diff --git a/comps/cores/proto/api_protocol.py b/comps/cores/proto/api_protocol.py
index bd52d7274..382982d27 100644
--- a/comps/cores/proto/api_protocol.py
+++ b/comps/cores/proto/api_protocol.py
@@ -160,7 +160,7 @@ class ChatCompletionRequest(BaseModel):
     logit_bias: Optional[Dict[str, float]] = None
     logprobs: Optional[bool] = False
     top_logprobs: Optional[int] = 0
-    max_tokens: Optional[int] = 16  # use https://platform.openai.com/docs/api-reference/completions/create
+    max_tokens: Optional[int] = 1024  # use https://platform.openai.com/docs/api-reference/completions/create
     n: Optional[int] = 1
     presence_penalty: Optional[float] = 0.0
     response_format: Optional[ResponseFormat] = None
diff --git a/comps/dataprep/pgvector/langchain/requirements.txt b/comps/dataprep/pgvector/langchain/requirements.txt
index 87680d503..84fd48e52 100644
--- a/comps/dataprep/pgvector/langchain/requirements.txt
+++ b/comps/dataprep/pgvector/langchain/requirements.txt
@@ -22,6 +22,7 @@ psycopg2-binary
 pymupdf
 pyspark
 python-docx
+python-multipart
 python-pptx
 sentence_transformers
 shortuuid
diff --git a/comps/dataprep/qdrant/README.md b/comps/dataprep/qdrant/README.md
index 24f58fc09..30aefb43d 100644
--- a/comps/dataprep/qdrant/README.md
+++ b/comps/dataprep/qdrant/README.md
@@ -47,7 +47,7 @@ docker build -t opea/dataprep-qdrant:latest --build-arg https_proxy=$https_proxy
 ## Run Docker with CLI
 
 ```bash
-docker run -d --name="dataprep-qdrant-server" -p 6000:6000 --ipc=host -e http_proxy=$http_proxy -e https_proxy=$https_proxy opea/dataprep-qdrant:latest
+docker run -d --name="dataprep-qdrant-server" -p 6007:6007 --ipc=host -e http_proxy=$http_proxy -e https_proxy=$https_proxy opea/dataprep-qdrant:latest
 ```
 
 ## Setup Environment Variables
@@ -55,7 +55,7 @@ docker run -d --name="dataprep-qdrant-server" -p 6000:6000 --ipc=host -e http_pr
 ```bash
 export http_proxy=${your_http_proxy}
 export https_proxy=${your_http_proxy}
-export QDRANT=${host_ip}
+export QDRANT_HOST=${host_ip}
 export QDRANT_PORT=6333
 export COLLECTION_NAME=${your_collection_name}
 ```
@@ -72,13 +72,21 @@ docker compose -f docker-compose-dataprep-qdrant.yaml up -d
 Once document preparation microservice for Qdrant is started, user can use below command to invoke the microservice to convert the document to embedding and save to the database.
 
 ```bash
-curl -X POST -H "Content-Type: application/json" -d '{"path":"/path/to/document"}' http://localhost:6000/v1/dataprep
+curl -X POST \
+    -H "Content-Type: multipart/form-data" \
+    -F "files=@./file1.txt" \
+    http://localhost:6007/v1/dataprep
 ```
 
 You can specify chunk_size and chunk_size by the following commands.
 
 ```bash
-curl -X POST -H "Content-Type: application/json" -d '{"path":"/path/to/document","chunk_size":1500,"chunk_overlap":100}' http://localhost:6000/v1/dataprep
+curl -X POST \
+    -H "Content-Type: multipart/form-data" \
+    -F "files=@./file1.txt" \
+    -F "chunk_size=1500" \
+    -F "chunk_overlap=100" \
+    http://localhost:6007/v1/dataprep
 ```
 
 We support table extraction from pdf documents. You can specify process_table and table_strategy by the following commands. "table_strategy" refers to the strategies to understand tables for table retrieval. As the setting progresses from "fast" to "hq" to "llm," the focus shifts towards deeper table understanding at the expense of processing speed. The default strategy is "fast".
@@ -86,5 +94,10 @@ We support table extraction from pdf documents. You can specify process_table an
 Note: If you specify "table_strategy=llm", You should first start TGI Service, please refer to 1.2.1, 1.3.1 in https://github.com/opea-project/GenAIComps/tree/main/comps/llms/README.md, and then `export TGI_LLM_ENDPOINT="http://${your_ip}:8008"`.
 
 ```bash
-curl -X POST -H "Content-Type: application/json" -d '{"path":"/path/to/document","process_table":true,"table_strategy":"hq"}' http://localhost:6000/v1/dataprep
+curl -X POST \
+    -H "Content-Type: multipart/form-data" \
+    -F "files=@./your_file.pdf" \
+    -F "process_table=true" \
+    -F "table_strategy=hq" \
+    http://localhost:6007/v1/dataprep
 ```
diff --git a/comps/dataprep/qdrant/config.py b/comps/dataprep/qdrant/config.py
index 2b30a3682..7cf37f404 100644
--- a/comps/dataprep/qdrant/config.py
+++ b/comps/dataprep/qdrant/config.py
@@ -7,7 +7,7 @@
 EMBED_MODEL = os.getenv("EMBED_MODEL", "sentence-transformers/all-MiniLM-L6-v2")
 
 # Qdrant configuration
-QDRANT_HOST = os.getenv("QDRANT", "localhost")
+QDRANT_HOST = os.getenv("QDRANT_HOST", "localhost")
 QDRANT_PORT = int(os.getenv("QDRANT_PORT", 6333))
 COLLECTION_NAME = os.getenv("COLLECTION_NAME", "rag-qdrant")
 
diff --git a/comps/dataprep/qdrant/docker/Dockerfile b/comps/dataprep/qdrant/docker/Dockerfile
index bdf0315e2..ff9f6b253 100644
--- a/comps/dataprep/qdrant/docker/Dockerfile
+++ b/comps/dataprep/qdrant/docker/Dockerfile
@@ -12,6 +12,7 @@ RUN apt-get update -y && apt-get install -y --no-install-recommends --fix-missin
     build-essential \
     libgl1-mesa-glx \
     libjemalloc-dev \
+    default-jre \
     vim
 
 RUN useradd -m -s /bin/bash user && \
@@ -22,13 +23,18 @@ USER user
 
 COPY comps /home/user/comps
 
-RUN pip install --no-cache-dir --upgrade pip && \
-    if [ ${ARCH} = "cpu" ]; then pip install torch --index-url https://download.pytorch.org/whl/cpu; fi && \
+RUN pip install --no-cache-dir --upgrade pip setuptools && \
+    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/dataprep/qdrant/requirements.txt
 
 ENV PYTHONPATH=$PYTHONPATH:/home/user
 
+USER root
+
+RUN mkdir -p /home/user/comps/dataprep/qdrant/uploaded_files && chown -R user /home/user/comps/dataprep/qdrant/uploaded_files
+
+USER user
+
 WORKDIR /home/user/comps/dataprep/qdrant
 
