diff --git a/comps/retrievers/langchain/README.md b/comps/retrievers/langchain/README.md
index 300fbc099..9d96ba14a 100644
--- a/comps/retrievers/langchain/README.md
+++ b/comps/retrievers/langchain/README.md
@@ -6,14 +6,18 @@ The service primarily utilizes similarity measures in vector space to rapidly re
 
 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.
 
-## Retriever Microservice with Redis
+# Retriever Microservice with Redis
 
 For details, please refer to this [readme](redis/README.md)
 
-## Retriever Microservice with Milvus
+# Retriever Microservice with Milvus
 
 For details, please refer to this [readme](milvus/README.md)
 
-## Retriever Microservice with PGVector
+# Retriever Microservice with PGVector
 
 For details, please refer to this [readme](pgvector/README.md)
+
+# Retriever Microservice with VDMS
+
+For details, please refer to this [readme](vdms/README.md)
diff --git a/comps/retrievers/langchain/vdms/README.md b/comps/retrievers/langchain/vdms/README.md
new file mode 100644
index 000000000..108ea5203
--- /dev/null
+++ b/comps/retrievers/langchain/vdms/README.md
@@ -0,0 +1,169 @@
+# Retriever Microservice
+
+This retriever microservice is a highly efficient search service designed for handling and retrieving embedding vectors. 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 host, port, and the index/collection 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.
+
+# Visual Data Management System (VDMS)
+
+VDMS is a storage solution for efficient access of big-”visual”-data that aims to achieve cloud scale by searching for relevant visual data via visual metadata stored as a graph and enabling machine friendly enhancements to visual data for faster access.
+
+VDMS offers the functionality of VectorDB. It provides multiple engines to index large number of embeddings and to search them for similarity. Based on the use case, the engine used will provide a tradeoff between indexing speed, search speed, total memory footprint, and search accuracy.
+
+VDMS also supports a graph database to store different metadata(s) associated with each vector embedding, and to retrieve them supporting a large variety of relationships ranging from simple to very complex relationships.
+
+In Summary, VDMS supports:
+
+K nearest neighbor search
+Euclidean distance (L2) and inner product (IP)
+Libraries for indexing and computing distances: TileDBDense, TileDBSparse, FaissFlat (Default), FaissIVFFlat, Flinng
+Embeddings for text, images, and video
+Vector and metadata searches
+Scalabity to allow for definition of different relationships across the metadata
+
+# 🚀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 Start TEI Service
+
+```bash
+export LANGCHAIN_TRACING_V2=true
+export LANGCHAIN_API_KEY=${your_langchain_api_key}
+export LANGCHAIN_PROJECT="opea/retriever"
+model=BAAI/bge-base-en-v1.5
+revision=refs/pr/4
+volume=$PWD/data
+docker run -d -p 6060:80 -v $volume:/data -e http_proxy=$http_proxy -e https_proxy=$https_proxy --pull always ghcr.io/huggingface/text-embeddings-inference:cpu-1.5 --model-id $model --revision $revision
+```
+
+## 1.3 Verify the TEI Service
+
+```bash
+curl 127.0.0.1:6060/rerank \
+    -X POST \
+    -d '{"query":"What is Deep Learning?", "texts": ["Deep Learning is not...", "Deep learning is..."]}' \
