07fe5 --- /dev/null +++ b/metadata/VehicleTitles__32__metadata.json @@ -0,0 +1,32 @@ +{ + "name": "VehicleTitles", + "retrainable": true, + "gpu": 0, + "processorType": "CPU", + "description": "Machine Learning model for extracting commonly occurring data points from Vehicle Titles, including header fields and line items. Please see more details including supported languages in the ML Packages section of the Document Understanding Guide: https://docs.uipath.com/document-understanding/docs/ml-packages", + "inputDescription": "ML Skills deployed using this ML Package can be integrated into RPA workflows using the Machine Learning Extractor activity in the Official feed or the MLModel activity in Connect feed. File formats accepted include pdf, tiff, jpg or png files. The activities also require an API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -> Other Services view.", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "cpu": 1, + "inputType": "JSON", + "displayName": "VehicleTitles", + "memory": 0, + "mlPackageLanguage": "PYTHON37_DU", + "projectId": "[project-id]", + "stagingUri": "[staging-uri]", + "projectName": "UiPath Document Understanding", + "projectDescription": "UiPath models to classify and extract information from images and pdfs.", + "tenantName": "UiPath", + "minAIFabricVersion": "22.10", + "languageVersion": 4, + "version": 32, + "customVersion": "24.10.3", + "imagePath": "du-semistructured:v24.10-3.27-rc02", + "parametersFileJSON": "{\"params\": [{\"name\": \"auto_retraining\",\"type\": [\"PIPELINE\"],\"value\": \"False\",\"help\": \"Enable this option when the Pipeline you are creating is a scheduled automatic pipeline, synchronized with automatic exports from Data Manager.\",\"options\": [\"True\",\"False\"]}],\"probes\": [{\"probe\": \"startup\",\"initialDelaySeconds\": \"120\",\"periodSeconds\": \"10\",\"timeoutSeconds\": \"2\",\"successThreshold\": \"1\",\"failureThreshold\": \"60\"}]}", + "settings": [ + { + "key": "package_type", + "type": "STRING", + "value": "vehicle_titles" + } + ] +} diff --git a/metadata/VehicleTitles__33__metadata.json b/metadata/VehicleTitles__33__metadata.json new file mode 100644 index 00000000..230adec6 --- /dev/null +++ b/metadata/VehicleTitles__33__metadata.json @@ -0,0 +1,33 @@ +{ + "name": "VehicleTitles", + "retrainable": true, + "gpu": 0, + "processorType": "CPU", + "description": "Machine Learning model for extracting commonly occurring data points from Vehicle Titles, including header fields and line items. Please see more details including supported languages in the ML Packages section of the Document Understanding Guide: https://docs.uipath.com/document-understanding/docs/ml-packages", + "inputDescription": "ML Skills deployed using this ML Package can be integrated into RPA workflows using the Machine Learning Extractor activity in the Official feed or the MLModel activity in Connect feed. File formats accepted include pdf, tiff, jpg or png files. The activities also require an API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -> Other Services view.", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "cpu": 1, + "inputType": "JSON", + "displayName": "VehicleTitles", + "memory": 0, + "mlPackageLanguage": "PYTHON37_DU", + "projectId": "[project-id]", + "stagingUri": "[staging-uri]", + "projectName": "UiPath Document Understanding", + "projectDescription": "UiPath models to classify and extract information from images and pdfs.", + "tenantName": "UiPath", + "minAIFabricVersion": "22.10", + "languageVersion": 4, + "version": 33, + "customVersion": "23.10.9", + "imagePath": "du-semistructured:v23.10-03.14-rc02", + "parametersFileJSON": "{\"params\": [{\"name\": \"auto_retraining\",\"type\": [\"PIPELINE\"],\"value\": \"False\",\"help\": \"Enable this option when the Pipeline you are creating is a scheduled automatic pipeline, synchronized with automatic exports from Data Manager.\",\"options\": [\"True\",\"False\"]}],\"probes\": [{\"probe\": \"startup\",\"initialDelaySeconds\": \"120\",\"periodSeconds\": \"10\",\"timeoutSeconds\": \"2\",\"successThreshold\": \"1\",\"failureThreshold\": \"60\"}]}", + "settings": [ + { + "key": "package_type", + "type": "STRING", + "value": "vehicle_titles" + } + ], + "maxAIFabricVersion": "24.10" +} diff --git a/metadata/VehicleTitles__34__metadata.json b/metadata/VehicleTitles__34__metadata.json new file mode 100644 index 00000000..a0bf7e15 --- /dev/null +++ b/metadata/VehicleTitles__34__metadata.json @@ -0,0 +1,33 @@ +{ + "name": "VehicleTitles", + "retrainable": true, + "gpu": 0, + "processorType": "CPU", + "description": "Machine Learning model for extracting commonly occurring data points from Vehicle Titles, including header fields and line items. Please see more details including supported languages in the ML Packages section of the Document Understanding Guide: https://docs.uipath.com/document-understanding/docs/ml-packages", + "inputDescription": "ML Skills deployed using this ML Package can be integrated into RPA workflows using the Machine Learning Extractor activity in the Official feed or the MLModel activity in Connect feed. File formats accepted include pdf, tiff, jpg or png files. The activities also require an API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -> Other Services view.", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "cpu": 1, + "inputType": "JSON", + "displayName": "VehicleTitles", + "memory": 0, + "mlPackageLanguage": "PYTHON37_DU", + "projectId": "[project-id]", + "stagingUri": "[staging-uri]", + "projectName": "UiPath Document Understanding", + "projectDescription": "UiPath models to classify and extract information from images and pdfs.", + "tenantName": "UiPath", + "minAIFabricVersion": "22.10", + "languageVersion": 4, + "version": 34, + "customVersion": "23.4.12", + "imagePath": "du-semistructured:v23.4-03.12-rc02", + "parametersFileJSON": "{\"params\": [{\"name\": \"auto_retraining\",\"type\": [\"PIPELINE\"],\"value\": \"False\",\"help\": \"Enable this option when the Pipeline you are creating is a scheduled automatic pipeline, synchronized with automatic exports from Data Manager.\",\"options\": [\"True\",\"False\"]}],\"probes\": [{\"probe\": \"startup\",\"initialDelaySeconds\": \"120\",\"periodSeconds\": \"10\",\"timeoutSeconds\": \"2\",\"successThreshold\": \"1\",\"failureThreshold\": \"60\"}]}", + "settings": [ + { + "key": "package_type", + "type": "STRING", + "value": "vehicle_titles" + } + ], + "maxAIFabricVersion": "23.10" +} diff --git a/metadata/VehicleTitles__35__metadata.json b/metadata/VehicleTitles__35__metadata.json new file mode 100644 index 00000000..f9d200b5 --- /dev/null +++ b/metadata/VehicleTitles__35__metadata.json @@ -0,0 +1,32 @@ +{ + "name": "VehicleTitles", + "retrainable": true, + "gpu": 0, + "processorType": "CPU", + "description": "Machine Learning model for extracting commonly occurring data points from Vehicle Titles, including header fields and line items. Please see more details including supported languages in the ML Packages section of the Document Understanding Guide: https://docs.uipath.com/document-understanding/docs/ml-packages", + "inputDescription": "ML Skills deployed using this ML Package can be integrated into RPA workflows using the Machine Learning Extractor activity in the Official feed or the MLModel activity in Connect feed. File formats accepted include pdf, tiff, jpg or png files. The activities also require an API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -> Other Services view.", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "cpu": 1, + "inputType": "JSON", + "displayName": "VehicleTitles", + "memory": 0, + "mlPackageLanguage": "PYTHON37_DU", + "projectId": "[project-id]", + "stagingUri": "[staging-uri]", + "projectName": "UiPath Document Understanding", + "projectDescription": "UiPath models to classify and extract information from images and pdfs.", + "tenantName": "UiPath", + "minAIFabricVersion": "22.10", + "languageVersion": 4, + "version": 35, + "customVersion": "24.10.4", + "imagePath": "du-semistructured:v24.10-6.16-rc04", + "parametersFileJSON": "{\"params\": [{\"name\": \"auto_retraining\",\"type\": [\"PIPELINE\"],\"value\": \"False\",\"help\": \"Enable this option when the Pipeline you are creating is a scheduled automatic pipeline, synchronized with automatic exports from Data Manager.\",\"options\": [\"True\",\"False\"]}],\"probes\": [{\"probe\": \"startup\",\"initialDelaySeconds\": \"120\",\"periodSeconds\": \"10\",\"timeoutSeconds\": \"2\",\"successThreshold\": \"1\",\"failureThreshold\": \"60\"}]}", + "settings": [ + { + "key": "package_type", + "type": "STRING", + "value": "vehicle_titles" + } + ] +} diff --git a/metadata/VehicleTitles__36__metadata.json b/metadata/VehicleTitles__36__metadata.json new file mode 100644 index 00000000..2f58ae78 --- /dev/null +++ b/metadata/VehicleTitles__36__metadata.json @@ -0,0 +1,33 @@ +{ + "name": "VehicleTitles", + "retrainable": true, + "gpu": 0, + "processorType": "CPU", + "description": "Machine Learning model for extracting commonly occurring data points from Vehicle Titles, including header fields and line items. Please see more details including supported languages in the ML Packages section of the Document Understanding Guide: https://docs.uipath.com/document-understanding/docs/ml-packages", + "inputDescription": "ML Skills deployed using this ML Package can be integrated into RPA workflows using the Machine Learning Extractor activity in the Official feed or the MLModel activity in Connect feed. File formats accepted include pdf, tiff, jpg or png files. The activities also require an API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -> Other Services view.", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "cpu": 1, + "inputType": "JSON", + "displayName": "VehicleTitles", + "memory": 0, + "mlPackageLanguage": "PYTHON37_DU", + "projectId": "[project-id]", + "stagingUri": "[staging-uri]", + "projectName": "UiPath Document Understanding", + "projectDescription": "UiPath models to classify and extract information from images and pdfs.", + "tenantName": "UiPath", + "minAIFabricVersion": "22.10", + "languageVersion": 4, + "version": 36, + "customVersion": "23.10.10", + "imagePath": "du-semistructured:v23.10-06.05-rc05", + "parametersFileJSON": "{\"params\": [{\"name\": \"auto_retraining\",\"type\": [\"PIPELINE\"],\"value\": \"False\",\"help\": \"Enable this option when the Pipeline you are creating is a scheduled automatic pipeline, synchronized with automatic exports from Data Manager.\",\"options\": [\"True\",\"False\"]}],\"probes\": [{\"probe\": \"startup\",\"initialDelaySeconds\": \"120\",\"periodSeconds\": \"10\",\"timeoutSeconds\": \"2\",\"successThreshold\": \"1\",\"failureThreshold\": \"60\"}]}", + "settings": [ + { + "key": "package_type", + "type": "STRING", + "value": "vehicle_titles" + } + ], + "maxAIFabricVersion": "24.10" +} diff --git a/metadata/VehicleTitles__37__metadata.json b/metadata/VehicleTitles__37__metadata.json new file mode 100644 index 00000000..d59916ea --- /dev/null +++ b/metadata/VehicleTitles__37__metadata.json @@ -0,0 +1,32 @@ +{ + "name": "VehicleTitles", + "retrainable": true, + "gpu": 0, + "processorType": "CPU", + "description": "Machine Learning model for extracting commonly occurring data points from Vehicle Titles, including header fields and line items. Please see more details including supported languages in the ML Packages section of the Document Understanding Guide: https://docs.uipath.com/document-understanding/docs/ml-packages", + "inputDescription": "ML Skills deployed using this ML Package can be integrated into RPA workflows using the Machine Learning Extractor activity in the Official feed or the MLModel activity in Connect feed. File formats accepted include pdf, tiff, jpg or png files. 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The activities also require an API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -> Other Services view.", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "changeLog": "", + "cpu": 1, + "inputType": "JSON", + "displayName": "W9", + "memory": 0, + "mlPackageLanguage": "PYTHON37_DU", + "projectId": "[project-id]", + "stagingUri": "[staging-uri]", + "projectName": "UiPath Document Understanding", + "projectDescription": "UiPath models to classify and extract information from images and pdfs.", + "tenantName": "UiPath", + "minAIFabricVersion": "22.10", + "languageVersion": 4, + "version": 36, + "customVersion": "23.10.9", + "imagePath": "du-semistructured:v23.10-03.14-rc02", + "parametersFileJSON": "{\"params\": [{\"name\": \"auto_retraining\",\"type\": [\"PIPELINE\"],\"value\": \"False\",\"help\": \"Enable this option when the Pipeline you are creating is a scheduled automatic pipeline, synchronized with automatic exports from Data Manager.\",\"options\": [\"True\",\"False\"]}],\"probes\": [{\"probe\": \"startup\",\"initialDelaySeconds\": \"120\",\"periodSeconds\": \"10\",\"timeoutSeconds\": \"2\",\"successThreshold\": \"1\",\"failureThreshold\": \"60\"}]}", + "settings": [ + { + "key": "package_type", + "type": "STRING", + "value": "w9" + } + ], + "maxAIFabricVersion": "24.10" +} diff --git a/metadata/W9__37__metadata.json b/metadata/W9__37__metadata.json new file mode 100644 index 00000000..3355ed00 --- /dev/null +++ b/metadata/W9__37__metadata.json @@ -0,0 +1,34 @@ +{ + "name": "W9", + "retrainable": true, + "gpu": 0, + "processorType": "CPU", + "description": "Machine Learning model for extracting commonly occurring data points from W9s, including header fields and line items. Please see more details including supported languages and link to Activities guide in the About Licensing -> Document Understanding API Key section of the UiPath Automation Cloud Guide here: https://docs.uipath.com/automation-cloud/docs/about-licensing#section-document-understanding-api-key", + "inputDescription": "ML Skills deployed using this ML Package can be integrated into RPA workflows using the Machine Learning Extractor activity in the Official feed or the MLModel activity in Connect feed. File formats accepted include pdf, tiff, jpg or png files. The activities also require an API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -> Other Services view.", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "changeLog": "", + "cpu": 1, + "inputType": "JSON", + "displayName": "W9", + "memory": 0, + "mlPackageLanguage": "PYTHON37_DU", + "projectId": "[project-id]", + "stagingUri": "[staging-uri]", + "projectName": "UiPath Document Understanding", + "projectDescription": "UiPath models to classify and extract information from images and pdfs.", + "tenantName": "UiPath", + "minAIFabricVersion": "22.10", + "languageVersion": 4, + "version": 37, + "customVersion": "23.4.12", + "imagePath": "du-semistructured:v23.4-03.12-rc02", + "parametersFileJSON": "{\"params\": [{\"name\": \"auto_retraining\",\"type\": [\"PIPELINE\"],\"value\": \"False\",\"help\": \"Enable this option when the Pipeline you are creating is a scheduled automatic pipeline, synchronized with automatic exports from Data Manager.\",\"options\": [\"True\",\"False\"]}],\"probes\": [{\"probe\": \"startup\",\"initialDelaySeconds\": \"120\",\"periodSeconds\": \"10\",\"timeoutSeconds\": \"2\",\"successThreshold\": \"1\",\"failureThreshold\": \"60\"}]}", + "settings": [ + { + "key": "package_type", + "type": "STRING", + "value": "w9" + } + ], + "maxAIFabricVersion": "23.10" +} diff --git a/metadata/W9__38__metadata.json b/metadata/W9__38__metadata.json new file mode 100644 index 00000000..523ccb84 --- /dev/null +++ b/metadata/W9__38__metadata.json @@ -0,0 +1,33 @@ +{ + "name": "W9", + "retrainable": true, + "gpu": 0, + "processorType": "CPU", + "description": "Machine Learning model for extracting commonly occurring data points from W9s, including header fields and line items. Please see more details including supported languages and link to Activities guide in the About Licensing -> Document Understanding API Key section of the UiPath Automation Cloud Guide here: https://docs.uipath.com/automation-cloud/docs/about-licensing#section-document-understanding-api-key", + "inputDescription": "ML Skills deployed using this ML Package can be integrated into RPA workflows using the Machine Learning Extractor activity in the Official feed or the MLModel activity in Connect feed. File formats accepted include pdf, tiff, jpg or png files. The activities also require an API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -> Other Services view.", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "changeLog": "", + "cpu": 1, + "inputType": "JSON", + "displayName": "W9", + "memory": 0, + "mlPackageLanguage": "PYTHON37_DU", + "projectId": "[project-id]", + "stagingUri": "[staging-uri]", + "projectName": "UiPath Document Understanding", + "projectDescription": "UiPath models to classify and extract information from images and pdfs.", + "tenantName": "UiPath", + "minAIFabricVersion": "22.10", + "languageVersion": 4, + "version": 38, + "customVersion": "24.10.4", + "imagePath": "du-semistructured:v24.10-6.16-rc04", + "parametersFileJSON": "{\"params\": [{\"name\": \"auto_retraining\",\"type\": [\"PIPELINE\"],\"value\": \"False\",\"help\": \"Enable this option when the Pipeline you are creating is a scheduled automatic pipeline, synchronized with automatic exports from Data Manager.\",\"options\": [\"True\",\"False\"]}],\"probes\": [{\"probe\": \"startup\",\"initialDelaySeconds\": \"120\",\"periodSeconds\": \"10\",\"timeoutSeconds\": \"2\",\"successThreshold\": \"1\",\"failureThreshold\": \"60\"}]}", + "settings": [ + { + "key": "package_type", + "type": "STRING", + "value": "w9" + } + ] +} diff --git a/metadata/W9__39__metadata.json b/metadata/W9__39__metadata.json new file mode 100644 index 00000000..547ff462 --- /dev/null +++ b/metadata/W9__39__metadata.json @@ -0,0 +1,34 @@ +{ + "name": "W9", + "retrainable": true, + "gpu": 0, + "processorType": "CPU", + "description": "Machine Learning model for extracting commonly occurring data points from W9s, including header fields and line items. Please see more details including supported languages and link to Activities guide in the About Licensing -> Document Understanding API Key section of the UiPath Automation Cloud Guide here: https://docs.uipath.com/automation-cloud/docs/about-licensing#section-document-understanding-api-key", + "inputDescription": "ML Skills deployed using this ML Package can be integrated into RPA workflows using the Machine Learning Extractor activity in the Official feed or the MLModel activity in Connect feed. File formats accepted include pdf, tiff, jpg or png files. The activities also require an API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -> Other Services view.", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "changeLog": "", + "cpu": 1, + "inputType": "JSON", + "displayName": "W9", + "memory": 0, + "mlPackageLanguage": "PYTHON37_DU", + "projectId": "[project-id]", + "stagingUri": "[staging-uri]", + "projectName": "UiPath Document Understanding", + "projectDescription": "UiPath models to classify and extract information from images and pdfs.", + "tenantName": "UiPath", + "minAIFabricVersion": "22.10", + "languageVersion": 4, + "version": 39, + "customVersion": "23.10.10", + "imagePath": "du-semistructured:v23.10-06.05-rc05", + "parametersFileJSON": "{\"params\": [{\"name\": \"auto_retraining\",\"type\": [\"PIPELINE\"],\"value\": \"False\",\"help\": \"Enable this option when the Pipeline you are creating is a scheduled automatic pipeline, synchronized with automatic exports from Data Manager.\",\"options\": [\"True\",\"False\"]}],\"probes\": [{\"probe\": \"startup\",\"initialDelaySeconds\": \"120\",\"periodSeconds\": \"10\",\"timeoutSeconds\": \"2\",\"successThreshold\": \"1\",\"failureThreshold\": \"60\"}]}", + "settings": [ + { + "key": "package_type", + "type": "STRING", + "value": "w9" + } + ], + "maxAIFabricVersion": "24.10" +} diff --git a/metadata/W9__3__metadata.json b/metadata/W9__3__metadata.json new file mode 100644 index 00000000..625255e5 --- /dev/null +++ b/metadata/W9__3__metadata.json @@ -0,0 +1,26 @@ +{ + "name": "W9", + "retrainable": true, + "gpu": 0, + "processorType": "CPU", + "description": "Machine Learning model for extracting commonly occurring data points from W9s, including header fields and line items. Please see more details including supported languages and link to Activities guide in the About Licensing -> Document Understanding API Key section of the UiPath Automation Cloud Guide here: https://docs.uipath.com/automation-cloud/docs/about-licensing#section-document-understanding-api-key", + "inputDescription": "ML Skills deployed using this ML Package can be integrated into RPA workflows using the Machine Learning Extractor activity in the Official feed or the MLModel activity in Connect feed. File formats accepted include pdf, tiff, jpg or png files. The activities also require an API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -> Other Services view.", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "changeLog": "", + "cpu": 1, + "inputType": "JSON", + "displayName": "W9", + "memory": 0, + "mlPackageLanguage": "PYTHON37_DU", + "projectId": "[project-id]", + "stagingUri": "[staging-uri]", + "projectName": "UiPath Document Understanding", + "projectDescription": "UiPath models to classify and extract information from images and pdfs.", + "tenantName": "UiPath", + "minAIFabricVersion": "22.4", + "languageVersion": 4, + "version": 3, + "customVersion": "22.4.1", + "contentUri": "https:///publicmodels/AIC/W9/22.4.1/w9_package.zip", + "maxAIFabricVersion": "22.4" +} \ No newline at end of file diff --git a/metadata/W9__40__metadata.json b/metadata/W9__40__metadata.json new file mode 100644 index 00000000..9b4a9633 --- /dev/null +++ b/metadata/W9__40__metadata.json @@ -0,0 +1,33 @@ +{ + "name": "W9", + "retrainable": true, + "gpu": 0, + "processorType": "CPU", + "description": "Machine Learning model for extracting commonly occurring data points from W9s, including header fields and line items. Please see more details including supported languages and link to Activities guide in the About Licensing -> Document Understanding API Key section of the UiPath Automation Cloud Guide here: https://docs.uipath.com/automation-cloud/docs/about-licensing#section-document-understanding-api-key", + "inputDescription": "ML Skills deployed using this ML Package can be integrated into RPA workflows using the Machine Learning Extractor activity in the Official feed or the MLModel activity in Connect feed. File formats accepted include pdf, tiff, jpg or png files. The activities also require an API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -> Other Services view.", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "changeLog": "", + "cpu": 1, + "inputType": "JSON", + "displayName": "W9", + "memory": 0, + "mlPackageLanguage": "PYTHON37_DU", + "projectId": "[project-id]", + "stagingUri": "[staging-uri]", + "projectName": "UiPath Document Understanding", + "projectDescription": "UiPath models to classify and extract information from images and pdfs.", + "tenantName": "UiPath", + "minAIFabricVersion": "22.10", + "languageVersion": 4, + "version": 40, + "customVersion": "24.10.5", + "imagePath": "du-semistructured:v24.10-8.25-rc02", + "parametersFileJSON": "{\"params\": [{\"name\": \"auto_retraining\",\"type\": [\"PIPELINE\"],\"value\": \"False\",\"help\": \"Enable this option when the Pipeline you are creating is a scheduled automatic pipeline, synchronized with automatic exports from Data Manager.