 ENTRYPOINT ["python", "prepare_doc_qdrant.py"]
-
diff --git a/comps/dataprep/qdrant/docker/docker-compose-dataprep-qdrant.yaml b/comps/dataprep/qdrant/docker/docker-compose-dataprep-qdrant.yaml
index e86dc2c4e..aaf2a17dd 100644
--- a/comps/dataprep/qdrant/docker/docker-compose-dataprep-qdrant.yaml
+++ b/comps/dataprep/qdrant/docker/docker-compose-dataprep-qdrant.yaml
@@ -9,19 +9,36 @@ services:
     ports:
       - "6333:6333"
       - "6334:6334"
+  tei-embedding-service:
+    image: ghcr.io/huggingface/text-embeddings-inference:cpu-1.5
+    container_name: tei-embedding-server
+    ports:
+      - "6006:80"
+    volumes:
+      - "./data:/data"
+    shm_size: 1g
+    environment:
+      no_proxy: ${no_proxy}
+      http_proxy: ${http_proxy}
+      https_proxy: ${https_proxy}
+    command: --model-id ${EMBEDDING_MODEL_ID} --auto-truncate
   dataprep-qdrant:
     image: opea/gen-ai-comps:dataprep-qdrant-xeon-server
     container_name: dataprep-qdrant-server
+    depends_on:
+      - qdrant-vector-db
+      - tei-embedding-service
     ports:
-      - "6000:6000"
+      - "6007:6007"
     ipc: host
     environment:
       no_proxy: ${no_proxy}
       http_proxy: ${http_proxy}
       https_proxy: ${https_proxy}
-      QDRANT: ${QDRANT}
+      QDRANT_HOST: ${QDRANT_HOST}
       QDRANT_PORT: ${QDRANT_PORT}
       COLLECTION_NAME: ${COLLECTION_NAME}
+      TEI_ENDPOINT: ${TEI_ENDPOINT}
     restart: unless-stopped
 
 networks:
diff --git a/comps/dataprep/qdrant/prepare_doc_qdrant.py b/comps/dataprep/qdrant/prepare_doc_qdrant.py
index 422854eec..fb8d66571 100644
--- a/comps/dataprep/qdrant/prepare_doc_qdrant.py
+++ b/comps/dataprep/qdrant/prepare_doc_qdrant.py
@@ -1,30 +1,31 @@
 # Copyright (C) 2024 Intel Corporation
 # SPDX-License-Identifier: Apache-2.0
 
-import os
+import json
+from typing import List, Optional, Union
 
-from config import COLLECTION_NAME, EMBED_MODEL, QDRANT_HOST, QDRANT_PORT
+from config import COLLECTION_NAME, EMBED_MODEL, QDRANT_HOST, QDRANT_PORT, TEI_EMBEDDING_ENDPOINT
+from fastapi import File, Form, HTTPException, UploadFile
 from langchain.text_splitter import RecursiveCharacterTextSplitter
-from langchain_community.embeddings import HuggingFaceBgeEmbeddings, HuggingFaceEmbeddings, HuggingFaceHubEmbeddings
+from langchain_community.embeddings import HuggingFaceBgeEmbeddings
 from langchain_community.vectorstores import Qdrant
+from langchain_huggingface import HuggingFaceEndpointEmbeddings
 from langchain_text_splitters import HTMLHeaderTextSplitter
 
-from comps import DocPath, opea_microservices, opea_telemetry, register_microservice
-from comps.dataprep.utils import document_loader, get_separators, get_tables_result
+from comps import DocPath, opea_microservices, register_microservice
+from comps.dataprep.utils import (
+    document_loader,
+    encode_filename,
+    get_separators,
+    get_tables_result,
+    parse_html,
+    save_content_to_local_disk,
+)
 
-tei_embedding_endpoint = os.getenv("TEI_ENDPOINT")
+upload_folder = "./uploaded_files/"
 
 
-@register_microservice(
-    name="opea_service@prepare_doc_qdrant",
-    endpoint="/v1/dataprep",
-    host="0.0.0.0",
-    port=6000,
-    input_datatype=DocPath,
-    output_datatype=None,
-)
-@opea_telemetry
-def ingest_documents(doc_path: DocPath):
+def ingest_data_to_qdrant(doc_path: DocPath):
     """Ingest document to Qdrant."""
     path = doc_path.path
     print(f"Parsing document {path}.")
@@ -38,23 +39,30 @@ def ingest_documents(doc_path: DocPath):
         text_splitter = HTMLHeaderTextSplitter(headers_to_split_on=headers_to_split_on)
     else:
         text_splitter = RecursiveCharacterTextSplitter(
-            chunk_size=doc_path.chunk_size, chunk_overlap=100, add_start_index=True, separators=get_separators()
+            chunk_size=doc_path.chunk_size,
+            chunk_overlap=doc_path.chunk_overlap,
+            add_start_index=True,
+            separators=get_separators(),
         )
 
     content = document_loader(path)
+
     chunks = text_splitter.split_text(content)
     if doc_path.process_table and path.endswith(".pdf"):
         table_chunks = get_tables_result(path, doc_path.table_strategy)
         chunks = chunks + table_chunks
     print("Done preprocessing. Created ", len(chunks), " chunks of the original pdf")
+
     # Create vectorstore
-    if tei_embedding_endpoint:
+    if TEI_EMBEDDING_ENDPOINT:
         # create embeddings using TEI endpoint service
-        embedder = HuggingFaceHubEmbeddings(model=tei_embedding_endpoint)
+        embedder = HuggingFaceEndpointEmbeddings(model=TEI_EMBEDDING_ENDPOINT)
     else:
         # create embeddings using local embedding model
         embedder = HuggingFaceBgeEmbeddings(model_name=EMBED_MODEL)
 
+    print("embedder created.")
+
     # Batch size
     batch_size = 32
     num_chunks = len(chunks)
@@ -71,6 +79,78 @@ def ingest_documents(doc_path: DocPath):
         )
         print(f"Processed batch {i//batch_size + 1}/{(num_chunks-1)//batch_size + 1}")
 
+    return True
+
+
+@register_microservice(
+    name="opea_service@prepare_doc_qdrant",
+    endpoint="/v1/dataprep",
+    host="0.0.0.0",
+    port=6007,
+    input_datatype=DocPath,
+    output_datatype=None,
+)
+async def ingest_documents(
+    files: Optional[Union[UploadFile, List[UploadFile]]] = File(None),
+    link_list: Optional[str] = Form(None),
+    chunk_size: int = Form(1500),
+    chunk_overlap: int = Form(100),
+    process_table: bool = Form(False),
+    table_strategy: str = Form("fast"),
+):
+    print(f"files:{files}")
+    print(f"link_list:{link_list}")
+
+    if files:
+        if not isinstance(files, list):
+            files = [files]
+        uploaded_files = []
+        for file in files:
+            encode_file = encode_filename(file.filename)
+            save_path = upload_folder + encode_file
+            await save_content_to_local_disk(save_path, file)
+            ingest_data_to_qdrant(
+                DocPath(
+                    path=save_path,
+                    chunk_size=chunk_size,
+                    chunk_overlap=chunk_overlap,
+                    process_table=process_table,
+                    table_strategy=table_strategy,
+                )
+            )
+            uploaded_files.append(save_path)
+            print(f"Successfully saved file {save_path}")
+
+        return {"status": 200, "message": "Data preparation succeeded"}
+
+    if link_list:
+        link_list = json.loads(link_list)  # Parse JSON string to list
+        if not isinstance(link_list, list):
+            raise HTTPException(status_code=400, detail="link_list should be a list.")
+        for link in link_list:
+            encoded_link = encode_filename(link)
+            save_path = upload_folder + encoded_link + ".txt"
+            content = parse_html([link])[0][0]
+            try:
+                await save_content_to_local_disk(save_path, content)
+                ingest_data_to_qdrant(
+                    DocPath(
+                        path=save_path,
+                        chunk_size=chunk_size,
+                        chunk_overlap=chunk_overlap,
+                        process_table=process_table,
+                        table_strategy=table_strategy,
+                    )
+                )
+            except json.JSONDecodeError:
+                raise HTTPException(status_code=500, detail="Fail to ingest data into qdrant.")
+
+            print(f"Successfully saved link {link}")
+
+        return {"status": 200, "message": "Data preparation succeeded"}
+
+    raise HTTPException(status_code=400, detail="Must provide either a file or a string list.")
+
 
