refactor: migrate docs of individual services to documentations repo

docs_src/use-cases/automated-self-checkout/services/common-service.md

@@ -0,0 +1,147 @@
+# Common-Service: LiDAR & Weight Sensor Microservice 
+This microservice manages **Barcode, LiDAR, and Weight sensors**  in a single container. It publishes sensor data over **MQTT** , **Kafka** , or **HTTP**  (or any combination), controlled entirely by environment variables.
+## 1. Overview 
+
+- **Sensors** 
+  - Barcode, LiDAR, & Weight support in the same codebase.
+
+  - Configuration for each sensor (e.g., ID, port, mock mode, intervals).
+
+- **Publishing**  
+  - `publisher.py` handles publishing to one or more protocols: 
+    - **MQTT**
+
+    - **Kafka**
+
+    - **HTTP**
+
+- **Apps**  
+  - Three main modules: 
+    - `barcode_app.py`
+
+    - `lidar_app.py`
+
+    - `weight_app.py`
+
+  - Each uses shared methods from `publisher.py` & `config.py`.
+
+## 2. Environment Variables 
+All settings are defined in `docker-compose.yml` under the `asc_common_service` section. Key variables include:
+### LiDAR 
+| Variable | Description | Example | 
+| --- | --- | --- | 
+| LIDAR_COUNT | Number of LiDAR sensors | 2 | 
+| LIDAR_SENSOR_ID_1 | Unique ID for first LiDAR sensor | lidar-001 | 
+| LIDAR_SENSOR_ID_2 | Unique ID for second LiDAR sensor (if any) | lidar-002 | 
+| LIDAR_MOCK_1 | Enable mock data for first LiDAR sensor (true/false) | true | 
+| LIDAR_MQTT_ENABLE | Toggle MQTT publishing | true | 
+| LIDAR_MQTT_BROKER_HOST | MQTT broker host | mqtt-broker or mqtt-broker_1 | 
+| LIDAR_MQTT_BROKER_PORT | MQTT broker port | 1883 | 
+| LIDAR_KAFKA_ENABLE | Toggle Kafka publishing | true | 
+| KAFKA_BOOTSTRAP_SERVERS | Kafka bootstrap server addresses | kafka:9093 | 
+| LIDAR_KAFKA_TOPIC | Kafka topic name for LiDAR data | lidar-data | 
+| LIDAR_HTTP_ENABLE | Toggle HTTP publishing | true | 
+| LIDAR_HTTP_URL | HTTP endpoint URL for LiDAR data | http://localhost:5000/api/lidar_data | 
+| LIDAR_PUBLISH_INTERVAL | Interval (in seconds) for LiDAR data publishing | 1.0 | 
+| LIDAR_LOG_LEVEL | Logging level (DEBUG, INFO, etc.) | INFO | 
+
+### Weight 
+| Variable | Description | Example | 
+| --- | --- | --- | 
+| WEIGHT_COUNT | Number of Weight sensors | 2 | 
+| WEIGHT_SENSOR_ID_1 | Unique ID for first Weight sensor | weight-001 | 
+| WEIGHT_SENSOR_ID_2 | Unique ID for second Weight sensor (if any) | weight-002 | 
+| WEIGHT_MOCK_1 | Enable mock data for first Weight sensor (true/false) | true | 
+| WEIGHT_MQTT_ENABLE | Toggle MQTT publishing | true | 
+| WEIGHT_MQTT_BROKER_HOST | MQTT broker host | mqtt-broker_1 | 
+| WEIGHT_MQTT_BROKER_PORT | MQTT broker port | 1883 | 
+| WEIGHT_KAFKA_ENABLE | Toggle Kafka publishing | false | 
+| WEIGHT_MQTT_TOPIC | MQTT topic name for Weight data | weight/data | 
+| WEIGHT_HTTP_ENABLE | Toggle HTTP publishing | false | 
+| WEIGHT_PUBLISH_INTERVAL | Interval (in seconds) for Weight data publishing | 1.0 | 
+| WEIGHT_LOG_LEVEL | Logging level (DEBUG, INFO, etc.) | INFO | 
+
+### Barcode
+| Variable | Description | Example |
+| --- | --- | --- |
+| BARCODE_COUNT | Number of Barcode sensors | 2 |