\",\"options\": [\"True\",\"False\"]}],\"probes\": [{\"probe\": \"startup\",\"initialDelaySeconds\": \"120\",\"periodSeconds\": \"10\",\"timeoutSeconds\": \"2\",\"successThreshold\": \"1\",\"failureThreshold\": \"60\"}]}", + "settings": [ + { + "key": "package_type", + "type": "STRING", + "value": "w9" + } + ] +} diff --git a/metadata/W9__41__metadata.json b/metadata/W9__41__metadata.json new file mode 100644 index 00000000..95a4e577 --- /dev/null +++ b/metadata/W9__41__metadata.json @@ -0,0 +1,34 @@ +{ + "name": "W9", + "retrainable": true, + "gpu": 0, + "processorType": "CPU", + "description": "Machine Learning model for extracting commonly occurring data points from W9s, including header fields and line items. Please see more details including supported languages and link to Activities guide in the About Licensing -> Document Understanding API Key section of the UiPath Automation Cloud Guide here: https://docs.uipath.com/automation-cloud/docs/about-licensing#section-document-understanding-api-key", + "inputDescription": "ML Skills deployed using this ML Package can be integrated into RPA workflows using the Machine Learning Extractor activity in the Official feed or the MLModel activity in Connect feed. File formats accepted include pdf, tiff, jpg or png files. The activities also require an API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -> Other Services view.", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "changeLog": "", + "cpu": 1, + "inputType": "JSON", + "displayName": "W9", + "memory": 0, + "mlPackageLanguage": "PYTHON37_DU", + "projectId": "[project-id]", + "stagingUri": "[staging-uri]", + "projectName": "UiPath Document Understanding", + "projectDescription": "UiPath models to classify and extract information from images and pdfs.", + "tenantName": "UiPath", + "minAIFabricVersion": "22.10", + "languageVersion": 4, + "version": 41, + "customVersion": "23.10.11", + "imagePath": "du-semistructured:v23.10-08.25-rc02", + "parametersFileJSON": "{\"params\": [{\"name\": \"auto_retraining\",\"type\": [\"PIPELINE\"],\"value\": \"False\",\"help\": \"Enable this option when the Pipeline you are creating is a scheduled automatic pipeline, synchronized with automatic exports from Data Manager.\",\"options\": [\"True\",\"False\"]}],\"probes\": [{\"probe\": \"startup\",\"initialDelaySeconds\": \"120\",\"periodSeconds\": \"10\",\"timeoutSeconds\": \"2\",\"successThreshold\": \"1\",\"failureThreshold\": \"60\"}]}", + "settings": [ + { + "key": "package_type", + "type": "STRING", + "value": "w9" + } + ], + "maxAIFabricVersion": "24.10" +} diff --git a/metadata/W9__42__metadata.json b/metadata/W9__42__metadata.json new file mode 100644 index 00000000..7b677cc9 --- /dev/null +++ b/metadata/W9__42__metadata.json @@ -0,0 +1,33 @@ +{ + "name": "W9", + "retrainable": true, + "gpu": 0, + "processorType": "CPU", + "description": "Machine Learning model for extracting commonly occurring data points from W9s, including header fields and line items. Please see more details including supported languages and link to Activities guide in the About Licensing -> Document Understanding API Key section of the UiPath Automation Cloud Guide here: https://docs.uipath.com/automation-cloud/docs/about-licensing#section-document-understanding-api-key", + "inputDescription": "ML Skills deployed using this ML Package can be integrated into RPA workflows using the Machine Learning Extractor activity in the Official feed or the MLModel activity in Connect feed. File formats accepted include pdf, tiff, jpg or png files. The activities also require an API Key input which you need to obtain from your UiPath Automation Cloud account, in the Licenses -> Other Services view.", + "outputDescription": "Please refer to the documentation of the Activity used to query the ML Skill.", + "changeLog": "", + "cpu": 1, + "inputType": "JSON", + "displayName": "W9", + "memory": 0, + "mlPackageLanguage": "PYTHON37_DU", + "projectId": "[project-id]", + "stagingUri": "[staging-uri]", + "projectName": "UiPath Document Understanding", + "projectDescription": "UiPath models to classify and extract information from images and pdfs.", + "tenantName": "UiPath", + "minAIFabricVersion": "22.10", + "languageVersion": 4, + "version": 42, + "customVersion": "25.10.0", + "imagePath": "du-semistructured:v25.10-10.17-rc04", + "parametersFileJSON": "{\"params\": [{\"name\": \"auto_retraining\",\"type\": [\"PIPELINE\"],\"value\": \"False\",\"help\": \"Enable this option when the Pipeline you are creating is a scheduled automatic pipeline, synchronized with automatic exports from Data Manager.\",\"options\": [\"True\",\"False\"]}],\"probes\": [{\"probe\": \"startup\",\"initialDelaySeconds\": \"120\",\"periodSeconds\": \"10\",\"timeoutSeconds\": \"2\",\"successThreshold\": \"1\",\"failureThreshold\": \"60\"}]}", + "settings": [ + { + "key": "package_type", + "type": "STRING", + "value": "w9" + } + ] +} diff --git a/metadata_generate_new_du_versions.ps1 b/metadata_generate_new_du_versions.ps1 new file mode 100644 index 00000000..9cd0c451 --- /dev/null +++ b/metadata_generate_new_du_versions.ps1 @@ -0,0 +1,110 @@ +param( + [Parameter()] + [String] $targetImage = '', + + [Parameter(Mandatory)] + [String] $newCustomVersion, + + [Parameter(Mandatory)] + [String] $previousCustomVersion, + + [Parameter(Mandatory)] + [String] $newTag +) + +function Remove-BomFromFile($Path) { + $Content = Get-Content -Path $Path -Raw + $Utf8NoBomEncoding = New-Object -TypeName System.Text.UTF8Encoding -ArgumentList $False + [System.IO.File]::WriteAllLines($Path, $Content, $Utf8NoBomEncoding) +} + +# Define the path to the folder containing the files +$folderPath = $PSScriptRoot + "/metadata" + +Write-Host "folderPath: $folderPath" + +# Get a list of all files in the folder that match the specified format +$fileList = Get-ChildItem $folderPath | Where-Object { $_.Name -match "^([a-zA-Z0-9_]+)__([0-9]+)__metadata\.json$" } + +# Create a hashtable to store the highest version number for each model +$maxModelVersions = @{} +$previousFileVersion = @{} + +Write-Host "fileList: $fileList" + +# Loop through each file and determine if it has a higher version number than any previously processed file for the same model +foreach ($file in $fileList) { + $fileName = $file.Name + Write-Host "Processing $fileName" + $match = [regex]::Match($fileName, "^([a-zA-Z0-9_]+)__([0-9]+)__metadata\.json$") + $model = $match.Groups[1].Value + $version = [int]$match.Groups[2].Value + + if ($maxModelVersions.ContainsKey($model)) { + $currentVersion = [int]$maxModelVersions[$model] + if ($version -gt $currentVersion) { + $maxModelVersions[$model] = $version + } + } + else { + $maxModelVersions[$model] = $version + } + + $json = Get-Content $file.FullName | ConvertFrom-Json + if ($json.customVersion -eq $previousCustomVersion){ + $previousFileVersion[$model] = $version + } +} + +# Loop through each file again and create a copy of the file with the previous version number for each model +foreach ($file in $fileList) { + $json = Get-Content $file.FullName | ConvertFrom-Json + $fileName = $file.Name + $match = [regex]::Match($fileName, "^([a-zA-Z0-9_]+)__([0-9]+)__metadata\.json$") + $model = $match.Groups[1].Value + $version = [int]$match.Groups[2].Value + + if ($version -eq $previousFileVersion[$model]) { + if ($json.customVersion -eq $newCustomVersion) { + Write-Host "Metadata with custom version $newCustomVersion exists. Updating it instead of creating a new one." + $newFilePath = $file.FullName #setting the newFilePath to the same file to update it + $newVersion = $version + $newFileName = $fileName + } else { + Write-Host "Metadata with custom version $newCustomVersion does not exist. Creating a new one." + $newVersion = $maxModelVersions[$model] + 1 + $newFileName = "$model" + "__" + "$newVersion" + "__metadata.json" + $newFilePath = Join-Path $folderPath $newFileName + } + + if ($json.mlPackageLanguage -like '*DU' -and $json.imagePath){ + #Write-Host "model: $model" + + $json.version = $newVersion + $json.customVersion = $newCustomVersion + + # Replace the specified text with the new text + $parts = $json.imagePath -split ':' + if ($targetImage -ne $null -and $targetImage -ne '' -and $targetImage -ne $parts[0]){ + Write-Host "No update needed for file $fileName because targetImage does not match imagePath: $($parts[0])" + continue + } + if ($parts[1] -eq $newTag) { + Write-Host "No update needed for file $fileName because imagePath already has the tag: $newTag" + continue + } + + Write-Host "Updating imagePath from $($parts[1]) to: $newTag" + + $json.imagePath = $parts[0] + ":" + $newTag + + $json | ConvertTo-Json -Depth 100 | Set-Content $newFilePath -Encoding ASCII + + # Copy the file's last write time to the new file + $newFile = Get-Item $newFilePath + $newFile.LastWriteTime = $file.LastWriteTime + + Write-Host "newFilePath: $newFilePath" + } + } +} \ No newline at end of file diff --git a/orchestrator/orchestratorAutomationAIC21-4.ps1 b/orchestrator/orchestratorAutomationAIC21-4.ps1 index 292134ba..ffc6dbc8 100644 --- a/orchestrator/orchestratorAutomationAIC21-4.ps1 +++ b/orchestrator/orchestratorAutomationAIC21-4.ps1 @@ -1,229 +1,228 @@ -#Requires -RunAsAdministrator - -<# - -.SYNOPSIS - Makes aifabric related changes to orchestrator web.config to enable aifabric installation and access. -.DESCRIPTION - Add entries in orchestrator web.config(if not exists) for orchestrator internal IDP and aifabric access from robot and orchestrator. - Removes cache to allow access to new controllers and resets iis to load new values. -.NOTES - Name: ./orchestratorAutomation.ps1 - Author: AIFabric Team - Pre-Requisites: script has to be executed throuh powershell in Administrator mode & before running script set execution policy to RemoteSigned by running "Set-ExecutionPolicy RemoteSigned" -.EXAMPLE - If aifabric is available at ww.xx.yy.zz, command to run would be - .\orchestratorAutomation.ps1 -aifip ww.xx.yy.zz - - If ai-app is accessed via domain instead of IP:PORT combo, then enable domainBasedAccess to true - .\orchestratorAutomation.ps1 -aifip "aif-sahil-aks.westeurope.cloudapp.azure.com" -portlessAccess "true" - - If Orchestrator Installation Path has to be specified, - .\orchestratorAutomation.ps1 -aifip ww.xx.yy.zz -config "C:\Program Files (x86)\UiPath\Orchestrator" - -#> - -Param ( - [Parameter(Mandatory = $true, ValueFromPipelineByPropertyName)] - [string] $aifip, - [Parameter(Mandatory = $false, ValueFromPipelineByPropertyName)] - [string] $config, - [Parameter(Mandatory = $false, ValueFromPipelineByPropertyName)] - [string] $aifport, - [Parameter(Mandatory = $false, ValueFromPipelineByPropertyName)] - [string] $portlessAccess, - [Parameter(Mandatory = $false, ValueFromPipelineByPropertyName)] - [string] $storageport -) - -Import-Module 'WebAdministration' - - -if(!$config){ - $config = "C:\Program Files (x86)\UiPath\Orchestrator" -} - -#if path does not end with \ add it -if( $config -notmatch '\\$' ){ - $config += '\' -} - -$dll_config = $config + 'UiPath.Orchestrator.dll.config' - -#Fetching Orchestrator version -if(Test-Path $dll_config){ - $orchestrator_version = [System.Diagnostics.FileVersionInfo]::GetVersionInfo($config + 'UiPath.Orchestrator.web.dll').FileVersion - echo "Orchestrator version : $orchestrator_version" -} - -if(Test-Path $dll_config){ - $config = $config + 'UiPath.Orchestrator.dll.config' - $configFile = 'UiPath.Orchestrator.dll.config' -} else { - $config = $config + 'web.config' - $configFile = 'web.config' -} - -#Check for the existence of config file -if(-not (Test-Path $config)){ - throw "$config File does not Exists. Please make sure that the Orchestrator installation folder is correct !" - exit -} - - -if(!$aifport){ - $aifport = "31390" -} - -if(!$storageport){ - $storageport = "31443" -} - -if($portlessAccess.Length -gt 0){ - $portlessAccess = $portlessAccess.ToString() -} else { - $portlessAccess = "false" -} - -echo "Path to Web config: "$config - -Copy-Item $config -Destination ("$config.original."+(Get-Date -Format "MMddyyyy.HH.mm.ss")) - - -if($portlessAccess -eq "true"){ - $hostName = $aifip -} else{ - $hostName = "$($aifip):$($aifport)" -} - - -#AiFabric Settings template -$STATIC_NODES_STRING=' - - - - - - - - - - - - - - - - - -' - -if($aifip.StartsWith("http://") -or $aifip.StartsWith("https://")) -{ - echo "aifip should not start with http or https" - throw "Invalid aifip input provided: $aifip" -} - - -# set nodes value -$STATIC_NODES_STRING = $STATIC_NODES_STRING.Replace("{{hostName}}",$hostName); -$STATIC_NODES_STRING = $STATIC_NODES_STRING.Replace("{{aifip}}",$aifip); -$STATIC_NODES_STRING = $STATIC_NODES_STRING.Replace("{{storageport}}",$storageport); -$STATIC_NODES = [xml]$STATIC_NODES_STRING - -# edit web config -function AifabricFixedConfig -{ - $nodes = Select-Xml -XPath '//add' -Xml $STATIC_NODES - - $file = gi $config - $xml = [xml](gc $file) - foreach($node in $nodes) - { - #remove existing nodes if they exist. They should not. - $key = $node.Node.key - $xml.SelectNodes("configuration/appSettings/add[@key='$key']") | %{$xml.configuration.appSettings.RemoveChild($_)} - $xml.configuration.appSettings.AppendChild($xml.ImportNode($node.Node,1)) - } - - $xml.Save($file.FullName) -} - -# Reset IIS to reload values -function Retry-IISRESET -{ - param ( - [Parameter(Mandatory=$false)][int]$retries = 5, - [Parameter(Mandatory=$false)][int]$secondsDelay = 2 - ) - - $retrycount = 0 - $completed = $false - - while (-not $completed) { - try { - iisreset | Tee-Object -Variable statusiisreset - if("$statusiisreset".Contains("failed")) - { - throw - } - Write-Verbose ("reset succeded") - $completed = $true - } catch { - if ($retrycount -ge $retries) { - throw - } else { - Start-Sleep $secondsDelay - $retrycount++ - } - } - } -} - -# remove cache -function EmptyAspNetCache -{ - $Framework32bitFolder = "\Framework\" - $Framework64bitFolder = "\Framework64\" - $temporaryAspNetFolder = "Temporary ASP.NET Files\root" - $ControllerCacheFileName = "MS-ApiControllerTypeCache.xml" - $aspNetCacheFolder = [System.Runtime.InteropServices.RuntimeEnvironment]::GetRuntimeDirectory()+$temporaryAspNetFolder - if ([Environment]::Is64BitOperatingSystem) - { - $aspNetCacheFolder = $aspNetCacheFolder.Replace($Framework32bitFolder, $Framework64bitFolder); - } - if (!(Test-Path $aspNetCacheFolder)) - { - echo $"Folder $aspNetCacheFolder not found for removing $ControllerCacheFileName" - return - } - echo $"Removing $ControllerCacheFileName files from ASP.NET cache folder $aspNetCacheFolder" - Get-Childitem –Path $aspNetCacheFolder -Include $ControllerCacheFileName -Recurse | ForEach { - $retrycount = 0 - $retries = 3 - $completed = $false - - while (-not $completed) { - try { - Remove-Item $_.FullName - $completed = $true - } catch { - if ($retrycount -ge $retries) { - throw - } else { - Start-Sleep 2 - $retrycount++ - } - } - } - - echo $"Removed $ControllerCacheFileName" - } -} - -#create the proper web.config with configuration -AifabricFixedConfig -EmptyAspNetCache -Retry-IISRESET 3 2 -Sleep 2 +#Requires -RunAsAdministrator + +<# + +.SYNOPSIS + Makes aifabric related changes to orchestrator web.config to enable aifabric installation and access. +.DESCRIPTION + Add entries in orchestrator web.config(if not exists) for orchestrator internal IDP and aifabric access from robot and orchestrator. + Removes cache to allow access to new controllers and resets iis to load new values. +.NOTES + Name: ./orchestratorAutomation.ps1 + Author: AIFabric Team + Pre-Requisites: script has to be executed throuh powershell in Administrator mode & before running script set execution policy to RemoteSigned by running "Set-ExecutionPolicy RemoteSigned" +.EXAMPLE + If aifabric is available at ww.xx.yy.zz, command to run would be + .\orchestratorAutomation.ps1 -aifip ww.xx.yy.zz + + If ai-app is accessed via domain instead of IP:PORT combo, then enable domainBasedAccess to true + .\orchestratorAutomation.ps1 -aifip "aif-sahil-aks.westeurope.cloudapp.azure.com" -portlessAccess "true" + + If Orchestrator Installation Path has to be specified, + .\orchestratorAutomation.ps1 -aifip ww.xx.yy.zz -config "C:\Program Files (x86)\UiPath\Orchestrator" + +#> + +Param ( + [Parameter(Mandatory = $true, ValueFromPipelineByPropertyName)] + [string] $aifip, + [Parameter(Mandatory = $false, ValueFromPipelineByPropertyName)] + [string] $config, + [Parameter(Mandatory = $false, ValueFromPipelineByPropertyName)] + [string] $aifport, + [Parameter(Mandatory = $false, ValueFromPipelineByPropertyName)] + [string] $portlessAccess, + [Parameter(Mandatory = $false, ValueFromPipelineByPropertyName)] + [string] $storageport +) + +Import-Module 'WebAdministration' + + +if(!$config){ + $config = "C:\Program Files (x86)\UiPath\Orchestrator" +} + +#if path does not end with \ add it +if( $config -notmatch '\\$' ){ + $config += '\' +} + +$dll_config = $config + 'UiPath.Orchestrator.dll.config' + +#Fetching Orchestrator version +if(Test-Path $dll_config){ + $orchestrator_version = [System.Diagnostics.FileVersionInfo]::GetVersionInfo($config + 'UiPath.Orchestrator.web.dll').FileVersion + echo "Orchestrator version : $orchestrator_version" +} + +if(Test-Path $dll_config){ + $config = $config + 'UiPath.Orchestrator.dll.config' + $configFile = 'UiPath.Orchestrator.dll.config' +} else{ + $config = $config + 'web.config' + $configFile = 'web.config' +} + +#Check for the existence of config file +if(-not (Test-Path $config)){ + throw "$config File does not Exists. Please make sure that the Orchestrator installation folder is correct !" + exit +} + +if(!$aifport){ + $aifport = "31390" +} + +if(!$storageport){ + $storageport = "31443" +} + +if($portlessAccess.Length -gt 0){ + $portlessAccess = $portlessAccess.ToString() +} else { + $portlessAccess = "false" +} + +echo "Path to Web config: "$config + +Copy-Item $config -Destination ("$config.original."+(Get-Date -Format "MMddyyyy.HH.mm.ss")) + + +if($portlessAccess -eq "true"){ + $hostName = $aifip +} else{ + $hostName = "$($aifip):$($aifport)" +} + + +#AiFabric Settings template +$STATIC_NODES_STRING=' + + + + + + + + + + + + + + + + + +' + +if($aifip.StartsWith("http://") -or $aifip.StartsWith("https://")) +{ + echo "aifip should not start with http or https" + throw "Invalid aifip input provided: $aifip" +} + + +# set nodes value +$STATIC_NODES_STRING = $STATIC_NODES_STRING.Replace("{{hostName}}",$hostName); +$STATIC_NODES_STRING = $STATIC_NODES_STRING.Replace("{{aifip}}",$aifip); +$STATIC_NODES_STRING = $STATIC_NODES_STRING.Replace("{{storageport}}",$storageport); +$STATIC_NODES = [xml]$STATIC_NODES_STRING + +# edit web config +function AifabricFixedConfig +{ + $nodes = Select-Xml -XPath '//add' -Xml $STATIC_NODES + + $file = gi $config + $xml = [xml](gc $file) + foreach($node in $nodes) + { + #remove existing nodes if they exist. They should not. + $key = $node.Node.key + $xml.SelectNodes("configuration/appSettings/add[@key='$key']") | %{$xml.configuration.appSettings.RemoveChild($_)} + $xml.configuration.appSettings.AppendChild($xml.ImportNode($node.Node,1)) + } + + $xml.Save($file.FullName) +} + +# Reset IIS to reload values +function Retry-IISRESET +{ + param ( + [Parameter(Mandatory=$false)][int]$retries = 5, + [Parameter(Mandatory=$false)][int]$secondsDelay = 2 + ) + + $retrycount = 0 + $completed = $false + + while (-not $completed) { + try { + iisreset | Tee-Object -Variable statusiisreset + if("$statusiisreset".Contains("failed")) + { + throw + } + Write-Verbose ("reset succeded") + $completed = $true + } catch { + if ($retrycount -ge $retries) { + throw + } else { + Start-Sleep $secondsDelay + $retrycount++ + } + } + } +} + +# remove cache +function EmptyAspNetCache +{ + $Framework32bitFolder = "\Framework\" + $Framework64bitFolder = "\Framework64\" + $temporaryAspNetFolder = "Temporary ASP.NET Files\root" + $ControllerCacheFileName = "MS-ApiControllerTypeCache.xml" + $aspNetCacheFolder = [System.Runtime.InteropServices.RuntimeEnvironment]::GetRuntimeDirectory()+$temporaryAspNetFolder + if ([Environment]::Is64BitOperatingSystem) + { + $aspNetCacheFolder = $aspNetCacheFolder.Replace($Framework32bitFolder, $Framework64bitFolder); + } + if (!