 if __name__ == "__main__":
     opea_microservices["opea_service@prepare_doc_qdrant"].start()
diff --git a/comps/dataprep/qdrant/requirements.txt b/comps/dataprep/qdrant/requirements.txt
index eb92e628f..e5bcf80b3 100644
--- a/comps/dataprep/qdrant/requirements.txt
+++ b/comps/dataprep/qdrant/requirements.txt
@@ -8,6 +8,7 @@ huggingface_hub
 langchain
 langchain-community
 langchain-text-splitters
+langchain_huggingface
 markdown
 numpy
 opentelemetry-api
diff --git a/comps/dataprep/redis/langchain_ray/requirements.txt b/comps/dataprep/redis/langchain_ray/requirements.txt
index b16a4ac82..50d57b20e 100644
--- a/comps/dataprep/redis/langchain_ray/requirements.txt
+++ b/comps/dataprep/redis/langchain_ray/requirements.txt
@@ -19,6 +19,7 @@ pyarrow
 pymupdf
 python-bidi==0.4.2
 python-docx
+python-multipart
 python-pptx
 ray
 redis
diff --git a/comps/embeddings/langchain-mosec/mosec-docker/Dockerfile b/comps/embeddings/langchain-mosec/mosec-docker/Dockerfile
index a8241e04e..945f7b90c 100644
--- a/comps/embeddings/langchain-mosec/mosec-docker/Dockerfile
+++ b/comps/embeddings/langchain-mosec/mosec-docker/Dockerfile
@@ -10,7 +10,6 @@ ARG DEBIAN_FRONTEND=noninteractive
 ENV GLIBC_TUNABLES glibc.cpu.x86_shstk=permissive
 RUN apt update && apt install -y python3 python3-pip 
 
-USER user
 COPY comps /home/user/comps
 
 RUN pip3 install torch==2.2.2 torchvision --index-url https://download.pytorch.org/whl/cpu 
@@ -19,7 +18,7 @@ RUN pip3 install transformers
 RUN pip3 install llmspec mosec
 
 RUN cd /home/user/ && export HF_ENDPOINT=https://hf-mirror.com && huggingface-cli download --resume-download BAAI/bge-large-zh-v1.5 --local-dir /home/user/bge-large-zh-v1.5
-
+USER user
 ENV EMB_MODEL="/home/user/bge-large-zh-v1.5/"
 
 WORKDIR /home/user/comps/embeddings/langchain-mosec/mosec-docker
diff --git a/comps/embeddings/langchain-mosec/mosec-docker/server-ipex.py b/comps/embeddings/langchain-mosec/mosec-docker/server-ipex.py
index 6db56fb88..9639b424a 100644
--- a/comps/embeddings/langchain-mosec/mosec-docker/server-ipex.py
+++ b/comps/embeddings/langchain-mosec/mosec-docker/server-ipex.py
@@ -13,7 +13,7 @@
 from llmspec import EmbeddingData, EmbeddingRequest, EmbeddingResponse, TokenUsage
 from mosec import ClientError, Runtime, Server, Worker
 
-DEFAULT_MODEL = "/root/bge-large-zh-v1.5/"
+DEFAULT_MODEL = "/home/user/bge-large-zh-v1.5/"
 
 
 class Embedding(Worker):
diff --git a/comps/embeddings/llama_index/local_embedding.py b/comps/embeddings/llama_index/local_embedding.py
index bccec24ca..f6a69afaf 100644
--- a/comps/embeddings/llama_index/local_embedding.py
+++ b/comps/embeddings/llama_index/local_embedding.py
@@ -2,7 +2,7 @@
 # SPDX-License-Identifier: Apache-2.0
 
 from langsmith import traceable
-from llama_index.embeddings.huggingface import HuggingFaceEmbedding
+from llama_index.embeddings.huggingface_api import HuggingFaceInferenceAPIEmbedding
 
 from comps import EmbedDoc, ServiceType, TextDoc, opea_microservices, register_microservice
 
@@ -24,5 +24,5 @@ def embedding(input: TextDoc) -> EmbedDoc:
 
 
 if __name__ == "__main__":
-    embeddings = HuggingFaceEmbedding(model_name="BAAI/bge-large-en-v1.5")
+    embeddings = HuggingFaceInferenceAPIEmbedding(model_name="BAAI/bge-large-en-v1.5")
     opea_microservices["opea_service@local_embedding"].start()
diff --git a/comps/embeddings/llama_index/requirements.txt b/comps/embeddings/llama_index/requirements.txt
index b1d2beba9..908c38b06 100644
--- a/comps/embeddings/llama_index/requirements.txt
+++ b/comps/embeddings/llama_index/requirements.txt
@@ -2,6 +2,7 @@ docarray[full]
 fastapi
 huggingface_hub
 langsmith
+llama-index-embeddings-huggingface-api
 llama-index-embeddings-text-embeddings-inference
 opentelemetry-api
 opentelemetry-exporter-otlp
diff --git a/comps/guardrails/llama_guard/README.md b/comps/guardrails/llama_guard/README.md
index 94bdcd952..084711125 100644
--- a/comps/guardrails/llama_guard/README.md
+++ b/comps/guardrails/llama_guard/README.md
@@ -36,7 +36,7 @@ pip install -r requirements.txt
 export HF_TOKEN=${your_hf_api_token}
 export LANGCHAIN_TRACING_V2=true
 export LANGCHAIN_API_KEY=${your_langchain_api_key}
-export LANGCHAIN_PROJECT="opea/gaurdrails"
+export LANGCHAIN_PROJECT="opea/guardrails"
 volume=$PWD/data
 model_id="meta-llama/Meta-Llama-Guard-2-8B"
 docker pull ghcr.io/huggingface/tgi-gaudi:2.0.1
diff --git a/comps/guardrails/llama_guard/guardrails_tgi.py b/comps/guardrails/llama_guard/guardrails_tgi.py
index 96a89b8c8..b415876ed 100644
--- a/comps/guardrails/llama_guard/guardrails_tgi.py
+++ b/comps/guardrails/llama_guard/guardrails_tgi.py
@@ -2,13 +2,14 @@
 # SPDX-License-Identifier: Apache-2.0
 
 import os
+from typing import List, Union
 
 from langchain_community.utilities.requests import JsonRequestsWrapper
 from langchain_huggingface import ChatHuggingFace
 from langchain_huggingface.llms import HuggingFaceEndpoint
 from langsmith import traceable
 
-from comps import ServiceType, TextDoc, opea_microservices, register_microservice
+from comps import GeneratedDoc, ServiceType, TextDoc, opea_microservices, register_microservice
 
 DEFAULT_MODEL = "meta-llama/LlamaGuard-7b"
 
