From ac30d1af2b4008ca39be0f4a63c07450db562d71 Mon Sep 17 00:00:00 2001
From: Tiep Le <tiep.le@intel.com>
Date: Thu, 29 Aug 2024 22:00:32 +0000
Subject: [PATCH 1/8] updates

Signed-off-by: Tiep Le <tiep.le@intel.com>
---
 .../multimodal_langchain/redis/README.md      | 120 ++++++++++++++++++
 .../multimodal_langchain/redis/__init__.py    |   2 +
 .../redis/docker/Dockerfile                   |  29 +++++
 .../docker/docker_compose_retriever.yaml      |  33 +++++
 .../redis/multimodal_config.py                |  80 ++++++++++++
 .../redis/redis_schema.yml                    |  20 +++
 .../redis/requirements.txt                    |  14 ++
 .../redis/retriever_redis.py                  | 100 +++++++++++++++
 8 files changed, 398 insertions(+)
 create mode 100644 comps/retrievers/multimodal_langchain/redis/README.md
 create mode 100644 comps/retrievers/multimodal_langchain/redis/__init__.py
 create mode 100644 comps/retrievers/multimodal_langchain/redis/docker/Dockerfile
 create mode 100644 comps/retrievers/multimodal_langchain/redis/docker/docker_compose_retriever.yaml
 create mode 100644 comps/retrievers/multimodal_langchain/redis/multimodal_config.py
 create mode 100644 comps/retrievers/multimodal_langchain/redis/redis_schema.yml
 create mode 100644 comps/retrievers/multimodal_langchain/redis/requirements.txt
 create mode 100644 comps/retrievers/multimodal_langchain/redis/retriever_redis.py

