diff --git a/.github/workflows/docker/compose/retrievers-compose.yaml b/.github/workflows/docker/compose/retrievers-compose.yaml
index dc49b95d5..24f5623e5 100644
--- a/.github/workflows/docker/compose/retrievers-compose.yaml
+++ b/.github/workflows/docker/compose/retrievers-compose.yaml
@@ -15,10 +15,6 @@ services:
     build:
       dockerfile: comps/retrievers/vdms/langchain/Dockerfile
     image: ${REGISTRY:-opea}/retriever-vdms:${TAG:-latest}
-  retriever-multimodal-redis:
-    build:
-      dockerfile: comps/retrievers/multimodal/redis/langchain/Dockerfile
-    image: ${REGISTRY:-opea}/retriever-multimodal-redis:${TAG:-latest}
   retriever-pgvector:
     build:
       dockerfile: comps/retrievers/pgvector/langchain/Dockerfile
diff --git a/comps/retrievers/README.md b/comps/retrievers/README.md
index eeba8860e..a05586d5f 100644
--- a/comps/retrievers/README.md
+++ b/comps/retrievers/README.md
@@ -29,7 +29,3 @@ For details, please refer to this [readme](qdrant/haystack/README.md)
 ## Retriever Microservice with VDMS
 
 For details, please refer to this [readme](vdms/langchain/README.md)
-
-## Retriever Microservice with Multimodal
-
-For details, please refer to this [readme](multimodal/redis/langchain/README.md)
diff --git a/comps/retrievers/milvus/langchain/ingest.py b/comps/retrievers/milvus/langchain/ingest.py
deleted file mode 100644
index 77b7a7823..000000000
--- a/comps/retrievers/milvus/langchain/ingest.py
+++ /dev/null
@@ -1,99 +0,0 @@
-#!/usr/bin/env python
-# -*- coding: utf-8 -*-
-# Copyright (C) 2024 Intel Corporation
-# SPDX-License-Identifier: Apache-2.0
-
-#
-
-import argparse
-import io
-import os
-
-import numpy as np
-from config import COLLECTION_NAME, EMBED_ENDPOINT, EMBED_MODEL, MILVUS_HOST, MILVUS_PORT
-from langchain.text_splitter import RecursiveCharacterTextSplitter
-from langchain_community.embeddings import HuggingFaceBgeEmbeddings, HuggingFaceHubEmbeddings
-from langchain_milvus.vectorstores import Milvus
-from PIL import Image
-
-
-def pdf_loader(file_path):
-    try:
-        import easyocr
-        import fitz
-    except ImportError:
-        raise ImportError(
-            "`PyMuPDF` or 'easyocr' package is not found, please install it with "
-            "`pip install pymupdf or pip install easyocr.`"
-        )
-
-    doc = fitz.open(file_path)
-    reader = easyocr.Reader(["en"])
-    result = ""
-    for i in range(doc.page_count):
-        page = doc.load_page(i)
-        pagetext = page.get_text().strip()
-        if pagetext:
-            result = result + pagetext
-        if len(doc.get_page_images(i)) > 0:
-            for img in doc.get_page_images(i):
-                if img:
-                    pageimg = ""
-                    xref = img[0]
-                    img_data = doc.extract_image(xref)
-                    img_bytes = img_data["image"]
-                    pil_image = Image.open(io.BytesIO(img_bytes))
-                    img = np.array(pil_image)
-                    img_result = reader.readtext(img, paragraph=True, detail=0)
-                    pageimg = pageimg + ", ".join(img_result).strip()
-                    if pageimg.endswith("!") or pageimg.endswith("?") or pageimg.endswith("."):
-                        pass
-                    else:
-                        pageimg = pageimg + "."
-                result = result + pageimg
-    return result
-
-
-def ingest_documents(folder_path, tag):
-    """Ingest PDF to Milvus from a directory."""
-    # Load list of pdfs
-    doc_path = [os.path.join(folder_path, file) for file in os.listdir(folder_path)][0]
-
-    print("Parsing...", doc_path)
-
-    text_splitter = RecursiveCharacterTextSplitter(chunk_size=1500, chunk_overlap=100, add_start_index=True)
-    content = pdf_loader(doc_path)
-    chunks = text_splitter.split_text(content)
-
-    print("Done preprocessing. Created ", len(chunks), " chunks of the original pdf")
-    # Create vectorstore
-    if EMBED_ENDPOINT:
-        # create embeddings using TEI endpoint service
-        embedder = HuggingFaceHubEmbeddings(model=EMBED_ENDPOINT)
-    else:
-        # create embeddings using local embedding model
-        embedder = HuggingFaceBgeEmbeddings(model_name=EMBED_MODEL)
-
-    # Batch size
-    batch_size = 32
-    num_chunks = len(chunks)
-    for i in range(0, num_chunks, batch_size):
-        batch_chunks = chunks[i : i + batch_size]
-        batch_texts = [f"Tag: {tag}. " + chunk for chunk in batch_chunks]
-
-        _ = Milvus.from_texts(
-            texts=batch_texts,
-            embedding=embedder,
-            collection_name=COLLECTION_NAME,
-            connection_args={"host": MILVUS_HOST, "port": MILVUS_PORT},
-        )