+    -H 'Content-Type: application/json'
+```
+
+## 1.4 Setup VectorDB Service
+
+You need to setup your own VectorDB service (VDMS in this example), and ingest your knowledge documents into the vector database.
+
+As for VDMS, you could start a docker container using the following commands.
+Remember to ingest data into it manually.
+
+```bash
+docker run -d --name="vdms-vector-db" -p 55555:55555 intellabs/vdms:latest
+```
+
+## 1.5 Start Retriever Service
+
+```bash
+export TEI_EMBEDDING_ENDPOINT="http://${your_ip}:6060"
+python langchain/retriever_vdms.py
+```
+
+# 🚀2. Start Microservice with Docker (Option 2)
+
+## 2.1 Setup Environment Variables
+
+```bash
+export RETRIEVE_MODEL_ID="BAAI/bge-base-en-v1.5"
+export INDEX_NAME=${your_index_name or collection_name}
+export TEI_EMBEDDING_ENDPOINT="http://${your_ip}:6060"
+export LANGCHAIN_TRACING_V2=true
+export LANGCHAIN_API_KEY=${your_langchain_api_key}
+export LANGCHAIN_PROJECT="opea/retrievers"
+```
+
+## 2.2 Build Docker Image
+
+```bash
+cd ../../
+docker build -t opea/retriever-vdms:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f comps/retrievers/langchain/vdms/docker/Dockerfile .
+```
+
+To start a docker container, you have two options:
+
+- A. Run Docker with CLI
+- B. Run Docker with Docker Compose
+
+You can choose one as needed.
+
+## 2.3 Run Docker with CLI (Option A)
+
+```bash
+docker run -d --name="retriever-vdms-server" -p 7000:7000 --ipc=host -e http_proxy=$http_proxy -e https_proxy=$https_proxy -e INDEX_NAME=$INDEX_NAME -e TEI_EMBEDDING_ENDPOINT=$TEI_EMBEDDING_ENDPOINT opea/retriever-vdms:latest
+```
+
+## 2.4 Run Docker with Docker Compose (Option B)
+
+```bash
+cd langchain/vdms/docker
+docker compose -f docker_compose_retriever.yaml up -d
+```
+
+# 🚀3. Consume Retriever Service
+
+## 3.1 Check Service Status
+
+```bash
+curl http://localhost:7000/v1/health_check \
+  -X GET \
+  -H 'Content-Type: application/json'
+```
+
+## 3.2 Consume Embedding Service
+
+To consume the Retriever Microservice, you can generate a mock embedding vector of length 768 with Python.
+
+```bash
+your_embedding=$(python -c "import random; embedding = [random.uniform(-1, 1) for _ in range(768)]; 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=$(python -c "import random; embedding = [random.uniform(-1, 1) for _ in range(768)]; print(embedding)")
+curl http://localhost:7000/v1/retrieval \
+  -X POST \
+  -d "{\"text\":\"What is the revenue of Nike in 2023?\",\"embedding\":${your_embedding},\"search_type\":\"similarity\", \"k\":4}" \
+  -H 'Content-Type: application/json'
+```
+
+```bash
+your_embedding=$(python -c "import random; embedding = [random.uniform(-1, 1) for _ in range(768)]; print(embedding)")
+curl http://localhost:7000/v1/retrieval \
+  -X POST \
+  -d "{\"text\":\"What is the revenue of Nike in 2023?\",\"embedding\":${your_embedding},\"search_type\":\"similarity_distance_threshold\", \"k\":4, \"distance_threshold\":1.0}" \
+  -H 'Content-Type: application/json'
+```
+
+```bash
+your_embedding=$(python -c "import random; embedding = [random.uniform(-1, 1) for _ in range(768)]; print(embedding)")
+curl http://localhost:7000/v1/retrieval \
+  -X POST \