(Test-Path $aspNetCacheFolder)) + { + echo $"Folder $aspNetCacheFolder not found for removing $ControllerCacheFileName" + return + } + echo $"Removing $ControllerCacheFileName files from ASP.NET cache folder $aspNetCacheFolder" + Get-Childitem -Path $aspNetCacheFolder -Include $ControllerCacheFileName -Recurse | ForEach { + $retrycount = 0 + $retries = 3 + $completed = $false + + while (-not $completed) { + try { + Remove-Item $_.FullName + $completed = $true + } catch { + if ($retrycount -ge $retries) { + throw + } else { + Start-Sleep 2 + $retrycount++ + } + } + } + + echo $"Removed $ControllerCacheFileName" + } +} + +#create the proper web.config with configuration +AifabricFixedConfig +EmptyAspNetCache +Retry-IISRESET 3 2 +Sleep 2 echo "Orchestrator configured successfully" \ No newline at end of file diff --git a/orchestrator/orchestratorAutomationAIF20-10.ps1 b/orchestrator/orchestratorAutomationAIF20-10.ps1 index d8d2063a..cba1a7b9 100644 --- a/orchestrator/orchestratorAutomationAIF20-10.ps1 +++ b/orchestrator/orchestratorAutomationAIF20-10.ps1 @@ -3,20 +3,20 @@ <# .SYNOPSIS - Makes aifabric related changes to orchestrator web.config to enable aifabric installation and access. + Makes aicenter related changes to orchestrator web.config to enable aicenter installation and access. .DESCRIPTION - Add entries in orchestrator web.config(if not exists) for orchestrator internal IDP and aifabric access from robot and orchestrator. + Add entries in orchestrator web.config(if not exists) for orchestrator internal IDP and aicenter access from robot and orchestrator. Removes cache to allow access to new controllers and resets iis to load new values. .NOTES Name: ./orchestratorAutomation.ps1 - Author: AIFabric Team + Author: AICenter Team Pre-Requisites: script has to be executed throuh powershell in Administrator mode & before running script set execution policy to RemoteSigned by running "Set-ExecutionPolicy RemoteSigned" .EXAMPLE - If orchestrator is hosted at orchestrator.uipath.com and aifabric is available at ww.xx.yy.zz, command to run would be + If orchestrator is hosted at orchestrator.uipath.com and aicenter is available at ww.xx.yy.zz, command to run would be ./orchestratorAutomation.ps1 -aifip ww.xx.yy.zz -orcname orchestrator.uipath.com If ai-app is accessed via domain instead of IP:PORT combo, then enable domainBasedAccess to true - .\orchestratorAutomation.ps1 -aifip "aif-sahil-aks.westeurope.cloudapp.azure.com" -orcname "aifabricdevorch.northeurope.cloudapp.azure.com" -portlessAccess "true" + .\orchestratorAutomation.ps1 -aifip "aif-sahil-aks.westeurope.cloudapp.azure.com" -orcname "aicenterdevorch.northeurope.cloudapp.azure.com" -portlessAccess "true" If Orchestrator Installation Path has to be specified, ./orchestratorAutomation.ps1 -aifip ww.xx.yy.zz -orcname orchestrator.uipath.com -config "C:\Program Files (x86)\UiPath\Orchestrator" diff --git a/platform/onebox/backup_and_restore/ceph/README.md b/platform/onebox/backup_and_restore/ceph/README.md new file mode 100644 index 00000000..0624f13a --- /dev/null +++ b/platform/onebox/backup_and_restore/ceph/README.md @@ -0,0 +1,36 @@ +# Ceph Backup-Restore + + +## Purpose +To provide backup and restore scripts for object storage in ceph to handle DR scenarios. +This backs up: +* Packages (zips) +* Datasets +* Pipeline Artifacts/Logs +... + + +## Requirements +The Machine where backup/restore runs needs the following: +* Access to AIF machine (public ip address can be obtained via dig) +* aws s3, s3cmd, jq to be installed, e.g. on Ubuntu ```sudo apt install -y jq awscli s3cmd``` +* User logged in with permission to run the script and access to above tools + + +## Usage +* Run get-credentials.sh on AIF machine. It generates a file storage-creds.json. Copy it over to the backup/restore VM. +* [Optionally] Move the credentials to some cluster manager and make changes to the scripts to read from there +# Ceph Backup-Restore +* Make sure to use absolute path as basepath in below scripts + +### For Backup +``` +./export.sh +``` +It creates a folder /ceph which contains 1 folder per bucket containing all the blobs of that bucket + +### For Restore +``` +./import.sh +``` +This looks for a folder ceph inside basePath, creates a bucket per folder inside ceph and then uploads all blobs diff --git a/platform/onebox/backup_and_restore/ceph/export.sh b/platform/onebox/backup_and_restore/ceph/export.sh new file mode 100644 index 00000000..b3a69438 --- /dev/null +++ b/platform/onebox/backup_and_restore/ceph/export.sh @@ -0,0 +1,132 @@ +#!/bin/bash + +: ' +This scipt will export all data stored in blob storage from target environments. +# $1 - json file with credentials, change the script to work with own credential manager +# $2 - path to export +Script will generate folders like path/ceph/bucket1 path/ceph/bucket2 each containing data from 1 bucket +[Script Version -> 21.4] +' +red=$(tput setaf 1) +green=$(tput setaf 2) +yellow=$(tput setaf 3) +default=$(tput sgr0) + +echo "$green $(date) Starting sync of object storage to local disk $default" + +readonly CREDENTIALS_FILE=$1 +readonly BASE_PATH=$2 + +# Validate file provided by user exists or not, It may be relative path or absolute path +# $1 - File path +function validate_file_path() { + if [ ! -f "$1" ]; then + echo "$red $(date) $1 file does not exist, Please check ... Exiting $default" + exit 1 + fi +} + +# Validate dependency module +# $1 - Name of the dependency module +# $2 - Command to validate module +function validate_dependency() { + eval $2 + # Next statement is checking last command success aws --version has some issue + if [ $? -ne 0 ]; then + echo "$red $(date) Please install ******** $1 *********** ... Exiting $default" + exit 1 + fi +} + +# Validate required modules exits in target setup +function validate_setup() { + validate_dependency "aws s3" "aws --version" + validate_dependency "jq" "jq --version" + echo "$(date) Successfully validated required dependencies" +} + +function initialize_variables() { + # Validate file path + validate_file_path $CREDENTIALS_FILE + + export AWS_HOST=$(cat $CREDENTIALS_FILE | jq -r 'select(.AWS_HOST != null) | .AWS_HOST') + export AWS_ENDPOINT=$(cat $CREDENTIALS_FILE | jq -r 'select(.AWS_ENDPOINT != null) | .AWS_ENDPOINT') + export AWS_ACCESS_KEY_ID=$(cat $CREDENTIALS_FILE | jq -r 'select(.AWS_ACCESS_KEY_ID != null) | .AWS_ACCESS_KEY_ID') + export AWS_SECRET_ACCESS_KEY=$(cat $CREDENTIALS_FILE | jq -r 'select(.AWS_SECRET_ACCESS_KEY != null) | .AWS_SECRET_ACCESS_KEY') + readonly FOLDER=${BASE_PATH}/ceph/ + mkdir -p ${FOLDER} +} + +function list_buckets() { + temp_buckets=$(aws s3 --endpoint-url $AWS_ENDPOINT --no-verify-ssl ls) + readonly BUCKETS=${temp_buckets} +} + +function get_cors_policy() { + BUCKET_NAME=${1} + aws --endpoint-url $AWS_ENDPOINT --no-verify-ssl s3api get-bucket-cors --bucket ${BUCKET_NAME} >${FOLDER}${BUCKET_NAME}-cors.json +} + +function download_blob() { + BUCKET_NAME=${1} + echo "$green $(date) Starting sync of object storage to local disk for bucket ${BUCKET_NAME} $default" + mkdir -p ${FOLDER}${BUCKET_NAME} + aws s3 --endpoint-url $AWS_ENDPOINT --no-verify-ssl sync s3://${BUCKET_NAME} ${FOLDER}${BUCKET_NAME} --delete + echo "$green $(date) Finsihed sync of object storage to local disk for bucket ${BUCKET_NAME} $default" +} + +function download_blob_old() { + BUCKET_NAME=${1} + PREFIX=${2} + echo "$green $(date) Starting sync of object storage to local disk for bucket ${BUCKET_NAME} and prefix ${PREFIX} $default" + mkdir -p ${FOLDER}${BUCKET_NAME}/${PREFIX} + # check if less than 1000 blobs + blob_count=$(aws s3api --endpoint-url $AWS_ENDPOINT --no-verify-ssl list-objects --bucket ${BUCKET_NAME} --prefix "${PREFIX}" --output json --query "length(Contents[])") + if [ "$blob_count" -gt 1000 ] + then + # sync root level, caveat that it will only sync 100 + aws s3 --endpoint-url $AWS_ENDPOINT --no-verify-ssl sync s3://${BUCKET_NAME}/${PREFIX} ${FOLDER}${BUCKET_NAME}/${PREFIX} --exclude "*/*" --delete + # get subfolders & call recursively + folders=($(aws s3api --endpoint-url $AWS_ENDPOINT --no-verify-ssl list-objects --bucket ${BUCKET_NAME} --delimiter "/" --prefix "${PREFIX}" --output json | jq -r 'select(.CommonPrefixes != null and (.CommonPrefixes |type) == "array") | .CommonPrefixes[] | select(.Prefix != null).Prefix')) + for subprefix in "${folders[@]}" + do + : + download_blob_old ${BUCKET_NAME} ${subprefix} + done + else + # sync all + aws s3 --endpoint-url $AWS_ENDPOINT --no-verify-ssl sync s3://${BUCKET_NAME}/${PREFIX} ${FOLDER}${BUCKET_NAME}/${PREFIX} --delete + fi + echo "$green $(date) Finsihed sync of object storage to local disk for bucket ${BUCKET_NAME} and prefix ${PREFIX} $default" +} + +function sync_buckets() { + #Ceph issue limits it to 1000 blobs for rook 1.0.6 so find rook first + old_rook="v1.0." + toolbox_pod=$(kubectl -n rook-ceph get pod -l app=rook-ceph-tools -o jsonpath="{.items[0].metadata.name}") + rook_version=$(kubectl -n rook-ceph exec -it $toolbox_pod -- sh -c 'rook version | head -n 1 | cut -d ':' -f2') + while read line; do + echo "Response line: '${line}'" + bucket=$(echo ${line} | cut -d" " -f3) + # get cors policy on bucket + get_cors_policy $bucket + # download bucket contents + if [[ "$rook_version" =~ "$old_rook".* ]]; then + download_blob_old $bucket "" + else + download_blob $bucket + fi + done <<<"$BUCKETS" +} + +# Validate Setup +validate_setup + +# Update ENV Variables +initialize_variables + +# List buckets +list_buckets + +# Sync Buckets +sync_buckets diff --git a/platform/onebox/backup_and_restore/ceph/get-credentials.sh b/platform/onebox/backup_and_restore/ceph/get-credentials.sh new file mode 100644 index 00000000..a0079515 --- /dev/null +++ b/platform/onebox/backup_and_restore/ceph/get-credentials.sh @@ -0,0 +1,43 @@ +#!/bin/bash + +: ' +This scipt will generate json file for creds to be used with import export +Script will generate file storage-creds.json +Run it from the VM running aifabric. +Use it as it is [insecure] or transfer to some credsManager and then change backup/restore scripts to fetch from credsmanager instead of json file +# $1 - [Optional but recommended] pass private ip of the aif machine on which it is accesible from other vms in the same network +[Script Version -> 21.4] +' + +readonly PRIVATE_IP=$1 + +function initialize_variables() { + if [ -z "$PRIVATE_IP" ]; then + # Gets private ip of machine so that it can be connected within the VM + # Seems to be set as localhost on some customer machines + #OBJECT_GATEWAY_EXTERNAL_HOST=$(hostname -i) + PRIVATE_ADDRESS=$(ip -o route get to 8.8.8.8 | sed -n 's/.*src \([0-9.]\+\).*/\1/p') + #This is needed on k8s 1.18.x as $PRIVATE_ADDRESS is found to have a newline + OBJECT_GATEWAY_EXTERNAL_HOST=$(echo "$PRIVATE_ADDRESS" | tr -d '\n') + else + OBJECT_GATEWAY_EXTERNAL_HOST=$PRIVATE_IP + fi + echo "$green $(date) Private IP was $PRIVATE_IP and OBJECT_GATEWAY_EXTERNAL_HOST is $OBJECT_GATEWAY_EXTERNAL_HOST" + + OBJECT_GATEWAY_EXTERNAL_PORT=31443 + + STORAGE_ACCESS_KEY=$(kubectl -n aifabric get secret storage-secrets -o json | jq '.data.OBJECT_STORAGE_ACCESSKEY' | sed -e 's/^"//' -e 's/"$//' | base64 -d) + STORAGE_SECRET_KEY=$(kubectl -n aifabric get secret storage-secrets -o json | jq '.data.OBJECT_STORAGE_SECRETKEY' | sed -e 's/^"//' -e 's/"$//' | base64 -d) + + readonly AWS_HOST=$OBJECT_GATEWAY_EXTERNAL_HOST + readonly AWS_ENDPOINT="https://${OBJECT_GATEWAY_EXTERNAL_HOST}:${OBJECT_GATEWAY_EXTERNAL_PORT}" + readonly AWS_ACCESS_KEY_ID=$STORAGE_ACCESS_KEY + readonly AWS_SECRET_ACCESS_KEY=$STORAGE_SECRET_KEY +} + +function generate_json() { + echo '{"AWS_HOST": "'$AWS_HOST'", "AWS_ENDPOINT": "'$AWS_ENDPOINT'", "AWS_ACCESS_KEY_ID": "'$AWS_ACCESS_KEY_ID'", "AWS_SECRET_ACCESS_KEY": "'$AWS_SECRET_ACCESS_KEY'"}' > storage-creds.json +} + +initialize_variables +generate_json \ No newline at end of file diff --git a/platform/onebox/backup_and_restore/ceph/import.sh b/platform/onebox/backup_and_restore/ceph/import.sh new file mode 100644 index 00000000..8d547aa6 --- /dev/null +++ b/platform/onebox/backup_and_restore/ceph/import.sh @@ -0,0 +1,116 @@ +#!/bin/bash + +: ' +This scipt will import all data stored at a path to blob storage in target environments. +# $1 - json file with credentials, change the script to work with own credential manager +# $2 - path to import from +Script will look for folders like path/ceph/bucket1 path/ceph/bucket2 each containing data from 1 bucket and create bucket and upload +[Script Version -> 21.4] +' + +red=$(tput setaf 1) +green=$(tput setaf 2) +yellow=$(tput setaf 3) +default=$(tput sgr0) + +echo "$green $(date) Starting sync of object storage to local disk $default" + +readonly CREDENTIALS_FILE=$1 +readonly BASE_PATH=$2 + +# Validate file provided by user exists or not, It may be relative path or absolute path +# $1 - File path +function validate_file_path() { + if [ ! -f "$1" ]; then + echo "$red $(date) $1 file does not exist, Please check ... Exiting $default" + exit 1 + fi +} + +function initialize_variables() { + # Validate file path + validate_file_path $CREDENTIALS_FILE + + export AWS_HOST=$(cat $CREDENTIALS_FILE | jq -r 'select(.AWS_HOST != null) | .AWS_HOST') + export AWS_ENDPOINT=$(cat $CREDENTIALS_FILE | jq -r 'select(.AWS_ENDPOINT != null) | .AWS_ENDPOINT') + export AWS_ACCESS_KEY_ID=$(cat $CREDENTIALS_FILE | jq -r 'select(.AWS_ACCESS_KEY_ID != null) | .AWS_ACCESS_KEY_ID') + export AWS_SECRET_ACCESS_KEY=$(cat $CREDENTIALS_FILE | jq -r 'select(.AWS_SECRET_ACCESS_KEY != null) | .AWS_SECRET_ACCESS_KEY') + readonly FOLDER=${BASE_PATH}/ceph/ + +} + +function list_buckets() { + cd $FOLDER + dirs=$(find . -maxdepth 1 -mindepth 1 -type d -printf '%f\n') + readonly DIRS=${dirs} + cd - +} + +function upload_blob() { + BUCKET_NAME=${1} + DIR_NAME=${2} + # create bucket if not exists + local check_bucket=$(s3cmd info --host=${AWS_ENDPOINT} --host-bucket= s3://${BUCKET_NAME} --no-check-certificate -q) + if [ -z "$check_bucket" ]; then + echo "$green $(date) Creating bucket ${BUCKET_NAME} $default" + s3cmd mb --host=${AWS_ENDPOINT} --host-bucket= s3://${BUCKET_NAME} --no-check-certificate + else + echo "$yellow $(date) Bucket exists: ${BUCKET_NAME}, skipping $default" + fi + # sync folder to bucket + echo "$green $(date) Starting sync of object storage to local disk for bucket ${BUCKET_NAME} $default" + aws s3 --endpoint-url ${AWS_ENDPOINT} --no-verify-ssl --only-show-errors sync ${FOLDER}${DIR_NAME}/ s3://${BUCKET_NAME} + echo "$green $(date) Finsihed sync of object storage to local disk for bucket ${BUCKET_NAME} $default" +} + +function update_cors_policy() { + BUCKET_NAME=${1} + DIR_NAME=${2} + if [ ! -f "${FOLDER}${DIR_NAME}-cors.json" ]; then + echo "$red $(date) ${FOLDER}${DIR_NAME}-cors.json file does not exist, Please check ... Skipping cors creation $default" + return + fi + aws --endpoint-url $AWS_ENDPOINT --no-verify-ssl s3api put-bucket-cors --bucket ${BUCKET_NAME} --cors-configuration file://${FOLDER}${DIR_NAME}-cors.json +} + +function process_buckets() { + while read dir; do + echo "Processing directory: '${dir}'" + # aws doesn't allow underscores in bucket name + bucket=${dir//_/-} + # Create and sync bucket contents + upload_blob ${bucket} ${dir} + update_cors_policy ${bucket} ${dir} + done <<<"$DIRS" +} + +# Validate dependency module +# $1 - Name of the dependency module +# $2 - Command to validate module +function validate_dependency() { + eval $2 + # Next statement is checking last command success aws --version has some issue + if [ $? -ne 0 ]; then + echo "$red $(date) Please install ******** $1 *********** ... Exiting $default" + exit 1 + fi +} + +# Validate required modules exits in target setup +function validate_setup() { + validate_dependency "aws s3" "aws --version" + validate_dependency s3cmd "s3cmd --version" + echo "$(date) Successfully validated required dependencies" +} + +# Validate Setup +validate_setup + +# Update ENV Variables +initialize_variables + +# List Buckets +list_buckets + +# Process data inside buckets +process_buckets diff --git a/platform/onebox/backup_and_restore/clusterResources/export.sh b/platform/onebox/backup_and_restore/clusterResources/export.sh new file mode 100644 index 00000000..5ab98649 --- /dev/null +++ b/platform/onebox/backup_and_restore/clusterResources/export.sh @@ -0,0 +1,67 @@ +#!/bin/bash + +: ' +This script will export user oriented cluster resources from one enviornmnet to another envrionment +[Script Version -> 21.4]' + +red=$(tput setaf 1) +green=$(tput setaf 2) +yellow=$(tput setaf 3) +default=$(tput sgr0) + +echo "$green $(date) Starting export of user namespaces and pipeline cron jobs $default" + +# Validate dependency module +# $1 - Name of the dependency module +# $2 - Command to validate module +function validate_dependency() { + eval $2 + # Next statement is checking last command success + if [ $? -ne 0 ]; then + echo "$red $(date) Please install ******** $1 *********** ... Exiting $default" + exit 1 + fi +} + +# Validate required modules exits in target setup +function validate_setup() { + validate_dependency velero "velero version" + echo "$(date) Successfully validated required dependencies" +} + +# Backup all UUID namespace using velero +function backup_namespace() { + echo "$(date) Process of user namespaces backup started" + declare -a NAMESPACES=() + + echo "$(date) Fetching list of all UUID namespaces" + readonly NAMESPACES=$(kubectl get ns -A | awk '/[0-9a-fA-F]{8}\-[0-9a-fA-F]{4}\-[0-9a-fA-F]{4}\-[0-9a-fA-F]{4}\-[0-9a-fA-F]{12}/ {print $1}') + local namespaces_list=$(echo "$NAMESPACES" | paste -s -d, /dev/stdin) + + backup_name="ai-center-onpremise-backup"-$(date +%s) + velero backup create $backup_name --include-namespaces $namespaces_list + + echo "$(date) Successfully backup all user namespaces with name $backup_name" +} + +# Backup all cron jobs using velero +function backup_cronjobs() { + echo "$(date) Process of cronjobs backup started" + + readonly local backup_name="ai-center-cronjobs-onpremise-backup"-$(date +%s) + readonly local backup_cronjobs_ns="aifabric" + + #backup pipeline cron jobs + velero backup create $backup_name --include-resources cronjobs --include-namespaces $backup_cronjobs_ns + + echo "$(date) Successfully backup all cron jobs in namespace $backup_cronjobs_ns with name $backup_name" +} + +# Validate setup +validate_setup + +# Backup namespaces +backup_namespace + +# Backup cron jobs +backup_cronjobs \ No newline at end of file diff --git a/platform/onebox/backup_and_restore/clusterResources/import.sh b/platform/onebox/backup_and_restore/clusterResources/import.sh new file mode 100644 index 00000000..d99e03c6 --- /dev/null +++ b/platform/onebox/backup_and_restore/clusterResources/import.sh @@ -0,0 +1,155 @@ +#!/bin/bash + +: ' +This script will import user triggered cluster resources from one environment to another environment +[Script Version -> 21.4]' + +red=$(tput setaf 1) +green=$(tput setaf 2) +yellow=$(tput setaf 3) +default=$(tput sgr0) + +echo "$green $(date) Starting export of user namespaces and pipeline related cron jobs $default" + +readonly CLUSTER_RESOURCES_EXPORT_FILE=$1 +readonly CORE_SERVICE_NAMESPACE=aifabric +readonly KOTS_REGISTRY_SECRET=kotsadm-replicated-registry + +# Validate dependency module +# $1 - Name of the dependency module +# $2 - Command to validate module +function validate_dependency() { + eval $2 + # Next statement is checking last command success + if [ $? -ne 0 ]; then + echo "$red $(date) Please install ******** $1 *********** ... Exiting $default" + exit 1 + fi +} + +function validate_file_path() { + if [ ! -f "$1" ]; then + echo "$red $(date) $1 file does not exist, Please check ... Exiting $default" + exit 1 + fi +} + +# Validate required modules exits in target setup +function validate_setup() { + validate_dependency velero "velero version" + echo "$(date) Successfully validated required dependencies" +} + +# Validate input provided by end user +function validate_input() { + + # Validate file path + validate_file_path $CLUSTER_RESOURCES_EXPORT_FILE + + readonly NAMESPACES_BACKUP_NAME=$(cat $CLUSTER_RESOURCES_EXPORT_FILE | jq -r 'select(.namespaceBackupName != null) | .namespaceBackupName') + readonly CRONJOBS_BACKUP_NAME=$(cat $CLUSTER_RESOURCES_EXPORT_FILE | jq -r 'select(.cronjobsBackupName != null) | .cronjobsBackupName') + + if [[ -z $NAMESPACES_BACKUP_NAME || -z $CRONJOBS_BACKUP_NAME ]]; then + echo "$red $(date) Input is invalid or missing, Please check ... Exiting $default" + exit 1 + fi + + echo "$green $(date) Successfully validated user input $default" + } + +# Restore all user related namespaces +function restore_namespace() { + echo "$(date) Process of namespace restoration started" + + # Restore namespaces + velero restore create --from-backup $NAMESPACES_BACKUP_NAME + + echo "$(date) Successfully restore all user namespaces from backup $NAMESPACES_BACKUP_NAME" +} + +# Restore cronjobs +function restore_cronjobs() { + echo "$(date) Process of cronjob restoration started" + + # Restore namespaces + velero restore create --from-backup $CRONJOBS_BACKUP_NAME + echo "$(date) Successfully restore all cronjob from backup $CRONJOBS_BACKUP_NAME" +} + +# Patch secrets in namespaces +function update_secrets_in_namespaces() { + + echo "$(date) Updating secrets" + + # Sleep is required for namespaces to be created + sleep 30 + + # Get docker config secrets + readonly registryCredentials=$(kubectl -n $CORE_SERVICE_NAMESPACE get configmap registry-config -o jsonpath="{.data.REGISTRY_CREDENTIALS_PULL}") + local registryInternalIp=$(kubectl get svc registry -n kurl -o jsonpath={.spec.clusterIP}) + + NAMESPACES=($(kubectl get ns -A | awk '/[0-9a-fA-F]{8}\-[0-9a-fA-F]{4}\-[0-9a-fA-F]{4}\-[0-9a-fA-F]{4}\-[0-9a-fA-F]{12}/ {print $1}')) + + for ((ns = 0; ns < ${#NAMESPACES[@]}; ns = ns + 1)); do + local defaultTokenSecrets=$(kubectl get secrets -n ${NAMESPACES[ns]} --no-headers -o custom-columns=":metadata.name" | grep default-token) + + echo "$(date) Deleting $defaultTokenSecrets in namespace ${NAMESPACES[ns]}" + kubectl -n ${NAMESPACES[ns]} delete secrets $defaultTokenSecrets + + # Deleting storage credentials, will be updated from post resource + if (echo ${NAMESPACES[ns]} | grep "training-"); + then + local trainingStorageSecrets=$(kubectl get secrets -n ${NAMESPACES[ns]} --no-headers -o custom-columns=":metadata.name" | grep training-storage-credentials) + echo "$(date) Deleting $trainingStorageSecrets in namespace ${NAMESPACES[ns]}" + kubectl -n ${NAMESPACES[ns]} delete secrets $trainingStorageSecrets + echo "$(date) Deleting all active jobs if any in namespace: ${NAMESPACES[ns]}" + kubectl delete jobs --all -n ${NAMESPACES[ns]} + fi + + # Deleting storage credentials, will be updated from post resource + if (echo ${NAMESPACES[ns]} | grep "data-manager-"); + then + local dataManagerNewSecrets=$(kubectl -n $CORE_SERVICE_NAMESPACE get secrets $KOTS_REGISTRY_SECRET -o jsonpath='{.data.