-        print(f"Processed batch {i//batch_size + 1}/{(num_chunks-1)//batch_size + 1}")
-
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser(description="Ingest documents from a specified folder with a tag")
-    parser.add_argument("folder_path", type=str, help="Path to the folder containing documents")
-    parser.add_argument("--tag", type=str, default="", help="Tag to be used as an identifier")
-
-    args = parser.parse_args()
-    ingest_documents(args.folder_path, args.tag)
diff --git a/comps/retrievers/multimodal/redis/langchain/Dockerfile b/comps/retrievers/multimodal/redis/langchain/Dockerfile
deleted file mode 100644
index 5c339069f..000000000
--- a/comps/retrievers/multimodal/redis/langchain/Dockerfile
+++ /dev/null
@@ -1,28 +0,0 @@
-# Copyright (C) 2024 Intel Corporation
-# SPDX-License-Identifier: Apache-2.0
-
-FROM python:3.11-slim
-
-ARG ARCH="cpu"
-
-RUN apt-get update -y && apt-get install -y --no-install-recommends --fix-missing \
-    libgl1-mesa-glx \
-    libjemalloc-dev
-
-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 setuptools && \
-    if [ ${ARCH} = "cpu" ]; then pip install --no-cache-dir torch torchvision --index-url https://download.pytorch.org/whl/cpu; fi && \
-    pip install --no-cache-dir -r /home/user/comps/retrievers/multimodal/redis/langchain/requirements.txt
-
-ENV PYTHONPATH=$PYTHONPATH:/home/user
-
-WORKDIR /home/user/comps/retrievers/multimodal/redis/langchain
-
-ENTRYPOINT ["python", "retriever_redis.py"]
diff --git a/comps/retrievers/multimodal/redis/langchain/README.md b/comps/retrievers/multimodal/redis/langchain/README.md
deleted file mode 100644
index c67dc9e77..000000000
--- a/comps/retrievers/multimodal/redis/langchain/README.md
+++ /dev/null
@@ -1,123 +0,0 @@
-# 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/blob/main/comps/dataprep/multimodal/redis/langchain/README.md).
-
-### 1.4 Start Retriever Service
-
-```bash
-python retriever_redis.py
-```
-
-## 🚀2. Start Microservice with Docker (Option 2)
-
-### 2.1 Setup Environment Variables
-
-```bash
-export your_ip=$(hostname -I | awk '{print $1}')
-export REDIS_URL="redis://${your_ip}:6379"
-export INDEX_NAME=${your_index_name}
-```
-
-### 2.2 Build Docker Image
-
-```bash
-cd ../../../../../
-docker build -t opea/retriever-multimodal-redis:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f comps/retrievers/multimodal/redis/langchain/Dockerfile .
-```
-
-To start a docker container, you have two options:
-
-- A. Run Docker with CLI
-- B. Run Docker with Docker Compose
-
-You can choose one as needed.
-
-### 2.3 Run Docker with CLI (Option A)
-
-```bash
-docker run -d --name="retriever-multimodal-redis" -p 7000:7000 --ipc=host -e http_proxy=$http_proxy -e https_proxy=$https_proxy -e REDIS_URL=$REDIS_URL -e INDEX_NAME=$INDEX_NAME opea/retriever-multimodal-redis:latest
-```
-
-### 2.4 Run Docker with Docker Compose (Option B)
-
-```bash
-cd docker
-docker compose -f docker_compose_retriever.yaml up -d
-```
-
-## 🚀3. Consume Retriever Service
-
-### 3.1 Consume Embedding Service
-
-To consume the Retriever Microservice, you can generate a mock embedding vector of length 512 with Python.
-
-```bash
-export your_embedding=$(python -c "import random; embedding = [random.uniform(-1, 1) for _ in range(512)]; print(embedding)")
-curl http://${your_ip}:7000/v1/multimodal_retrieval \
-  -X POST \
-  -d "{\"text\":\"What is the revenue of Nike in 2023?\",\"embedding\":${your_embedding}}" \
-  -H 'Content-Type: application/json'
-```
-
-You can set the parameters for the retriever.
-
-```bash
-export your_embedding=$(python -c "import random; embedding = [random.uniform(-1, 1) for _ in range(512)]; print(embedding)")
-curl http://localhost:7000/v1/multimodal_retrieval \
-  -X POST \
-  -d "{\"text\":\"What is the revenue of Nike in 2023?\",\"embedding\":${your_embedding},\"search_type\":\"similarity\", \"k\":4}" \
-  -H 'Content-Type: application/json'
-```
-
-```bash
-export your_embedding=$(python -c "import random; embedding = [random.uniform(-1, 1) for _ in range(512)]; print(embedding)")
-curl http://localhost:7000/v1/multimodal_retrieval \
-  -X POST \
-  -d "{\"text\":\"What is the revenue of Nike in 2023?\",\"embedding\":${your_embedding},\"search_type\":\"similarity_distance_threshold\", \"k\":4, \"distance_threshold\":1.0}" \
-  -H 'Content-Type: application/json'
-```
-
-```bash
-export your_embedding=$(python -c "import random; embedding = [random.uniform(-1, 1) for _ in range(512)]; print(embedding)")
-curl http://localhost:7000/v1/multimodal_retrieval \
-  -X POST \
-  -d "{\"text\":\"What is the revenue of Nike in 2023?\",\"embedding\":${your_embedding},\"search_type\":\"similarity_score_threshold\", \"k\":4, \"score_threshold\":0.2}" \