+  -d "{\"text\":\"What is the revenue of Nike in 2023?\",\"embedding\":${your_embedding},\"search_type\":\"similarity_score_threshold\", \"k\":4, \"score_threshold\":0.2}" \
+  -H 'Content-Type: application/json'
+```
+
+```bash
+your_embedding=$(python -c "import random; embedding = [random.uniform(-1, 1) for _ in range(768)]; print(embedding)")
+curl http://localhost:7000/v1/retrieval \
+  -X POST \
+  -d "{\"text\":\"What is the revenue of Nike in 2023?\",\"embedding\":${your_embedding},\"search_type\":\"mmr\", \"k\":4, \"fetch_k\":20, \"lambda_mult\":0.5}" \
+  -H 'Content-Type: application/json'
+```
diff --git a/comps/retrievers/langchain/vdms/__init__.py b/comps/retrievers/langchain/vdms/__init__.py
new file mode 100644
index 000000000..916f3a44b
--- /dev/null
+++ b/comps/retrievers/langchain/vdms/__init__.py
@@ -0,0 +1,2 @@
+# Copyright (C) 2024 Intel Corporation
+# SPDX-License-Identifier: Apache-2.0
diff --git a/comps/retrievers/langchain/vdms/docker/Dockerfile b/comps/retrievers/langchain/vdms/docker/Dockerfile
new file mode 100644
index 000000000..de4a3b50c
--- /dev/null
+++ b/comps/retrievers/langchain/vdms/docker/Dockerfile
@@ -0,0 +1,49 @@
+
+# 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 \
+    iputils-ping \
+    vim
+
+RUN useradd -m -s /bin/bash user && \
+    mkdir -p /home/user && \
+    chown -R user /home/user/
+
+COPY comps /home/user/comps
+
+# RUN chmod +x /home/user/comps/retrievers/langchain/vdms/run.sh
+
+USER user
+RUN pip install --no-cache-dir --upgrade pip && \
+    if [ ${ARCH} = "cpu" ]; then pip install torch torchvision --index-url https://download.pytorch.org/whl/cpu; fi && \
+    pip install --no-cache-dir -r /home/user/comps/retrievers/langchain/vdms/requirements.txt
+
+RUN pip install -U langchain
+RUN pip install -U langchain-community
+
+RUN pip install --upgrade huggingface-hub
+
+ENV PYTHONPATH=$PYTHONPATH:/home/user
+
+ENV HUGGINGFACEHUB_API_TOKEN=dummy
+
+ENV USECLIP 0
+
+ENV no_proxy=localhost,127.0.0.1
+
+ENV http_proxy=""
+ENV https_proxy=""
+
+WORKDIR /home/user/comps/retrievers/langchain/vdms
+
+#ENTRYPOINT ["/home/user/comps/retrievers/langchain/vdms/run.sh"]
+#ENTRYPOINT ["/bin/bash"]
+
+ENTRYPOINT ["python", "retriever_vdms.py"]
diff --git a/comps/retrievers/langchain/vdms/docker/docker_compose_retriever.yaml b/comps/retrievers/langchain/vdms/docker/docker_compose_retriever.yaml
new file mode 100644
index 000000000..75efdf36a
--- /dev/null
+++ b/comps/retrievers/langchain/vdms/docker/docker_compose_retriever.yaml
@@ -0,0 +1,32 @@
+# Copyright (C) 2024 Intel Corporation
+# SPDX-License-Identifier: Apache-2.0
+
+version: "3.8"
+
+services:
+  tei_xeon_service:
+    image: ghcr.io/huggingface/text-embeddings-inference:cpu-1.5
+    container_name: tei-xeon-server
+    ports:
+      - "6060:80"
+    volumes:
+      - "./data:/data"
+    shm_size: 1g
+    command: --model-id ${RETRIEVE_MODEL_ID}
+  retriever:
+    image: opea/retriever-vdms:latest
+    container_name: retriever-vdms-server
+    ports:
+      - "7000:7000"
+    ipc: host
+    environment:
+      no_proxy: ${no_proxy}
+      http_proxy: ${http_proxy}
+      https_proxy: ${https_proxy}
+      INDEX_NAME: ${INDEX_NAME}
+      LANGCHAIN_API_KEY: ${LANGCHAIN_API_KEY}
+    restart: unless-stopped
+
+networks:
+  default:
+    driver: bridge