\.dockerconfigjson}') + local appHelperDataManager=$(kubectl get secrets -n ${NAMESPACES[ns]} --no-headers -o custom-columns=":metadata.name" | grep app-data-manager) + local appDataHelperManager=$(kubectl get secrets -n ${NAMESPACES[ns]} --no-headers -o custom-columns=":metadata.name" | grep app-helper-data-manager) + kubectl patch secret $appHelperDataManager -n ${NAMESPACES[ns]} --type='json' -p="[{'op' : 'replace' ,'path' : '/data/.dockerconfigjson' ,'value' : '$dataManagerNewSecrets'}]" + kubectl patch secret $appDataHelperManager -n ${NAMESPACES[ns]} --type='json' -p="[{'op' : 'replace' ,'path' : '/data/.dockerconfigjson' ,'value' : '$dataManagerNewSecrets'}]" + kubectl delete --all deployments --namespace=${NAMESPACES[ns]} + kubectl delete --all svc --namespace=${NAMESPACES[ns]} + fi + + echo "$(date) Updating registry credentials in namespace ${NAMESPACES[ns]}" + kubectl patch secret kurl-registry -n ${NAMESPACES[ns]} --type='json' -p="[{'op' : 'replace' ,'path' : '/data/.dockerconfigjson' ,'value' : '$registryCredentials'}]" + + if [ ${#NAMESPACES[ns]} == 36 ]; then + declare -a DEPLOYMENTS=() + echo "$(date) Fetching list of deployments in skill namespace ${NAMESPACES[ns]}" + DEPLOYMENTS=($(kubectl get deployments --no-headers -o custom-columns=":metadata.name" -n ${NAMESPACES[ns]})) + echo "$(date) Total deployments in namespace ${NAMESPACES[ns]} is ${#DEPLOYMENTS[@]}" + for ((dp = 0; dp < ${#DEPLOYMENTS[@]}; dp = dp + 1)); do + echo "$(date) Patching deployments ${DEPLOYMENTS[dp]} in namespace ${NAMESPACES[ns]}" + imageName=$(kubectl get deployment ${DEPLOYMENTS[dp]} -n ${NAMESPACES[ns]} -o=jsonpath="{..image}") + newImage=$(echo $imageName | sed -r 's/(\b[0-9]{1,3}\.){3}[0-9]{1,3}\b'/"$registryInternalIp"/) + echo "$(date) Old image path: $imageName, new image path: $newImage" + kubectl patch deployment ${DEPLOYMENTS[dp]} -n ${NAMESPACES[ns]} --type json -p="[{'op': 'replace', 'path': '/spec/template/spec/containers/0/image', 'value': '$newImage'}]" + done + fi + done + + echo "$(date) Restore process is completed successfully" +} + +# Validate setup +validate_setup + +# Validate input +validate_input + +# Restore namespaces +restore_namespace + +# Restore cron jobs +restore_cronjobs + +# Update secrets in namespaces +update_secrets_in_namespaces \ No newline at end of file diff --git a/platform/onebox/backup_and_restore/credentials/idputils.sh b/platform/onebox/backup_and_restore/credentials/idputils.sh new file mode 100644 index 00000000..3da2aa78 --- /dev/null +++ b/platform/onebox/backup_and_restore/credentials/idputils.sh @@ -0,0 +1,116 @@ +#!/bin/bash + +: ' +This scipt provides methods to fetch access token and register and de-register clients from identity server +The following arguments are to be set by calling script so that they are available to is.sh +# $1 - identityServerEndPoint: End point where identity server is hosted +# $2 - hostTenantName: Host Tenant name registered in identity server +# $3 - hostTenantIdOrEmailId: Host tenant id or email Id +# $4 - hostTenantPassword: Host tenant password +[Script Version -> 21.4] +' + +red=$(tput setaf 1) +green=$(tput setaf 2) +yellow=$(tput setaf 3) +default=$(tput sgr0) + +# Fetch admin token from identity server end point using host tenant +function internal_fetch_identity_server_token_to_register_client() { + echo "$(date) Fetching identity server client registeration token" + + # Generate required endpoints + readonly local antif=https://$IDENTITY_SERVER_ENDPOINT"/identity/api/antiforgery/generate" + readonly local login=https://$IDENTITY_SERVER_ENDPOINT"/identity/api/Account/Login" + readonly local tokenUrl=https://$IDENTITY_SERVER_ENDPOINT"/identity/api/Account/ClientAccessToken" + + dataLogin='{ + "tenant": "'$HOST_TENANT_NAME'", + "usernameOrEmail": "'$HOST_TENANT_USER_ID_OR_EMAIL'", + "password": "'$HOST_TENANT_PASSWORD'", + "rememberLogin": true + }' + + cookie_file="cookiefile.txt" + cookie_file_new="cookiefile_new.txt" + + # Get token and construct the cookie, save the returned token. + curl --silent --fail --show-error -k -c $cookie_file --request GET "$antif" + + # Replace headers + sed 's/XSRF-TOKEN-IS/XSRF-TOKEN/g' $cookie_file >$cookie_file_new + + token=$(cat $cookie_file_new | grep XSRF-TOKEN | cut -f7 -d$'\t') + + # Authentication -> POST to $login_url with the token in header "X-CSRF-Token: $token". + curl --silent --fail --show-error -k -H "X-XSRF-TOKEN: $token" -c $cookie_file_new -b $cookie_file_new -d "$dataLogin" --request POST "$login" -H "Content-Type: application/json" + + # Fetch Acces token + CLIENT_INSTALLTION_TOKEN=$(curl --silent --fail --show-error -k -H "X-XSRF-TOKEN: $token" -b $cookie_file_new "$tokenUrl" -H "Content-Type: application/json") + + if [ -z "$CLIENT_INSTALLTION_TOKEN" ]; then + echo "$(date) $red Failed to generate token to register client ... Exiting $default" + exit 1 + fi +} + +# Fetch access token to call backens server +function internal_fetch_identity_server_access_token() { + echo "$(date) Getting access token for client $IS_AIFABRIC_CLIENT_NAME from $IDENTITY_SERVER_ENDPOINT" + + readonly access_token_response=$( + curl -k --silent --fail --show-error -X --location --request POST "https://${IDENTITY_SERVER_ENDPOINT}/identity/connect/token" \ + -H 'Content-Type: application/x-www-form-urlencoded' \ + --data-urlencode "client_Id=$IS_AIFABRIC_CLIENT_ID" \ + --data-urlencode "client_secret=$IS_AIFABRIC_CLIENT_SECRET" \ + --data-urlencode "grant_type=client_credentials" + ) + + if [ -z "$access_token_response" ]; then + echo "$(date) $red Failed to generate access token to call backend server ... Exiting $default" + deregister_client + exit 1 + fi + + export ACCESS_TOKEN=$(echo "$access_token_response" | jq -r 'select(.access_token != null) | .access_token') + + if [ -z "$ACCESS_TOKEN" ]; then + echo "$(date) $red Failed to extract access token ... Exiting $default" + deregister_client + exit 1 + fi + + echo "$(date) Successfully fetched access token to call backend server " +} + +# De-register clients, all values needed for it to work would be set already by calling register_client_and_fetch_access_token +function deregister_client() { + echo "$default $(date) Deregistering client from $IDENTITY_SERVER_ENDPOINT with name $IS_AIFABRIC_CLIENT_NAME" + curl -k -i --silent --fail --show-error -X DELETE "https://${IDENTITY_SERVER_ENDPOINT}/identity/api/Client/$IS_AIFABRIC_CLIENT_ID" -H "Authorization: Bearer ${CLIENT_INSTALLTION_TOKEN}" +} + +# Register client and fetch Access token +function register_client_and_fetch_access_token() { + + export IS_AIFABRIC_CLIENT_ID="aifabric-"$(openssl rand -hex 10) + export IS_AIFABRIC_CLIENT_SECRET=$(openssl rand -hex 32) + export IS_AIFABRIC_CLIENT_NAME="aifabric-"$(openssl rand -hex 10) + + # Fetch admin token + internal_fetch_identity_server_token_to_register_client + + # Register client + echo "$(date) Registering client by name $IS_AIFABRIC_CLIENT_NAME with client id $IS_AIFABRIC_CLIENT_ID" + + client_creation_response=$(curl -k --silent --fail --show-error -X POST "https://${IDENTITY_SERVER_ENDPOINT}/identity/api/Client" -H "Connection: keep-alive" -H "accept: text/plain" -H "Authorization: Bearer ${CLIENT_INSTALLTION_TOKEN}" -H "Content-Type: application/json-patch+json" -H "Accept-Encoding: gzip, deflate, br" -H "Accept-Language: en-US,en;q=0.9" -d "{\"clientId\":\"${IS_AIFABRIC_CLIENT_ID}\",\"clientName\":\"${IS_AIFABRIC_CLIENT_NAME}\",\"clientSecrets\":[\"${IS_AIFABRIC_CLIENT_SECRET}\"],\"requireConsent\":false,\"requireClientSecret\": true,\"allowOfflineAccess\":true,\"alwaysSendClientClaims\":true,\"allowAccessTokensViaBrowser\":true,\"allowOfflineAccess\":true,\"alwaysIncludeUserClaimsInIdToken\":true,\"accessTokenLifetime\":${ACCESS_TOKEN_LIFE_TIME},\"identityTokenLifetime\":${ACCESS_TOKEN_LIFE_TIME},\"authorizationCodeLifetime\":${ACCESS_TOKEN_LIFE_TIME},\"absoluteRefreshTokenLifetime\":${ACCESS_TOKEN_LIFE_TIME},\"slidingRefreshTokenLifetime\":${ACCESS_TOKEN_LIFE_TIME},\"RequireRequestObject\":true,\"Claims\":true,\"AlwaysIncludeUserClaimsInIdToken\":true,\"allowedGrantTypes\":[\"client_credentials\",\"authorization_code\"],\"allowedResponseTypes\":[\"id_token\"],\"allowedScopes\":[\"openid\",\"profile\",\"email\",\"AiFabric\",\"IdentityServerApi\",\"Orchestrator\",\"OrchestratorApiUserAccess\"]}") + + if [ -z "$client_creation_response" ]; then + echo "$(date) $red Failed to register client $IS_AIFABRIC_CLIENT_NAME with identity server $IDENTITY_SERVER_ENDPOINT ... Exiting $default" + exit 1 + fi + + # Fetch access token authorize backend server call + internal_fetch_identity_server_access_token +} + +echo "$red Please source the script & call individual methods instead of calling the script directly $default" \ No newline at end of file diff --git a/platform/onebox/backup_and_restore/packages/export.sh b/platform/onebox/backup_and_restore/packages/export.sh new file mode 100644 index 00000000..8377cb7b --- /dev/null +++ b/platform/onebox/backup_and_restore/packages/export.sh @@ -0,0 +1,442 @@ +#!/bin/bash + +: ' +This scipt will download ML package from target environment, expect cloned public packages. +# $1 - ML package export json file path + +[ Structure of ML Package export file with exact key name ] + - hostOrFQDN: Public end point from where backend service can be accessible + - identityServerEndPoint: End point where identity server is hosted + - hostTenantName: Host Tenant name registered in identity server + - hostTenantIdOrEmailId: Host tenant id or email Id + - hostTenantPassword: Host tenant password + - tenantName: Name of the tenant from where ML package export will be carried out + - projectName: Project Name from where ML package will be exported + - mlPackageName: Name of ML package which will be downloaded from target environment + - mlPackageVersion: ML package version number which will be downloaded. It should be in format like 3.2 or 3.1 etc +[Script Version -> 20.10.1.2] +' + +red=$(tput setaf 1) +green=$(tput setaf 2) +yellow=$(tput setaf 3) +default=$(tput sgr0) + +echo "$green $(date) Starting export of ML Package $default" + +readonly PAGE_SIZE=10000 +readonly ACCESS_TOKEN_LIFE_TIME=345600 +readonly ML_PACKAGE_EXPORT_INPUT_FILE=$1 + +# Validate API response +# $1 - Api response code +# $2 - Expected response code +# $3 - Success Message +# $4 - Error message +function validate_response_from_api() { + if [ $1 = $2 ]; then + echo "$(date) $3" + elif [ "$1" = "DEFAULT" ]; then + echo "$red $(date) Please validate access token or check internet. If fine check curl status code ... Exiting $default" + deregister_client + exit 1 + else + echo "$4 $(date) $default" + deregister_client + exit 1 + fi +} + +# Download ML package from blob storage using generated signed url +# $1 - Signed Url +# $2 - ML Package name to be saved +function download_ml_package_using_signedUrl() { + local signed_url=$1 + local ml_package=$2 + + echo "$yellow $(date) Downloading ML package ... $default" + curl --progress-bar -L -k -o $ml_package $signed_url + + if [ $? -ne 0 ]; then + echo "$red $(date) Failed to download ML package $default" + remove_directory + deregister_client + exit 1 + fi +} + +# Get signed for ML package located at blob storage +# $1 - Signing Method Type +# $2 - Encoded URl +function get_signed_url() { + local signing_method=$1 + local encoded_url=$2 + generated_signed_url=$(curl --silent --fail --show-error -k 'https://'"$INGRESS_HOST_OR_FQDN"'/ai-pkgmanager/v1/signedURL?mlPackageName='"$blob_path"'&signingMethod='"$signing_method"'&encodedUrl='"$encoded_url"'' -H 'tenant-id: '"$TENANT_ID"'' -H 'authorization: Bearer '"$ACCESS_TOKEN"'') + + local resp_code=DEFAULT + if [ ! -z "$generated_signed_url" ]; then + resp_code=$(echo "$generated_signed_url" | jq -r 'select(.respCode != null) | .respCode') + fi + + validate_response_from_api $resp_code "200" "Signed url generated successfully for blob path: $blob_path, signing method: $1, encoded url: $2" "$red Failed to generate signed url for blob path $1, signing method $2 ... Exiting !!!" +} + +# Generate ML package blob path +# $1 - ML package Id +# $2 - ML package file name with extention +function generate_ml_package_blobPath() { + local account_id=$(echo "$ml_package_version" | jq -r 'select(.accountId != null) | .accountId') + local tenant_id=$(echo "$ml_package_version" | jq -r 'select(.tenantId != null) | .tenantId') + local project_id=$(echo "$ml_package_version" | jq -r 'select(.projectId != null) | .projectId') + local ml_package_version_id=$(echo "$ml_package_version" | jq -r 'select(.id != null) | .id') + + blob_path=$account_id/$tenant_id/$project_id/$1/$ml_package_version_id/$2 +} + +# Fetch List of ML packages version given by ML package id +# $1 - ML Package id +function fetch_ml_package_versions_by_ml_package_id() { + local ml_package_id=$1 + local project_id=$2 + readonly mlpackage_versions_of_ml_package=$(curl -k --silent --fail --show-error 'https://'"$INGRESS_HOST_OR_FQDN"'/ai-pkgmanager/v1/mlpackages/'"$ml_package_id"'/versions?pageSize='"$PAGE_SIZE"'&sortBy=createdOn&sortOrder=DESC&projectId='"$project_id"'' -H 'tenant-id: '"$TENANT_ID"'' -H 'authorization: Bearer '"$ACCESS_TOKEN"'') + + local resp_code=DEFAULT + if [ ! -z "$mlpackage_versions_of_ml_package" ]; then + resp_code=$(echo "$mlpackage_versions_of_ml_package" | jq -r 'select(.respCode != null) | .respCode') + fi + + validate_response_from_api $resp_code "200" "Successfully fetched ML package versions for ML package $ML_PACKAGE_NAME" "$red Failed to fetch ML package versions for ML package $ML_PACKAGE_NAME ... Exiting" +} + +# Fetch list of all ML packages +# $1 - Project Id under which ML packages is present +function fetch_ml_packages() { + local project_id=$1 + ml_packages=$(curl -k --silent --fail --show-error 'https://'"$INGRESS_HOST_OR_FQDN"'/ai-pkgmanager/v1/mlpackages?pageSize='"$PAGE_SIZE"'&sortBy=createdOn&sortOrder=DESC&projectId='"$project_id"'' -H 'tenant-id: '"$TENANT_ID"'' -H 'authorization: Bearer '"$ACCESS_TOKEN"'') + + local resp_code=DEFAULT + if [ ! -z "$ml_packages" ]; then + resp_code=$(echo "$ml_packages" | jq -r 'select(.respCode != null) | .respCode') + fi + + validate_response_from_api $resp_code "200" "Successfully fetched ML packages" "$red 1 Failed to fetch ML packages ... Exiting" +} + +# Find all projects +function fetch_projects() { + projects=$(curl -k --silent --fail --show-error 'https://'"$INGRESS_HOST_OR_FQDN"'/ai-pkgmanager/v1/projects?pageSize='"$PAGE_SIZE"'' -H 'tenant-id: '"$TENANT_ID"'' -H 'authorization: Bearer '"$ACCESS_TOKEN"'') + + local resp_code=DEFAULT + if [ ! -z "$projects" ]; then + resp_code=$(echo "$projects" | jq -r 'select(.respCode != null) | .respCode') + fi + + validate_response_from_api $resp_code "200" "Successfully fetched projects" "$red Failed to fetch projects ... Exiting" +} + +# Download ML package +function download_ml_package() { + + # Fetch tenant details + get_tenant_details + + # Fetch Projects + fetch_projects + + # Fetch project by name + local project=$(echo "$projects" | jq ".data.dataList[] | select(.name != null) | select(.name==\"$PROJECT_NAME\")") + + if [ ! -z "$project" ]; then + echo "$(date) Project by name $PROJECT_NAME fetched successfully" + else + echo "$red $(date) Failed to find project by name $PROJECT_NAME, Please check Project Name ... Exiting $default" + deregister_client + exit 1 + fi + + local project_id=$(echo "$project" | jq -r 'select(.id != null) | .id') + + if [ -z "$project_id" ]; then + echo "$red $(date) Failed to extract project id from list of projects ... Exiting $default" + deregister_client + exit 1 + fi + + # Fetch list of all ML packages from target environment + fetch_ml_packages $project_id + + # Fetch ML Package by name from list of Packages + local ml_package=$(echo "$ml_packages" | jq ".data.dataList[] | select(.name != null) | select(.name==\"$ML_PACKAGE_NAME\")") + + if [ ! -z "$ml_package" ]; then + echo "$(date) ML package by name $ML_PACKAGE_NAME fetched successfully" + else + echo "$red $(date) Failed to find ML Package by name $ML_PACKAGE_NAME, Please check ML Package name ... Exiting $default" + deregister_client + exit 1 + fi + + # Extract ML package Id + ml_package_id=$(echo "$ml_package" | jq -r 'select(.id != null) | .id') + + if [ -z "$ml_package_id" ]; then + echo "$red $(date) Failed to extract ML package id from list of ML Packages ... Exiting $default" + deregister_client + exit 1 + fi + + # Extract ML package major and minor version from input + local minor_ml_package_version=${ML_PACKAGE_VERSION##*.} + local major_ml_package_version=${ML_PACKAGE_VERSION%.*} + + # Fetch list of ML Packages versions by ML Package Id + fetch_ml_package_versions_by_ml_package_id $ml_package_id $project_id + + # Fetch ML package version fron list of versions + ml_package_version=$(echo "$mlpackage_versions_of_ml_package" | jq '.data.dataList[] | select((.version != null) and (.trainingVersion != null) and (.version=='$major_ml_package_version' and .trainingVersion=='$minor_ml_package_version')) | select(.status == ("UNDEPLOYED", "DEPLOYED", "DEPLOYING"))') + + if [ ! -z "$ml_package_version" ]; then + echo "$(date) ML package verison $ML_PACKAGE_VERSION fetched successfully" + else + echo "$red $(date) Failed to fetch ML Package version $ML_PACKAGE_VERSION for ML package $ML_PACKAGE_NAME in status [UNDEPLOYED, DEPLOYED, DEPLOYING] in project $PROJECT_NAME... Exiting $default" + deregister_client + exit 1 + fi + + # Extract content uri + local ml_package_version_content_uri=$(echo "$ml_package_version" | jq 'select(.contentUri != null) | .contentUri') + + if [ -z "$ml_package_version_content_uri" ]; then + echo "$red $(date) We can only download version and trained packages, not cloned version, please verify ML package version ... Exiting $default" + deregister_client + exit 1 + fi + + # Extract zip file name + zip_path="${ml_package_version_content_uri##*/}" + ml_package_zip_file_name=${zip_path%.*}.zip + + metadata_file_name=${zip_path%.