-  -H 'Content-Type: application/json'
-```
-
-```bash
-export your_embedding=$(python -c "import random; embedding = [random.uniform(-1, 1) for _ in range(512)]; print(embedding)")
-curl http://localhost:7000/v1/multimodal_retrieval \
-  -X POST \
-  -d "{\"text\":\"What is the revenue of Nike in 2023?\",\"embedding\":${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/redis/langchain/__init__.py b/comps/retrievers/multimodal/redis/langchain/__init__.py
deleted file mode 100644
index 916f3a44b..000000000
--- a/comps/retrievers/multimodal/redis/langchain/__init__.py
+++ /dev/null
@@ -1,2 +0,0 @@
-# Copyright (C) 2024 Intel Corporation
-# SPDX-License-Identifier: Apache-2.0
diff --git a/comps/retrievers/multimodal/redis/langchain/docker_compose_retriever.yaml b/comps/retrievers/multimodal/redis/langchain/docker_compose_retriever.yaml
deleted file mode 100644
index 6c4f76ad0..000000000
--- a/comps/retrievers/multimodal/redis/langchain/docker_compose_retriever.yaml
+++ /dev/null
@@ -1,23 +0,0 @@
-# Copyright (C) 2024 Intel Corporation
-# SPDX-License-Identifier: Apache-2.0
-
-version: "1.0"
-
-services:
-  retriever-multimodal-redis:
-    image: opea/retriever-multimodal-redis:latest
-    container_name: retriever-multimodal-redis
-    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}
-    restart: unless-stopped
-
-networks:
-  default:
-    driver: bridge
diff --git a/comps/retrievers/multimodal/redis/langchain/multimodal_config.py b/comps/retrievers/multimodal/redis/langchain/multimodal_config.py
deleted file mode 100644
index 79ac5698f..000000000
--- a/comps/retrievers/multimodal/redis/langchain/multimodal_config.py
+++ /dev/null
@@ -1,83 +0,0 @@
-# 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.
-
-    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", "schema.yml")
-schema_path = os.path.join(parent_dir, REDIS_SCHEMA)
-INDEX_SCHEMA = schema_path
diff --git a/comps/retrievers/multimodal/redis/langchain/requirements.txt b/comps/retrievers/multimodal/redis/langchain/requirements.txt
deleted file mode 100644
index b7cef2866..000000000
--- a/comps/retrievers/multimodal/redis/langchain/requirements.txt
+++ /dev/null
@@ -1,11 +0,0 @@
-docarray[full]
-fastapi
-langchain_community
-opentelemetry-api
-opentelemetry-exporter-otlp
-opentelemetry-sdk
-prometheus-fastapi-instrumentator
-redis
-shortuuid
-transformers
-uvicorn
diff --git a/comps/retrievers/multimodal/redis/langchain/retriever_redis.py b/comps/retrievers/multimodal/redis/langchain/retriever_redis.py
deleted file mode 100644
index a01b3e20c..000000000
--- a/comps/retrievers/multimodal/redis/langchain/retriever_redis.py
+++ /dev/null
@@ -1,93 +0,0 @@
-# Copyright (C) 2024 Intel Corporation
-# SPDX-License-Identifier: Apache-2.0
-
-import time
-from typing import Union
-
-from langchain_community.vectorstores import Redis
-from multimodal_config import INDEX_NAME, INDEX_SCHEMA, REDIS_URL
-
-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,
-)
-from comps.embeddings.multimodal.bridgetower import BridgeTowerEmbedding
-
-
-@register_microservice(
-    name="opea_service@multimodal_retriever_redis",
-    service_type=ServiceType.RETRIEVER,
-    endpoint="/v1/multimodal_retrieval",
-    host="0.0.0.0",
-    port=7000,
-)
-@register_statistics(names=["opea_service@multimodal_retriever_redis"])
-async 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 the Redis index has data, perform the search
-        if input.search_type == "similarity":
-            search_res = await vector_db.asimilarity_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 = await vector_db.asimilarity_search_by_vector(
-                embedding=input.embedding, k=input.k, distance_threshold=input.distance_threshold
-            )
-        elif input.search_type == "similarity_score_threshold":
-            docs_and_similarities = await vector_db.asimilarity_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 = await vector_db.amax_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@multimodal_retriever_redis"].append_latency(time.time() - start, None)
-    return result
-
-
-if __name__ == "__main__":
-
-    embeddings = BridgeTowerEmbedding()
-    vector_db = Redis(embedding=embeddings, index_name=INDEX_NAME, index_schema=INDEX_SCHEMA, redis_url=REDIS_URL)
-    opea_microservices["opea_service@multimodal_retriever_redis"].start()
diff --git a/comps/retrievers/qdrant/haystack/ingest.py b/comps/retrievers/qdrant/haystack/ingest.py
deleted file mode 100644
index d14dfbbb0..000000000
--- a/comps/retrievers/qdrant/haystack/ingest.py
+++ /dev/null
@@ -1,110 +0,0 @@
-#!/usr/bin/env python