+| BARCODE_SENSOR_ID_1 | Unique ID for first Barcode sensor | barcode-001 |
+| BARCODE_SENSOR_ID_2 | Unique ID for second Barcode sensor (if any) | barcode-002 |
+| BARCODE_MOCK_1 | Enable mock data for first Barcode sensor (true/false) | true |
+| BARCODE_MQTT_ENABLE | Toggle MQTT publishing | true |
+| BARCODE_MQTT_BROKER_HOST | MQTT broker host | mqtt-broker_1 |
+| BARCODE_MQTT_BROKER_PORT | MQTT broker port | 1883 |
+| BARCODE_KAFKA_ENABLE | Toggle Kafka publishing | false |
+| BARCODE_MQTT_TOPIC | MQTT topic name for Barcode data | barcode/data |
+| BARCODE_HTTP_ENABLE | Toggle HTTP publishing | false |
+| BARCODE_PUBLISH_INTERVAL | Interval (in seconds) for Barcode data publishing | 1.0 |
+| BARCODE_LOG_LEVEL | Logging level (DEBUG, INFO, etc.) | INFO |
+
+> **Note:**  Change `"true"` or `"false"` to enable or disable each protocol. Adjust intervals, logging levels, or sensor counts as needed.
+## 3. Usage 
+
+1. **Build and Run** 
+
+```bash
+make build-sensors
+make run-sensors
+```
+This spins up the `asc_common_service` container (and related services like Mosquitto or Kafka, depending on your configuration).
+
+2. **Data Flow**  
+  - By default, LiDAR publishes to `lidar/data` (MQTT, if enabled) or `lidar-data` (Kafka), or an HTTP endpoint if configured.
+
+  - Weight sensor similarly publishes to `weight/data` or `weight-data`.
+
+3. **Mock Mode**  
+  - Setting `LIDAR_MOCK_1="true"` (or `WEIGHT_MOCK_1="true"`) forces the sensor to generate **random**  data rather than reading from actual hardware.
+
+## 4. Testing 
+
+### A. MQTT 
+
+- **Grafana** : A pre-loaded dashboard named *Retail Analytics Dashboard* is available at [http://localhost:3000](http://localhost:3000/)  (default credentials `admin`/`admin`).
+
+- Check that the MQTT data source in Grafana points to `tcp://mqtt-broker_1:1883` (or `tcp://mqtt-broker:1883`, depending on the network).
+
+### B. Kafka 
+
+- Enable Kafka for LiDAR/Weight by setting `LIDAR_KAFKA_ENABLE="true"` and/or `WEIGHT_KAFKA_ENABLE="true"`.
+
+- Test from inside the container:
+
+```bash
+docker exec asc_common_service python kafka_publisher_test.py --topic lidar-data
+```
+You should see incoming messages in the console.
+
+### C. HTTP 
+
+1️ **Local Test (Inside Docker)**
+
+- Set `LIDAR_HTTP_URL="http://localhost:5000/api/lidar_data"` in the environment.
+- Run `make run-sensors` and wait for all containers to start.
+- Once up, execute:
+
+```bash
+docker exec asc_common_service python http_publisher_test.py
+```
+
+- This will trigger the HTTP publisher and display the received data inside the container.
+
+2️ **Using an External Webhook Service**
+
+- Visit [Webhook.site](https://webhook.site/) and get a unique URL.
+- Set `LIDAR_HTTP_URL` to this URL.
+- Run `make run-sensors`, and you should see the HTTP requests arriving on the Webhook.site dashboard.
+
+
+
+## 5. Contributing & Development 
+
+- **Code Structure**  
+  - `publisher.py`: Core publishing logic (MQTT, Kafka, HTTP).
+
+  - `config.py`: Loads environment variables and configures each sensor.
+
+  - `barcode_app.py`, `lidar_app.py`, and `weight_app.py`: Sensor-specific logic.