*}_v${ML_PACKAGE_VERSION}_metadata + + # Generate ML package blob path + generate_ml_package_blobPath $ml_package_id $ml_package_zip_file_name + + # Get Signed url + get_signed_url GET false + + signed_url=$(echo "$generated_signed_url" | jq -r 'select(.data != null) | .data' | jq -r 'select(.url != null) | .url') + + if [ -z "$signed_url" ]; then + echo "$red $(date) Failed to extract signed url ... Exiting $default" + deregister_client + exit 1 + fi + + # create new directory + create_directory + + echo $ml_package_version >$metadata_file_name.json + echo "$yellow $(date) Successfully saved ML package version $ML_PACKAGE_VERSION metadata json file with name $metadata_file_name in [$(pwd)] directory $default" + + # Download ML package using genearted signed url + download_ml_package_using_signedUrl $signed_url $ml_package_zip_file_name +} + +# Get details of tenant by name +function get_tenant_details() { + echo "$(date) Fetching Tenant details for tenant $TENANT_NAME" + local aif_tenant_details=$(curl -k --silent --fail --show-error 'https://'"$INGRESS_HOST_OR_FQDN"'/ai-deployer/v1/tenant/tenantdetails?tenantName='"$TENANT_NAME"'' -H 'authorization: Bearer '"$ACCESS_TOKEN"'') + + local resp_code=DEFAULT + if [ ! -z "$aif_tenant_details" ]; then + resp_code=$(echo "$aif_tenant_details" | jq -r 'select(.respCode != null) | .respCode') + fi + validate_response_from_api $resp_code "200" "Successfully fetched tenant details for tenant $TENANT_NAME" "$red Failed to fetch tenant details for tenant $TENANT_NAME ... Exiting" + + # Extract tenant Id + TENANT_ID=$(echo "$aif_tenant_details" | jq -r 'select(.data.provisionedTenantId != null) | .data.provisionedTenantId') + + if [ -z "$TENANT_ID" ]; then + echo "$red $(date) Failed to extract tenant id... Exiting $default" + deregister_client + exit 1 + fi +} + +# Fetch admin token from identity server end point using host tenant +function fetch_identity_server_token_to_register_client() { + echo "$(date) Fetching identity server client registeration token" + + # Generate required endpoints + readonly local antif=https://$IDENTITY_SERVER_ENDPOINT"/identity/api/antiforgery/generate" + readonly local login=https://$IDENTITY_SERVER_ENDPOINT"/identity/api/Account/Login" + readonly local tokenUrl=https://$IDENTITY_SERVER_ENDPOINT"/identity/api/Account/ClientAccessToken" + + dataLogin='{ + "tenant": "'$HOST_TENANT_NAME'", + "usernameOrEmail": "'$HOST_TENANT_USER_ID_OR_EMAIL'", + "password": "'$HOST_TENANT_PASSWORD'", + "rememberLogin": true + }' + + cookie_file="cookfile.txt" + cookie_file_new="cookfile_new.txt" + + # Get token and construct the cookie, save the returned token. + curl --silent --fail --show-error -k -c $cookie_file --request GET "$antif" + + # Replace headers + sed 's/XSRF-TOKEN-IS/XSRF-TOKEN/g' $cookie_file >$cookie_file_new + + token=$(cat $cookie_file_new | grep XSRF-TOKEN | cut -f7 -d$'\t') + + # Authentication -> POST to $login_url with the token in header "X-CSRF-Token: $token". + curl --silent --fail --show-error -k -H "X-XSRF-TOKEN: $token" -c $cookie_file_new -b $cookie_file_new -d "$dataLogin" --request POST "$login" -H "Content-Type: application/json" + + # Fetch Acces token + CLIENT_INSTALLTION_TOKEN=$(curl --silent --fail --show-error -k -H "X-XSRF-TOKEN: $token" -b $cookie_file_new "$tokenUrl" -H "Content-Type: application/json") + + if [ -z "$CLIENT_INSTALLTION_TOKEN" ]; then + echo "$(date) $red Failed to generate token to register client ... Exiting $default" + exit 1 + fi +} + +# Fetch access token to call backens server +function fetch_identity_server_access_token() { + echo "$(date) Getting access token for client $IS_AIFABRIC_CLIENT_NAME from $IDENTITY_SERVER_ENDPOINT" + + readonly access_token_response=$( + curl -k --silent --fail --show-error -X --location --request POST "https://${IDENTITY_SERVER_ENDPOINT}/identity/connect/token" \ + -H 'Content-Type: application/x-www-form-urlencoded' \ + --data-urlencode "client_Id=$IS_AIFABRIC_CLIENT_ID" \ + --data-urlencode "client_secret=$IS_AIFABRIC_CLIENT_SECRET" \ + --data-urlencode "grant_type=client_credentials" + ) + + if [ -z "$access_token_response" ]; then + echo "$(date) $red Failed to generate access token to call backend server ... Exiting $default" + deregister_client + exit 1 + fi + + ACCESS_TOKEN=$(echo "$access_token_response" | jq -r 'select(.access_token != null) | .access_token') + + if [ -z "$ACCESS_TOKEN" ]; then + echo "$(date) $red Failed to extract access token ... Exiting $default" + deregister_client + exit 1 + fi + + echo "$(date) Successfully fetched access token to call backend server " +} + +function deregister_client() { + echo "$default $(date) Deregistering client from $IDENTITY_SERVER_ENDPOINT with name $IS_AIFABRIC_CLIENT_NAME" + curl -k -i --silent --fail --show-error -X DELETE "https://${IDENTITY_SERVER_ENDPOINT}/identity/api/Client/$IS_AIFABRIC_CLIENT_ID" -H "Authorization: Bearer ${CLIENT_INSTALLTION_TOKEN}" +} + +# Register client and fetch Access token +function register_client_and_fetch_access_token() { + + readonly IS_AIFABRIC_CLIENT_ID="aifabric-"$(openssl rand -hex 10) + readonly IS_AIFABRIC_CLIENT_SECRET=$(openssl rand -hex 32) + readonly IS_AIFABRIC_CLIENT_NAME="aifabric-"$(openssl rand -hex 10) + + # Fetch admin token + fetch_identity_server_token_to_register_client + + # Register client + echo "$(date) Registering client by name $IS_AIFABRIC_CLIENT_NAME with client id $IS_AIFABRIC_CLIENT_ID" + + client_creation_response=$(curl -k --silent --fail --show-error -X POST "https://${IDENTITY_SERVER_ENDPOINT}/identity/api/Client" -H "Connection: keep-alive" -H "accept: text/plain" -H "Authorization: Bearer ${CLIENT_INSTALLTION_TOKEN}" -H "Content-Type: application/json-patch+json" -H "Accept-Encoding: gzip, deflate, br" -H "Accept-Language: en-US,en;q=0.9" -d "{\"clientId\":\"${IS_AIFABRIC_CLIENT_ID}\",\"clientName\":\"${IS_AIFABRIC_CLIENT_NAME}\",\"clientSecrets\":[\"${IS_AIFABRIC_CLIENT_SECRET}\"],\"requireConsent\":false,\"requireClientSecret\": true,\"allowOfflineAccess\":true,\"alwaysSendClientClaims\":true,\"allowAccessTokensViaBrowser\":true,\"allowOfflineAccess\":true,\"alwaysIncludeUserClaimsInIdToken\":true,\"accessTokenLifetime\":${ACCESS_TOKEN_LIFE_TIME},\"identityTokenLifetime\":${ACCESS_TOKEN_LIFE_TIME},\"authorizationCodeLifetime\":${ACCESS_TOKEN_LIFE_TIME},\"absoluteRefreshTokenLifetime\":${ACCESS_TOKEN_LIFE_TIME},\"slidingRefreshTokenLifetime\":${ACCESS_TOKEN_LIFE_TIME},\"RequireRequestObject\":true,\"Claims\":true,\"AlwaysIncludeUserClaimsInIdToken\":true,\"allowedGrantTypes\":[\"client_credentials\",\"authorization_code\"],\"allowedResponseTypes\":[\"id_token\"],\"allowedScopes\":[\"openid\",\"profile\",\"email\",\"AiFabric\",\"IdentityServerApi\",\"Orchestrator\",\"OrchestratorApiUserAccess\"]}") + + if [ -z "$client_creation_response" ]; then + echo "$(date) $red Failed to register client $IS_AIFABRIC_CLIENT_NAME with identity server $IDENTITY_SERVER_ENDPOINT ... Exiting $default" + exit 1 + fi + + # Fetch access token authorize backend server call + fetch_identity_server_access_token +} + +# Validate file provided by user exists or not, It may be relative path or absolute path +# $1 - File path +function validate_file_path() { + if [ ! -f "$1" ]; then + echo "$red $(date) $1 file does not exist, Please check ... Exiting $default" + exit 1 + fi +} + +# Validate input provided by end user +function validate_input() { + + # Validate file path + validate_file_path $ML_PACKAGE_EXPORT_INPUT_FILE + + readonly INGRESS_HOST_OR_FQDN=$(cat $ML_PACKAGE_EXPORT_INPUT_FILE | jq -r 'select(.hostOrFQDN != null) | .hostOrFQDN') + readonly TENANT_NAME=$(cat $ML_PACKAGE_EXPORT_INPUT_FILE | jq -r 'select(.tenantName != null) | .tenantName') + readonly PROJECT_NAME=$(cat $ML_PACKAGE_EXPORT_INPUT_FILE | jq -r 'select(.projectName != null) | .projectName') + readonly ML_PACKAGE_NAME=$(cat $ML_PACKAGE_EXPORT_INPUT_FILE | jq -r 'select(.mlPackageName != null) | .mlPackageName') + readonly ML_PACKAGE_VERSION=$(cat $ML_PACKAGE_EXPORT_INPUT_FILE | jq -r 'select(.mlPackageVersion != null) | .mlPackageVersion') + readonly IDENTITY_SERVER_ENDPOINT=$(cat $ML_PACKAGE_EXPORT_INPUT_FILE | jq -r 'select(.identityServerEndPoint != null) | .identityServerEndPoint') + readonly HOST_TENANT_NAME=$(cat $ML_PACKAGE_EXPORT_INPUT_FILE | jq -r 'select(.hostTenantName != null) | .hostTenantName') + readonly HOST_TENANT_USER_ID_OR_EMAIL=$(cat $ML_PACKAGE_EXPORT_INPUT_FILE | jq -r 'select(.hostTenantIdOrEmailId != null) | .hostTenantIdOrEmailId') + readonly HOST_TENANT_PASSWORD=$(cat $ML_PACKAGE_EXPORT_INPUT_FILE | jq -r 'select(.hostTenantPassword != null) | .hostTenantPassword') + + if [[ -z $INGRESS_HOST_OR_FQDN || -z $PROJECT_NAME || -z $ML_PACKAGE_NAME || -z $ML_PACKAGE_VERSION || -z TENANT_NAME || -z IDENTITY_SERVER_ENDPOINT || -z HOST_TENANT_NAME || -z HOST_TENANT_USER_ID_OR_EMAIL || -z HOST_TENANT_PASSWORD ]]; then + echo "$red $(date) Input is invalid or missing, Please check ... Exiting $default" + exit 1 + fi + + echo "$(date) Successfully validated user input" +} + +# Validate dependency module +# $1 - Name of the dependency module +# $2 - Command to validate module +function validate_dependency() { + eval $2 + # Next statement is checking last command success + if [ $? -ne 0 ]; then + echo "$red $(date) Please install ******** $1 *********** ... Exiting $default" + exit 1 + fi +} + +# Validate required modules exits in target setup +function validate_setup() { + validate_dependency curl "curl --version" + validate_dependency jq "jq --version" + echo "$(date) Successfully validated required dependencies" +} + +# Create directory +function create_directory() { + dir_name=${ML_PACKAGE_NAME}_v${ML_PACKAGE_VERSION}_$(date +%s) + mkdir -p $dir_name + cd $dir_name +} + +# Remove directory +function remove_directory() { + rm -rf $dir_name +} + +# Validate Setup +validate_setup + +# Validate Input +validate_input + +# Register Client and fetch access token +register_client_and_fetch_access_token + +# Download requested ML package +download_ml_package + +# Register client +deregister_client + +echo "$green $(date) Successfully downloaded $ML_PACKAGE_NAME V$ML_PACKAGE_VERSION in [$(pwd)] directory $default" diff --git a/platform/onebox/backup_and_restore/packages/import.sh b/platform/onebox/backup_and_restore/packages/import.sh new file mode 100644 index 00000000..00e2deb8 --- /dev/null +++ b/platform/onebox/backup_and_restore/packages/import.sh @@ -0,0 +1,654 @@ +#!/bin/bash + +: ' +This scipt will upload ML package to target environment. +# $1 - ML package import json file path + +[ Structure of ML Package import json file along with exact key name] + - hostOrFQDN: Public end point from where backend service can be accessible + - identityServerEndPoint: End point where identity server is hosted + - hostTenantName: Host Tenant name registered in identity server + - hostTenantIdOrEmailId: Host tenant id or email Id + - hostTenantPassword: Host tenant password + - tenantName: Name of tenant where ML package import will be carried out + - projectName: Project Name to which ML package will be imported + - mlPackageName: Name of ML package to which new version will be uploaded if exists, otherwise new ML package by same name will be created + - mlPackageMajorVersionForPrivatePackage: Used to upload new minor version like 3.X. Used for only private packages. Default value should be zero + - mlPackageZipFilePath: ML package zip file path with extension that will be uploaded to target environment + - mlPackageMetadataFilePath: ML package import metadata json file path with extension +[Script Version -> 20.10.1.2] +' + +red=$(tput setaf 1) +green=$(tput setaf 2) +yellow=$(tput setaf 3) +default=$(tput sgr0) + +# Will be used in public package upload +source_ml_package_owned_by_accountId="" +source_ml_package_owned_by_tenantId="" +source_ml_package_owned_by_projectId="" +source_ml_package_id="" +source_ml_package_version_id="" + +readonly ML_PACKAGE_IMPORT_INPUT_FILE=$1 +readonly PAGE_SIZE=10000 +readonly ACCESS_TOKEN_LIFE_TIME=345600 + +echo "$green $(date) Starting import of ML Package $default" + +# Validate API response +# $1 - Api response code +# $2 - Expected response code +# $3 - Success Message +# $4 - Error message +function validate_response_from_api() { + if [ $1 = $2 ]; then + echo "$(date) $3" + elif [ "$1" = "DEFAULT" ]; then + echo "$red $(date) Please validate access token or internet. If fine check returned curl status code ... Exiting $default" + deregister_client + exit 1 + else + echo "$(date) $4 $default" + deregister_client + exit 1 + fi +} + +# Create public ML package +function create_public_ml_package_metadata() { + echo "$(date) Creating public ML package metadata for ML package $ML_PACKAGE_NAME in project $PROJECT_NAME" + local create_public_ml_package=$(curl -k --silent --fail --show-error 'https://'"$INGRESS_HOST_OR_FQDN"'/ai-pkgmanager/v1/mlpackages/clone' -H 'tenant-id: '"$TENANT_ID"'' -H 'accept: application/json, text/plain, */*' -H 'authorization: Bearer '"$ACCESS_TOKEN"'' -H 'content-type: application/json;charset=UTF-8' --data-binary ''"$extractedMetadata"'') + + local resp_code=DEFAULT + if [ ! -z "$create_public_ml_package" ]; then + resp_code=$(echo "$create_public_ml_package" | jq -r 'select(.respCode != null) | .respCode') + fi + + validate_response_from_api $resp_code "201" "$green Successfully created ML package $ML_PACKAGE_NAME $default" "$red Failed to create ML package ... Exiting !!!" +} + +# Create public ML package version +# $1 - ML package id +function create_public_ml_package_version_metadata() { + local ml_package_id=$1 + echo "$(date) Creating public ML package version metadata for ML package $ML_PACKAGE_NAME in project $PROJECT_NAME" + local create_public_ml_package_version=$(curl -k --silent --fail --show-error 'https://'"$INGRESS_HOST_OR_FQDN"'/ai-pkgmanager/v1/mlpackages/'"$ml_package_id"'/versions/clone' -H 'tenant-id: '"$TENANT_ID"'' -H 'accept: application/json, text/plain, */*' -H 'authorization: Bearer '"$ACCESS_TOKEN"'' \ + -H 'content-type: application/json;charset=UTF-8' --data-binary ''"$extractedMetadata"'') + + local resp_code=DEFAULT + if [ ! -z "$create_public_ml_package_version" ]; then + resp_code=$(echo "$create_public_ml_package_version" | jq -r 'select(.respCode != null) | .respCode') + local major_package_version=$(echo "$create_public_ml_package_version" | jq '.data | select(.version != null) | .version') + local minor_package_version=$(echo "$create_public_ml_package_version" | jq '.data | select(.trainingVersion != null) | .trainingVersion') + fi + + validate_response_from_api $resp_code "201" "$green Successfully created ML package version v$major_package_version.$minor_package_version for ML package $ML_PACKAGE_NAME $default" "$red Failed to create ML package version ... Exiting !!!" +} + +# Create ML package version metadata +# $1 - ML Package Id +function create_ml_package_version_metadata() { + echo "$(date) Creating new ML package version for ML package $ML_PACKAGE_NAME in project $PROJECT_NAME" + local ml_package_id=$1 + local ml_package_version_creation=$(curl --silent --fail --show-error -k 'https://'"$INGRESS_HOST_OR_FQDN"'/ai-pkgmanager/v1/mlpackages/'"$ml_package_id"'/versions?mlPackageCreationType=ML_PACKAGE_VERSION_IMPORT' -H 'tenant-id: '"$TENANT_ID"'' -H 'authorization: Bearer '"$ACCESS_TOKEN"'' -H 'content-type: application/json;charset=UTF-8' --data-binary ''"$extractedMetadata"'') + + local resp_code=DEFAULT + if [ ! -z "$ml_package_version_creation" ]; then + resp_code=$(echo "$ml_package_version_creation" | jq -r 'select(.respCode != null) | .respCode') + local major_package_version=$(echo "$ml_package_version_creation" | jq '.data | select(.version != null) | .version') + local minor_package_version=$(echo "$ml_package_version_creation" | jq '.data | select(.trainingVersion != null) | .trainingVersion') + fi + + validate_response_from_api $resp_code "201" "$green Successfully created ML package version v$major_package_version.$minor_package_version for ML package $ML_PACKAGE_NAME $default" "$red Failed to create ML package version ... Exiting !!!" +} + +# Create ML package metadata +function create_ml_package_metadata() { + echo "$(date) Creating new ML package $ML_PACKAGE_NAME in project $PROJECT_NAME" + local ml_package_creation=$(curl --silent --fail --show-error -k 'https://'"$INGRESS_HOST_OR_FQDN"'/ai-pkgmanager/v1/mlpackages' -H 'tenant-id: '"$TENANT_ID"'' -H 'content-type: application/json;charset=UTF-8' -H 'authorization: Bearer '"$ACCESS_TOKEN"'' --data-binary ''"$extractedMetadata"'') + + local resp_code=DEFAULT + if [ ! -z "$ml_package_creation" ]; then + resp_code=$(echo "$ml_package_creation" | jq -r 'select(.respCode != null) | .respCode') + fi + + validate_response_from_api $resp_code "201" "$green Successfully created ML package" "$red Failed to create ML package version ... Exiting !!!" +} + +# Validate if last command executed successfully +# $1- Error message +function validate_last_command_executed_succesfully() { + if [ $? -ne 0 ]; then + echo "$red $(date) $1 $default" + deregister_client + exit 1 + fi +} + +# Create package upload metadata +# $1 - Project id +# $2 - Flag to validate if public package +# $3 - Is Private package version upload +function create_package_upload_payload() { + local project_id=$1 + local is_public_package=$2 + local is_private_package_version_upload=$3 + + if [ "$is_public_package" = true ]; then + extractedMetadata=$(cat $ML_PACKAGE_METADATA_FILE_PATH | jq '{description,displayName,inputDescription,mlPackageOwnedByAccountId,mlPackageOwnedByTenantId,mlPackageOwnedByProjectId,sourcePackageId,sourcePackageVersionId,name,outputDescription,settings,projectId,stagingUri}' | jq -M ". + {name:\"$ML_PACKAGE_NAME\",displayName:\"$ML_PACKAGE_NAME\",projectId:\"$project_id\",stagingUri:\"$signed_url\",mlPackageOwnedByAccountId:\"$source_ml_package_owned_by_accountId\",mlPackageOwnedByTenantId:\"$source_ml_package_owned_by_tenantId\",mlPackageOwnedByProjectId:\"$source_ml_package_owned_by_projectId\",sourcePackageId:\"$source_ml_package_id\",sourcePackageVersionId:\"$source_ml_package_version_id\"}") + else + if [ "$is_private_package_version_upload" = false ]; then + extractedMetadata=$(cat $ML_PACKAGE_METADATA_FILE_PATH | jq '{gpu,displayName,name,description,inputDescription,outputDescription,mlPackageLanguage,inputType,languageVersion,retrainable,changeLog,projectId,stagingUri}' | jq -M ". + {name:\"$ML_PACKAGE_NAME\", displayName:\"$ML_PACKAGE_NAME\",projectId:\"$project_id\",stagingUri:\"$signed_url\"}") + else + extractedMetadata=$(cat $ML_PACKAGE_METADATA_FILE_PATH | jq '{gpu,displayName,name,description,inputDescription,outputDescription,mlPackageLanguage,inputType,languageVersion,retrainable,changeLog,projectId,stagingUri}' | jq -M ". + {name:\"$ML_PACKAGE_NAME\", displayName:\"$ML_PACKAGE_NAME\",projectId:\"$project_id\",stagingUri:\"$signed_url\",version:\"$ML_PACKAGE_MAJOR_VERSION_FOR_PRIVATE_PACKAGE\"}") + fi + fi + + # Check if language version exist for older packages + local isLanguageVersion=$(cat $ML_PACKAGE_METADATA_FILE_PATH | jq -r 'select(.languageVersion != null) | .languageVersion') + + # Adding default language version for private packages if not exists for backward compatibility + if [[ -z "$isLanguageVersion" && "$is_public_package" == false ]]; then + echo "$yellow $(date) Langugae version is null for private packages, adding 0 as default language version $default" + extractedMetadata=$(echo $extractedMetadata | jq -M ". + {languageVersion:"0"}") + fi + + # Check if image path exists and then update the package payload + local isImagePath=$(cat $ML_PACKAGE_METADATA_FILE_PATH | jq -r 'select(.imagePath != null) | .imagePath') + if [[ ! -z "$isImagePath" && "$is_public_package" == false ]]; then + echo "$yellow $(date) Image path provided is not null for private packages, updating the image path" + extractedMetadata=$(echo $extractedMetadata | jq -M ". + {imagePath:\"$isImagePath\"}") + fi + + validate_last_command_executed_succesfully "$red Failed to extract ML package metadata from $ML_PACKAGE_METADATA_FILE_PATH" +} + +# Upload ML package to blob storage for target environment +function upload_ml_package_using_signed_url() { + + # Get Signed url + get_signed_url PUT false $ML_PACKAGE_ZIP_FILE_PATH + + # Global signed url will be used in payload creation + signed_url=$(echo "$generated_signed_url" | jq -r 'select(.data != null) | .data' | jq -r 'select(.url != null) | .url') + + if [ -z "$signed_url" ]; then + echo "$red $(date) Failed to extract signed url ... Exiting $default" + deregister_client + exit 1 + fi + + echo "$yellow $(date) Uploading ML package $default" + + curl -k -L $signed_url -X 'PUT' -H 'content-type: application/x-zip-compressed' --data-binary @$ML_PACKAGE_ZIP_FILE_PATH + + validate_last_command_executed_succesfully "$red Failed to upload ML package" + + echo "$(date) Successfully uploaded ML package zip file" +} + +# Get signed for ML package located at blob storage +# $1 - Signing Method Type +# $2 - Encoded URl +# $3 - Zip file +function get_signed_url() { + local signing_method=$1 + local encoded_url=$2 + local ml_package_file=$3 + + # Change forward to backslash if any for compatibiity + local ml_package_zip_path=$(echo $ml_package_file | sed 's/\\/\//g') + local blob_name=${ml_package_zip_path##*/} + + generated_signed_url=$(curl -k --silent --fail --show-error 'https://'"$INGRESS_HOST_OR_FQDN"'/ai-pkgmanager/v1/signedURL?contentType=application/x-zip-compressed&mlPackageName='"$blob_name"'&signingMethod='"$signing_method"'&encodedUrl='"$encoded_url"'' -H 'tenant-id: '"$TENANT_ID"'' -H 'authorization: Bearer '"$ACCESS_TOKEN"'') + + local resp_code=DEFAULT + if [ ! -z "$generated_signed_url" ]; then + resp_code=$(echo "$generated_signed_url" | jq -r 'select(.respCode != null) | .respCode') + fi + + validate_response_from_api $resp_code "200" "Signed Url generated successfully for ML Package: $ml_package_zip_path, signing method: $signing_method, encoded url: $encoded_url" "$red Failed to generate signed url for blob $ml_package_zip_path, signing method $signing_method ... Exiting !!!" +} + +# Fetch list of all ML packages +# $1 - Project Id under which ML packages are present +function fetch_ml_packages() { + local project_id=$1 + ml_packages=$(curl -k --silent --fail --show-error 'https://'"$INGRESS_HOST_OR_FQDN"'/ai-pkgmanager/v1/mlpackages?pageSize='"$PAGE_SIZE"'&sortBy=createdOn&sortOrder=DESC&projectId='"$project_id"'' -H 'tenant-id: '"$TENANT_ID"'' -H 'authorization: Bearer '"$ACCESS_TOKEN"'') + + local resp_code=DEFAULT + if [ ! -z "$ml_packages" ]; then + resp_code=$(echo "$ml_packages" | jq -r 'select(.respCode != null) | .respCode') + fi + + validate_response_from_api $resp_code "200" "Successfully fetched ML packages" "$red Failed to fetch ML packages ... Exiting" +} + +# Find all projects +function fetch_projects() { + projects=$(curl -k --silent --fail --show-error 'https://'"$INGRESS_HOST_OR_FQDN"'/ai-pkgmanager/v1/projects?pageSize='"$PAGE_SIZE"'' -H 'tenant-id: '"$TENANT_ID"'' -H 'authorization: Bearer '"$ACCESS_TOKEN"'') + + local resp_code=DEFAULT + if [ ! -z "$projects" ]; then + resp_code=$(echo "$projects" | jq -r 'select(.respCode != null) | .respCode') + fi + + validate_response_from_api $resp_code "200" "Successfully fetched projects" "$red Failed to fetch projects ... Exiting" +} + +# Extract destination public ML package metadata +# $1 - Existing public ML package name in target environment +# $2 - Public ML package major version in target environment +function extract_public_package_additional_metadata_from_target_environment() { + + local public_ml_package_name=$1 + local major_ml_package_version=$2 + local minor_ml_package_version=0 + + echo "$(date) Extracting additional metadata for public package $public_ml_package_name with version $major_ml_package_version.