diff --git a/comps/retrievers/multimodal_langchain/redis/README.md b/comps/retrievers/multimodal_langchain/redis/README.md
new file mode 100644
index 0000000000..a8d8e2fb0c
--- /dev/null
+++ b/comps/retrievers/multimodal_langchain/redis/README.md
@@ -0,0 +1,120 @@
+# Retriever Microservice
+
+This retriever microservice is a highly efficient search service designed for handling and retrieving embedding vectors from multimodal data. It operates by receiving an embedding vector as input and conducting a similarity search against vectors stored in a VectorDB database. Users must specify the VectorDB's URL and the index name, and the service searches within that index to find documents with the highest similarity to the input vector.
+
+The service primarily utilizes similarity measures in vector space to rapidly retrieve contentually similar documents. The vector-based retrieval approach is particularly suited for handling large datasets, offering fast and accurate search results that significantly enhance the efficiency and quality of information retrieval.
+
+Overall, this microservice provides robust backend support for applications requiring efficient similarity searches, playing a vital role in scenarios such as recommendation systems, information retrieval, or any other context where precise measurement of document similarity is crucial.
+
+## 🚀1. Start Microservice with Python (Option 1)
+
+To start the retriever microservice, you must first install the required python packages.
+
+### 1.1 Install Requirements
+
+```bash
+pip install -r requirements.txt
+```
+### 1.2 Setup VectorDB Service
+
+You need to setup your own VectorDB service (Redis in this example), and ingest your knowledge documents into the vector database.
+
+As for Redis, you could start a docker container using the following commands.
+Remember to ingest data into it manually.
+
+```bash
+docker run -d --name="redis-vector-db" -p 6379:6379 -p 8001:8001 redis/redis-stack:7.2.0-v9
+```
+### 1.3 Ingest images or video
+
+Upload a video or images using the dataprep microservice, instructions can be found  [here](https://github.com/opea-project/GenAIComps/tree/main/comps/dataprep/redis/multimodal_langchain/README.md).
+
+### 1.4 Start Retriever Service
+
+```bash
+python langchain_multimodal/retriever_redis.py
+```
+
+## 🚀2. Start Microservice with Docker (Option 2)
+
+### 2.1 Setup Environment Variables
+
+```bash
+export REDIS_URL="redis://${your_ip}:6379"
+export INDEX_NAME=${your_index_name}
+```
+
+### 2.2 Build Docker Image
+
+```bash
+cd ../../
+docker build -t opea/multimodal-retriever-redis:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f comps/retrievers/langchain_multimodal/redis/docker/Dockerfile .
+```
+
+To start a docker container, you have two options:
+
+- A. Run Docker with CLI
+- B. Run Docker with Docker Compose
+
+You can choose one as needed.
+
+### 2.3 Run Docker with CLI (Option A)
+
+```bash
+docker run -d --name="retriever-redis-server" -p 7000:7000 --ipc=host -e http_proxy=$http_proxy -e https_proxy=$https_proxy -e REDIS_URL=$REDIS_URL -e INDEX_NAME=$INDEX_NAME opea/multimodal-retriever-redis:latest
+```
+
+### 2.4 Run Docker with Docker Compose (Option B)
+
+```bash
+cd langchain_multimodal/docker
+docker compose -f docker_compose_retriever.yaml up -d
+```
+
+## 🚀3. Consume Retriever Service
+
+### 3.1 Consume Embedding Service
+
+To consume the Retriever Microservice, you can generate a mock embedding vector of length 512 with Python.
+
+```bash
+your_embedding=$(python -c "import random; embedding = [random.uniform(-1, 1) for _ in range(512)]; print(embedding)")
+curl http://${your_ip}:7000/v1/retrieval \
+  -X POST \
+  -d {\"text\":\"What is the revenue of Nike in 2023?\",\"embedding\":${your_embedding}}" \
+  -H 'Content-Type: application/json'
+```
+
+You can set the parameters for the retriever.
+
+```bash
+your_embedding=$(python -c "import random; embedding = [random.uniform(-1, 1) for _ in range(512)]; print(embedding)")
+curl http://localhost:7000/v1/retrieval \
+  -X POST \
+  -d "{\"text\":\"What is the revenue of Nike in 2023?\",\"embedding\":${your_embedding},\"search_type\":\"similarity\", \"k\":4}" \
+  -H 'Content-Type: application/json'
+```
+
+```bash
+your_embedding=$(python -c "import random; embedding = [random.uniform(-1, 1) for _ in range(512)]; print(embedding)")
+curl http://localhost:7000/v1/retrieval \
+  -X POST \
+  -d "{\"text\":\"What is the revenue of Nike in 2023?\",\"embedding\":${your_embedding},\"search_type\":\"similarity_distance_threshold\", \"k\":4, \"distance_threshold\":1.0}" \
+  -H 'Content-Type: application/json'
+```
+
+```bash
+your_embedding=$(python -c "import random; embedding = [random.uniform(-1, 1) for _ in range(512)]; print(embedding)")
+curl http://localhost:7000/v1/retrieval \
+  -X POST \
+  -d "{\"text\":\"What is the revenue of Nike in 2023?\",\"embedding\":${your_embedding},\"search_type\":\"similarity_score_threshold\", \"k\":4, \"score_threshold\":0.2}" \
+  -H 'Content-Type: application/json'
+```
+
+```bash
+your_embedding=$(python -c "import random; embedding = [random.uniform(-1, 1) for _ in range(512)]; print(embedding)")
+curl http://localhost:7000/v1/retrieval \
+  -X POST \
+  -d "{\"text\":\"What is the revenue of Nike in 2023?\",\"embedding\":${your_embedding},\"search_type\":\"mmr\", \"k\":4, \"fetch_k\":20, \"lambda_mult\":0.5}" \
+  -H 'Content-Type: application/json'
+```