diff --git a/comps/retrievers/langchain/vdms/requirements.txt b/comps/retrievers/langchain/vdms/requirements.txt
new file mode 100644
index 000000000..9138aea2e
--- /dev/null
+++ b/comps/retrievers/langchain/vdms/requirements.txt
@@ -0,0 +1,16 @@
+docarray[full]
+easyocr
+einops
+fastapi
+langchain-community
+langchain-core
+langchain-huggingface
+opentelemetry-api
+opentelemetry-exporter-otlp
+opentelemetry-sdk
+prometheus-fastapi-instrumentator
+pymupdf
+sentence_transformers
+shortuuid
+uvicorn
+vdms
diff --git a/comps/retrievers/langchain/vdms/retriever_vdms.py b/comps/retrievers/langchain/vdms/retriever_vdms.py
new file mode 100644
index 000000000..58970db9c
--- /dev/null
+++ b/comps/retrievers/langchain/vdms/retriever_vdms.py
@@ -0,0 +1,119 @@
+# Copyright (C) 2024 Intel Corporation
+# SPDX-License-Identifier: Apache-2.0
+
+import os
+import time
+
+from langchain_community.embeddings import HuggingFaceBgeEmbeddings, HuggingFaceHubEmbeddings
+from langchain_community.vectorstores.vdms import VDMS, VDMS_Client
+from langchain_huggingface.embeddings import HuggingFaceEndpointEmbeddings
+from vdms_config import DEBUG, DISTANCE_STRATEGY, EMBED_MODEL, INDEX_NAME, SEARCH_ENGINE, VDMS_HOST, VDMS_PORT
+
+from comps import (
+    EmbedDoc,
+    SearchedDoc,
+    SearchedMultimodalDoc,
+    ServiceType,
+    TextDoc,
+    opea_microservices,
+    register_microservice,
+    register_statistics,
+    statistics_dict,
+)
+
+tei_embedding_endpoint = os.getenv("TEI_EMBEDDING_ENDPOINT")
+hf_token = os.getenv("HUGGINGFACEHUB_API_TOKEN")
+use_clip = int(os.getenv("USECLIP"))
+
+if use_clip:
+    import sys
+
+    sys.path.append("../../../embeddings/langchain_multimodal/")
+    from embeddings_clip import vCLIP
+
+# Debugging
+if DEBUG:
+    all_variables = dir()
+
+    for name in all_variables:
+        # Print the item if it doesn't start with '__'
+        if not name.startswith("__"):
+            myvalue = eval(name)
+            print(name, "is", type(myvalue), "and = ", myvalue)
+
+
+client = VDMS_Client(VDMS_HOST, VDMS_PORT)
+
+
+@register_microservice(
+    name="opea_service@retriever_vdms",
+    service_type=ServiceType.RETRIEVER,
+    endpoint="/v1/retrieval",
+    host="0.0.0.0",
+    port=7000,
+)
+@register_statistics(names=["opea_service@retriever_vdms"])
+def retrieve(input: EmbedDoc) -> SearchedMultimodalDoc:
+    start = time.time()
+
+    if input.search_type == "similarity":
+        search_res = vector_db.similarity_search_by_vector(
+            embedding=input.embedding, k=input.k, filter=input.constraints
+        )
+    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, filter=input.constraints
+        )
+    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, filter=input.constraints
+        )
+        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, filter=input.constraints
+        )
+    searched_docs = []
+    metadata_list = []
+    for r in search_res:
+        searched_docs.append(TextDoc(text=r.page_content))
+        metadata_list.append(r.metadata)
+    result = SearchedMultimodalDoc(retrieved_docs=searched_docs, metadata=metadata_list, initial_query=input.text)
+    statistics_dict["opea_service@retriever_vdms"].append_latency(time.time() - start, None)
+    return result
+
+
+if __name__ == "__main__":
+    # Create vectorstore
+