docs_src/use-cases/automated-self-checkout/services/pipeline-server.md

@@ -0,0 +1,69 @@
+# Pipeline Server
+
+## Prerequisites
+
+  * Prepare models
+
+      Use the model downloader [available here](https://github.com/dlstreamer/pipeline-server/tree/main/tools/model_downloader) to download new models. Point `MODEL_DIR` to the directory containing the new models. The following section assumes that the new models are available under `$(pwd)/models`.
+   ```bash
+    $ export MODEL_DIR=$(pwd)/models
+   ```
+object_detection/yolov5s/yolov5s.json
+object_detection/yolov5s/FP32
+
+ * Prepare pipelines
+
+     Use [these docs](https://github.com/dlstreamer/pipeline-server/blob/main/docs/defining_pipelines.md) to get started with defining new pipelines. Once the new pipelines have been defined, point `PIPELINE_DIR` to the directory containing the new pipelines. The following section assumes that the new pipelines are available under `$(pwd)/pipelines`.
+    ```bash
+    $ export PIPELINE_DIR=$(pwd)/pipelines
+   ```
+
+ * Run the image with new models and pipelines mounted into the container
+ ```bash
+   $ docker run -itd \
+      --privileged \
+      --device=/dev:/dev \
+      --device-cgroup-rule='c 189:* rmw' \
+      --device-cgroup-rule='c 209:* rmw' \
+      --group-add 109 \
+      --name evam \
+      -p 8080:8080 \
+      -p 8554:8554 \
+      -e ENABLE_RTSP=true \
+      -e RTSP_PORT=8554 \
+      -e ENABLE_WEBRTC=true \
+      -e WEBRTC_SIGNALING_SERVER=ws://localhost:8443 \
+      -e RUN_MODE=EVA \
+      -e DETECTION_DEVICE=CPU \
+      -e CLASSIFICATION_DEVICE=CPU \
+      -v ./models:/home/pipeline-server/models \
+      -v ./src/pipelines:/home/pipeline-server/pipelines \
+      dlstreamer:dev
+ ```
+## Starting pipelines
+ * We can trigger pipelines using the *pipeline server's* REST endpoints, here is an example cURL command, the output is available as a RTSP stream at *rtsp://<host ip>/pipeline-server*
+ ```bash
+    $ curl localhost:8080/pipelines/object_detection/yolov5 -X POST -H \
+      'Content-Type: application/json' -d \
+      '{
+         "source": {
+            "uri": "rtsp://192.168.1.141:8555/camera_0",
+            "type": "uri"
+         },
+         "destination": {
+            "metadata": {
+               "type": "file",
+               "path": "/tmp/results.jsonl",
+               "format": "json-lines"
+            },
+            "frame": {
+               "type": "rtsp",
+               "path": "pipeline-server"
+            }
+         },
+         "parameters": {
+            "detection-device": "CPU",
+            "network": "FP16-INT8"
+         }
+      }'
+ ```

mkdocs.yml

@@ -56,6 +56,9 @@ nav:
           - Catalog:
               - Overview: 'use-cases/automated-self-checkout/catalog/Overview.md'
               - Getting Started: 'use-cases/automated-self-checkout/catalog/Get-Started-Guide.md'
+          - Services:
+              - Common Service: 'use-cases/automated-self-checkout/services/common-service.md'
+              - Pipeline Server: 'use-cases/automated-self-checkout/services/pipeline-server.md'
       - AI Connect for Scientific Data (AiCSD):
           - Overview: 'use-cases/AiCSD/aicsd.md'
           - GRPC Yolov5s Pipeline: 'use-cases/AiCSD/pipeline-grpc-go.md'