diff --git a/comps/retrievers/multimodal_langchain/redis/__init__.py b/comps/retrievers/multimodal_langchain/redis/__init__.py
new file mode 100644
index 0000000000..916f3a44b2
--- /dev/null
+++ b/comps/retrievers/multimodal_langchain/redis/__init__.py
@@ -0,0 +1,2 @@
+# Copyright (C) 2024 Intel Corporation
+# SPDX-License-Identifier: Apache-2.0
diff --git a/comps/retrievers/multimodal_langchain/redis/docker/Dockerfile b/comps/retrievers/multimodal_langchain/redis/docker/Dockerfile
new file mode 100644
index 0000000000..ed1258c579
--- /dev/null
+++ b/comps/retrievers/multimodal_langchain/redis/docker/Dockerfile
@@ -0,0 +1,29 @@
+# Copyright (C) 2024 Intel Corporation
+# SPDX-License-Identifier: Apache-2.0
+
+FROM langchain/langchain:latest
+
+ARG ARCH="cpu"
+
+RUN apt-get update -y && apt-get install -y --no-install-recommends --fix-missing \
+    libgl1-mesa-glx \
+    libjemalloc-dev \
+    vim
+
+RUN useradd -m -s /bin/bash user && \
+    mkdir -p /home/user && \
+    chown -R user /home/user/
+
+COPY comps /home/user/comps
+
+USER user
+
+RUN pip install --no-cache-dir --upgrade pip && \
+    if [ ${ARCH} = "cpu" ]; then pip install torch torchvision --index-url https://download.pytorch.org/whl/cpu; fi && \
+    pip install --no-cache-dir -r /home/user/comps/retrievers/langchain_multimodal/redis/requirements.txt
+
+ENV PYTHONPATH=$PYTHONPATH:/home/user
+
+WORKDIR /home/user/comps/retrievers/multimodal_langchain/redis
+
+ENTRYPOINT ["python", "retriever_redis.py"]
diff --git a/comps/retrievers/multimodal_langchain/redis/docker/docker_compose_retriever.yaml b/comps/retrievers/multimodal_langchain/redis/docker/docker_compose_retriever.yaml
new file mode 100644
index 0000000000..4129a4c782
--- /dev/null
+++ b/comps/retrievers/multimodal_langchain/redis/docker/docker_compose_retriever.yaml
@@ -0,0 +1,33 @@
+# Copyright (C) 2024 Intel Corporation
+# SPDX-License-Identifier: Apache-2.0
+
+version: "3.8"
+
+services:
+  tei_xeon_service:
+    image: retriever-redis:cpu-1.2
+    container_name: tei-xeon-server
+    ports:
+      - "6060:80"
+    volumes:
+      - "./data:/data"
+    shm_size: 1g
+    command: --model-id "BridgeTower/bridgetower-large-itm-mlm-itc"
+  retriever:
+    image: opea/retriever-redis:latest
+    container_name: retriever-redis-server
+    ports:
+      - "7000:7000"
+    ipc: host
+    environment:
+      no_proxy: ${no_proxy}
+      http_proxy: ${http_proxy}
+      https_proxy: ${https_proxy}
+      REDIS_URL: ${REDIS_URL}
+      INDEX_NAME: ${INDEX_NAME}
+      LANGCHAIN_API_KEY: ${LANGCHAIN_API_KEY}
+    restart: unless-stopped
+
+networks:
+  default:
+    driver: bridge
diff --git a/comps/retrievers/multimodal_langchain/redis/multimodal_config.py b/comps/retrievers/multimodal_langchain/redis/multimodal_config.py
new file mode 100644
index 0000000000..2ae183bfb8
--- /dev/null
+++ b/comps/retrievers/multimodal_langchain/redis/multimodal_config.py
@@ -0,0 +1,80 @@
+import os
+
+
+def get_boolean_env_var(var_name, default_value=False):
+    """Retrieve the boolean value of an environment variable.
+    Args:
+    var_name (str): The name of the environment variable to retrieve.
+    default_value (bool): The default value to return if the variable
+    is not found.
+    Returns:
+    bool: The value of the environment variable, interpreted as a boolean.
+    """
+    true_values = {"true", "1", "t", "y", "yes"}
+    false_values = {"false", "0", "f", "n", "no"}
+
+    # Retrieve the environment variable's value
+    value = os.getenv(var_name, "").lower()
+
+    # Decide the boolean value based on the content of the string
+    if value in true_values:
+        return True
+    elif value in false_values:
+        return False
+    else:
+        return default_value
+
+
+# Check for openai API key
+#if "OPENAI_API_KEY" not in os.environ:
+#    raise Exception("Must provide an OPENAI_API_KEY as an env var.")
+
+
+# Whether or not to enable langchain debugging
+DEBUG = get_boolean_env_var("DEBUG", False)
+# Set DEBUG env var to "true" if you wish to enable LC debugging module
+if DEBUG:
+    import langchain
+
+    langchain.debug = True
+
+
+# Embedding model
+EMBED_MODEL = os.getenv("EMBED_MODEL", "BridgeTower/bridgetower-large-itm-mlm-itc")
+
+# Redis Connection Information
+REDIS_HOST = os.getenv("REDIS_HOST", "localhost")
+REDIS_PORT = int(os.getenv("REDIS_PORT", 6379))
+
+
+def format_redis_conn_from_env():
+    redis_url = os.getenv("REDIS_URL", None)
+    if redis_url:
+        return redis_url
+    else:
+        using_ssl = get_boolean_env_var("REDIS_SSL", False)
+        start = "rediss://" if using_ssl else "redis://"
+
+        # if using RBAC
+        password = os.getenv("REDIS_PASSWORD", None)
+        username = os.getenv("REDIS_USERNAME", "default")
+        if password is not None:
+            start += f"{username}:{password}@"
+
+        return start + f"{REDIS_HOST}:{REDIS_PORT}"
+
+
+REDIS_URL = format_redis_conn_from_env()
+
+# Vector Index Configuration
+INDEX_NAME = os.getenv("INDEX_NAME", "test-index")
+
+current_file_path = os.path.abspath(__file__)
+parent_dir = os.path.dirname(current_file_path)
+REDIS_SCHEMA = os.getenv("REDIS_SCHEMA", "redis_schema.yml")
+schema_path = os.path.join(parent_dir, REDIS_SCHEMA)
+INDEX_SCHEMA = schema_path
+TGI_ENDPOINT = os.getenv("TGI_ENDPOINT", "http://localhost:8080")
+TGI_ENDPOINT_NO_RAG = os.getenv("TGI_ENDPOINT_NO_RAG", "http://localhost:8081")
+NUM_RETRIEVED_RESULTS = int(os.getenv("NUM_RETRIEVED_RESULTS", 1))
+MULTIMODAL_CHAT_MODEL = os.getenv("MULTIMODAL_CHAT_MODEL", "llava-hf/llava-1.5-13b-hf")