+    if use_clip:
+        embeddings = vCLIP({"model_name": "openai/clip-vit-base-patch32", "num_frm": 4})
+        dimensions = embeddings.get_embedding_length()
+    elif tei_embedding_endpoint:
+        embeddings = HuggingFaceEndpointEmbeddings(model=tei_embedding_endpoint, huggingfacehub_api_token=hf_token)
+    else:
+        embeddings = HuggingFaceBgeEmbeddings(model_name=EMBED_MODEL)
+        # create embeddings using local embedding model
+
+    if use_clip:
+        vector_db = VDMS(
+            client=client,
+            embedding=embeddings,
+            collection_name=INDEX_NAME,
+            embedding_dimensions=dimensions,
+            distance_strategy=DISTANCE_STRATEGY,
+            engine=SEARCH_ENGINE,
+        )
+    else:
+        vector_db = VDMS(
+            client=client,
+            embedding=embeddings,
+            collection_name=INDEX_NAME,
+            # embedding_dimensions=768,
+            distance_strategy=DISTANCE_STRATEGY,
+            engine=SEARCH_ENGINE,
+        )
+
+    opea_microservices["opea_service@retriever_vdms"].start()
diff --git a/comps/retrievers/langchain/vdms/run.sh b/comps/retrievers/langchain/vdms/run.sh
new file mode 100644
index 000000000..ac5875e93
--- /dev/null
+++ b/comps/retrievers/langchain/vdms/run.sh
@@ -0,0 +1,9 @@
+#!/bin/sh
+
+# Copyright (C) 2024 Intel Corporation
+# SPDX-License-Identifier: Apache-2.0
+
+cd /home/user/comps/retrievers/langchain/vdms
+#python ingest.py
+
+python retriever_vdms.py
diff --git a/comps/retrievers/langchain/vdms/vdms_config.py b/comps/retrievers/langchain/vdms/vdms_config.py
new file mode 100644
index 000000000..d388add9a
--- /dev/null
+++ b/comps/retrievers/langchain/vdms/vdms_config.py
@@ -0,0 +1,80 @@
+# Copyright (C) 2024 Intel Corporation
+# SPDX-License-Identifier: Apache-2.0
+
+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
+
+
+# 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", "BAAI/bge-base-en-v1.5")
+
+
+# VDMS Connection Information
+VDMS_HOST = os.getenv("VDMS_HOST", "localhost")
+VDMS_PORT = int(os.getenv("VDMS_PORT", 55555))
+
+
+# def format_vdms_conn_from_env():
+#     vdms_url = os.getenv("VDMS_URL", None)
+#     if vdms_url:
+#         return vdms_url
+#     else:
+#         using_ssl = get_boolean_env_var("VDMS_SSL", False)
+#         start = "vdmss://" if using_ssl else "vdms://"
+
+#         # if using RBAC
+#         password = os.getenv("VDMS_PASSWORD", None)
+#         username = os.getenv("VDMS_USERNAME", "default")
+#         if password is not None:
+#             start += f"{username}:{password}@"
+
+#         return start + f"{VDMS_HOST}:{VDMS_PORT}"
+
+
+# VDMS_URL = format_vdms_conn_from_env()
+
+# Vector Index Configuration
+INDEX_NAME = os.getenv("INDEX_NAME", "rag-vdms")
+# HUGGINGFACEHUB_API_TOKEN ="dummy-token"
+
+
+# current_file_path = os.path.abspath(__file__)
+# parent_dir = os.path.dirname(current_file_path)
+# VDMS_SCHEMA = os.getenv("VDMS_SCHEMA", "vdms_schema.yml")
+# INDEX_SCHEMA = os.path.join(parent_dir, VDMS_SCHEMA)
+SEARCH_ENGINE = "FaissFlat"
+DISTANCE_STRATEGY = "L2"
diff --git a/comps/vectorstores/README.md b/comps/vectorstores/README.md
index 492ef970f..643029812 100644
--- a/comps/vectorstores/README.md
+++ b/comps/vectorstores/README.md
@@ -2,22 +2,26 @@
 