$minor_ml_package_version from target environment" + + local public_projects=$(curl -k --silent --fail --show-error 'https://'"$INGRESS_HOST_OR_FQDN"'/ai-pkgmanager/v1/projects/public?pageSize='"$PAGE_SIZE"'' -H 'tenant-id: '"$TENANT_ID"'' -H 'authorization: Bearer '"$ACCESS_TOKEN"'') + + local resp_code=DEFAULT + if [ ! -z "$public_projects" ]; then + resp_code=$(echo "$public_projects" | jq -r 'select(.respCode != null) | .respCode') + fi + + validate_response_from_api $resp_code "200" "Successfully fetched public project" "$red Failed to fetch public project ... Exiting" + + for ((i = 0; i < $(echo "$public_projects" | jq -r ".data | length"); i = i + 1)); do + local public_project=$(echo "$public_projects" | jq -r ".data[$i]") + local mlPackage_owned_by_accountId=$(echo "$public_project" | jq -r 'select(.accountId != null) | .accountId') + local mlPackage_owned_by_tenantId=$(echo "$public_project" | jq -r 'select(.tenantId != null) | .tenantId') + local mlPackage_owned_by_projectId=$(echo "$public_project" | jq -r 'select(.id != null) | .id') + local project_name=$(echo "$public_project" | jq -r 'select(.name != null) | .name') + + local public_ml_packages_under_public_project=$(curl -k --silent --fail --show-error 'https://'"$INGRESS_HOST_OR_FQDN"'/ai-pkgmanager/v1/mlpackages?pageSize='"$PAGE_SIZE"'&status=DEPLOYING,DEPLOYED,UNDEPLOYED&mlPackageOwnedByAccountId='"$mlPackage_owned_by_accountId"'&mlPackageOwnedByTenantId='"$mlPackage_owned_by_tenantId"'&mlPackageOwnedByProjectId='"$mlPackage_owned_by_projectId"'' -H 'tenant-id: '"$TENANT_ID"'' -H 'authorization: Bearer '"$ACCESS_TOKEN"'') + + local resp_code=DEFAULT + if [ ! -z "$public_ml_packages_under_public_project" ]; then + resp_code=$(echo "$public_projects" | jq -r 'select(.respCode != null) | .respCode') + fi + + validate_response_from_api $resp_code "200" "Successfully fetched ML packages under project $project_name" "$red Failed to fetch ml packages under project $project_name ... Exiting" + + local public_ml_package=$(echo "$public_ml_packages_under_public_project" | jq ".data.dataList[] | select(.name != null) | select(.name==\"$public_ml_package_name\")") + + if [ ! -z "$public_ml_package" ]; then + echo "$(date) ML Package with name $public_ml_package_name found" + local public_ml_package_id=$(echo "$public_ml_package" | jq -r 'select(.id != null) | .id') + + local public_ml_package_version_under_public_project=$(curl -k --silent --fail --show-error 'https://'"$INGRESS_HOST_OR_FQDN"'/ai-pkgmanager/v1/mlpackages/'"$public_ml_package_id"'??pageSize='"$PAGE_SIZE"'&status=PURGED,VALIDATION_FAILED,VALIDATING,THREAT_DETECTED&mlPackageOwnedByAccountId='"$mlPackage_owned_by_accountId"'&mlPackageOwnedByTenantId='"$mlPackage_owned_by_tenantId"'&mlPackageOwnedByProjectId='"$mlPackage_owned_by_projectId"'' -H 'tenant-id: '"$TENANT_ID"'' -H 'authorization: Bearer '"$ACCESS_TOKEN"'') + + local resp_code=DEFAULT + if [ ! -z "$public_ml_package_version_under_public_project" ]; then + resp_code=$(echo "$public_ml_package_version_under_public_project" | jq -r 'select(.respCode != null) | .respCode') + fi + + validate_response_from_api $resp_code "200" "Successfully fetched ML package versions for ML package $public_ml_package_name" "$red Failed to fetch ML package versions for ml package $public_ml_package_name ... Exiting" + + local public_ml_package_version=$(echo "$public_ml_package_version_under_public_project" | jq '.data.mlPackageVersions[] | select((.version != null) and (.trainingVersion != null) and (.version=='$major_ml_package_version' and .trainingVersion=='$minor_ml_package_version')) | select(.status == ("UNDEPLOYED", "DEPLOYED", "DEPLOYING"))') + + if [ ! -z "$public_ml_package_version" ]; then + echo "$(date) ML package version $major_ml_package_version.$minor_ml_package_version found for ML package $public_ml_package_name" + + # update all required properties for public model + source_ml_package_owned_by_accountId=$mlPackage_owned_by_accountId + source_ml_package_owned_by_tenantId=$mlPackage_owned_by_tenantId + source_ml_package_owned_by_projectId=$mlPackage_owned_by_projectId + source_ml_package_id=$public_ml_package_id + source_ml_package_version_id=$(echo "$public_ml_package_version" | jq -r 'select(.id != null) | .id') + + # Return, we have found required ML package version in target environment + return + fi + fi + done +} + +function upload_ml_package() { + + # Fetch tenant details + get_tenant_details + + # Fetch Projects + fetch_projects + + # Fetch project by name + local project=$(echo "$projects" | jq -r ".data.dataList[] | select(.name != null) | select(.name==\"$PROJECT_NAME\")") + + if [ ! -z "$project" ]; then + echo "$(date) Project by name $PROJECT_NAME fetched successfully" + else + echo "$red $(date) Failed to project by name $PROJECT_NAME, Please check Project Name ... Exiting $default" + deregister_client + exit 1 + fi + + local project_id=$(echo "$project" | jq -r 'select(.id != null) | .id') + + if [ -z "$project_id" ]; then + echo "$red $(date) Failed to extract Project id from list of projects ... Exiting $default" + deregister_client + exit 1 + fi + + # Fetch list of all ML packages from target environment + fetch_ml_packages $project_id + + # Fetch ML Package by name from list of Packages + local ml_package=$(echo "$ml_packages" | jq ".data.dataList[] | select(.name != null) | select(.name==\"$ML_PACKAGE_NAME\")") + + if [ ! -z "$ml_package" ]; then + echo "$yellow $(date) ML package by name $ML_PACKAGE_NAME fetched successfully, New ML version will be uploaded $default" + + # Extract ML package Id + ml_package_id=$(echo "$ml_package" | jq -r 'select(.id != null) | .id') + + if [ -z "$ml_package_id" ]; then + echo "$red $(date) Failed to extract ML package id from list of ML Packages ... Exiting $default" + deregister_client + exit 1 + fi + + # Upload ML package + upload_ml_package_using_signed_url + + # Validate if ML package metadata is for public package or not + local is_public_package=$(cat $ML_PACKAGE_METADATA_FILE_PATH | jq -r -e '.sourcePackageName?') + + if [ "$is_public_package" != null ]; then + echo "$(date) ML package version metadata belong to public package" + + local is_ml_package_also_public=$(echo $ml_package | jq -r -e '.sourcePackageName?') + + if [ "$is_ml_package_also_public" = null ]; then + echo "$red $(date) ML package should also be public for public packge metadata, please check metadata file ... Exiting $default" + deregister_client + exit 1 + fi + + local sourcePackageVersion=$(cat $ML_PACKAGE_METADATA_FILE_PATH | jq -r '.sourcePackageVersion') + extract_public_package_additional_metadata_from_target_environment $is_public_package $sourcePackageVersion + + # Validate public ML package metadata extracted successfully + validate_extracted_public_ml_package_metadata + + # create payload for ML package + create_package_upload_payload $project_id "true" "false" + + # Create public ML package + create_public_ml_package_version_metadata $ml_package_id + else + echo "$(date) ML package version metadata belong to private package" + + local is_ml_package_not_public=$(echo $ml_package | jq -r -e '.sourcePackageName?') + + if [ "$is_ml_package_not_public" != null ]; then + echo "$red $(date) ML package metadata should also be non public for non public package, please check metadata file ... Exiting $default" + deregister_client + exit 1 + fi + + # create payload for package upload + create_package_upload_payload $project_id "false" "true" + + # Crate ML Package version package + create_ml_package_version_metadata $ml_package_id + fi + else + echo "$yellow $(date) Failed to find ML Package by name $ML_PACKAGE_NAME, new ML package will be uploaded $default" + + # validate unique name for ML package + validate_unique_ml_package_name $ML_PACKAGE_NAME + + # Upload ML package + upload_ml_package_using_signed_url + + # Validate if ML package metadata is for public package or not + local is_public_package=$(cat $ML_PACKAGE_METADATA_FILE_PATH | jq -r -e '.sourcePackageName?') + + if [ "$is_public_package" != null ]; then + echo "$(date) ML package version metadata belong to public package" + local sourcePackageVersion=$(cat $ML_PACKAGE_METADATA_FILE_PATH | jq -r '.sourcePackageVersion') + extract_public_package_additional_metadata_from_target_environment $is_public_package $sourcePackageVersion + + # Validate public ML package metadata extracted successfully + validate_extracted_public_ml_package_metadata + + # Create payload for ML package + create_package_upload_payload $project_id "true" "false" + + # Create public ML package version + create_public_ml_package_metadata + else + echo "$(date) ML package metadata belong to private package" + + # Create payload for ML package + create_package_upload_payload $project_id "false" "false" + + # Create ML package version metadata + create_ml_package_metadata + fi + fi +} + +# Get details of tenant by name +function get_tenant_details() { + local aif_tenant_details=$(curl -k --silent --fail --show-error 'https://'"$INGRESS_HOST_OR_FQDN"'/ai-deployer/v1/tenant/tenantdetails?tenantName='"$TENANT_NAME"'' -H 'authorization: Bearer '"$ACCESS_TOKEN"'') + + local resp_code=DEFAULT + if [ ! -z "$aif_tenant_details" ]; then + resp_code=$(echo "$aif_tenant_details" | jq -r 'select(.respCode != null) | .respCode') + fi + + validate_response_from_api $resp_code "200" "Successfully fetched tenant details for tenant $TENANT_NAME" "$red Failed to fetch tenant details for tenant $TENANT_NAME ... Exiting" + + # Extract tenant Id + TENANT_ID=$(echo "$aif_tenant_details" | jq -r 'select(.data.provisionedTenantId != null) | .data.provisionedTenantId') + + if [ -z "$TENANT_ID" ]; then + echo "$red $(date) Failed to extract tenant id... Exiting $default" + deregister_client + exit 1 + fi +} + +# Fetch admin token from identity server end point using host tenant +function fetch_identity_server_token_to_register_client() { + echo "$(date) Fetching identity server client registeration token" + + # Generate required endpoints + readonly local antif=https://$IDENTITY_SERVER_ENDPOINT"/identity/api/antiforgery/generate" + readonly local login=https://$IDENTITY_SERVER_ENDPOINT"/identity/api/Account/Login" + readonly local tokenUrl=https://$IDENTITY_SERVER_ENDPOINT"/identity/api/Account/ClientAccessToken" + + dataLogin='{ + "tenant": "'$HOST_TENANT_NAME'", + "usernameOrEmail": "'$HOST_TENANT_USER_ID_OR_EMAIL'", + "password": "'$HOST_TENANT_PASSWORD'", + "rememberLogin": true + }' + + cookie_file="cookfile.txt" + cookie_file_new="cookfile_new.txt" + + # Get token and construct the cookie, save the returned token. + curl --silent --fail --show-error -k -c $cookie_file --request GET "$antif" + + # Replace headers + sed 's/XSRF-TOKEN-IS/XSRF-TOKEN/g' $cookie_file >$cookie_file_new + + token=$(cat $cookie_file_new | grep XSRF-TOKEN | cut -f7 -d$'\t') + + # Authentication -> POST to $login_url with the token in header "X-CSRF-Token: $token". + curl --silent --fail --show-error -k -H "X-XSRF-TOKEN: $token" -c $cookie_file_new -b $cookie_file_new -d "$dataLogin" --request POST "$login" -H "Content-Type: application/json" + + # Fetch Acces token + CLIENT_INSTALLTION_TOKEN=$(curl --silent --fail --show-error -k -H "X-XSRF-TOKEN: $token" -b $cookie_file_new "$tokenUrl" -H "Content-Type: application/json") + + if [ -z "$CLIENT_INSTALLTION_TOKEN" ]; then + echo "$(date) $red Failed to generate token to register client ... Exiting $default" + exit 1 + fi + + echo "$(date) Successfully fetched client register token" +} + +# Fetch access token to call backens server +function fetch_identity_server_access_token() { + echo "$(date) Getting access token for client $IS_AIFABRIC_CLIENT_NAME from $IDENTITY_SERVER_ENDPOINT" + + readonly local access_token_response=$( + curl -k --silent --fail --show-error -X --location --request POST "https://${IDENTITY_SERVER_ENDPOINT}/identity/connect/token" \ + -H 'Content-Type: application/x-www-form-urlencoded' \ + --data-urlencode "client_Id=$IS_AIFABRIC_CLIENT_ID" \ + --data-urlencode "client_secret=$IS_AIFABRIC_CLIENT_SECRET" \ + --data-urlencode "grant_type=client_credentials" + ) + + if [ -z "$access_token_response" ]; then + echo "$(date) $red Failed to generate access token to call backend server ... Exiting $default" + deregister_client + exit 1 + fi + + ACCESS_TOKEN=$(echo "$access_token_response" | jq -r 'select(.access_token != null) | .access_token') + + if [ -z "$ACCESS_TOKEN" ]; then + echo "$(date) $red Failed to extract access token ... Exiting $default" + deregister_client + exit 1 + fi + + echo "$(date) Successfully fetched access token to call backend server " +} + +function deregister_client() { + echo "$(date) Deregistering client from $IDENTITY_SERVER_ENDPOINT with name $IS_AIFABRIC_CLIENT_NAME" + curl -k -i --silent --fail --show-error -X DELETE "https://${IDENTITY_SERVER_ENDPOINT}/identity/api/Client/$IS_AIFABRIC_CLIENT_ID" -H "Authorization: Bearer ${CLIENT_INSTALLTION_TOKEN}" +} + +# Register client and fetch Access token +function register_client_and_fetch_access_token() { + + readonly IS_AIFABRIC_CLIENT_ID="aifabric-"$(openssl rand -hex 10) + readonly IS_AIFABRIC_CLIENT_SECRET=$(openssl rand -hex 32) + readonly IS_AIFABRIC_CLIENT_NAME="aifabric-"$(openssl rand -hex 10) + + # Fetch admin token + fetch_identity_server_token_to_register_client + + # Register client + echo "$(date) Registering client by name $IS_AIFABRIC_CLIENT_NAME with client id $IS_AIFABRIC_CLIENT_ID" + + local client_creation_response=$(curl -k --silent --fail --show-error -X POST "https://${IDENTITY_SERVER_ENDPOINT}/identity/api/Client" -H "Connection: keep-alive" -H "accept: text/plain" -H "Authorization: Bearer ${CLIENT_INSTALLTION_TOKEN}" -H "Content-Type: application/json-patch+json" -H "Accept-Encoding: gzip, deflate, br" -H "Accept-Language: en-US,en;q=0.9" -d "{\"clientId\":\"${IS_AIFABRIC_CLIENT_ID}\",\"clientName\":\"${IS_AIFABRIC_CLIENT_NAME}\",\"clientSecrets\":[\"${IS_AIFABRIC_CLIENT_SECRET}\"],\"requireConsent\":false,\"requireClientSecret\": true,\"allowOfflineAccess\":true,\"alwaysSendClientClaims\":true,\"allowAccessTokensViaBrowser\":true,\"allowOfflineAccess\":true,\"alwaysIncludeUserClaimsInIdToken\":true,\"accessTokenLifetime\":${ACCESS_TOKEN_LIFE_TIME},\"identityTokenLifetime\":${ACCESS_TOKEN_LIFE_TIME},\"authorizationCodeLifetime\":${ACCESS_TOKEN_LIFE_TIME},\"absoluteRefreshTokenLifetime\":${ACCESS_TOKEN_LIFE_TIME},\"slidingRefreshTokenLifetime\":${ACCESS_TOKEN_LIFE_TIME},\"RequireRequestObject\":true,\"Claims\":true,\"AlwaysIncludeUserClaimsInIdToken\":true,\"allowedGrantTypes\":[\"client_credentials\",\"authorization_code\"],\"allowedResponseTypes\":[\"id_token\"],\"allowedScopes\":[\"openid\",\"profile\",\"email\",\"AiFabric\",\"IdentityServerApi\",\"Orchestrator\",\"OrchestratorApiUserAccess\"]}") + + if [ -z "$client_creation_response" ]; then + echo "$(date) $red Failed to register client $IS_AIFABRIC_CLIENT_NAME with identity server $IDENTITY_SERVER_ENDPOINT ... Exiting $default" + exit 1 + fi + + # Fetch access token authorize backend server call + fetch_identity_server_access_token +} + +# Valiate if ML package name is unique acros all projects in target environment +# $1 - ML package name +function validate_unique_ml_package_name() { + + echo "$(date) Validating uniqueness of ML package name" + local ml_package_name=$1 + local is_unique_ml_package=$(curl -k --silent --fail --show-error 'https://'"$INGRESS_HOST_OR_FQDN"'/ai-pkgmanager/v1/mlpackages/search?name='"$ml_package_name"'' -H 'tenant-id: '"$TENANT_ID"'' -H 'authorization: Bearer '"$ACCESS_TOKEN"'') + + if [ -z "$is_unique_ml_package" ]; then + echo "$red $(date) ML package with name $1 alreday exits in target enviornment, can't create new ML package ... Exiting $default" + deregister_client + exit 1 + fi +} + +function validate_extracted_public_ml_package_metadata() { + + if [[ -z $source_ml_package_owned_by_accountId || -z $source_ml_package_owned_by_tenantId || -z $source_ml_package_owned_by_projectId || -z $source_ml_package_id || -z $source_ml_package_version_id ]]; then + echo "$red $(date) Some of ML package metadata is still empty after extration ... Exiting $default" + deregister_client + exit 1 + fi +} + +# Validate file provided by user exists or not, It may be relative path or absolute path +# $1 - File path +function validate_file_path() { + if [ ! -f "$1" ]; then + echo "$red $(date) $1 file does not exist, Please check ... Exiting $default" + exit 1 + fi +} + +# Validate dependency module +# $1 - Name of the dependency module +# $2 - Command to validate module +function validate_dependency() { + eval $2 + # Next statement is checking last command success + if [ $? -ne 0 ]; then + echo "$red $(date) Please install ******** $1 *********** ... Exiting $default" + exit 1 + fi +} + +# Validate input provided by end user +function validate_input() { + + # Validate file path + validate_file_path $ML_PACKAGE_IMPORT_INPUT_FILE + + readonly INGRESS_HOST_OR_FQDN=$(cat $ML_PACKAGE_IMPORT_INPUT_FILE | jq -r 'select(.hostOrFQDN != null) | .hostOrFQDN') + readonly TENANT_NAME=$(cat $ML_PACKAGE_IMPORT_INPUT_FILE | jq -r 'select(.tenantName != null) | .tenantName') + readonly PROJECT_NAME=$(cat $ML_PACKAGE_IMPORT_INPUT_FILE | jq -r 'select(.projectName != null) | .projectName') + readonly ML_PACKAGE_NAME=$(cat $ML_PACKAGE_IMPORT_INPUT_FILE | jq -r 'select(.mlPackageName != null) | .mlPackageName') + readonly IDENTITY_SERVER_ENDPOINT=$(cat $ML_PACKAGE_IMPORT_INPUT_FILE | jq -r 'select(.identityServerEndPoint != null) | .identityServerEndPoint') + readonly HOST_TENANT_NAME=$(cat $ML_PACKAGE_IMPORT_INPUT_FILE | jq -r 'select(.hostTenantName != null) | .hostTenantName') + readonly HOST_TENANT_USER_ID_OR_EMAIL=$(cat $ML_PACKAGE_IMPORT_INPUT_FILE | jq -r 'select(.hostTenantIdOrEmailId != null) | .hostTenantIdOrEmailId') + readonly HOST_TENANT_PASSWORD=$(cat $ML_PACKAGE_IMPORT_INPUT_FILE | jq -r 'select(.hostTenantPassword != null) | .hostTenantPassword') + readonly ML_PACKAGE_MAJOR_VERSION_FOR_PRIVATE_PACKAGE=$(cat $ML_PACKAGE_IMPORT_INPUT_FILE | jq -r 'select(.mlPackageMajorVersionForPrivatePackage != null) | .mlPackageMajorVersionForPrivatePackage') + readonly ML_PACKAGE_ZIP_FILE_PATH=$(cat $ML_PACKAGE_IMPORT_INPUT_FILE | jq -r 'select(.mlPackageZipFilePath != null) | .mlPackageZipFilePath') + readonly ML_PACKAGE_METADATA_FILE_PATH=$(cat $ML_PACKAGE_IMPORT_INPUT_FILE | jq -r 'select(.mlPackageMetadataFilePath != null) | .mlPackageMetadataFilePath') + + if [[ -z $INGRESS_HOST_OR_FQDN || -z $PROJECT_NAME || -z $ML_PACKAGE_NAME || -z $ML_PACKAGE_MAJOR_VERSION_FOR_PRIVATE_PACKAGE || -z $ML_PACKAGE_ZIP_FILE_PATH || -z $ML_PACKAGE_METADATA_FILE_PATH || -z TENANT_NAME || -z IDENTITY_SERVER_ENDPOINT || -z HOST_TENANT_NAME || -z HOST_TENANT_USER_ID_OR_EMAIL || -z HOST_TENANT_PASSWORD ]]; then + echo "$red $(date) Input is invalid or missing, Please check ... Exiting $default" + exit 1 + fi + + validate_file_path $ML_PACKAGE_ZIP_FILE_PATH + validate_file_path $ML_PACKAGE_METADATA_FILE_PATH + + echo "$(date) Successfully validated user input" +} + +# Validate required modules exits in target setup +function validate_setup() { + validate_dependency curl "curl --version" + validate_dependency jq "jq --version" + echo "$(date) Successfully validated required dependencies" +} + +# Validate Setup +validate_setup + +# Validate Input +validate_input + +# Register Client and fetch access token +register_client_and_fetch_access_token + +# Upload requested ML package +upload_ml_package + +# Register client +deregister_client + +echo "$green $(date) Successfully uploaded ML Package under project $PROJECT_NAME in target environment $default" \ No newline at end of file diff --git a/platform/onebox/backup_and_restore/packages/sanitize.sh b/platform/onebox/backup_and_restore/packages/sanitize.sh new file mode 100644 index 00000000..e7bd5ed5 --- /dev/null +++ b/platform/onebox/backup_and_restore/packages/sanitize.sh @@ -0,0 +1,72 @@ +#!/bin/bash + +: ' +This scipt will sanitize model zips to make them usable with new wrapper codes +# $1 - ml-package-folder to scan and update in place +[Script Version -> 21.10] +' + +red=$(tput setaf 1) +green=$(tput setaf 2) +yellow=$(tput setaf 3) +default=$(tput sgr0) + +readonly BASE_PATH=$1 + +echo "$green $(date) Starting mlpackage zip updates... $default" + +if [ ! -d "$1" ]; then + echo "$red $(date) $1 dir does not exist, Please check ... Exiting $default" + exit 1 +fi + + +# Validate dependency module +# $1 - Name of the dependency module +# $2 - Command to validate module +function validate_dependency() { + eval $2 + # Next statement is checking last command success aws --version has some issue + if [ $? -ne 0 ]; then + echo "$red $(date) Please install ******** $1 *********** ... Exiting $default" + exit 1 + fi +} + +# Validate required modules exits in target setup +function validate_setup() { + validate_dependency "zip" "zip -v" + echo "$(date) Successfully validated required dependencies" +} + +function process_file() { + echo "$green $(date) Processing file $1... $default" + fullFileName=${1##*/} + onlyFileName=${fullFileName%.*} + zip -d $1 "$onlyFileName/uipath_wrapper_config.json" || true + zip -d $1 "$onlyFileName/uipath_core.tar.gz" || true +} + +function process_files() { + cd $BASE_PATH + while read file; do + process_file ${file} + done <<<"$FILES" + cd - +} + +function list_files() { + cd $BASE_PATH + files=$(find . -type f) + readonly FILES=${files} + cd - +} + +# Validate Setup +validate_setup + +# List Buckets +list_files + +# Process data inside buckets +process_files \ No newline at end of file diff --git a/platform/onebox/backup_and_restore/registry/README.md b/platform/onebox/backup_and_restore/registry/README.md new file mode 100644 index 00000000..7cff40b5 --- /dev/null +++ b/platform/onebox/backup_and_restore/registry/README.md @@ -0,0 +1,34 @@ +# Registry Backup-Restore + + +## Purpose +To provide backup and restore scripts for registry to handle DR scenarios. +This backs up only the images currently used by any skills +... + + +## Requirements +The Machine where backup/restore runs needs the following: +* Access to AIF machine (public ip address can be obtained via dig) +* jq, sqlcmd to be installed, e.g. on Ubuntu ```sudo apt install -y jq``` +* User logged in with permission to run the script and access to above tools +* AIF machines need registry accessible via nodeport. It can be done via ```kubectl -n kurl apply -f registry-np.yaml``` + + +## Usage +* Run get-credentials.sh on AIF machines. It generates a file registry-creds.json. Copy it over to the backup/restore VM. +* [Optionally] Move the credentials to some cluster manager and make changes to the scripts to read from there +# Registry Backup-Restore +* Make sure to use absolute path as basepath in below scripts + +### For Backup +``` +./backup.sh +``` +It creates a folder /registry which contains 1 tar file for every image + +### For Restore +``` +./restore.sh +``` +This looks for a folder registry inside basePath, loads image from tar and pushes to local registry diff --git a/platform/onebox/backup_and_restore/registry/export.sh b/platform/onebox/backup_and_restore/registry/export.sh new file mode 100644 index 00000000..a6a447c9 --- /dev/null +++ b/platform/onebox/backup_and_restore/registry/export.sh @@ -0,0 +1,149 @@ +#!