diff --git a/comps/retrievers/multimodal_langchain/redis/redis_schema.yml b/comps/retrievers/multimodal_langchain/redis/redis_schema.yml
new file mode 100644
index 0000000000..f0d1bd5674
--- /dev/null
+++ b/comps/retrievers/multimodal_langchain/redis/redis_schema.yml
@@ -0,0 +1,20 @@
+# Copyright (C) 2024 Intel Corporation
+# SPDX-License-Identifier: Apache-2.0
+
+text:
+- name: content
+- name: b64_img_str
+- name: video_id
+- name: source_video
+- name: embedding_type
+- name: title
+- name: description
+- name: transcript_for_inference
+numeric:
+- name: time_of_frame_ms
+vector:
+- name: content_vector
+  algorithm: HNSW
+  datatype: FLOAT32
+  dims: 512
+  distance_metric: COSINE
diff --git a/comps/retrievers/multimodal_langchain/redis/requirements.txt b/comps/retrievers/multimodal_langchain/redis/requirements.txt
new file mode 100644
index 0000000000..3720190d3e
--- /dev/null
+++ b/comps/retrievers/multimodal_langchain/redis/requirements.txt
@@ -0,0 +1,14 @@
+docarray[full]
+easyocr
+fastapi
+langchain_community
+langsmith
+opentelemetry-api
+opentelemetry-exporter-otlp
+opentelemetry-sdk
+prometheus-fastapi-instrumentator
+pymupdf
+redis
+sentence_transformers
+shortuuid
+uvicorn
diff --git a/comps/retrievers/multimodal_langchain/redis/retriever_redis.py b/comps/retrievers/multimodal_langchain/redis/retriever_redis.py
new file mode 100644
index 0000000000..bb90623421
--- /dev/null
+++ b/comps/retrievers/multimodal_langchain/redis/retriever_redis.py
@@ -0,0 +1,100 @@
+# Copyright (C) 2024 Intel Corporation
+# SPDX-License-Identifier: Apache-2.0
+
+import os
+import time
+from typing import Union
+
+from comps import BridgeTowerEmbedding
+from langchain_community.vectorstores import Redis
+from langsmith import traceable
+from multimodal_config import INDEX_NAME, REDIS_URL, REDIS_SCHEMA
+
+from comps import (
+    EmbedMultimodalDoc,
+    SearchedMultimodalDoc,
+    ServiceType,
+    TextDoc,
+    opea_microservices,
+    register_microservice,
+    register_statistics,
+    statistics_dict,
+)
+from comps.cores.proto.api_protocol import (
+    ChatCompletionRequest,
+    RetrievalRequest,
+    RetrievalResponse,
+    RetrievalResponseData,
+)
+
+@register_microservice(
+    name="opea_service@retriever_redis",
+    service_type=ServiceType.RETRIEVER,
+    endpoint="/v1/retrieval",
+    host="0.0.0.0",
+    port=7000,
+)
+@traceable(run_type="retriever")
+@register_statistics(names=["opea_service@retriever_redis"])
+def retrieve(
+    input: Union[EmbedMultimodalDoc, RetrievalRequest, ChatCompletionRequest]
+) -> Union[SearchedMultimodalDoc, RetrievalResponse, ChatCompletionRequest]:
+
+    start = time.time()
+    # check if the Redis index has data
+    if vector_db.client.keys() == []:
+        search_res = []
+    else:
+        if isinstance(input, EmbedMultimodalDoc):
+            query = input.text
+        else:
+            # for RetrievalRequest, ChatCompletionRequest
+            query = input.input
+        # if the Redis index has data, perform the search
+        if input.search_type == "similarity":
+            search_res = vector_db.similarity_search_by_vector(embedding=input.embedding, k=input.k)
+        elif input.search_type == "similarity_distance_threshold":
+            if input.distance_threshold is None:
+                raise ValueError("distance_threshold must be provided for " + "similarity_distance_threshold retriever")
+            search_res = vector_db.similarity_search_by_vector(
+                embedding=input.embedding, k=input.k, distance_threshold=input.distance_threshold
+            )
+        elif input.search_type == "similarity_score_threshold":
+            docs_and_similarities = vector_db.similarity_search_with_relevance_scores(
+                query=input.text, k=input.k, score_threshold=input.score_threshold
+            )
+            search_res = [doc for doc, _ in docs_and_similarities]
+        elif input.search_type == "mmr":
+            search_res = vector_db.max_marginal_relevance_search(
+                query=input.text, k=input.k, fetch_k=input.fetch_k, lambda_mult=input.lambda_mult
+            )
+        else:
+            raise ValueError(f"{input.search_type} not valid")
+
+    # return different response format
+    retrieved_docs = []
+    if isinstance(input, EmbedMultimodalDoc):
+        metadata_list = []
+        for r in search_res:
+            metadata_list.append(r.metadata)
+            retrieved_docs.append(TextDoc(text=r.page_content))
+        result = SearchedMultimodalDoc(retrieved_docs=retrieved_docs, initial_query=input.text, metadata=metadata_list)
+    else:
+        for r in search_res:
+            retrieved_docs.append(RetrievalResponseData(text=r.page_content, metadata=r.metadata))
+        if isinstance(input, RetrievalRequest):
+            result = RetrievalResponse(retrieved_docs=retrieved_docs)
+        elif isinstance(input, ChatCompletionRequest):
+            input.retrieved_docs = retrieved_docs
+            input.documents = [doc.text for doc in retrieved_docs]
+            result = input
+
+    statistics_dict["opea_service@retriever_redis"].append_latency(time.time() - start, None)
+    return result
+
+
+if __name__ == "__main__":
+
+    embeddings = BridgeTowerEmbedding()
+    vector_db = Redis.from_existing_index(embedding=embeddings, schema=REDIS_SCHEMA, index_name=INDEX_NAME, redis_url=REDIS_URL)
+    opea_microservices["opea_service@retriever_redis"].start()