@@ -59,12 +60,17 @@ def get_tgi_service_model_id(endpoint_url, default=DEFAULT_MODEL):
     endpoint="/v1/guardrails",
     host="0.0.0.0",
     port=9090,
-    input_datatype=TextDoc,
+    input_datatype=Union[GeneratedDoc, TextDoc],
     output_datatype=TextDoc,
 )
 @traceable(run_type="llm")
-def safety_guard(input: TextDoc) -> TextDoc:
-    response_input_guard = llm_engine_hf.invoke([{"role": "user", "content": input.text}]).content
+def safety_guard(input: Union[GeneratedDoc, TextDoc]) -> TextDoc:
+    if isinstance(input, GeneratedDoc):
+        messages = [{"role": "user", "content": input.prompt}, {"role": "assistant", "content": input.text}]
+    else:
+        messages = [{"role": "user", "content": input.text}]
+    response_input_guard = llm_engine_hf.invoke(messages).content
+
     if "unsafe" in response_input_guard:
         unsafe_dict = get_unsafe_dict(llm_engine_hf.model_id)
         policy_violation_level = response_input_guard.split("\n")[1].strip()
@@ -75,7 +81,6 @@ def safety_guard(input: TextDoc) -> TextDoc:
         )
     else:
         res = TextDoc(text=input.text)
-
     return res
 
 
diff --git a/comps/guardrails/llama_guard/requirements.txt b/comps/guardrails/llama_guard/requirements.txt
index 5fd992e66..5eda60170 100644
--- a/comps/guardrails/llama_guard/requirements.txt
+++ b/comps/guardrails/llama_guard/requirements.txt
@@ -1,6 +1,7 @@
 docarray[full]
 fastapi
-huggingface_hub
+# Fix for issue with langchain-huggingface not using InferenceClient `base_url` kwarg
+huggingface-hub<=0.24.0
 langchain-community
 langchain-huggingface
 langsmith
diff --git a/comps/retrievers/haystack/qdrant/README.md b/comps/retrievers/haystack/qdrant/README.md
index 70d2845ed..66da3c627 100644
--- a/comps/retrievers/haystack/qdrant/README.md
+++ b/comps/retrievers/haystack/qdrant/README.md
@@ -1,49 +1,54 @@
 # Retriever Microservice with Qdrant
 
-# 🚀Start Microservice with Python
+# 1. 🚀Start Microservice with Python (Option 1)
 
-## Install Requirements
+## 1.1 Install Requirements
 
 ```bash
 pip install -r requirements.txt
 ```
 
-## Start Qdrant Server
+## 1.2 Start Qdrant Server
 
 Please refer to this [readme](../../../vectorstores/langchain/qdrant/README.md).
 
-## Setup Environment Variables
+## 1.3 Setup Environment Variables
 
 ```bash
-export http_proxy=${your_http_proxy}
-export https_proxy=${your_https_proxy}
 export QDRANT_HOST=${your_qdrant_host_ip}
 export QDRANT_PORT=6333
 export EMBED_DIMENSION=${your_embedding_dimension}
 export INDEX_NAME=${your_index_name}
-export TEI_EMBEDDING_ENDPOINT=${your_tei_endpoint}
 ```
 
-## Start Retriever Service
+## 1.4 Start Retriever Service
 
 ```bash
 export TEI_EMBEDDING_ENDPOINT="http://${your_ip}:6060"
 python haystack/qdrant/retriever_qdrant.py
 ```
 
-# 🚀Start Microservice with Docker
+# 2. 🚀Start Microservice with Docker (Option 2)
 
-## Build Docker Image
+## 2.1 Setup Environment Variables
+
+```bash
+export QDRANT_HOST=${your_qdrant_host_ip}
+export QDRANT_PORT=6333
+export TEI_EMBEDDING_ENDPOINT="http://${your_ip}:6060"
+```
+
+## 2.2 Build Docker Image
 
 ```bash
 cd ../../
 docker build -t opea/retriever-qdrant:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f comps/retrievers/haystack/qdrant/docker/Dockerfile .
 ```
 
-## Run Docker with CLI
+## 2.3 Run Docker with CLI
 
 ```bash
-docker run -d --name="retriever-qdrant-server" -p 7000:7000 --ipc=host -e http_proxy=$http_proxy -e https_proxy=$https_proxy -e TEI_EMBEDDING_ENDPOINT=${your_tei_endpoint} -e QDRANT_HOST=${your_qdrant_host_ip} -e QDRANT_PORT=${your_qdrant_port} opea/retriever-qdrant:latest
+docker run -d --name="retriever-qdrant-server" -p 7000:7000 --ipc=host -e http_proxy=$http_proxy -e https_proxy=$https_proxy -e TEI_EMBEDDING_ENDPOINT=$TEI_EMBEDDING_ENDPOINT -e QDRANT_HOST=$QDRANT_HOST -e QDRANT_PORT=$QDRANT_PORT opea/retriever-qdrant:latest
 ```
 
 # 🚀3. Consume Retriever Service
diff --git a/tests/test_agent_langchain.sh b/tests/test_agent_langchain.sh
index ad9aae145..6bed5d23d 100644
--- a/tests/test_agent_langchain.sh
+++ b/tests/test_agent_langchain.sh
@@ -12,7 +12,7 @@ function build_docker_images() {
     echo "Building the docker images"
     cd $WORKPATH
     echo $WORKPATH
-    docker build -t opea/comps-agent-langchain:latest -f comps/agent/langchain/docker/Dockerfile .
+    docker build -t opea/comps-agent-langchain:comps -f comps/agent/langchain/docker/Dockerfile .
 
 }
 
@@ -24,19 +24,19 @@ function start_service() {
 
     #single card
     echo "start tgi gaudi service"
-    docker run -d --runtime=habana --name "comps-tgi-gaudi-service" -p 8080:80 -v ./data:/data -e HF_TOKEN=$HF_TOKEN -e HABANA_VISIBLE_DEVICES=all -e OMPI_MCA_btl_vader_single_copy_mechanism=none --cap-add=sys_nice --ipc=host ghcr.io/huggingface/tgi-gaudi:latest --model-id $model --max-input-tokens 4096 --max-total-tokens 8092
+    docker run -d --runtime=habana --name "test-comps-tgi-gaudi-service" -p 8080:80 -v ./data:/data -e HF_TOKEN=$HF_TOKEN -e HABANA_VISIBLE_DEVICES=all -e OMPI_MCA_btl_vader_single_copy_mechanism=none --cap-add=sys_nice --ipc=host ghcr.io/huggingface/tgi-gaudi:latest --model-id $model --max-input-tokens 4096 --max-total-tokens 8092
     sleep 5s
-    docker logs comps-tgi-gaudi-service
+    docker logs test-comps-tgi-gaudi-service
 