-# -*- coding: utf-8 -*-
-# Copyright (C) 2024 Intel Corporation
-# SPDX-License-Identifier: Apache-2.0
-
-#
-
-import argparse
-import io
-import os
-import uuid
-
-import numpy as np
-from haystack.components.embedders import HuggingFaceTEIDocumentEmbedder, SentenceTransformersDocumentEmbedder
-from haystack.dataclasses.document import Document
-from haystack_integrations.document_stores.qdrant import QdrantDocumentStore
-from langchain.text_splitter import RecursiveCharacterTextSplitter
-from PIL import Image
-from qdrant_config import EMBED_DIMENSION, EMBED_ENDPOINT, EMBED_MODEL, INDEX_NAME, QDRANT_HOST, QDRANT_PORT
-
-
-def pdf_loader(file_path):
-    try:
-        import easyocr
-        import fitz
-    except ImportError:
-        raise ImportError(
-            "`PyMuPDF` or 'easyocr' package is not found, please install it with "
-            "`pip install pymupdf or pip install easyocr.`"
-        )
-
-    doc = fitz.open(file_path)
-    reader = easyocr.Reader(["en"])
-    result = ""
-    for i in range(doc.page_count):
-        page = doc.load_page(i)
-        pagetext = page.get_text().strip()
-        if pagetext:
-            result = result + pagetext
-        if len(doc.get_page_images(i)) > 0:
-            for img in doc.get_page_images(i):
-                if img:
-                    pageimg = ""
-                    xref = img[0]
-                    img_data = doc.extract_image(xref)
-                    img_bytes = img_data["image"]
-                    pil_image = Image.open(io.BytesIO(img_bytes))
-                    img = np.array(pil_image)
-                    img_result = reader.readtext(img, paragraph=True, detail=0)
-                    pageimg = pageimg + ", ".join(img_result).strip()
-                    if pageimg.endswith("!") or pageimg.endswith("?") or pageimg.endswith("."):
-                        pass
-                    else:
-                        pageimg = pageimg + "."
-                result = result + pageimg
-    return result
-
-
-def ingest_documents(folder_path, tag):
-    """Ingest PDF to Qdrant from the a given path."""
-    # Load list of pdfs
-    doc_path = [os.path.join(folder_path, file) for file in os.listdir(folder_path)][0]
-
-    print("Parsing...", doc_path)
-
-    text_splitter = RecursiveCharacterTextSplitter(chunk_size=1500, chunk_overlap=100, add_start_index=True)
-    content = pdf_loader(doc_path)
-    chunks = text_splitter.split_text(content)
-
-    print("Done preprocessing. Created ", len(chunks), " chunks of the original pdf")
-    # Create vectorstore
-    if EMBED_ENDPOINT:
-        # create embeddings using TEI endpoint service
-        embedder = HuggingFaceTEIDocumentEmbedder(url=EMBED_ENDPOINT)
-    else:
-        # create embeddings using local embedding model
-        embedder = SentenceTransformersDocumentEmbedder(model=EMBED_MODEL)
-        embedder.warm_up()
-
-    # Initialize Qdrant store
-    qdrant_store = QdrantDocumentStore(
-        host=QDRANT_HOST,
-        port=QDRANT_PORT,
-        embedding_dim=EMBED_DIMENSION,
-        index=INDEX_NAME,
-        embedding_field="embedding",
-        similarity="cosine",
-        recreate_index=True,
-    )
-
-    # Batch size
-    batch_size = 32
-    num_chunks = len(chunks)
-    for i in range(0, num_chunks, batch_size):
-        batch_chunks = chunks[i : i + batch_size]
-        batch_texts = [f"Tag: {tag}. " + chunk for chunk in batch_chunks]
-        documents = [Document(id=str(uuid.uuid4()), content=content) for content in batch_texts]
-        documents_with_embeddings = embedder.run(documents)["documents"]
-        qdrant_store.write_documents(documents_with_embeddings)
-
-        print(f"Processed batch {i//batch_size + 1}/{(num_chunks-1)//batch_size + 1}")
-
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser(description="Ingest documents from a specified folder with a tag")
-    parser.add_argument("folder_path", type=str, help="Path to the folder containing documents")
-    parser.add_argument("--tag", type=str, default="", help="Tag to be used as an identifier")
-
-    args = parser.parse_args()
-    ingest_documents(args.folder_path, args.tag)
diff --git a/comps/retrievers/redis/data/nke-10k-2023.pdf b/comps/retrievers/redis/data/nke-10k-2023.pdf
deleted file mode 100644
index 6ade8863e..000000000
Binary files a/comps/retrievers/redis/data/nke-10k-2023.pdf and /dev/null differ
diff --git a/comps/retrievers/redis/langchain/README.md b/comps/retrievers/redis/langchain/README.md
index db3161192..2172bcbc1 100644
--- a/comps/retrievers/redis/langchain/README.md
+++ b/comps/retrievers/redis/langchain/README.md
@@ -57,12 +57,24 @@ python retriever_redis.py
 