From 77632f2686fbdebe723edc80b3ee6faba7532f30 Mon Sep 17 00:00:00 2001
From: siddhivelankar23 <siddhi.velankar@intel.com>
Date: Fri, 30 Aug 2024 19:04:08 +0000
Subject: [PATCH 2/8] cosmetic

Signed-off-by: siddhivelankar23 <siddhi.velankar@intel.com>
---
 comps/retrievers/multimodal_langchain/redis/README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/comps/retrievers/multimodal_langchain/redis/README.md b/comps/retrievers/multimodal_langchain/redis/README.md
index a8d8e2fb0c..31bdaff384 100644
--- a/comps/retrievers/multimodal_langchain/redis/README.md
+++ b/comps/retrievers/multimodal_langchain/redis/README.md
@@ -8,7 +8,7 @@ Overall, this microservice provides robust backend support for applications requ
 
 ## 🚀1. Start Microservice with Python (Option 1)
 
-To start the retriever microservice, you must first install the required python packages.
+To start the retriever microservice, you must first install the required python packages. 
 
 ### 1.1 Install Requirements
 

From 4c3ea33bfa9848107282561522a1b97fb61e048f Mon Sep 17 00:00:00 2001
From: siddhivelankar23 <siddhi.velankar@intel.com>
Date: Tue, 3 Sep 2024 00:17:07 +0000
Subject: [PATCH 3/8] update redis schema

Signed-off-by: siddhivelankar23 <siddhi.velankar@intel.com>
---
 .../redis/redis_schema.yml                    | 27 +++++++++----------
 1 file changed, 13 insertions(+), 14 deletions(-)

diff --git a/comps/retrievers/multimodal_langchain/redis/redis_schema.yml b/comps/retrievers/multimodal_langchain/redis/redis_schema.yml
index f0d1bd5674..32f4a79ae4 100644
--- a/comps/retrievers/multimodal_langchain/redis/redis_schema.yml
+++ b/comps/retrievers/multimodal_langchain/redis/redis_schema.yml
@@ -2,19 +2,18 @@
 # SPDX-License-Identifier: Apache-2.0
 
 text:
-- name: content
-- name: b64_img_str
-- name: video_id
-- name: source_video
-- name: embedding_type
-- name: title
-- name: description
-- name: transcript_for_inference
+  - name: content
+  - name: b64_img_str
+  - name: video_id
+  - name: source_video
+  - name: embedding_type
+  - name: title
+  - name: transcript_for_inference
 numeric:
-- name: time_of_frame_ms
+  - name: time_of_frame_ms
 vector:
-- name: content_vector
-  algorithm: HNSW
-  datatype: FLOAT32
-  dims: 512
-  distance_metric: COSINE
+  - name: content_vector
+    algorithm: HNSW
+    datatype: FLOAT32
+    dims: 512
+    distance_metric: COSINE

From 168803315e27f58767ba3b7d5e3ab7aeaf2527d1 Mon Sep 17 00:00:00 2001
From: siddhivelankar23 <siddhi.velankar@intel.com>
Date: Tue, 3 Sep 2024 00:30:48 +0000
Subject: [PATCH 4/8] update multimodal config and docker compose retriever

Signed-off-by: siddhivelankar23 <siddhi.velankar@intel.com>
---
 .../redis/docker/docker_compose_retriever.yaml       | 12 +-----------
 .../multimodal_langchain/redis/multimodal_config.py  | 12 ++++++------
 2 files changed, 7 insertions(+), 17 deletions(-)

diff --git a/comps/retrievers/multimodal_langchain/redis/docker/docker_compose_retriever.yaml b/comps/retrievers/multimodal_langchain/redis/docker/docker_compose_retriever.yaml
index 4129a4c782..044e723ac0 100644
--- a/comps/retrievers/multimodal_langchain/redis/docker/docker_compose_retriever.yaml
+++ b/comps/retrievers/multimodal_langchain/redis/docker/docker_compose_retriever.yaml
@@ -1,18 +1,9 @@
 # Copyright (C) 2024 Intel Corporation
 # SPDX-License-Identifier: Apache-2.0
 
-version: "3.8"
+version: "1.0"
 
 services:
-  tei_xeon_service:
-    image: retriever-redis:cpu-1.2
-    container_name: tei-xeon-server
-    ports:
-      - "6060:80"
-    volumes:
-      - "./data:/data"
-    shm_size: 1g
-    command: --model-id "BridgeTower/bridgetower-large-itm-mlm-itc"
   retriever:
     image: opea/retriever-redis:latest
     container_name: retriever-redis-server
@@ -25,7 +16,6 @@ services:
       https_proxy: ${https_proxy}
       REDIS_URL: ${REDIS_URL}
       INDEX_NAME: ${INDEX_NAME}
-      LANGCHAIN_API_KEY: ${LANGCHAIN_API_KEY}
     restart: unless-stopped
 
 networks:
diff --git a/comps/retrievers/multimodal_langchain/redis/multimodal_config.py b/comps/retrievers/multimodal_langchain/redis/multimodal_config.py
index 2ae183bfb8..f92d5755d7 100644
--- a/comps/retrievers/multimodal_langchain/redis/multimodal_config.py
+++ b/comps/retrievers/multimodal_langchain/redis/multimodal_config.py
@@ -1,5 +1,10 @@
+# Copyright (C) 2024 Intel Corporation
+# SPDX-License-Identifier: Apache-2.0
+
 import os
 
+current_file_path = os.path.abspath(__file__)
+parent_dir = os.path.dirname(current_file_path)
 
 def get_boolean_env_var(var_name, default_value=False):
     """Retrieve the boolean value of an environment variable.
@@ -69,12 +74,7 @@ def format_redis_conn_from_env():
 # Vector Index Configuration
 INDEX_NAME = os.getenv("INDEX_NAME", "test-index")
 
-current_file_path = os.path.abspath(__file__)
-parent_dir = os.path.dirname(current_file_path)
 REDIS_SCHEMA = os.getenv("REDIS_SCHEMA", "redis_schema.yml")
 schema_path = os.path.join(parent_dir, REDIS_SCHEMA)
 INDEX_SCHEMA = schema_path
-TGI_ENDPOINT = os.getenv("TGI_ENDPOINT", "http://localhost:8080")
-TGI_ENDPOINT_NO_RAG = os.getenv("TGI_ENDPOINT_NO_RAG", "http://localhost:8081")
-NUM_RETRIEVED_RESULTS = int(os.getenv("NUM_RETRIEVED_RESULTS", 1))
-MULTIMODAL_CHAT_MODEL = os.getenv("MULTIMODAL_CHAT_MODEL", "llava-hf/llava-1.5-13b-hf")
+NUM_RETRIEVED_RESULTS = int(os.getenv("NUM_RETRIEVED_RESULTS", 1))
\ No newline at end of file

From bc1699f2b0efad7348249139af563ff54728bf19 Mon Sep 17 00:00:00 2001
From: siddhivelankar23 <siddhi.velankar@intel.com>
Date: Tue, 3 Sep 2024 00:34:02 +0000
Subject: [PATCH 5/8] update requirements