     echo "Starting agent microservice"
-    docker run -d --runtime=runc --name="comps-langchain-agent-endpoint" -v $WORKPATH/comps/agent/langchain/tools:/home/user/comps/agent/langchain/tools -p 9090:9090 --ipc=host -e HUGGINGFACEHUB_API_TOKEN=${HUGGINGFACEHUB_API_TOKEN} -e model=${model} -e strategy=react -e llm_endpoint_url=http://${ip_address}:8080 -e llm_engine=tgi -e recursion_limit=5 -e require_human_feedback=false -e tools=/home/user/comps/agent/langchain/tools/custom_tools.yaml opea/comps-agent-langchain:latest
+    docker run -d --runtime=runc --name="test-comps-langchain-agent-endpoint" -v $WORKPATH/comps/agent/langchain/tools:/home/user/comps/agent/langchain/tools -p 9090:9090 --ipc=host -e HUGGINGFACEHUB_API_TOKEN=${HUGGINGFACEHUB_API_TOKEN} -e model=${model} -e strategy=react -e llm_endpoint_url=http://${ip_address}:8080 -e llm_engine=tgi -e recursion_limit=5 -e require_human_feedback=false -e tools=/home/user/comps/agent/langchain/tools/custom_tools.yaml opea/comps-agent-langchain:comps
     sleep 5s
-    docker logs comps-langchain-agent-endpoint
+    docker logs test-comps-langchain-agent-endpoint
 
     echo "Waiting tgi gaudi ready"
     n=0
     until [[ "$n" -ge 100 ]] || [[ $ready == true ]]; do
-        docker logs comps-tgi-gaudi-service > ${WORKPATH}/tests/tgi-gaudi-service.log
+        docker logs test-comps-tgi-gaudi-service > ${WORKPATH}/tests/tgi-gaudi-service.log
         n=$((n+1))
         if grep -q Connected ${WORKPATH}/tests/tgi-gaudi-service.log; then
             break
@@ -44,7 +44,7 @@ function start_service() {
         sleep 5s
     done
     sleep 5s
-    docker logs comps-tgi-gaudi-service
+    docker logs test-comps-tgi-gaudi-service
     echo "Service started successfully"
 }
 
@@ -72,17 +72,17 @@ function validate_microservice() {
     local EXIT_CODE="${EXIT_CODE:0-1}"
     echo "return value is $EXIT_CODE"
     if [ "$EXIT_CODE" == "1" ]; then
-        docker logs comps-tgi-gaudi-service &> ${LOG_PATH}/test-comps-tgi-gaudi-service.log
-        docker logs comps-langchain-agent-endpoint &> ${LOG_PATH}/test-comps-langchain-agent-endpoint.log
+        docker logs test-comps-tgi-gaudi-service &> ${LOG_PATH}/test-comps-tgi-gaudi-service.log
+        docker logs test-comps-langchain-agent-endpoint &> ${LOG_PATH}/test-comps-langchain-agent-endpoint.log
         exit 1
     fi
 }
 
 function stop_docker() {
-    cid=$(docker ps -aq --filter "name=comps-tgi-gaudi-service")
+    cid=$(docker ps -aq --filter "name=test-comps-tgi-gaudi-service")
     echo "Stopping the docker containers "${cid}
     if [[ ! -z "$cid" ]]; then docker rm $cid -f && sleep 1s; fi
-    cid=$(docker ps -aq --filter "name=comps-langchain-agent-endpoint")
+    cid=$(docker ps -aq --filter "name=test-comps-langchain-agent-endpoint")
     echo "Stopping the docker containers "${cid}
     if [[ ! -z "$cid" ]]; then docker rm $cid -f && sleep 1s; fi
     echo "Docker containers stopped successfully"
diff --git a/tests/test_dataprep_pgvector.sh b/tests/test_dataprep_pgvector.sh
index c4c892ee9..05ea23d83 100755
--- a/tests/test_dataprep_pgvector.sh
+++ b/tests/test_dataprep_pgvector.sh
@@ -2,7 +2,7 @@
 # Copyright (C) 2024 Intel Corporation
 # SPDX-License-Identifier: Apache-2.0
 
-set -xe
+set -x
 
 WORKPATH=$(dirname "$PWD")
 LOG_PATH="$WORKPATH/tests"
@@ -16,7 +16,7 @@ function build_docker_images() {
     docker pull pgvector/pgvector:0.7.0-pg16
 
     # build dataprep image for pgvector
-    docker build -t opea/dataprep-pgvector:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f $WORKPATH/comps/dataprep/pgvector/langchain/docker/Dockerfile .
+    docker build -t opea/dataprep-pgvector:comps --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f $WORKPATH/comps/dataprep/pgvector/langchain/docker/Dockerfile .
 }
 
 function start_service() {
@@ -24,12 +24,11 @@ function start_service() {
     export POSTGRES_PASSWORD=testpwd
     export POSTGRES_DB=vectordb
 
-
-    docker run --name vectorstore-postgres -e POSTGRES_USER=${POSTGRES_USER} -e POSTGRES_HOST_AUTH_METHOD=trust -e POSTGRES_DB=${POSTGRES_DB} -e POSTGRES_PASSWORD=${POSTGRES_PASSWORD} -p 5432:5432 -d -v $WORKPATH/comps/vectorstores/langchain/pgvector/init.sql:/docker-entrypoint-initdb.d/init.sql pgvector/pgvector:0.7.0-pg16
+    docker run --name test-comps-vectorstore-postgres -e POSTGRES_USER=${POSTGRES_USER} -e POSTGRES_HOST_AUTH_METHOD=trust -e POSTGRES_DB=${POSTGRES_DB} -e POSTGRES_PASSWORD=${POSTGRES_PASSWORD} -p 5432:5432 -d -v $WORKPATH/comps/vectorstores/langchain/pgvector/init.sql:/docker-entrypoint-initdb.d/init.sql pgvector/pgvector:0.7.0-pg16
 
     sleep 10s
 
-    docker run -d --name="dataprep-pgvector" -p ${dataprep_service_port}:6007 --ipc=host -e http_proxy=$http_proxy -e https_proxy=$https_proxy -e PG_CONNECTION_STRING=postgresql+psycopg2://${POSTGRES_USER}:${POSTGRES_PASSWORD}@$ip_address:5432/${POSTGRES_DB} opea/dataprep-pgvector:latest
+    docker run -d --name="test-comps-dataprep-pgvector" -p ${dataprep_service_port}:6007 --ipc=host -e http_proxy=$http_proxy -e https_proxy=$https_proxy -e PG_CONNECTION_STRING=postgresql+psycopg2://${POSTGRES_USER}:${POSTGRES_PASSWORD}@$ip_address:5432/${POSTGRES_DB} opea/dataprep-pgvector:comps
 
     sleep 3m
 }
@@ -50,12 +49,12 @@ function validate_microservice() {
             echo "[ dataprep ] Content is as expected."
         else
             echo "[ dataprep ] Content does not match the expected result: $CONTENT"
-            docker logs dataprep-pgvector >> ${LOG_PATH}/dataprep.log
+            docker logs test-comps-dataprep-pgvector >> ${LOG_PATH}/dataprep.log
             exit 1
         fi
     else
         echo "[ dataprep ] HTTP status is not 200. Received status was $HTTP_STATUS"
-        docker logs dataprep-pgvector >> ${LOG_PATH}/dataprep.log
+        docker logs test-comps-dataprep-pgvector >> ${LOG_PATH}/dataprep.log
         exit 1
     fi
 
@@ -70,12 +69,12 @@ function validate_microservice() {
             echo "[ dataprep - file ] Content is as expected."
         else
             echo "[ dataprep - file ] Content does not match the expected result: $CONTENT"
-            docker logs dataprep-pgvector >> ${LOG_PATH}/dataprep_file.log
+            docker logs test-comps-dataprep-pgvector >> ${LOG_PATH}/dataprep_file.log
             exit 1
         fi
     else
         echo "[ dataprep - file ] HTTP status is not 200. Received status was $HTTP_STATUS"
-        docker logs dataprep-pgvector >> ${LOG_PATH}/dataprep_file.log
+        docker logs test-comps-dataprep-pgvector >> ${LOG_PATH}/dataprep_file.log
         exit 1
     fi
 