 ### 2.1 Setup Environment Variables
 
+Two versions of retriever are supported for redis: text retriever and multimodal retriever.
+Users need to setup different environment variables for each type of retriever as below.
+
 ```bash
+# for text retriever
+export your_ip=$(hostname -I | awk '{print $1}')
 export RETRIEVE_MODEL_ID="BAAI/bge-base-en-v1.5"
 export REDIS_URL="redis://${your_ip}:6379"
 export INDEX_NAME=${your_index_name}
 export TEI_EMBEDDING_ENDPOINT="http://${your_ip}:6060"
 export HUGGINGFACEHUB_API_TOKEN=${your_hf_token}
+
+# for multimodal retriever
+export your_ip=$(hostname -I | awk '{print $1}')
+export RETRIEVE_MODEL_ID="BAAI/bge-base-en-v1.5"
+export REDIS_URL="redis://${your_ip}:6379"
+export INDEX_NAME=${your_index_name}
+export BRIDGE_TOWER_EMBEDDING=true
 ```
 
 ### 2.2 Build Docker Image
@@ -82,7 +94,10 @@ You can choose one as needed.
 ### 2.3 Run Docker with CLI (Option A)
 
 ```bash
+# Start a text retriever server
 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 -e TEI_EMBEDDING_ENDPOINT=$TEI_EMBEDDING_ENDPOINT -e HUGGINGFACEHUB_API_TOKEN=$HUGGINGFACEHUB_API_TOKEN opea/retriever-redis:latest
+# start a multimodal retriever server
+docker run -d --name="retriever-multimodal-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 -e BRIDGE_TOWER_EMBEDDING=${BRIDGE_TOWER_EMBEDDING} opea/retriever-redis:latest
 ```
 
 ### 2.4 Run Docker with Docker Compose (Option B)
@@ -103,10 +118,20 @@ curl http://localhost:7000/v1/health_check \
 
 ### 3.2 Consume Embedding Service
 
-To consume the Retriever Microservice, you can generate a mock embedding vector of length 768 with Python.
+To consume the Retriever Microservice, you can generate a mock embedding vector with Python.
+
+Same here, users need to validate text/multimodal embedding service with different lengths of vectors. Then use the `curl` command to validate.
 
 ```bash
+# for text retriever
 export your_embedding=$(python -c "import random; embedding = [random.uniform(-1, 1) for _ in range(768)]; print(embedding)")
+# for multimodal retriever
+export your_embedding=$(python -c "import random; embedding = [random.uniform(-1, 1) for _ in range(512)]; print(embedding)")
+```
+
+Default validation.
+
+```bash
 curl http://${your_ip}:7000/v1/retrieval \
   -X POST \
   -d "{\"text\":\"What is the revenue of Nike in 2023?\",\"embedding\":${your_embedding}}" \
@@ -116,7 +141,6 @@ curl http://${your_ip}:7000/v1/retrieval \
 You can set the parameters for the retriever.
 
 ```bash
-export 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}" \
@@ -124,7 +148,6 @@ curl http://localhost:7000/v1/retrieval \
 ```
 
 ```bash
-export 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}" \
@@ -132,7 +155,6 @@ curl http://localhost:7000/v1/retrieval \
 ```
 
 ```bash
-export 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}" \
@@ -140,7 +162,6 @@ curl http://localhost:7000/v1/retrieval \
 ```
 
 ```bash
-export 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}" \
diff --git a/comps/retrievers/redis/langchain/docker_compose_retriever.yaml b/comps/retrievers/redis/langchain/docker_compose_retriever.yaml
index 90c4f6a7f..5ce13af87 100644
--- a/comps/retrievers/redis/langchain/docker_compose_retriever.yaml
+++ b/comps/retrievers/redis/langchain/docker_compose_retriever.yaml
@@ -27,6 +27,7 @@ services:
       INDEX_NAME: ${INDEX_NAME}
       TEI_EMBEDDING_ENDPOINT: ${TEI_EMBEDDING_ENDPOINT}
       HUGGINGFACEHUB_API_TOKEN: ${HUGGINGFACEHUB_API_TOKEN}
+      BRIDGE_TOWER_EMBEDDING: ${BRIDGE_TOWER_EMBEDDING}
     restart: unless-stopped
 