Signed-off-by: siddhivelankar23 <siddhi.velankar@intel.com>
---
 comps/retrievers/multimodal_langchain/redis/requirements.txt | 5 +----
 1 file changed, 1 insertion(+), 4 deletions(-)

diff --git a/comps/retrievers/multimodal_langchain/redis/requirements.txt b/comps/retrievers/multimodal_langchain/redis/requirements.txt
index 3720190d3e..e6ceddd4ef 100644
--- a/comps/retrievers/multimodal_langchain/redis/requirements.txt
+++ b/comps/retrievers/multimodal_langchain/redis/requirements.txt
@@ -1,14 +1,11 @@
 docarray[full]
-easyocr
 fastapi
 langchain_community
-langsmith
 opentelemetry-api
 opentelemetry-exporter-otlp
 opentelemetry-sdk
 prometheus-fastapi-instrumentator
-pymupdf
 redis
-sentence_transformers
 shortuuid
 uvicorn
+transformers
\ No newline at end of file

From 5ecbfdb11c56bee8b3fcca90a6404762df8a3fe1 Mon Sep 17 00:00:00 2001
From: siddhivelankar23 <siddhi.velankar@intel.com>
Date: Tue, 3 Sep 2024 00:47:09 +0000
Subject: [PATCH 6/8] update retriever redis

Signed-off-by: siddhivelankar23 <siddhi.velankar@intel.com>
---
 .../redis/retriever_redis.py                  | 21 +++++++------------
 1 file changed, 7 insertions(+), 14 deletions(-)

diff --git a/comps/retrievers/multimodal_langchain/redis/retriever_redis.py b/comps/retrievers/multimodal_langchain/redis/retriever_redis.py
index bb90623421..50d567f821 100644
--- a/comps/retrievers/multimodal_langchain/redis/retriever_redis.py
+++ b/comps/retrievers/multimodal_langchain/redis/retriever_redis.py
@@ -1,13 +1,11 @@
 # Copyright (C) 2024 Intel Corporation
 # SPDX-License-Identifier: Apache-2.0
 
-import os
 import time
 from typing import Union
 
-from comps import BridgeTowerEmbedding
+from comps.embeddings.multimodal_embeddings.bridgetower import BridgeTowerEmbedding
 from langchain_community.vectorstores import Redis
-from langsmith import traceable
 from multimodal_config import INDEX_NAME, REDIS_URL, REDIS_SCHEMA
 
 from comps import (
@@ -28,14 +26,14 @@
 )
 
 @register_microservice(
-    name="opea_service@retriever_redis",
+    name="opea_service@multimodal_retriever_redis",
     service_type=ServiceType.RETRIEVER,
-    endpoint="/v1/retrieval",
+    endpoint="/v1/multimodal_retrieval",
     host="0.0.0.0",
     port=7000,
 )
-@traceable(run_type="retriever")
-@register_statistics(names=["opea_service@retriever_redis"])
+
+@register_statistics(names=["opea_service@multimodal_retriever_redis"])
 def retrieve(
     input: Union[EmbedMultimodalDoc, RetrievalRequest, ChatCompletionRequest]
 ) -> Union[SearchedMultimodalDoc, RetrievalResponse, ChatCompletionRequest]:
@@ -45,11 +43,6 @@ def retrieve(
     if vector_db.client.keys() == []:
         search_res = []
     else:
-        if isinstance(input, EmbedMultimodalDoc):
-            query = input.text
-        else:
-            # for RetrievalRequest, ChatCompletionRequest
-            query = input.input
         # if the Redis index has data, perform the search
         if input.search_type == "similarity":
             search_res = vector_db.similarity_search_by_vector(embedding=input.embedding, k=input.k)
@@ -89,7 +82,7 @@ def retrieve(
             input.documents = [doc.text for doc in retrieved_docs]
             result = input
 
-    statistics_dict["opea_service@retriever_redis"].append_latency(time.time() - start, None)
+    statistics_dict["opea_service@multimodal_retriever_redis"].append_latency(time.time() - start, None)
     return result
 
 
@@ -97,4 +90,4 @@ def retrieve(
 
     embeddings = BridgeTowerEmbedding()
     vector_db = Redis.from_existing_index(embedding=embeddings, schema=REDIS_SCHEMA, index_name=INDEX_NAME, redis_url=REDIS_URL)
-    opea_microservices["opea_service@retriever_redis"].start()
+    opea_microservices["opea_service@multimodal_retriever_redis"].start()

From bc2329071c454db261821634ce0f85f9f3e99f0c Mon Sep 17 00:00:00 2001
From: siddhivelankar23 <siddhi.velankar@intel.com>
Date: Tue, 3 Sep 2024 02:17:28 +0000
Subject: [PATCH 7/8] multimodal retriever implementation

Signed-off-by: siddhivelankar23 <siddhi.velankar@intel.com>
---
 .../multimodal_langchain/redis/README.md      | 21 ++++++++++---------
 .../redis/docker/Dockerfile                   |  2 +-
 .../docker/docker_compose_retriever.yaml      |  4 ++--
 3 files changed, 14 insertions(+), 13 deletions(-)

diff --git a/comps/retrievers/multimodal_langchain/redis/README.md b/comps/retrievers/multimodal_langchain/redis/README.md
index 31bdaff384..1b7fa72584 100644
--- a/comps/retrievers/multimodal_langchain/redis/README.md
+++ b/comps/retrievers/multimodal_langchain/redis/README.md
@@ -40,6 +40,7 @@ python langchain_multimodal/retriever_redis.py
 ### 2.1 Setup Environment Variables
 
 ```bash
+export your_ip=$(hostname -I | awk '{print $1}')
 export REDIS_URL="redis://${your_ip}:6379"
 export INDEX_NAME=${your_index_name}
 ```
@@ -47,8 +48,8 @@ export INDEX_NAME=${your_index_name}
 ### 2.2 Build Docker Image
 
 ```bash
-cd ../../
-docker build -t opea/multimodal-retriever-redis:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f comps/retrievers/langchain_multimodal/redis/docker/Dockerfile .
+cd ../../../../
+docker build -t opea/multimodal-retriever-redis:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f comps/retrievers/multimodal_langchain/redis/docker/Dockerfile .
 ```
 