@@ -84,19 +83,19 @@ function validate_microservice() {
     HTTP_STATUS=$(curl -s -o /dev/null -w "%{http_code}" -X POST -d '{"file_path": "dataprep_file.txt"}' -H 'Content-Type: application/json' "$URL")
     if [ "$HTTP_STATUS" -eq 200 ]; then
         echo "[ dataprep - del ] HTTP status is 200."
-        docker logs dataprep-pgvector >> ${LOG_PATH}/dataprep_del.log
+        docker logs test-comps-dataprep-pgvector >> ${LOG_PATH}/dataprep_del.log
     else
         echo "[ dataprep - del ] HTTP status is not 200. Received status was $HTTP_STATUS"
-        docker logs dataprep-pgvector >> ${LOG_PATH}/dataprep_del.log
+        docker logs test-comps-dataprep-pgvector >> ${LOG_PATH}/dataprep_del.log
         exit 1
     fi
 }
 
 function stop_docker() {
-    cid=$(docker ps -aq --filter "name=vectorstore-postgres*")
+    cid=$(docker ps -aq --filter "name=test-comps-vectorstore-postgres*")
     if [[ ! -z "$cid" ]]; then docker stop $cid && docker rm $cid && sleep 1s; fi
 
-    cid=$(docker ps -aq --filter "name=dataprep-pgvector*")
+    cid=$(docker ps -aq --filter "name=test-comps-dataprep-pgvector*")
     if [[ ! -z "$cid" ]]; then docker stop $cid && docker rm $cid && sleep 1s; fi
 }
 
diff --git a/tests/test_dataprep_qdrant_langchain.sh b/tests/test_dataprep_qdrant_langchain.sh
new file mode 100644
index 000000000..7d9e47708
--- /dev/null
+++ b/tests/test_dataprep_qdrant_langchain.sh
@@ -0,0 +1,116 @@
+#!/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 $WORKPATH
+
+    # dataprep qdrant image
+    docker build --no-cache -t opea/dataprep-qdrant:comps --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f comps/dataprep/qdrant/docker/Dockerfile .
+}
+
+function start_service() {
+    QDRANT_PORT=6360
+    docker run -d --name="test-comps-dataprep-qdrant-langchain" -e http_proxy=$http_proxy -e https_proxy=$https_proxy -p $QDRANT_PORT:6333 -p 6334:6334 --ipc=host qdrant/qdrant
+    tei_embedding_port=6361
+    model="BAAI/bge-base-en-v1.5"
+    docker run -d --name="test-comps-dataprep-qdrant-langchain-tei" -e http_proxy=$http_proxy -e https_proxy=$https_proxy -p $tei_embedding_port:80 -v ./data:/data --pull always ghcr.io/huggingface/text-embeddings-inference:cpu-1.5 --model-id $model
+    dataprep_service_port=6362
+    TEI_EMBEDDING_ENDPOINT="http://${ip_address}:${tei_embedding_port}"
+    COLLECTION_NAME="rag-qdrant"
+    docker run -d --name="test-comps-dataprep-qdrant-langchain-server" -e http_proxy=$http_proxy -e https_proxy=$https_proxy -e QDRANT_HOST=$ip_address -e QDRANT_PORT=$QDRANT_PORT -e COLLECTION_NAME=$COLLECTION_NAME -e TEI_ENDPOINT=$TEI_EMBEDDING_ENDPOINT -p ${dataprep_service_port}:6007 --ipc=host opea/dataprep-qdrant:comps
+    sleep 1m
+}
+
+function validate_services() {
+    local URL="$1"
+    local EXPECTED_RESULT="$2"
+    local SERVICE_NAME="$3"
+    local DOCKER_NAME="$4"
+    local INPUT_DATA="$5"
+
+    if [[ $SERVICE_NAME == *"dataprep_upload_file"* ]]; then
+        cd $LOG_PATH
+        HTTP_RESPONSE=$(curl --silent --write-out "HTTPSTATUS:%{http_code}" -X POST -F 'files=@./dataprep_file.txt' -H 'Content-Type: multipart/form-data' "$URL")
+    elif [[ $SERVICE_NAME == *"dataprep_upload_link"* ]]; then
+        HTTP_RESPONSE=$(curl --silent --write-out "HTTPSTATUS:%{http_code}" -X POST -F 'link_list=["https://www.ces.tech/"]' "$URL")
+    else
+        HTTP_RESPONSE=$(curl --silent --write-out "HTTPSTATUS:%{http_code}" -X POST -d "$INPUT_DATA" -H 'Content-Type: application/json' "$URL")
+    fi
+    HTTP_STATUS=$(echo $HTTP_RESPONSE | tr -d '\n' | sed -e 's/.*HTTPSTATUS://')
+    RESPONSE_BODY=$(echo $HTTP_RESPONSE | sed -e 's/HTTPSTATUS\:.*//g')
+
+    docker logs ${DOCKER_NAME} >> ${LOG_PATH}/${SERVICE_NAME}.log
+
+    # check response status
+    if [ "$HTTP_STATUS" -ne "200" ]; then
+        echo "[ $SERVICE_NAME ] HTTP status is not 200. Received status was $HTTP_STATUS"
+        exit 1
+    else
+        echo "[ $SERVICE_NAME ] HTTP status is 200. Checking content..."
+    fi
+    # check response body
+    if [[ "$RESPONSE_BODY" != *"$EXPECTED_RESULT"* ]]; then
+        echo "[ $SERVICE_NAME ] Content does not match the expected result: $RESPONSE_BODY"
+        exit 1
+    else
+        echo "[ $SERVICE_NAME ] Content is as expected."
+    fi
+
+    sleep 1s
+}
+
+function validate_microservice() {
+    # tei for embedding service
+    validate_services \
+        "${ip_address}:6361/embed" \
+        "[[" \
+        "tei_embedding" \
+        "test-comps-dataprep-qdrant-langchain-tei" \
+        '{"inputs":"What is Deep Learning?"}'
+
+    # dataprep upload file
+    echo "Deep learning is a subset of machine learning that utilizes neural networks with multiple layers to analyze various levels of abstract data representations. It enables computers to identify patterns and make decisions with minimal human intervention by learning from large amounts of data." > $LOG_PATH/dataprep_file.txt
+    validate_services \
+        "${ip_address}:6362/v1/dataprep" \
+        "Data preparation succeeded" \
+        "dataprep_upload_file" \
+        "test-comps-dataprep-qdrant-langchain-server"
+
+    # dataprep upload link
+    validate_services \
+        "${ip_address}:6362/v1/dataprep" \
+        "Data preparation succeeded" \
+        "dataprep_upload_link" \
+        "test-comps-dataprep-qdrant-langchain-server"
+
+}
+
+function stop_docker() {
+    cid=$(docker ps -aq --filter "name=test-comps-dataprep-qdrant-langchain*")
+    if [[ ! -z "$cid" ]]; then docker stop $cid && docker rm $cid && sleep 1s; fi
+
+    rm $LOG_PATH/dataprep_file.txt
+}
+
+function main() {
+
+    stop_docker
+
+    build_docker_images
+    start_service
+
+    validate_microservice
+
+    stop_docker
+    echo y | docker system prune
+
+}
+
+main
diff --git a/tests/test_dataprep_redis_langchain_ray.sh b/tests/test_dataprep_redis_langchain_ray.sh
index f377d9ef5..81feed9ac 100644
--- a/tests/test_dataprep_redis_langchain_ray.sh
+++ b/tests/test_dataprep_redis_langchain_ray.sh
@@ -11,7 +11,7 @@ ip_address=$(hostname -I | awk '{print $1}')
 function build_docker_images() {
     echo "Building the docker images"
     cd $WORKPATH
-    docker build -t opea/dataprep-on-ray-redis:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f comps/dataprep/redis/langchain_ray/docker/Dockerfile .
+    docker build -t opea/dataprep-on-ray-redis:comps --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f comps/dataprep/redis/langchain_ray/docker/Dockerfile .
     echo "Docker image built successfully"
 }
 