 networks:
diff --git a/comps/retrievers/redis/langchain/ingest.py b/comps/retrievers/redis/langchain/ingest.py
deleted file mode 100644
index c064c4158..000000000
--- a/comps/retrievers/redis/langchain/ingest.py
+++ /dev/null
@@ -1,121 +0,0 @@
-#!/usr/bin/env python
-# -*- coding: utf-8 -*-
-# Copyright (C) 2024 Intel Corporation
-# SPDX-License-Identifier: Apache-2.0
-
-#
-
-import io
-import os
-import time
-
-import numpy as np
-from langchain.text_splitter import RecursiveCharacterTextSplitter
-from langchain_community.embeddings import HuggingFaceBgeEmbeddings, HuggingFaceHubEmbeddings
-from langchain_community.vectorstores import Redis
-from PIL import Image
-from redis_config import EMBED_MODEL, INDEX_NAME, REDIS_URL
-
-tei_embedding_endpoint = os.getenv("TEI_EMBEDDING_ENDPOINT")
-
-timeout = 180  # Timeout in seconds
-check_interval = 5  # Check every 5 seconds
-
-
-def check_embedding_endpoint(embedder):
-    try:
-        test_sentence = "embedder available test."
-        embedder.embed_documents([test_sentence])
-        return True
-    except Exception as e:
-        print(f"Error embedder is unavailable: {e}")
-        return False
-
-
-def pdf_loader(file_path):
-    try:
-        import easyocr
-        import fitz
-    except ImportError:
-        raise ImportError(
-            "`PyMuPDF` or 'easyocr' package is not found, please install it with "
-            "`pip install pymupdf or pip install easyocr.`"
-        )
-
-    doc = fitz.open(file_path)
-    reader = easyocr.Reader(["en"])
-    result = ""
-    for i in range(doc.page_count):
-        page = doc.load_page(i)
-        pagetext = page.get_text().strip()
-        if pagetext:
-            result = result + pagetext
-        if len(doc.get_page_images(i)) > 0:
-            for img in doc.get_page_images(i):
-                if img:
-                    pageimg = ""
-                    xref = img[0]
-                    img_data = doc.extract_image(xref)
-                    img_bytes = img_data["image"]
-                    pil_image = Image.open(io.BytesIO(img_bytes))
-                    img = np.array(pil_image)
-                    img_result = reader.readtext(img, paragraph=True, detail=0)
-                    pageimg = pageimg + ", ".join(img_result).strip()
-                    if pageimg.endswith("!") or pageimg.endswith("?") or pageimg.endswith("."):
-                        pass
-                    else:
-                        pageimg = pageimg + "."
-                result = result + pageimg
-    return result
-
-
-def ingest_documents():
-    """Ingest PDF to Redis from the data/ directory that
-    contains Edgar 10k filings data for Nike."""
-    # Load list of pdfs
-    company_name = "Nike"
-    data_path = "data/"
-    doc_path = [os.path.join(data_path, file) for file in os.listdir(data_path)][0]
-
-    print("Parsing 10k filing doc for NIKE", doc_path)
-
-    text_splitter = RecursiveCharacterTextSplitter(chunk_size=1500, chunk_overlap=100, add_start_index=True)
-    content = pdf_loader(doc_path)
-    chunks = text_splitter.split_text(content)
-
-    print("Done preprocessing. Created ", len(chunks), " chunks of the original pdf")
-    # Create vectorstore
-    if tei_embedding_endpoint:
-        # create embeddings using TEI endpoint service
-        embedder = HuggingFaceHubEmbeddings(model=tei_embedding_endpoint)
-        start_time = time.time()
-        while time.time() - start_time < timeout:
-            is_available = check_embedding_endpoint(embedder)
-            if is_available:
-                print("Embedder endpoint is available.")
-                break
-            else:
-                print("Embedder endpoint is not available. Checking again in 5 seconds...")
-                time.sleep(check_interval)
-    else:
-        # create embeddings using local embedding model
-        embedder = HuggingFaceBgeEmbeddings(model_name=EMBED_MODEL)
-
-    # Batch size
-    batch_size = 32
-    num_chunks = len(chunks)
-    for i in range(0, num_chunks, batch_size):
-        batch_chunks = chunks[i : i + batch_size]
-        batch_texts = [f"Company: {company_name}. " + chunk for chunk in batch_chunks]
-
-        _ = Redis.from_texts(
-            texts=batch_texts,
-            embedding=embedder,
-            index_name=INDEX_NAME,
-            redis_url=REDIS_URL,
-        )
-        print(f"Processed batch {i//batch_size + 1}/{(num_chunks-1)//batch_size + 1}")
-
-
-if __name__ == "__main__":
-    ingest_documents()
diff --git a/comps/retrievers/redis/langchain/redis_config.py b/comps/retrievers/redis/langchain/redis_config.py
index 35233d8ff..ade017e7b 100644
--- a/comps/retrievers/redis/langchain/redis_config.py
+++ b/comps/retrievers/redis/langchain/redis_config.py
@@ -73,3 +73,6 @@ def format_redis_conn_from_env():
 