 To start a docker container, you have two options:
@@ -61,13 +62,13 @@ You can choose one as needed.
 ### 2.3 Run Docker with CLI (Option A)
 
 ```bash
-docker run -d --name="retriever-redis-server" -p 7000:7000 --ipc=host -e http_proxy=$http_proxy -e https_proxy=$https_proxy -e REDIS_URL=$REDIS_URL -e INDEX_NAME=$INDEX_NAME opea/multimodal-retriever-redis:latest
+docker run -d --name="multimodal-retriever-redis-server" -p 7000:7000 --ipc=host -e http_proxy=$http_proxy -e https_proxy=$https_proxy -e REDIS_URL=$REDIS_URL -e INDEX_NAME=$INDEX_NAME opea/multimodal-retriever-redis:latest
 ```
 
 ### 2.4 Run Docker with Docker Compose (Option B)
 
 ```bash
-cd langchain_multimodal/docker
+cd docker
 docker compose -f docker_compose_retriever.yaml up -d
 ```
 
@@ -79,9 +80,9 @@ To consume the Retriever Microservice, you can generate a mock embedding vector
 
 ```bash
 your_embedding=$(python -c "import random; embedding = [random.uniform(-1, 1) for _ in range(512)]; print(embedding)")
-curl http://${your_ip}:7000/v1/retrieval \
+curl http://${your_ip}:7000/v1/multimodal_retrieval \
   -X POST \
-  -d {\"text\":\"What is the revenue of Nike in 2023?\",\"embedding\":${your_embedding}}" \
+  -d "{\"text\":\"What is the revenue of Nike in 2023?\",\"embedding\":${your_embedding}}" \
   -H 'Content-Type: application/json'
 ```
 
@@ -89,7 +90,7 @@ You can set the parameters for the retriever.
 
 ```bash
 your_embedding=$(python -c "import random; embedding = [random.uniform(-1, 1) for _ in range(512)]; print(embedding)")
-curl http://localhost:7000/v1/retrieval \
+curl http://localhost:7000/v1/multimodal_retrieval \
   -X POST \
   -d "{\"text\":\"What is the revenue of Nike in 2023?\",\"embedding\":${your_embedding},\"search_type\":\"similarity\", \"k\":4}" \
   -H 'Content-Type: application/json'
@@ -97,7 +98,7 @@ curl http://localhost:7000/v1/retrieval \
 
 ```bash
 your_embedding=$(python -c "import random; embedding = [random.uniform(-1, 1) for _ in range(512)]; print(embedding)")
-curl http://localhost:7000/v1/retrieval \
+curl http://localhost:7000/v1/multimodal_retrieval \
   -X POST \
   -d "{\"text\":\"What is the revenue of Nike in 2023?\",\"embedding\":${your_embedding},\"search_type\":\"similarity_distance_threshold\", \"k\":4, \"distance_threshold\":1.0}" \
   -H 'Content-Type: application/json'
@@ -105,7 +106,7 @@ curl http://localhost:7000/v1/retrieval \
 
 ```bash
 your_embedding=$(python -c "import random; embedding = [random.uniform(-1, 1) for _ in range(512)]; print(embedding)")
-curl http://localhost:7000/v1/retrieval \
+curl http://localhost:7000/v1/multimodal_retrieval \
   -X POST \
   -d "{\"text\":\"What is the revenue of Nike in 2023?\",\"embedding\":${your_embedding},\"search_type\":\"similarity_score_threshold\", \"k\":4, \"score_threshold\":0.2}" \
   -H 'Content-Type: application/json'
@@ -113,7 +114,7 @@ curl http://localhost:7000/v1/retrieval \
 
 ```bash
 your_embedding=$(python -c "import random; embedding = [random.uniform(-1, 1) for _ in range(512)]; print(embedding)")
-curl http://localhost:7000/v1/retrieval \
+curl http://localhost:7000/v1/multimodal_retrieval \
   -X POST \
   -d "{\"text\":\"What is the revenue of Nike in 2023?\",\"embedding\":${your_embedding},\"search_type\":\"mmr\", \"k\":4, \"fetch_k\":20, \"lambda_mult\":0.5}" \
   -H 'Content-Type: application/json'
diff --git a/comps/retrievers/multimodal_langchain/redis/docker/Dockerfile b/comps/retrievers/multimodal_langchain/redis/docker/Dockerfile
index ed1258c579..33077c3037 100644
--- a/comps/retrievers/multimodal_langchain/redis/docker/Dockerfile
+++ b/comps/retrievers/multimodal_langchain/redis/docker/Dockerfile
@@ -20,7 +20,7 @@ USER user
 
 RUN pip install --no-cache-dir --upgrade pip && \
     if [ ${ARCH} = "cpu" ]; then pip install torch torchvision --index-url https://download.pytorch.org/whl/cpu; fi && \
-    pip install --no-cache-dir -r /home/user/comps/retrievers/langchain_multimodal/redis/requirements.txt
+    pip install --no-cache-dir -r /home/user/comps/retrievers/multimodal_langchain/redis/requirements.txt
 