/bin/bash + +: ' +This script exports the images corresponding to currently available skills +needs a json for creds and export directory +[Script Version -> 21.4] +' + +red=$(tput setaf 1) +green=$(tput setaf 2) +yellow=$(tput setaf 3) +default=$(tput sgr0) + +readonly REGISTRY_EXPORT_FILE=$1 +readonly EXPORT_PATH=$2/registry + +# Validate dependency module +# $1 - Name of the dependency module +# $2 - Command to validate module +function validate_dependency() { + eval $2 + # Next statement is checking last command success + if [ $? -ne 0 ]; then + echo "$red $(date) Please install ******** $1 *********** ... Exiting $default" + exit 1 + fi +} + +# Validate required modules exits in target setup +function validate_setup() { + validate_dependency curl "curl --version" + validate_dependency jq "jq --version" + validate_dependency sqlcmd "sqlcmd -?" + validate_dependency gzip "gzip --version" + validate_dependency docker "docker --version" + echo "$(date) Successfully validated required dependencies" +} + + +function validate_file_path() { + if [ ! -f "$1" ]; then + echo "$red $(date) $1 file does not exist, Please check ... Exiting $default" + exit 1 + fi +} + +function validate_input() { + + # Validate file path + validate_file_path $REGISTRY_EXPORT_FILE + + readonly DB_CONN=$(cat $REGISTRY_EXPORT_FILE | jq -r 'select(.dbConnection != null) | .dbConnection') + readonly DB_NAME=$(cat $REGISTRY_EXPORT_FILE | jq -r 'select(.dbName != null) | .dbName') + readonly DB_USER=$(cat $REGISTRY_EXPORT_FILE | jq -r 'select(.dbUser != null) | .dbUser') + readonly DB_PASSWORD="$(cat $REGISTRY_EXPORT_FILE | jq -r 'select(.dbPassword != null) | .dbPassword')" + readonly REGISTRY_ENDPOINT=$(cat $REGISTRY_EXPORT_FILE | jq -r 'select(.registryEndpoint != null) | .registryEndpoint') + readonly REGISTRY_USER=$(cat $REGISTRY_EXPORT_FILE | jq -r 'select(.registryUser != null) | .registryUser') + readonly REGISTRY_PASSWORD=$(cat $REGISTRY_EXPORT_FILE | jq -r 'select(.registryPassword != null) | .registryPassword') + readonly OLD_REGISTRY_ENDPOINT=$(cat $REGISTRY_EXPORT_FILE | jq -r 'select(.oldRegistryEndpoint != null) | .oldRegistryEndpoint') + + + if [[ -z $DB_CONN || -z $DB_NAME || -z $DB_USER || -z $DB_PASSWORD || -z TENANT_NAME || -z REGISTRY_ENDPOINT || -z REGISTRY_USER || -z REGISTRY_PASSWORD || -z EXPORT_PATH || -z OLD_REGISTRY_ENDPOINT ]]; then + echo "$red $(date) Input is invalid or missing, Please check ... Exiting $default" + exit 1 + fi + + echo "$green $(date) Successfully validated user input $default" +} + +function get_image_list() { + # sqlcmd -S tcp:${DB_CONN} -d ${DB_NAME} -U ${DB_USER} -P ${DB_PASSWORD} -i getimages.sql -o images.txt -h -1 -W + sqlcmd -S tcp:${DB_CONN} -d ${DB_NAME} -U ${DB_USER} -P ${DB_PASSWORD} -o images.txt -h -1 -W -Q "set nocount on; select distinct mpi.image_uri from ml_package_images mpi inner join ml_skill_versions msv on msv.ml_package_version_id = mpi.version_id and msv.processor = mpi.processor where msv.status in ('UPDATING', 'COMPLETED', 'VALIDATING_DEPLOYMENT') and mpi.status = 'ACTIVE'" +} + +formulate_docker_command() { + docker images registry + if [[ $? -ne 0 ]]; then + echo "sudo permission required for docker" + DOCKER_COMMAND="sudo docker" + else + DOCKER_COMMAND="docker" + fi +} + +function docker_setup() { + # Mark docker registry as unauth => Adapt for other envs + echo "{\"insecure-registries\": [\"${REGISTRY_ENDPOINT}\"]}" > insecure.json + sudo touch /etc/docker/daemon.json + daemondiff=$(sudo jq -s '.[0] as $o1 | .[1] as $o2 | ($o1 + $o2) | ."insecure-registries" = ($o1."insecure-registries" + $o2."insecure-registries" | unique)' /etc/docker/daemon.json insecure.json) + echo $daemondiff | sudo tee /etc/docker/daemon.json + # Restart docker + sudo service docker restart + # Login to docker registry + ${DOCKER_COMMAND} login ${REGISTRY_ENDPOINT} -u ${REGISTRY_USER} -p ${REGISTRY_PASSWORD} +} + +function save_image() { + ${DOCKER_COMMAND} pull $1 + if [ $? -ne 0 ]; + then + echo "$red $(date) Not able to pull image for $1 $default" + exit 1 + fi + set -o pipefail + ${DOCKER_COMMAND} save $1 | gzip > $2 + if [ $? -ne 0 ]; + then + echo "$red $(date) Not able to save image for $1 in $2 $default" + rm -rf $2 + exit 1 + fi +} + +function save_images() { + mkdir -p ${EXPORT_PATH} + # loop over images, replace existing registry tag with the one from nodeport and generate tars if they don't exist in destination + # Pass full image and a file name to save_image + while read img; do + # each line will be json + echo "$img" + + registry=$(echo $img | jq -r 'select(.repository != null) | .repository') + image=$(echo $img | jq -r 'select(.imageName != null) | .imageName') + tag=$(echo $img | jq -r 'select(.imageTag != null) | .imageTag') + + if [[ $registry == ${OLD_REGISTRY_ENDPOINT}* ]]; then + newimg=${REGISTRY_ENDPOINT}/${image}:${tag} + imgName=$(echo ${newimg##*/}) + if [ ! -f "${EXPORT_PATH}/$imgName.tar.gz" ]; then + echo "$green $(date) ${EXPORT_PATH}/$imgName file does not exist, Generating image tar $default" + save_image $newimg ${EXPORT_PATH}/${imgName}.tar.gz + else + echo "$green $(date) ${EXPORT_PATH}/$imgName file exists, Skipping $default" + fi + fi + done 21.4] +' + +red=$(tput setaf 1) +green=$(tput setaf 2) +yellow=$(tput setaf 3) +default=$(tput sgr0) + +readonly PRIVATE_IP=$1 + +# Validate dependecny module +# $1 - Name of the dependecny module +# $2 - Command to validate module +function validate_dependency() { + list=$($2) + if [ -z "$list" ]; then + echo "$red $(date) Please install ******** $1 *********** ... Exiting $default" + exit 1 + fi +} + +# Validate required modules exits in target setup +function validate_setup() { + validate_dependency kubectl "kubectl version" + echo "$(date) Successfully validated required dependencies" +} + +function get_db_details() { + + conn=$(kubectl -n aifabric get deployment ai-deployer-deployment -o yaml | grep -A1 'SPRING_DATASOURCE_URL' | grep -v 'SPRING_DATASOURCE_URL') + line=$(echo $conn | grep -o 'sqlserver://.*;') + readonly DB_CONN=$(basename ${line//;/}) + line=$(echo $conn | grep -o 'databaseName=[a-zA-Z0-9_-]*') + readonly DB_NAME=${line##*=} + user=$(kubectl -n aifabric get deployment ai-deployer-deployment -o yaml | grep -A1 'SPRING_DATASOURCE_USER' | grep -v 'SPRING_DATASOURCE_USER') + readonly DB_USER=${user##* } + pass=$(kubectl -n aifabric get secret ai-deployer-secrets -o yaml | grep 'DATASOURCE_PASSWORD' | grep -v ':DATASOURCE_PASSWORD') + readonly DB_PASSWORD=$(echo ${pass##* } | base64 -d) + + if [[ -z $DB_CONN || -z $DB_USER || -z $DB_PASSWORD || -z $DB_NAME ]]; then + echo "$red $(date) Failed to fetch one or more db info, Please check ... Exiting $default" + exit 1 + fi +} + +function get_registry_details() { + # get ip + if [ -z "$PRIVATE_IP" ]; then + # Gets private ip of machine so that it can be connected within the VM + # Seems to be set as localhost on some customer machines + #OBJECT_GATEWAY_EXTERNAL_HOST=$(hostname -i) + PRIVATE_ADDRESS=$(ip -o route get to 8.8.8.8 | sed -n 's/.*src \([0-9.]\+\).*/\1/p') + #This is needed on k8s 1.18.x as $PRIVATE_ADDRESS is found to have a newline + REGISTRY_IP=$(echo "$PRIVATE_ADDRESS" | tr -d '\n') + else + REGISTRY_IP=$PRIVATE_IP + fi + echo "$green $(date) Private IP was $PRIVATE_IP and REGISTRY_IP is $REGISTRY_IP" + # check if nodeport exists for registry + regnp=$(kubectl -n kurl get svc registry-np) + if [ -z "$regnp" ]; then + echo "$yellow $(date) Registry service not exposed as nodeport ... Creating $default" + result=kubectl -n kurl apply -f registry_np.yaml + if [ -z "$result" ]; then + echo "$red $(date) Failed to expose Registry service as nodeport ... Exiting $default" + exit 1 + fi + fi + port=$(kubectl -n kurl get service/registry-np -o jsonpath='{.spec.ports[0].nodePort}') + if [ -z "$port" ]; then + echo "$red $(date) Failed to fetch nodeport of Registry service ... Exiting $default" + exit 1 + fi + readonly REGISTRY_CONN=${REGISTRY_IP}:${port} + + # get credentials + line=$(kubectl -n aifabric get configmap registry-config -o yaml | grep 'REGISTRY_USERNAME' | grep -v 'f:REGISTRY_USERNAME') + readonly REGISTRY_USER=${line##* } + line=$(kubectl -n aifabric get configmap registry-config -o yaml | grep 'REGISTRY_PASSWORD' | grep -v 'f:REGISTRY_PASSWORD') + readonly REGISTRY_PASSWORD=${line##* } + + # get old ip + old=$(kubectl -n kurl get service/registry -o jsonpath='{.spec.clusterIP}') + if [ -z "$old" ]; then + echo "$red $(date) Failed to fetch clusterip of Registry service ... Exiting $default" + exit 1 + fi + readonly REGISTRY_OLD=${old} + if [[ -z $REGISTRY_CONN || -z $REGISTRY_USER || -z $REGISTRY_PASSWORD || -z $REGISTRY_OLD ]]; then + echo "$red $(date) Failed to fetch one or more registry info, Please check ... Exiting $default" + exit 1 + fi +} + +function generate_json() { + echo '{"dbConnection": "'$DB_CONN'", "dbName": "'$DB_NAME'", "dbUser": "'$DB_USER'", "dbPassword": "'$DB_PASSWORD'", "registryEndpoint": "'$REGISTRY_CONN'", "registryUser": "'$REGISTRY_USER'", "registryPassword": "'$REGISTRY_PASSWORD'", "oldRegistryEndpoint": "'$REGISTRY_OLD'"}' > registry-creds.json + echo "$green $(date) Successfully generated credentials: registry-creds.json ... Exiting $default" +} + + +validate_setup + +get_db_details +get_registry_details + +generate_json diff --git a/platform/onebox/backup_and_restore/registry/getimages.sql b/platform/onebox/backup_and_restore/registry/getimages.sql new file mode 100644 index 00000000..4f1b02b8 --- /dev/null +++ b/platform/onebox/backup_and_restore/registry/getimages.sql @@ -0,0 +1,3 @@ +set nocount on; +select distinct mpi.image_uri from ml_package_images mpi inner join ml_skill_versions msv on msv.ml_package_version_id = mpi.version_id and msv.processor = mpi.processor +where msv.status in ('UPDATING', 'COMPLETED', 'VALIDATING_DEPLOYMENT') and mpi.status = 'ACTIVE'; \ No newline at end of file diff --git a/platform/onebox/backup_and_restore/registry/import.sh b/platform/onebox/backup_and_restore/registry/import.sh new file mode 100644 index 00000000..f4bd9385 --- /dev/null +++ b/platform/onebox/backup_and_restore/registry/import.sh @@ -0,0 +1,141 @@ +#!/bin/bash + +: ' +This script exports the images corresponding to currently available skills +needs a json for creds and import directory +[Script Version -> 21.4] +' + +red=$(tput setaf 1) +green=$(tput setaf 2) +yellow=$(tput setaf 3) +default=$(tput sgr0) + +readonly REGISTRY_IMPORT_FILE=$1 +readonly IMPORT_PATH=$2/registry + +# Validate dependecny module +# $1 - Name of the dependecny module +# $2 - Command to validate module +function validate_dependency() { + eval $2 + # Next statement is checking last command success + if [ $? -ne 0 ]; then + echo "$red $(date) Please install ******** $1 *********** ... Exiting $default" + exit 1 + fi +} + +# Validate required modules exits in target setup +function validate_setup() { + validate_dependency curl "curl --version" + validate_dependency jq "jq --version" + validate_dependency sqlcmd "sqlcmd -?" + validate_dependency gzip "gzip --version" + validate_dependency docker "docker --version" + echo "$(date) Successfully validated required dependencies" +} + + +function validate_file_path() { + if [ ! -f "$1" ]; then + echo "$red $(date) $1 file does not exist, Please check ... Exiting $default" + exit 1 + fi +} + +function validate_input() { + + # Validate file path + validate_file_path $REGISTRY_IMPORT_FILE + + readonly DB_CONN=$(cat $REGISTRY_IMPORT_FILE | jq -r 'select(.dbConnection != null) | .dbConnection') + readonly DB_NAME=$(cat $REGISTRY_IMPORT_FILE | jq -r 'select(.dbName != null) | .dbName') + readonly DB_USER=$(cat $REGISTRY_IMPORT_FILE | jq -r 'select(.dbUser != null) | .dbUser') + readonly DB_PASSWORD="$(cat $REGISTRY_IMPORT_FILE | jq -r 'select(.dbPassword != null) | .dbPassword')" + readonly REGISTRY_ENDPOINT=$(cat $REGISTRY_IMPORT_FILE | jq -r 'select(.registryEndpoint != null) | .registryEndpoint') + readonly REGISTRY_USER=$(cat $REGISTRY_IMPORT_FILE | jq -r 'select(.registryUser != null) | .registryUser') + readonly REGISTRY_PASSWORD=$(cat $REGISTRY_IMPORT_FILE | jq -r 'select(.registryPassword != null) | .registryPassword') + readonly OLD_REGISTRY_ENDPOINT=$(cat $REGISTRY_IMPORT_FILE | jq -r 'select(.oldRegistryEndpoint != null) | .oldRegistryEndpoint') + + + if [[ -z $DB_CONN || -z $DB_NAME || -z $DB_USER || -z $DB_PASSWORD || -z TENANT_NAME || -z REGISTRY_ENDPOINT || -z REGISTRY_USER || -z REGISTRY_PASSWORD || -z EXPORT_PATH || -z OLD_REGISTRY_ENDPOINT ]]; then + echo "$red $(date) Input is invalid or missing, Please check ... Exiting $default" + exit 1 + fi + + echo "$green $(date) Successfully validated user input $default" +} + +formulate_docker_command() { + docker images registry + if [[ $? -ne 0 ]]; then + echo "sudo permission required for docker" + DOCKER_COMMAND="sudo docker" + else + DOCKER_COMMAND="docker" + fi +} + +function docker_setup() { + # Mark docker registry as unauth => Adapt for other envs + echo "{\"insecure-registries\": [\"${REGISTRY_ENDPOINT}\"]}" > insecure.json + sudo touch /etc/docker/daemon.json + daemondiff=$(sudo jq -s '.[0] as $o1 | .[1] as $o2 | ($o1 + $o2) | ."insecure-registries" = ($o1."insecure-registries" + $o2."insecure-registries" | unique)' /etc/docker/daemon.json insecure.json) + echo $daemondiff | sudo tee /etc/docker/daemon.json + # Restart docker + sudo service docker restart + # Login to docker registry + ${DOCKER_COMMAND} login ${REGISTRY_ENDPOINT} -u ${REGISTRY_USER} -p ${REGISTRY_PASSWORD} +} + +function load_image() { + LOAD_IMAGE_EXP="Loaded image: " + ERROR_RESPONSE="Response:Error" + + tarfile=$1 + echo "$green $(date) Loading file: ${tarfile} $default" + + LOAD_IMAGE_RESPONSE=$(${DOCKER_COMMAND} load < ${tarfile}) + if [[ "${LOAD_IMAGE_RESPONSE}" == *${ERROR_RESPONSE}* ]]; then + echo "${LOAD_IMAGE_RESPONSE}" + echo "Failed to load image: ${file}" + exit 1 + fi + + if [[ "${LOAD_IMAGE_RESPONSE}" == *${LOAD_IMAGE_EXP}* ]]; then + IMAGE_NAME=$(echo ${LOAD_IMAGE_RESPONSE}|sed -r "s|${LOAD_IMAGE_EXP}||g") + echo "Tag ${IMAGE_NAME} image to local registry" + existing_registry=$(echo ${IMAGE_NAME%%/*}) + LOCAL_IMAGE_NAME=$(echo ${IMAGE_NAME}|sed -r "s|${existing_registry}|${REGISTRY_ENDPOINT}|g") + echo "Local image name is $LOCAL_IMAGE_NAME" + # tag failure means an error in our script which will be caught in testing + ${DOCKER_COMMAND} tag ${IMAGE_NAME} ${LOCAL_IMAGE_NAME} + PUSH_IMAGE_RESPONSE=$(${DOCKER_COMMAND} push ${LOCAL_IMAGE_NAME}) + if [[ "${PUSH_IMAGE_RESPONSE}" == *${ERROR_RESPONSE}* ]]; then + echo "${PUSH_IMAGE_RESPONSE}" + echo "Failed to load image: ${file}" + exit 1 + fi + fi +} + +function load_images() { + for file in $(ls ${IMPORT_PATH}); do + load_image ${IMPORT_PATH}/${file} + # TODO: Update image ref in db + done +} + +validate_setup + +validate_input + +formulate_docker_command + +docker_setup + +## import +load_images + + diff --git a/platform/onebox/backup_and_restore/registry/registry-np.yaml b/platform/onebox/backup_and_restore/registry/registry-np.yaml new file mode 100644 index 00000000..2bd10e4b --- /dev/null +++ b/platform/onebox/backup_and_restore/registry/registry-np.yaml @@ -0,0 +1,17 @@ +apiVersion: v1 +kind: Service +metadata: + labels: + app: registry-np + name: registry-np + namespace: kurl +spec: + ports: + - name: registry-np + port: 443 + protocol: TCP + targetPort: 443 + selector: + app: registry + sessionAffinity: None + type: NodePort \ No newline at end of file diff --git a/platform/onebox/backup_and_restore/sanitization/postrestore.sh b/platform/onebox/backup_and_restore/sanitization/postrestore.sh new file mode 100644 index 00000000..2e41992a --- /dev/null +++ b/platform/onebox/backup_and_restore/sanitization/postrestore.sh @@ -0,0 +1,366 @@ +#!/bin/bash + +: ' +This script will update in-flight operation to terminal state +[ Structure of Json file with exact key name ] + - hostOrFQDN: Public end point from where backend service can be accessible + - identityServerEndPoint: End point where identity server is hosted + - hostTenantName: Host Tenant name registered in identity server + - hostTenantIdOrEmailId: Host tenant id or email Id + - hostTenantPassword: Host tenant password +[Script Version -> 21.4]' + +red=$(tput setaf 1) +green=$(tput setaf 2) +yellow=$(tput setaf 3) +default=$(tput sgr0) + +echo "$green $(date) Process of Updating in flight status to terminal state started $default" +readonly POST_RESTORE_CONFIG_FILE=$1 +readonly CORE_SERVICE_NAMESPACE=aifabric +readonly ACCESS_TOKEN_LIFE_TIME=345600 + +# Fetch admin token from identity server end point using host tenant +function fetch_identity_server_token_to_register_client() { + echo "$(date) Fetching identity server client registration token" + + # Generate required endpoints + readonly local antif=https://$IDENTITY_SERVER_ENDPOINT"/identity/api/antiforgery/generate" + readonly local login=https://$IDENTITY_SERVER_ENDPOINT"/identity/api/Account/Login" + readonly local tokenUrl=https://$IDENTITY_SERVER_ENDPOINT"/identity/api/Account/ClientAccessToken" + + dataLogin='{ + "tenant": "'$HOST_TENANT_NAME'", + "usernameOrEmail": "'$HOST_TENANT_USER_ID_OR_EMAIL'", + "password": "'$HOST_TENANT_PASSWORD'", + "rememberLogin": true + }' + + cookie_file="cookfile.txt" + cookie_file_new="cookfile_new.txt" + + # Get token and construct the cookie, save the returned token. + curl --silent --fail --show-error -k -c $cookie_file --request GET "$antif" + + # Replace headers + sed 's/XSRF-TOKEN-IS/XSRF-TOKEN/g' $cookie_file >$cookie_file_new + + token=$(cat $cookie_file_new | grep XSRF-TOKEN | cut -f7 -d$'\t') + + # Authentication -> POST to $login_url with the token in header "X-CSRF-Token: $token". + curl --silent --fail --show-error -k -H "X-XSRF-TOKEN: $token" -c $cookie_file_new -b $cookie_file_new -d "$dataLogin" --request POST "$login" -H "Content-Type: application/json" + + # Fetch Acces token + CLIENT_INSTALLTION_TOKEN=$(curl --silent --fail --show-error -k -H "X-XSRF-TOKEN: $token" -b $cookie_file_new "$tokenUrl" -H "Content-Type: application/json") + + if [ -z "$CLIENT_INSTALLTION_TOKEN" ]; then + echo "$(date) $red Failed to generate token to register client ... Exiting $default" + exit 1 + fi + + echo "$(date) Successfully fetched client register token" +} + +# Fetch access token to call backend server +function fetch_identity_server_access_token() { + echo "$(date) Getting access token for client $IS_AIFABRIC_CLIENT_NAME from $IDENTITY_SERVER_ENDPOINT" + + readonly local access_token_response=$( + curl -k --silent --fail --show-error -X --location --request POST "https://${IDENTITY_SERVER_ENDPOINT}/identity/connect/token" \ + -H 'Content-Type: application/x-www-form-urlencoded' \ + --data-urlencode "client_Id=$IS_AIFABRIC_CLIENT_ID" \ + --data-urlencode "client_secret=$IS_AIFABRIC_CLIENT_SECRET" \ + --data-urlencode "grant_type=client_credentials" + ) + + if [ -z "$access_token_response" ]; then + echo "$(date) $red Failed to generate access token to call backend server ... Exiting $default" + deregister_client + exit 1 + fi + + ACCESS_TOKEN=$(echo "$access_token_response" | jq -r 'select(.access_token != null) | .access_token') + + if [ -z "$ACCESS_TOKEN" ]; then + echo "$(date) $red Failed to extract access token ... Exiting $default" + deregister_client + exit 1 + fi + + echo "$(date) Successfully fetched access token to call backend server " +} + +function deregister_client() { + echo "$(date) De-registering client from $IDENTITY_SERVER_ENDPOINT with name $IS_AIFABRIC_CLIENT_NAME" + curl -k -i --silent --fail --show-error -X DELETE "https://${IDENTITY_SERVER_ENDPOINT}/identity/api/Client/$IS_AIFABRIC_CLIENT_ID" -H "Authorization: Bearer ${CLIENT_INSTALLTION_TOKEN}" +} + +# Register client and fetch Access token +function register_client_and_fetch_access_token() { + + readonly IS_AIFABRIC_CLIENT_ID="aifabric-"$(openssl rand -hex 10) + readonly IS_AIFABRIC_CLIENT_SECRET=$(openssl rand -hex 32) + readonly IS_AIFABRIC_CLIENT_NAME="aifabric-"$(openssl rand -hex 10) + + # Fetch admin token + fetch_identity_server_token_to_register_client + + # Register client + echo "$(date) Registering client by name $IS_AIFABRIC_CLIENT_NAME with client id $IS_AIFABRIC_CLIENT_ID" + + local client_creation_response=$(curl -k --silent --fail --show-error -X POST "https://${IDENTITY_SERVER_ENDPOINT}/identity/api/Client" -H "Connection: keep-alive" -H "accept: text/plain" -H "Authorization: Bearer ${CLIENT_INSTALLTION_TOKEN}" -H "Content-Type: application/json-patch+json" -H "Accept-Encoding: gzip, deflate, br" -H "Accept-Language: en-US,en;q=0.9" -d "{\"clientId\":\"${IS_AIFABRIC_CLIENT_ID}\",\"clientName\":\"${IS_AIFABRIC_CLIENT_NAME}\",\"clientSecrets\":[\"${IS_AIFABRIC_CLIENT_SECRET}\"],\"requireConsent\":false,\"requireClientSecret\": true,\"allowOfflineAccess\":true,\"alwaysSendClientClaims\":true,\"allowAccessTokensViaBrowser\":true,\"allowOfflineAccess\":true,\"alwaysIncludeUserClaimsInIdToken\":true,\"accessTokenLifetime\":${ACCESS_TOKEN_LIFE_TIME},\"identityTokenLifetime\":${ACCESS_TOKEN_LIFE_TIME},\"authorizationCodeLifetime\":${ACCESS_TOKEN_LIFE_TIME},\"absoluteRefreshTokenLifetime\":${ACCESS_TOKEN_LIFE_TIME},\"slidingRefreshTokenLifetime\":${ACCESS_TOKEN_LIFE_TIME},\"RequireRequestObject\":true,\"Claims\":true,\"AlwaysIncludeUserClaimsInIdToken\":true,\"allowedGrantTypes\":[\"client_credentials\",\"authorization_code\"],\"allowedResponseTypes\":[\"id_token\"],\"allowedScopes\":[\"openid\",\"profile\",\"email\",\"AiFabric\",\"IdentityServerApi\",\"Orchestrator\",\"OrchestratorApiUserAccess\"]}") + + if [ -z "$client_creation_response" ]; then + echo "$(date) $red Failed to register client $IS_AIFABRIC_CLIENT_NAME with identity