 current_file_path = os.path.abspath(__file__)
 parent_dir = os.path.dirname(current_file_path)
+REDIS_SCHEMA = os.getenv("REDIS_SCHEMA", "redis_schema_multi.yml")
+schema_path = os.path.join(parent_dir, REDIS_SCHEMA)
+INDEX_SCHEMA = schema_path
diff --git a/comps/retrievers/multimodal/redis/langchain/schema.yml b/comps/retrievers/redis/langchain/redis_schema_multi.yml
similarity index 100%
rename from comps/retrievers/multimodal/redis/langchain/schema.yml
rename to comps/retrievers/redis/langchain/redis_schema_multi.yml
diff --git a/comps/retrievers/redis/langchain/requirements.txt b/comps/retrievers/redis/langchain/requirements.txt
index f29317dd9..96a303bd7 100644
--- a/comps/retrievers/redis/langchain/requirements.txt
+++ b/comps/retrievers/redis/langchain/requirements.txt
@@ -11,4 +11,5 @@ pymupdf
 redis
 sentence_transformers
 shortuuid
+transformers
 uvicorn
diff --git a/comps/retrievers/redis/langchain/retriever_redis.py b/comps/retrievers/redis/langchain/retriever_redis.py
index ada07d236..da531adb4 100644
--- a/comps/retrievers/redis/langchain/retriever_redis.py
+++ b/comps/retrievers/redis/langchain/retriever_redis.py
@@ -8,12 +8,14 @@
 from langchain_community.embeddings import HuggingFaceBgeEmbeddings
 from langchain_community.vectorstores import Redis
 from langchain_huggingface import HuggingFaceEndpointEmbeddings
-from redis_config import EMBED_MODEL, INDEX_NAME, REDIS_URL
+from redis_config import EMBED_MODEL, INDEX_NAME, INDEX_SCHEMA, REDIS_URL
 
 from comps import (
     CustomLogger,
     EmbedDoc,
+    EmbedMultimodalDoc,
     SearchedDoc,
+    SearchedMultimodalDoc,
     ServiceType,
     TextDoc,
     opea_microservices,
@@ -28,11 +30,13 @@
     RetrievalResponse,
     RetrievalResponseData,
 )
+from comps.embeddings.multimodal.bridgetower import BridgeTowerEmbedding
 
 logger = CustomLogger("retriever_redis")
 logflag = os.getenv("LOGFLAG", False)
 
 tei_embedding_endpoint = os.getenv("TEI_EMBEDDING_ENDPOINT")
+bridge_tower_embedding = os.getenv("BRIDGE_TOWER_EMBEDDING")
 
 
 @register_microservice(
@@ -44,8 +48,8 @@
 )
 @register_statistics(names=["opea_service@retriever_redis"])
 async def retrieve(
-    input: Union[EmbedDoc, RetrievalRequest, ChatCompletionRequest]
-) -> Union[SearchedDoc, RetrievalResponse, ChatCompletionRequest]:
+    input: Union[EmbedDoc, EmbedMultimodalDoc, RetrievalRequest, ChatCompletionRequest]
+) -> Union[SearchedDoc, SearchedMultimodalDoc, RetrievalResponse, ChatCompletionRequest]:
     if logflag:
         logger.info(input)
     start = time.time()
@@ -53,21 +57,16 @@ async def retrieve(
     if vector_db.client.keys() == []:
         search_res = []
     else:
-        if isinstance(input, EmbedDoc):
-            query = input.text
+        if isinstance(input, EmbedDoc) or isinstance(input, EmbedMultimodalDoc):
             embedding_data_input = input.embedding
         else:
             # for RetrievalRequest, ChatCompletionRequest
-            query = input.input
             if isinstance(input.embedding, EmbeddingResponse):
                 embeddings = input.embedding.data
                 embedding_data_input = []
                 for emb in embeddings:
                     # each emb is EmbeddingResponseData
-                    # print("Embedding data: ", emb.embedding)
-                    # print("Embedding data length: ",len(emb.embedding))
                     embedding_data_input.append(emb.embedding)
-                # print("All Embedding data length: ",len(embedding_data_input))
             else:
                 embedding_data_input = input.embedding
 
@@ -98,6 +97,12 @@ async def retrieve(
         for r in search_res:
             retrieved_docs.append(TextDoc(text=r.page_content))
         result = SearchedDoc(retrieved_docs=retrieved_docs, initial_query=input.text)
+    elif 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))
@@ -119,9 +124,14 @@ async def retrieve(
     if tei_embedding_endpoint:
         # create embeddings using TEI endpoint service
         embeddings = HuggingFaceEndpointEmbeddings(model=tei_embedding_endpoint)
+        vector_db = Redis(embedding=embeddings, index_name=INDEX_NAME, redis_url=REDIS_URL)
+    elif bridge_tower_embedding:
+        # create embeddings using BridgeTower service
+        embeddings = BridgeTowerEmbedding()
+        vector_db = Redis(embedding=embeddings, index_name=INDEX_NAME, index_schema=INDEX_SCHEMA, redis_url=REDIS_URL)
     else:
         # create embeddings using local embedding model
         embeddings = HuggingFaceBgeEmbeddings(model_name=EMBED_MODEL)
+        vector_db = Redis(embedding=embeddings, index_name=INDEX_NAME, redis_url=REDIS_URL)
 