@@ -24,7 +24,7 @@ function start_service() {
     export REDIS_URL="redis://${ip_address}:6382"
     export INDEX_NAME="rag-redis"
     echo "Starting dataprep-redis-server"
-    docker run -d --name="test-comps-dataprep-redis-ray-server" --runtime=runc -p 6009:6007 -p 6010:6008 --ipc=host -e http_proxy=$http_proxy -e https_proxy=$https_proxy -e REDIS_URL=$REDIS_URL -e INDEX_NAME=$INDEX_NAME -e TEI_ENDPOINT=$TEI_ENDPOINT -e TIMEOUT_SECONDS=600 opea/dataprep-on-ray-redis:latest
+    docker run -d --name="test-comps-dataprep-redis-ray-server" --runtime=runc -p 6009:6007 -p 6010:6008 --ipc=host -e http_proxy=$http_proxy -e https_proxy=$https_proxy -e REDIS_URL=$REDIS_URL -e INDEX_NAME=$INDEX_NAME -e TEI_ENDPOINT=$TEI_ENDPOINT -e TIMEOUT_SECONDS=600 opea/dataprep-on-ray-redis:comps
 
     sleep 10
     echo "Service started successfully"
diff --git a/tests/test_embeddings_llama_index.sh b/tests/test_embeddings_llama_index.sh
index 006a2c259..cf1dbfb9f 100644
--- a/tests/test_embeddings_llama_index.sh
+++ b/tests/test_embeddings_llama_index.sh
@@ -11,24 +11,24 @@ ip_address=$(hostname -I | awk '{print $1}')
 function build_docker_images() {
     cd $WORKPATH
     echo $(pwd)
-    docker build --no-cache -t opea/embedding-tei:comps --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f comps/embeddings/llama_index/docker/Dockerfile .
+    docker build --no-cache -t opea/embedding-tei-llamaindex:comps --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f comps/embeddings/llama_index/docker/Dockerfile .
 }
 
 function start_service() {
     tei_endpoint=5001
     model="BAAI/bge-large-en-v1.5"
     revision="refs/pr/5"
-    docker run -d --name="test-comps-embedding-tei-endpoint" -p $tei_endpoint:80 -v ./data:/data -e http_proxy=$http_proxy -e https_proxy=$https_proxy --pull always ghcr.io/huggingface/text-embeddings-inference:cpu-1.2 --model-id $model --revision $revision
+    docker run -d --name="test-comps-embedding-tei-llamaindex-endpoint" -p $tei_endpoint:80 -v ./data:/data -e http_proxy=$http_proxy -e https_proxy=$https_proxy --pull always ghcr.io/huggingface/text-embeddings-inference:cpu-1.2 --model-id $model --revision $revision
     export TEI_EMBEDDING_ENDPOINT="http://${ip_address}:${tei_endpoint}"
     tei_service_port=5010
-    docker run -d --name="test-comps-embedding-tei-server" -e http_proxy=$http_proxy -e https_proxy=$https_proxy -p ${tei_service_port}:6000 --ipc=host -e TEI_EMBEDDING_ENDPOINT=$TEI_EMBEDDING_ENDPOINT  opea/embedding-tei:comps
+    docker run -d --name="test-comps-embedding-tei-llamaindex-server" -e http_proxy=$http_proxy -e https_proxy=$https_proxy -p ${tei_service_port}:6000 --ipc=host -e TEI_EMBEDDING_ENDPOINT=$TEI_EMBEDDING_ENDPOINT  opea/embedding-tei-llamaindex:comps
     sleep 3m
 }
 
 function validate_microservice() {
     tei_service_port=5010
     URL="http://${ip_address}:$tei_service_port/v1/embeddings"
-    docker logs test-comps-embedding-tei-server >> ${LOG_PATH}/embedding.log
+    docker logs test-comps-embedding-tei-llamaindex-server >> ${LOG_PATH}/embedding.log
     HTTP_STATUS=$(curl -s -o /dev/null -w "%{http_code}" -X POST -d '{"text":"What is Deep Learning?"}' -H 'Content-Type: application/json' "$URL")
     if [ "$HTTP_STATUS" -eq 200 ]; then
         echo "[ embedding - llama_index ] HTTP status is 200. Checking content..."
@@ -38,12 +38,12 @@ function validate_microservice() {
             echo "[ embedding - llama_index ] Content is as expected."
         else
             echo "[ embedding - llama_index ] Content does not match the expected result: $CONTENT"
-            docker logs test-comps-embedding-tei-server >> ${LOG_PATH}/embedding.log
+            docker logs test-comps-embedding-tei-llamaindex-server >> ${LOG_PATH}/embedding.log
             exit 1
         fi
     else
         echo "[ embedding - llama_index ] HTTP status is not 200. Received status was $HTTP_STATUS"
-        docker logs test-comps-embedding-tei-server >> ${LOG_PATH}/embedding.log
+        docker logs test-comps-embedding-tei-llamaindex-server >> ${LOG_PATH}/embedding.log
         exit 1
     fi
 }
diff --git a/tests/test_guardrails_llama_guard.sh b/tests/test_guardrails_llama_guard.sh
index 1462611aa..b174cbd64 100644
--- a/tests/test_guardrails_llama_guard.sh
+++ b/tests/test_guardrails_llama_guard.sh
@@ -11,7 +11,7 @@ function build_docker_images() {
     echo "Start building docker images for microservice"
     cd $WORKPATH
     docker pull ghcr.io/huggingface/tgi-gaudi:2.0.1
-    docker build --no-cache -t opea/guardrails-tgi:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f comps/guardrails/llama_guard/docker/Dockerfile .
+    docker build --no-cache -t opea/guardrails-tgi:comps --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f comps/guardrails/llama_guard/docker/Dockerfile .
     echo "Docker images built"
 }
 
@@ -21,19 +21,18 @@ function start_service() {
     export SAFETY_GUARD_MODEL_ID="meta-llama/Meta-Llama-Guard-2-8B"
     export SAFETY_GUARD_ENDPOINT=http://${ip_address}:8088/v1/chat/completions
 