server $IDENTITY_SERVER_ENDPOINT ... Exiting $default" + exit 1 + fi + + # Fetch access token authorize backend server call + fetch_identity_server_access_token +} + +# Validate dependency module +# $1 - Name of the dependency module +# $2 - Command to validate module +function validate_dependency() { + eval $2 + # Next statement is checking last command success + if [ $? -ne 0 ]; then + echo "$red $(date) Please install ******** $1 *********** ... Exiting $default" + exit 1 + fi +} + +# Validate API response +# $1 - Api response code +# $2 - Expected response code +# $3 - Success Message +# $4 - Error message +function validate_response_from_api() { + if [ $1 = $2 ]; then + echo "$(date) $3" + elif [ "$1" = "DEFAULT" ]; then + echo "$red $(date) Please validate access token or internet. If fine check returned curl status code ... Exiting $default" + deregister_client + exit 1 + else + echo "$(date) $4 $default" + deregister_client + exit 1 + fi +} + +# Wait for core service pods to come up +# $1 - Service label +function wait_for_service_pods_liveness() { + echo "$(date) Waiting for core service $1 pod to come up" + local wait_cmd="kubectl -n $CORE_SERVICE_NAMESPACE wait --field-selector status.phase=Running --for=condition=ready --timeout=60s pod -l app=$1" + local sleep_int=10 + local start_time + local pod_ready_timeout=300 + local current_time + local elapsed_time + + # Initial sleep is required + start_time=$(date +"%s") + sleep $sleep_int + eval "$wait_cmd" + while [[ $? -ne 0 ]] + do + sleep $sleep_int + current_time=$(date +"%s") + elapsed_time=$(( current_time - start_time )) + if [[ $elapsed_time -gt $pod_ready_timeout ]] + then + echo "$(date) Timeout waiting for core service: $1 pods/pods to come alive in namespace: $CORE_SERVICE_NAMESPACE" + deregister_client + exit 1 + fi + eval "$wait_cmd" + done +} + +# Validate if data manager is enabled, returns 0 for true, 1 for false +function isDataManagerEnabled() { + local feature_flag_name=data-labeling-enabled + local isDataManagerEnabled=$(kubectl -n $CORE_SERVICE_NAMESPACE get deployment ai-app-deployment -o yaml | grep FEATURE_FLAGS -A 1 | grep $feature_flag_name) + if [ -z "$isDataManagerEnabled" ]; + then + echo "$(date) Data manager is not enabled in this platform" + return 1; + fi + return 0; +} + +# Update core service specific env variables +function update_core_service_env_variables_for_recovery() { + kubectl -n $CORE_SERVICE_NAMESPACE set env deployment/ai-pkgmanager-deployment S2S_RECOVERY_CLIENT_ID=$IS_AIFABRIC_CLIENT_ID S2S_RECOVERY_AUDIENCE=AiFabric + kubectl -n $CORE_SERVICE_NAMESPACE set env deployment/ai-deployer-deployment S2S_RECOVERY_CLIENT_ID=$IS_AIFABRIC_CLIENT_ID S2S_RECOVERY_AUDIENCE=AiFabric + kubectl -n $CORE_SERVICE_NAMESPACE set env deployment/ai-trainer-deployment S2S_RECOVERY_CLIENT_ID=$IS_AIFABRIC_CLIENT_ID S2S_RECOVERY_AUDIENCE=AiFabric + + + # Sleep is needed for pods status to be updated + sleep 2 + + # Update pkg-manager service + wait_for_service_pods_liveness "ai-pkgmanager-deployment" + + # Update deployer service + wait_for_service_pods_liveness "ai-deployer-deployment" + + # Update trainer service + wait_for_service_pods_liveness "ai-trainer-deployment" + + sleep 2 + + if isDataManagerEnabled; + then + # Update data manager service + kubectl -n $CORE_SERVICE_NAMESPACE set env deployment/ai-appmanager-deployment S2S_RECOVERY_CLIENT_ID=$IS_AIFABRIC_CLIENT_NAME S2S_RECOVERY_AUDIENCE=AiFabric + wait_for_service_pods_liveness "ai-appmanager-deployment" + fi +} + +function parse_sanitize_response() { + if [ -z "$1" ]; + then + echo "$red $(date) No response received from server for sanitizing $2, retry may fix it ... Exiting !!! $default" + exit 1 + fi + + resp_code=$(echo "$1" | grep -v '100 Continue' | grep HTTP | awk '{print $2}') + + if [[ "${resp_code}" =~ ^2 ]]; + then + echo "$green $(date) $2 sanitized Successfully $default" + else + echo "$red $(date) Sanitization failed for $2 with message: $1 $default" + exit 1 + fi +} + +# Sanitize in flight ML packages +function sanitize_in_flight_ml_packages() { + response=$(curl -i -k --show-error -X POST 'https://'"$INGRESS_HOST_OR_FQDN"'/ai-pkgmanager/v1/system/mlpackage/recover' -H 'authorization: Bearer '"$ACCESS_TOKEN"'') + parse_sanitize_response "$response" "MLPackages" +} + +# Sanitize in flight projects +function sanitize_in_flight_projects() { + response=$(curl -i -k --show-error -X POST 'https://'"$INGRESS_HOST_OR_FQDN"'/ai-pkgmanager/v1/system/project/recover' -H 'authorization: Bearer '"$ACCESS_TOKEN"'') + parse_sanitize_response "$response" "Projects" +} + +# Sanitize in flight ML Skills +function sanitize_in_flight_ml_skills() { + response=$(curl -i -k --show-error -X POST 'https://'"$INGRESS_HOST_OR_FQDN"'/ai-deployer/v1/system/mlskills/recover' -H 'authorization: Bearer '"$ACCESS_TOKEN"'') + parse_sanitize_response "$response" "MLSkills" +} + +# Sanitize in flight trainer namespaces +function sanitize_in_flight_namespaces() { + response=$(curl -i -k --show-error -X POST 'https://'"$INGRESS_HOST_OR_FQDN"'/ai-trainer/v1/system/namespace/recover' -H 'authorization: Bearer '"$ACCESS_TOKEN"'') + parse_sanitize_response "$response" "TrainerNamespaces" +} + +# Sanitize in flight trainer namespaces +function sanitize_in_flight_deployer_namespaces() { + response=$(curl -i -k --show-error -X POST 'https://'"$INGRESS_HOST_OR_FQDN"'/ai-deployer/v1/system/namespace/recover' -H 'authorization: Bearer '"$ACCESS_TOKEN"'') + parse_sanitize_response "$response" "DeployerNamespaces" +} + + +# Sanitize in flight ML Pipelines +function sanitize_in_flight_pipelines() { + response=$(curl -i -k --show-error -X POST 'https://'"$INGRESS_HOST_OR_FQDN"'/ai-trainer/v1/system/pipeline/recover' -H 'authorization: Bearer '"$ACCESS_TOKEN"'') + parse_sanitize_response "$response" "Pipelines" +} + +# Sanitize in flight Tenants +function sanitize_in_flight_tenants() { + response=$(curl -i -k --show-error -X POST 'https://'"$INGRESS_HOST_OR_FQDN"'/ai-deployer/v1/system/tenant/recover' -H 'authorization: Bearer '"$ACCESS_TOKEN"'') + parse_sanitize_response "$response" "Tenants" +} + +# Sanitize in flight data manager apps +function sanitize_in_flight_data_manager_apps() { + response=$(curl -i -k --show-error -X POST 'https://'"$INGRESS_HOST_OR_FQDN"'/ai-appmanager/v1/system/app/recover' -H 'authorization: Bearer '"$ACCESS_TOKEN"'') + parse_sanitize_response "$response" "DataManager" +} + +function sanitize_core_services_in_flight_operation() { + echo "$(date) Sanitizing core services in flight operations" + sanitize_in_flight_ml_packages + sleep 5 + sanitize_in_flight_pipelines + sleep 5 + sanitize_in_flight_namespaces + sleep 5 + sanitize_in_flight_deployer_namespaces + sleep 5 + sanitize_in_flight_ml_skills + sleep 5 + sanitize_in_flight_projects + sleep 5 + sanitize_in_flight_tenants + + if isDataManagerEnabled; + then + # Update data manager service + sanitize_in_flight_data_manager_apps + fi + echo "$(date) Successfully sanitized in-flight core services operations" +} + +# Validate file provided by user exists or not, It may be relative path or absolute path +# $1 - File path +function validate_file_path() { + if [ ! -f "$1" ]; then + echo "$red $(date) $1 file does not exist, Please check ... Exiting $default" + exit 1 + fi +} + +# Validate required modules exits in target setup +function validate_setup() { + validate_dependency curl "curl --version" + validate_dependency jq "jq --version" + echo "$(date) Successfully validated required dependencies" +} + +# Validate input provided by end user +function validate_input() { + + # Validate file path + validate_file_path $POST_RESTORE_CONFIG_FILE + + readonly INGRESS_HOST_OR_FQDN=$(cat $POST_RESTORE_CONFIG_FILE | jq -r 'select(.hostOrFQDN != null) | .hostOrFQDN') + readonly IDENTITY_SERVER_ENDPOINT=$(cat $POST_RESTORE_CONFIG_FILE | jq -r 'select(.identityServerEndPoint != null) | .identityServerEndPoint') + readonly HOST_TENANT_NAME=$(cat $POST_RESTORE_CONFIG_FILE | jq -r 'select(.hostTenantName != null) | .hostTenantName') + readonly HOST_TENANT_USER_ID_OR_EMAIL=$(cat $POST_RESTORE_CONFIG_FILE | jq -r 'select(.hostTenantIdOrEmailId != null) | .hostTenantIdOrEmailId') + readonly HOST_TENANT_PASSWORD=$(cat $POST_RESTORE_CONFIG_FILE | jq -r 'select(.hostTenantPassword != null) | .hostTenantPassword') + + if [[ -z $INGRESS_HOST_OR_FQDN || -z $IDENTITY_SERVER_ENDPOINT || -z $HOST_TENANT_NAME || -z $HOST_TENANT_USER_ID_OR_EMAIL || -z $HOST_TENANT_PASSWORD ]]; then + echo "$red $(date) Input is invalid or missing, Please check ... Exiting $default" + exit 1 + fi + + echo "$green $(date) Successfully validated user input $default" +} + +# Validate setup +validate_setup + +# Validate input +validate_input + +# Register Client and fetch access token +register_client_and_fetch_access_token + +# Update core service env variables +update_core_service_env_variables_for_recovery + +# Sanitize core services in flight operations +sanitize_core_services_in_flight_operation + +echo "$green $(date) Successfully updated in flight user operations to end state" \ No newline at end of file diff --git a/storage-checks.sh b/storage-checks.sh new file mode 100644 index 00000000..1d8856e5 --- /dev/null +++ b/storage-checks.sh @@ -0,0 +1,393 @@ +#!/bin/bash + +: ' +This scipt will validate blob storage from AIC perspective. +# $1 - json file with credentials +[Script Version -> 21.7] +Credentials file structure +{"AWS_HOST": , "AWS_ENDPOINT": , "AWS_ACCESS_KEY_ID": , "AWS_SECRET_ACCESS_KEY": , "BUCKET_1": , "BUCKET_2": } +where the access_key and secret correspond to aws crednetials which has access to the two buckets (customer creates buckets with appropriate policies) +Requirements: aws s3, jq +' + +red=$(tput setaf 1) +green=$(tput setaf 2) +yellow=$(tput setaf 3) +default=$(tput sgr0) + +json_schema='{"AWS_HOST": , "AWS_ENDPOINT": , "AWS_ACCESS_KEY_ID": , "AWS_SECRET_ACCESS_KEY": , "BUCKET_1": , "BUCKET_2": }' + +declare -A etags +declare -A errors + +function echoinfo() { + echo "$yellow $(date) $1 $default" +} + +function errecho() { + echo "$red $(date) $1 $default" +} + + +echo "$green $(date) Starting validation of object storage $default" + +if [ "$#" -ne 1 ]; then + errecho "Illegal number of arguments, scripts requires a single argument as filepath to crednetials json file" + exit 1 +fi + +readonly CREDENTIALS_FILE=$1 + + + +# Validate file provided by user exists or not, It may be relative path or absolute path +# $1 - File path +function validate_file_path() { + if [ ! -f "$1" ]; then + echo "$red $(date) $1 file does not exist, Please check ... Exiting $default" + exit 1 + fi +} + +# Validate dependency module +# $1 - Name of the dependency module +# $2 - Command to validate module +function validate_dependency() { + eval $2 + # Next statement is checking last command success aws --version has some issue + if [ $? -ne 0 ]; then + echo "$red $(date) Please install ******** $1 *********** ... Exiting $default" + exit 1 + fi +} + +function validate_file_is_json() { + if [ $(cat $1 | jq empty > /dev/null 2>&1; echo $?) -eq 0 ]; then + echo "$green $(date) JSON is valid in file $1 $default" + else + errecho "JSON is invalid in file $1, please ensure it conforms to the schema $2" + fi +} + +# Validate required modules exits in target setup +function validate_setup() { + validate_dependency "aws s3" "aws --version" + validate_dependency "jq" "jq --version" + #validate_dependency "fallocate" "fallocate -V" + validate_dependency "yes" "yes --version" + echo "$(date) Successfully validated required dependencies" +} + +function initialize_variables() { + # Validate file path + validate_file_path $CREDENTIALS_FILE + validate_file_is_json $CREDENTIALS_FILE $json_schema + + export AWS_HOST=$(cat $CREDENTIALS_FILE | jq -r 'select(.AWS_HOST != null) | .AWS_HOST') + export AWS_ENDPOINT=$(cat $CREDENTIALS_FILE | jq -r 'select(.AWS_ENDPOINT != null) | .AWS_ENDPOINT') + export AWS_ACCESS_KEY_ID=$(cat $CREDENTIALS_FILE | jq -r 'select(.AWS_ACCESS_KEY_ID != null) | .AWS_ACCESS_KEY_ID') + export AWS_SECRET_ACCESS_KEY=$(cat $CREDENTIALS_FILE | jq -r 'select(.AWS_SECRET_ACCESS_KEY != null) | .AWS_SECRET_ACCESS_KEY') + export BUCKET_1=$(cat $CREDENTIALS_FILE | jq -r 'select(.BUCKET_1 != null) | .BUCKET_1') + export BUCKET_2=$(cat $CREDENTIALS_FILE | jq -r 'select(.BUCKET_2 != null) | .BUCKET_2') + echoinfo "bucket1: $BUCKET_1, and bucket2: $BUCKET_2" + echoinfo "AWS_HOST: $AWS_HOST, and AWS_ENDPOINT: $AWS_ENDPOINT" + echoinfo "Please ensure that AWS_ACCESS_KEY_ID & AWS_SECRET_ACCESS_KEY are correct in the credentials file" +} + +function bucket_exists { + be_bucketname=$1 + + aws s3api --endpoint-url $AWS_ENDPOINT --no-verify-ssl head-bucket \ + --bucket $be_bucketname \ + >/dev/null 2>&1 + + if [[ ${?} -eq 0 ]]; then + return 0 + else + errecho "ERROR: Bucket doesn't exist: $be_bucketname" + errors['bucket_exists']="Missing bucket $be_bucketname" + return 1 + fi +} + +function copy_local_file_to_bucket { + cftb_bucketname=$1 + cftb_sourcefile=$2 + cftb_destfilename=$3 + local RESPONSE + + RESPONSE=$(aws s3api --endpoint-url $AWS_ENDPOINT --no-verify-ssl put-object \ + --bucket $cftb_bucketname \ + --body $cftb_sourcefile \ + --key $cftb_destfilename) + + if [[ ${?} -ne 0 ]]; then + errecho "ERROR: AWS reports put-object operation failed.\n$RESPONSE" + errors['copy_local_file_to_bucket']="Failed with RESPONSE: \n$RESPONSE" + return 1 + fi +} + +function copy_item_in_bucket { + ciib_bucketname=$1 + ciib_sourcefile=$2 + ciib_destfile=$3 + local RESPONSE + + RESPONSE=$(aws s3api --endpoint-url $AWS_ENDPOINT --no-verify-ssl copy-object \ + --bucket $ciib_bucketname \ + --copy-source $ciib_bucketname/$ciib_sourcefile \ + --key $ciib_destfile) + + if [[ $? -ne 0 ]]; then + errecho "ERROR: AWS reports s3api copy-object operation failed.\n$RESPONSE" + errors['copy_item_in_bucket']="Failed with RESPONSE: \n$RESPONSE" + return 1 + fi +} + +function copy_items_across_buckets { + local RESPONSE + RESPONSE=$(aws s3 --endpoint-url $AWS_ENDPOINT --no-verify-ssl cp s3://$1/$3 s3://$2/$3) + if [[ $? -ne 0 ]]; then + errecho "ERROR: AWS reports s3api cp operation failed (across buckets).\n$RESPONSE" + errors['copy_items_across_buckets']="Failed with RESPONSE: \n$RESPONSE" + return 1 + fi +} + +function list_items_in_bucket { + liib_bucketname=$1 + local RESPONSE + + RESPONSE=$(aws s3api --endpoint-url $AWS_ENDPOINT --no-verify-ssl list-objects \ + --bucket $liib_bucketname \ + --output text \ + --query 'Contents[].{Key: Key, Size: Size}' ) + + if [[ ${?} -eq 0 ]]; then + echo "$RESPONSE" + else + errecho "ERROR: AWS reports s3api list-objects operation failed.\n$RESPONSE" + errors['list_items_in_bucket']="Failed with RESPONSE: \n$RESPONSE" + return 1 + fi +} + +function list_items_in_bucket_paginated { + liib_bucketname=$1 + local RESPONSE + + RESPONSE=$(aws s3api --endpoint-url $AWS_ENDPOINT --no-verify-ssl list-objects \ + --bucket $liib_bucketname \ + --max-items=2 ) + + if [[ ${?} -eq 0 ]]; then + TOKEN=$(echo $RESPONSE | jq -r 'select(.NextToken != null) | .NextToken') + echoinfo "Paginated fetch gave nextToken as $TOKEN, should be non empty" + else + errecho "ERROR: AWS reports s3api list-objects operation failed.\n$RESPONSE" + errors['list_items_in_bucket_paginated']="Failed with RESPONSE: \n$RESPONSE" + return 1 + fi +} + +function delete_item_in_bucket { + diib_bucketname=$1 + diib_key=$2 + local RESPONSE + + RESPONSE=$(aws s3api --endpoint-url $AWS_ENDPOINT --no-verify-ssl delete-object \ + --bucket $diib_bucketname \ + --key $diib_key) + + if [[ $? -ne 0 ]]; then + errecho "ERROR: AWS reports s3api delete-object operation failed.\n$RESPONSE" + errors['delete_item_in_bucket']="Failed with RESPONSE: \n$RESPONSE" + return 1 + fi +} + +function delete_multiple { + #Objects=[{Key=string,VersionId=string},{Key=string,VersionId=string}],Quiet=boolean + local RESPONSE + RESPONSE=$(aws s3api --endpoint-url $AWS_ENDPOINT --no-verify-ssl delete-objects --bucket $1 --delete $2) + + if [[ $? -ne 0 ]]; then + errecho "ERROR: AWS reports s3api delete-objects operation failed.\n$RESPONSE" + errors['delete_multiple']="Failed with RESPONSE: \n$RESPONSE" + return 1 + fi +} + +function download_file { + local RESPONSE + RESPONSE=$(aws s3api --endpoint-url $AWS_ENDPOINT --no-verify-ssl get-object --bucket $1 --key $2 $2) + if [[ $? -ne 0 ]]; then + errecho "ERROR: AWS reports s3api get-object operation failed.\n$RESPONSE" + errors['download_file']="Failed with RESPONSE: \n$RESPONSE" + return 1 + fi +} + + +function validate_bucket_counts() { + blobs1=$(aws s3api --endpoint-url $AWS_ENDPOINT --no-verify-ssl list-objects --bucket ${BUCKET_1} --output json --query "length(Contents[])") + echo "$green $(date) $blobs1 objects in bucket: $BUCKET_1 $default" + blobs2=$(aws s3api --endpoint-url $AWS_ENDPOINT --no-verify-ssl list-objects --bucket ${BUCKET_2} --output json --query "length(Contents[])") + echo "$green $(date) $blobs2 objects in bucket: $BUCKET_2 $default" + # Policy??? +} + +# KEY MAY NEED SINGLE QUTOES AND UPLOAD_ID AND ETAG AS DOUBLEQUOTES +function create_multipart() { + local RESPONSE + RESPONSE=$(aws s3api --endpoint-url $AWS_ENDPOINT --no-verify-ssl create-multipart-upload --bucket $1 --key $2) + # Key, UploadId + if [[ $? -ne 0 ]]; then + errecho "ERROR: AWS reports s3api create-multipart-upload operation failed.\n$RESPONSE" + errors['multipart-upload']="Failed in create_multipart with RESPONSE: \n$RESPONSE" + return 1 + fi + export UPLOAD_ID=$(echo $RESPONSE | jq -r 'select(.UploadId != null) | .UploadId') +} + +function upload_part() { + local RESPONSE + RESPONSE=$(aws s3api --endpoint-url $AWS_ENDPOINT --no-verify-ssl upload-part --bucket $1 --key $2 --part-number $4 --body $5 --upload-id $3) + #Etag + if [[ $? -ne 0 ]]; then + errecho "ERROR: AWS reports s3api upload-part operation failed.\n$RESPONSE" + errors['multipart-upload']="Failed in upload_part with RESPONSE: \n$RESPONSE" + return 1 + fi + + local etag=$(echo $RESPONSE | jq -r 'select(.ETag != null) | .ETag') + etag="${etag%\"}" + etag="${etag#\"}" + etags[$4]=$etag +} + +function upload_part_copy() { + local RESPONSE + RESPONSE=$(aws s3api --endpoint-url $AWS_ENDPOINT --no-verify-ssl upload-part-copy --bucket $1 --key $2 --part-number $4 --upload-id $3 --copy-source "$1/$5") + #CopyPartResult.Etag + if [[ $? -ne 0 ]]; then + errecho "ERROR: AWS reports s3api upload-part-copy operation failed.\n$RESPONSE" + errors['multipart-upload']="Failed in upload_part_copy with RESPONSE: \n$RESPONSE" + return 1 + fi + + local etag=$(echo $RESPONSE | jq -r 'select(.CopyPartResult != null) | .CopyPartResult.ETag') + etag="${etag%\"}" + etag="${etag#\"}" + etags[$4]=$etag +} + +function complete_multipart() { + local RESPONSE + # file contains json {"Parts": [{"ETag":, "PartNumber":}]} + RESPONSE=$(aws s3api --endpoint-url $AWS_ENDPOINT --no-verify-ssl complete-multipart-upload --multipart-upload file://$4 --bucket $1 --key $2 --upload-id $3) + if [[ $? -ne 0 ]]; then + errecho "ERROR: AWS reports s3api complete-multipart-upload operation failed.\n$RESPONSE" + errors['multipart-upload']="Failed in complete_multipart with RESPONSE: \n$RESPONSE" + return 1 + fi +} + +function create_file() { + # pass desired size in mb and filename + let size_in_bytes=$1*1024*1024 + file_name=$2 + #fallocate -l ${size_in_bytes} ${file_name} + # head -c 5MB /dev/zero > ostechnix.txt + yes "file: $file_name" | head -c $1MB > $file_name +} + + +function storage_validations() { + # Create files for upload + create_file 6 1.txt + create_file 7 2.txt + create_file 9 3.txt + create_file 1 4.txt + # check both buckets exist + bucket_exists $BUCKET_1 + bucket_exists $BUCKET_2 + # upload files to one bucket + copy_local_file_to_bucket $BUCKET_1 1.txt 1.txt + copy_local_file_to_bucket $BUCKET_1 2.txt 2.txt + + copy_local_file_to_bucket $BUCKET_1 3.txt 3.txt + copy_local_file_to_bucket $BUCKET_1 4.txt 4.txt + + list_items_in_bucket_paginated $BUCKET_1 + + # validate list works, todo: pagination + echoinfo "verify 1/2/3/4.txt is present" + list_items_in_bucket $BUCKET_1 + + # delete multiple + delete_query='Objects=[{Key="3.txt"},{Key="4.txt"}]' + delete_multiple $BUCKET_1 $delete_query + echoinfo "verify 1/2.txt is present & 3/4.txt are no more" + list_items_in_bucket $BUCKET_1 + + # validate copy works within bucket + copy_item_in_bucket $BUCKET_1 1.txt 1-copy.txt + # validate copy to second bucket + copy_items_across_buckets $BUCKET_1 $BUCKET_2 1.txt + echoinfo "verify 1.txt is present" + list_items_in_bucket $BUCKET_2 + # validate counts + validate_bucket_counts + # validate delete & count post delete + delete_item_in_bucket $BUCKET_2 1.txt + # validate_bucket_counts + + # validate multipart upload + local key="combined.txt" + create_multipart $BUCKET_1 $key + upload_part_copy $BUCKET_1 $key $UPLOAD_ID 1 1.txt + upload_part $BUCKET_1 $key $UPLOAD_ID 2 3.txt + # last part < 5MB + upload_part $BUCKET_1 $key $UPLOAD_ID 3 4.txt + # write json of parts to parts.json + # for part in "${!etags[@]}"; do echo "$part - ${etags[$part]}"; done + myjson='{"Parts": []}' + for part in "${!etags[@]}" + do + myjson=$(echo -n "$myjson" | jq --arg pn $part --arg etag "${etags[$part]}" '.Parts += [{"PartNumber": ($pn | tonumber), "ETag": $etag}]') + done + echo $myjson > parts.json + + complete_multipart $BUCKET_1 $key $UPLOAD_ID parts.json + + # download full file + download_file $BUCKET_1 $key + echoinfo "verify that file size is about 15MB" + ls -lh $key + + len=${#errors[@]} + if [[ $len -ne 0 ]]; then + errecho "Failed tests:" + for part in "${!errors[@]}"; do errecho "Failed $part with error - ${errors[$part]}"; done + else + echoinfo "All tests passed successfully, please check individual logs above for any discrepancy" + fi + + + + # Get signedurl from bucket 1 + # upload using signedurl + # Check CORS??? +} + + +# Validate Setup +validate_setup + +# Update ENV Variables +initialize_variables + +storage_validations \ No newline at end of file