 The Vectorstores Microservice provides convenient way to start various vector database servers.
 
-## Vectorstores Microservice with Redis
+# Vectorstores Microservice with Redis
 
 For details, please refer to this [readme](langchain/redis/README.md)
 
-## Vectorstores Microservice with Qdrant
+# Vectorstores Microservice with Qdrant
 
 For details, please refer to this [readme](langchain/qdrant/README.md)
 
-## Vectorstores Microservice with PGVector
+# Vectorstores Microservice with PGVector
 
 For details, please refer to this [readme](langchain/pgvector/README.md)
 
-## Vectorstores Microservice with Pinecone
+# Vectorstores Microservice with Pinecone
 
 For details, please refer to this [readme](langchain/pinecone/README.md)
 
-## Vectorstores Microservice with Pathway
+# Vectorstores Microservice with Pathway
 
 For details, please refer to this [readme](langchain/pathway/README.md)
+
+# Vectorstores Microservice with VDMS
+
+For details, please refer to this [readme](langchain/vdms/README.md)
diff --git a/comps/vectorstores/langchain/vdms/README.md b/comps/vectorstores/langchain/vdms/README.md
new file mode 100644
index 000000000..959bbfd64
--- /dev/null
+++ b/comps/vectorstores/langchain/vdms/README.md
@@ -0,0 +1,13 @@
+# Start VDMS server
+
+## 1. Download VDMS image
+
+```bash
+docker pull intellabs/vdms:latest
+```
+
+## 2. Run VDMS service
+
+```bash
+docker run -d -p 55555:55555 intellabs/vdms:latest
+```
diff --git a/comps/vectorstores/langchain/vdms/__init__.py b/comps/vectorstores/langchain/vdms/__init__.py
new file mode 100644
index 000000000..916f3a44b
--- /dev/null
+++ b/comps/vectorstores/langchain/vdms/__init__.py
@@ -0,0 +1,2 @@
+# Copyright (C) 2024 Intel Corporation
+# SPDX-License-Identifier: Apache-2.0
diff --git a/comps/vectorstores/langchain/vdms/docker-compose-vdms.yml b/comps/vectorstores/langchain/vdms/docker-compose-vdms.yml
new file mode 100644
index 000000000..8e13a0f1b
--- /dev/null
+++ b/comps/vectorstores/langchain/vdms/docker-compose-vdms.yml
@@ -0,0 +1,10 @@
+# Copyright (C) 2024 Intel Corporation
+# SPDX-License-Identifier: Apache-2.0
+
+version: "3"
+services:
+  vdms-vector-db:
+    image: intellabs/vdms:latest
+    container_name: vdms-vector-db
+    ports:
+      - "55555:55555"
diff --git a/tests/test_retrievers_langchain_vdms.sh b/tests/test_retrievers_langchain_vdms.sh
new file mode 100755
index 000000000..ad7c1c242
--- /dev/null
+++ b/tests/test_retrievers_langchain_vdms.sh
@@ -0,0 +1,126 @@
+#!/bin/bash
+# Copyright (C) 2024 Intel Corporation
+# SPDX-License-Identifier: Apache-2.0
+
+set -xe
+
+WORKPATH=$(dirname "$PWD")
+LOG_PATH="$WORKPATH/tests"
+ip_address=$(hostname -I | awk '{print $1}')
+no_proxy=$no_proxy,$ip_address
+
+function build_docker_images() {
+    cd $WORKPATH
+    hf_token="dummy"
+    docker build --no-cache -t opea/retriever-vdms:comps \
+        --build-arg https_proxy=$https_proxy \
+        --build-arg http_proxy=$http_proxy \
+        --build-arg huggingfacehub_api_token=$hf_token\
+        -f comps/retrievers/langchain/vdms/docker/Dockerfile .
+
+}
+
+function start_service() {
+    #unset http_proxy
+    # vdms
+    vdms_port=55555
+    docker run -d --name test-comps-retriever-vdms-vector-db \
+        -p $vdms_port:$vdms_port   intellabs/vdms:latest
+    sleep 10s
+
+    # tei endpoint
+    tei_endpoint=5008
+    model="BAAI/bge-base-en-v1.5"
+    docker run -d --name="test-comps-retriever-tei-endpoint" \
+        -p $tei_endpoint:80 -v ./data:/data \
+        -e HTTPS_PROXY=$https_proxy -e HTTP_PROXY=$https_proxy \