-    vector_db = Redis(embedding=embeddings, index_name=INDEX_NAME, redis_url=REDIS_URL)
     opea_microservices["opea_service@retriever_redis"].start()
diff --git a/tests/retrievers/test_retrievers_multimodal_redis_langchain.sh b/tests/retrievers/test_retrievers_multimodal_redis_langchain.sh
deleted file mode 100644
index 873516ddc..000000000
--- a/tests/retrievers/test_retrievers_multimodal_redis_langchain.sh
+++ /dev/null
@@ -1,84 +0,0 @@
-#!/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 $WORKPATH
-    docker build --no-cache -t opea/retriever-multimodal-redis:comps --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f comps/retrievers/multimodal/redis/langchain/Dockerfile .
-    if [ $? -ne 0 ]; then
-        echo "opea/retriever-multimodal-redis built fail"
-        exit 1
-    else
-        echo "opea/retriever-multimodal-redis built successful"
-    fi
-}
-
-function start_service() {
-    # redis
-    docker run -d --name test-comps-multimodal-retriever-redis-vector-db -p 5689:6379 -p 5011:8001 -e HTTPS_PROXY=$https_proxy -e HTTP_PROXY=$https_proxy redis/redis-stack:7.2.0-v9
-    sleep 10s
-
-    # redis retriever
-    export REDIS_URL="redis://${ip_address}:5689"
-    export INDEX_NAME="rag-redis"
-    retriever_port=5434
-    unset http_proxy
-    docker run -d --name="test-comps-retriever-multimodal-redis" -p ${retriever_port}:7000 --ipc=host -e http_proxy=$http_proxy -e https_proxy=$https_proxy -e REDIS_URL=$REDIS_URL -e INDEX_NAME=$INDEX_NAME opea/retriever-multimodal-redis:comps
-
-    sleep 5m
-}
-
-function validate_microservice() {
-    retriever_port=5434
-    export PATH="${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-retriever-multimodal-redis >> ${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-multimodal-redis >> ${LOG_PATH}/retriever.log
-        exit 1
-    fi
-}
-
-function stop_docker() {
-    cid_retrievers=$(docker ps -aq --filter "name=test-comps-*")
-    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
diff --git a/tests/retrievers/test_retrievers_redis_langchain.sh b/tests/retrievers/test_retrievers_redis_langchain.sh
index dd34a2a0f..685d20ba4 100644
--- a/tests/retrievers/test_retrievers_redis_langchain.sh
+++ b/tests/retrievers/test_retrievers_redis_langchain.sh
@@ -42,14 +42,29 @@ function start_service() {
     sleep 3m
 }
 
+function start_multimodal_service() {
+    # redis
+    docker run -d --name test-comps-retriever-redis-vector-db -p 5689:6379 -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}:5689"
+    export INDEX_NAME="rag-redis"
+    retriever_port=5435
+    unset http_proxy
+    docker run -d --name="test-comps-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 -e BRIDGE_TOWER_EMBEDDING=true opea/retriever-redis:comps
+
+    sleep 2m
+}
+
 function validate_microservice() {
+    local test_embedding="$1"
+
     retriever_port=5435
     export PATH="${HOME}/miniforge3/bin:$PATH"
     source activate
     URL="http://${ip_address}:$retriever_port/v1/retrieval"
 
-    test_embedding=$(python -c "import random; embedding = [random.uniform(-1, 1) for _ in range(768)]; print(embedding)")
-
     HTTP_STATUS=$(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..."
@@ -60,13 +75,11 @@ function validate_microservice() {
         else
             echo "[ retriever ] Content does not match the expected result: $CONTENT"
             docker logs test-comps-retriever-redis-server >> ${LOG_PATH}/retriever.log
-            docker logs test-comps-retriever-redis-tei-endpoint >> ${LOG_PATH}/tei.log
             exit 1
         fi
     else
         echo "[ retriever ] HTTP status is not 200. Received status was $HTTP_STATUS"
         docker logs test-comps-retriever-redis-server >> ${LOG_PATH}/retriever.log
-        docker logs test-comps-retriever-redis-tei-endpoint >> ${LOG_PATH}/tei.log
         exit 1
     fi
 }
@@ -81,12 +94,20 @@ function stop_docker() {
 function main() {
 
     stop_docker
-
     build_docker_images
+
+    # test text retriever
     start_service
+    test_embedding=$(python -c "import random; embedding = [random.uniform(-1, 1) for _ in range(768)]; print(embedding)")
+    validate_microservice "$test_embedding"
+    stop_docker
 
-    validate_microservice
+    # test multimodal retriever
+    start_multimodal_service
+    test_embedding_multi=$(python -c "import random; embedding = [random.uniform(-1, 1) for _ in range(512)]; print(embedding)")
+    validate_microservice "$test_embedding_multi"
 
+    # clean env
     stop_docker
     echo y | docker system prune