 ENV PYTHONPATH=$PYTHONPATH:/home/user
 
diff --git a/comps/retrievers/multimodal_langchain/redis/docker/docker_compose_retriever.yaml b/comps/retrievers/multimodal_langchain/redis/docker/docker_compose_retriever.yaml
index 044e723ac0..efba29a4e1 100644
--- a/comps/retrievers/multimodal_langchain/redis/docker/docker_compose_retriever.yaml
+++ b/comps/retrievers/multimodal_langchain/redis/docker/docker_compose_retriever.yaml
@@ -5,8 +5,8 @@ version: "1.0"
 
 services:
   retriever:
-    image: opea/retriever-redis:latest
-    container_name: retriever-redis-server
+    image: opea/multimodal-retriever-redis:latest
+    container_name: multimodal-retriever-redis-server
     ports:
       - "7000:7000"
     ipc: host

From c56151d32e5143e181132495f1e9773e069accaa Mon Sep 17 00:00:00 2001
From: siddhivelankar23 <siddhi.velankar@intel.com>
Date: Tue, 3 Sep 2024 02:30:00 +0000
Subject: [PATCH 8/8] test for multimodal retriever

Signed-off-by: siddhivelankar23 <siddhi.velankar@intel.com>
---
 ...t_retrievers_multimodal_langchain_redis.sh | 84 +++++++++++++++++++
 1 file changed, 84 insertions(+)
 create mode 100644 tests/test_retrievers_multimodal_langchain_redis.sh

diff --git a/tests/test_retrievers_multimodal_langchain_redis.sh b/tests/test_retrievers_multimodal_langchain_redis.sh
new file mode 100644
index 0000000000..3d3c37ac30
--- /dev/null
+++ b/tests/test_retrievers_multimodal_langchain_redis.sh
@@ -0,0 +1,84 @@
+#!/bin/bash
+# Copyright (C) 2024 Intel Corporation
+# SPDX-License-Identifier: Apache-2.0
+
+set -x
+
+WORKPATH=$(dirname "$PWD")
+LOG_PATH="$WORKPATH/tests"
+ip_address=$(hostname -I | awk '{print $1}')
+
+function build_docker_images() {
+    cd $WORKPATH
+    docker build --no-cache -t opea/multimodal-retriever-redis:comps --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f comps/retrievers/multimodal_langchain/redis/docker/Dockerfile .
+    if [ $? -ne 0 ]; then
+        echo "opea/multimodal-retriever-redis built fail"
+        exit 1
+    else
+        echo "opea/multimodal-retriever-redis built successful"
+    fi
+}
+
+function start_service() {
+    # redis
+    docker run -d --name test-comps-multimodal-retriever-redis-vector-db -p 5010:6379 -p 5011:8001 -e HTTPS_PROXY=$https_proxy -e HTTP_PROXY=$https_proxy redis/redis-stack:7.2.0-v9
+    sleep 10s
+
+    # redis retriever
+    export REDIS_URL="redis://${ip_address}:5010"
+    export INDEX_NAME="rag-redis"
+    retriever_port=5009
+    unset http_proxy
+    docker run -d --name="test-comps-multimodal-retriever-redis-server" -p ${retriever_port}:7000 --ipc=host -e http_proxy=$http_proxy -e https_proxy=$https_proxy -e REDIS_URL=$REDIS_URL -e INDEX_NAME=$INDEX_NAME opea/multimodal-retriever-redis:comps
+
+    sleep 3m
+}
+
+function validate_microservice() {
+    retriever_port=5009
+    export PATH="${HOME}/miniforge3/bin:$PATH"
+    source activate
+    URL="http://${ip_address}:$retriever_port/v1/multimodal_retrieval"
+    test_embedding=$(python -c "import random; embedding = [random.uniform(-1, 1) for _ in range(512)]; print(embedding)")
+
+    HTTP_STATUS=$(curl -s -o /dev/null -w "%{http_code}" -X POST -d "{\"text\":\"test\",\"embedding\":${test_embedding}}" -H 'Content-Type: application/json' "$URL")
+    if [ "$HTTP_STATUS" -eq 200 ]; then
+        echo "[ retriever ] HTTP status is 200. Checking content..."
+        local CONTENT=$(curl -s -X POST -d "{\"text\":\"test\",\"embedding\":${test_embedding}}" -H 'Content-Type: application/json' "$URL" | tee ${LOG_PATH}/retriever.log)
+
+        if echo "$CONTENT" | grep -q "retrieved_docs"; then
+            echo "[ retriever ] Content is as expected."
+        else
+            echo "[ retriever ] Content does not match the expected result: $CONTENT"
+            docker logs test-comps-multimodal-retriever-redis-server >> ${LOG_PATH}/retriever.log
+            exit 1
+        fi
+    else
+        echo "[ retriever ] HTTP status is not 200. Received status was $HTTP_STATUS"
+        docker logs test-comps-multimodal-retriever-redis-server >> ${LOG_PATH}/retriever.log
+        exit 1
+    fi
+}
+
+function stop_docker() {
+    cid_retrievers=$(docker ps -aq --filter "name=test-comps-multimodal-retriever*")
+    if [[ ! -z "$cid_retrievers" ]]; then
+        docker stop $cid_retrievers && docker rm $cid_retrievers && sleep 1s
+    fi
+}
+
+function main() {
+
+    stop_docker
+
+    build_docker_images
+    start_service
+
+    validate_microservice
+
+    stop_docker
+    # echo y | docker system prune
+
+}
+
+main