+        --pull always ghcr.io/huggingface/text-embeddings-inference:cpu-1.5 \
+        --model-id $model
+    sleep 30s
+
+    export TEI_EMBEDDING_ENDPOINT="http://${ip_address}:${tei_endpoint}"
+
+    export INDEX_NAME="rag-vdms"
+
+    # vdms retriever
+    unset http_proxy
+    use_clip=0 #set to 1 if openai clip embedding should be used
+
+    docker run -d --name="test-comps-retriever-vdms-server" -p 5009:7000 --ipc=host \
+    -e INDEX_NAME=$INDEX_NAME -e VDMS_HOST=$ip_address \
+    -e https_proxy=$https_proxy -e http_proxy=$http_proxy \
+    -e VDMS_PORT=$vdms_port -e HUGGINGFACEHUB_API_TOKEN=$HUGGINGFACEHUB_API_TOKEN \
+    -e TEI_EMBEDDING_ENDPOINT=$TEI_EMBEDDING_ENDPOINT -e USECLIP=$use_clip \
+     opea/retriever-vdms:comps
+    sleep 3m
+}
+
+function validate_microservice() {
+
+
+    retriever_port=5009
+    URL="http://${ip_address}:$retriever_port/v1/retrieval"
+    #test_embedding=$(python -c "import random; embedding = [random.uniform(-1, 1) for _ in range(768)]; print(embedding)")
+
+    test_embedding="[0.3212316218862614, 0.05284697028105079, 0.792736615029739, -0.01450667589035648, -0.7358454555705813, -0.5159104761926909, 0.3535153166047822, -0.6465310827905328, -0.3260418169245214, 0.5427377177268364, 0.839674125021304, 0.27459120894125255, -0.9833857616143291, 0.4763752586395751, 0.7048355150785723, 0.4935209825796325, -0.09655411499027178, -0.5739389241976944, 0.34450497876796815, -0.03401327136919208, -0.8247080270670755, -0.9430721851019634, 0.4702688485035773, 0.3872526674852217, -0.13436894777006136, 0.27166203983338266, 0.7724679346611174, 0.49524109590526666, 0.9810730976435518, 0.2143402533230332, 0.35235793217357947, -0.3199320624935764, -0.3535996110405917, 0.1982603781951089, -0.37547349902996063, -0.6148649695355071, 0.388521078627599, 0.7073360849235228, 0.1768845283243352, -0.38289339223361885, 0.36390326284734775, -0.4790146416310761, -0.5412301982310956, 0.33793186533237507, -0.7028178009236765, -0.6850965350085609, -0.519584428926227, 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-0.2199845691372495, 0.7554317108927318, 0.9809450136647395, -0.9430090133566618, 0.23635288316941683]"
+
+
+
+    HTTP_STATUS=$(curl -s -o /dev/null -w "%{http_code}" -X POST -d "{\"text\":\"test\",\"embedding\":${test_embedding}}" -H 'Content-Type: application/json' "$URL")
+
+    #echo "HTTP_STATUS = $HTTP_STATUS"
+
+    if [ "$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-retriever-vdms-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-retriever-vdms-server >> ${LOG_PATH}/retriever.log
+        exit 1
+    fi
+
+    docker logs test-comps-retriever-tei-endpoint >> ${LOG_PATH}/tei.log
+}
+
+function stop_docker() {
+    cid_retrievers=$(docker ps -aq --filter "name=test-comps-retriever-tei-endpoint*")
+    if [[ ! -z "$cid_retrievers" ]]; then
+        docker stop $cid_retrievers && docker rm $cid_retrievers && sleep 1s
+    fi
+
+    cid_vdms=$(docker ps -aq --filter "name=test-comps-retriever-vdms-server")
+    if [[ ! -z "$cid_vdms" ]]; then
+        docker stop $cid_vdms && docker rm $cid_vdms && sleep 1s
+    fi
+
+    cid_vdmsdb=$(docker ps -aq --filter "name=test-comps-retriever-vdms-vector-db")
+    if [[ ! -z "$cid_vdmsdb" ]]; then
+        docker stop $cid_vdmsdb && docker rm $cid_vdmsdb && sleep 1s
+    fi
+
+
+}
+
+function main() {
+
+    stop_docker
+
+    build_docker_images
+    start_service
+
+    validate_microservice
+
+    stop_docker
+    echo y | docker system prune
+
+}
+
+main