-    docker run -d --name="test-guardrails-langchain-tgi-server" -p 8088:80 --runtime=habana -e HF_TOKEN=$HF_TOKEN -e HABANA_VISIBLE_DEVICES=all -e OMPI_MCA_btl_vader_single_copy_mechanism=none --cap-add=sys_nice --ipc=host -e HTTPS_PROXY=$https_proxy -e HTTP_PROXY=$https_proxy ghcr.io/huggingface/tgi-gaudi:2.0.1 --model-id $model_id --max-input-length 1024 --max-total-tokens 2048
+    docker run -d --name="test-comps-guardrails-langchain-tgi-server" -p 8088:80 --runtime=habana -e HF_TOKEN=$HF_TOKEN -e HABANA_VISIBLE_DEVICES=all -e OMPI_MCA_btl_vader_single_copy_mechanism=none --cap-add=sys_nice --ipc=host -e HTTPS_PROXY=$https_proxy -e HTTP_PROXY=$https_proxy ghcr.io/huggingface/tgi-gaudi:2.0.1 --model-id $model_id --max-input-length 1024 --max-total-tokens 2048
     sleep 4m
-    docker run -d --name="test-guardrails-langchain-service" -p 9090:9090 --ipc=host -e http_proxy=$http_proxy -e https_proxy=$https_proxy -e no_proxy=$no_proxy -e SAFETY_GUARD_MODEL_ID=$SAFETY_GUARD_MODEL_ID -e SAFETY_GUARD_ENDPOINT=$SAFETY_GUARD_ENDPOINT -e HUGGINGFACEHUB_API_TOKEN=$HF_TOKEN opea/guardrails-tgi:latest
+    docker run -d --name="test-comps-guardrails-langchain-service" -p 9090:9090 --ipc=host -e http_proxy=$http_proxy -e https_proxy=$https_proxy -e no_proxy=$no_proxy -e SAFETY_GUARD_MODEL_ID=$SAFETY_GUARD_MODEL_ID -e SAFETY_GUARD_ENDPOINT=$SAFETY_GUARD_ENDPOINT -e HUGGINGFACEHUB_API_TOKEN=$HF_TOKEN opea/guardrails-tgi:comps
     sleep 10s
-
     echo "Microservice started"
 }
 
 function validate_microservice() {
     echo "Validate microservice started"
     echo "test 1 - violated policies"
-    docker logs test-guardrails-langchain-tgi-server
-    docker logs test-guardrails-langchain-service
+    docker logs test-comps-guardrails-langchain-tgi-server
+    docker logs test-comps-guardrails-langchain-service
     result=$(http_proxy= curl http://localhost:9090/v1/guardrails  -X POST   -d '{"text":"How do you buy a tiger in the US?","parameters":{"max_new_tokens":32}}'   -H 'Content-Type: application/json')
     if [[ $result == *"Violated"* ]]; then
         echo "Result correct."
@@ -53,7 +52,7 @@ function validate_microservice() {
 }
 
 function stop_docker() {
-    cid=$(docker ps -aq --filter "name=test-guardrails-langchain*")
+    cid=$(docker ps -aq --filter "name=test-comps-guardrails-langchain*")
     echo "Shutdown legacy containers "$cid
     if [[ ! -z "$cid" ]]; then docker stop $cid && docker rm $cid && sleep 1s; fi
 }
diff --git a/tests/test_guardrails_pii_detection.sh b/tests/test_guardrails_pii_detection.sh
index 1ba8202f6..4a5391414 100644
--- a/tests/test_guardrails_pii_detection.sh
+++ b/tests/test_guardrails_pii_detection.sh
@@ -10,13 +10,13 @@ ip_address=$(hostname -I | awk '{print $1}')
 function build_docker_images() {
     echo "Start building docker images for microservice"
     cd $WORKPATH
-    docker build -t opea/guardrails-pii-detection:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f comps/guardrails/pii_detection/docker/Dockerfile .
+    docker build -t opea/guardrails-pii-detection:comps --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f comps/guardrails/pii_detection/docker/Dockerfile .
     echo "Docker images built"
 }
 
 function start_service() {
     echo "Starting microservice"
-    docker run -d --runtime=runc --name="test-guardrails-pii-detection-endpoint" -p 6357:6357 --ipc=host -e http_proxy=$http_proxy -e https_proxy=$https_proxy opea/guardrails-pii-detection:latest
+    docker run -d --runtime=runc --name="test-comps-guardrails-pii-detection-endpoint" -p 6357:6357 --ipc=host -e http_proxy=$http_proxy -e https_proxy=$https_proxy opea/guardrails-pii-detection:comps
     sleep 5
     echo "Microservice started"
 }
@@ -34,11 +34,11 @@ function validate_microservice() {
     echo "test 4 - 20 tasks in parallel - ml"
     python comps/guardrails/pii_detection/test.py --test_text --batch_size 20 --ip_addr $ip_address --strategy ml
     echo "Validate microservice completed"
-    docker logs test-guardrails-pii-detection-endpoint
+    docker logs test-comps-guardrails-pii-detection-endpoint
 }
 
 function stop_docker() {
-    cid=$(docker ps -aq --filter "name=test-guardrails-pii-detection-endpoint")
+    cid=$(docker ps -aq --filter "name=test-comps-guardrails-pii-detection-endpoint")
     echo "Shutdown legacy containers "$cid
     if [[ ! -z "$cid" ]]; then docker stop $cid && docker rm $cid && sleep 1s; fi
 }
diff --git a/tests/test_llms_text-generation_ray_serve.sh b/tests/test_llms_text-generation_ray_serve.sh
index 823971ecc..5ceabef6d 100644
--- a/tests/test_llms_text-generation_ray_serve.sh
+++ b/tests/test_llms_text-generation_ray_serve.sh
@@ -13,7 +13,7 @@ function build_docker_images() {
     docker build \
         -f comps/llms/text-generation/ray_serve/docker/Dockerfile.rayserve \
         --network=host \
-        -t ray_serve:habana .
+        -t ray_serve_habana:comps .
 
     ## Build OPEA microservice docker
     cd $WORKPATH
diff --git a/tests/test_llms_text-generation_vllm-openvino.sh b/tests/test_llms_text-generation_vllm-openvino.sh
index f2df98584..9494d678a 100755
--- a/tests/test_llms_text-generation_vllm-openvino.sh
+++ b/tests/test_llms_text-generation_vllm-openvino.sh
@@ -9,7 +9,7 @@ WORKPATH="$( cd "$( dirname "$0" )" && pwd )"
 # Define variables
 port=8123
 HF_CACHE_DIR=$HOME/.cache/huggingface
-DOCKER_IMAGE="vllm:openvino"
+DOCKER_IMAGE="vllm-openvino:comps"
 CONTAINER_NAME="vllm-openvino-container"
 
 function build_container() {
@@ -34,7 +34,7 @@ start_container() {
       -e HTTPS_PROXY=$https_proxy \
       -e HTTP_PROXY=$https_proxy \
       -v $HF_CACHE_DIR:/root/.cache/huggingface \
-      vllm:openvino /bin/bash -c "\
+      vllm-openvino:comps /bin/bash -c "\
         cd / && \
         export VLLM_CPU_KVCACHE_SPACE=50 && \
         python3 -m vllm.entrypoints.openai.api_server \
diff --git a/tests/test_llms_text-generation_vllm-ray.sh b/tests/test_llms_text-generation_vllm-ray.sh
index 7ab235a93..6364690e8 100644
--- a/tests/test_llms_text-generation_vllm-ray.sh
+++ b/tests/test_llms_text-generation_vllm-ray.sh
@@ -12,7 +12,7 @@ function build_docker_images() {
     cd $WORKPATH
     docker build \
         -f comps/llms/text-generation/vllm-ray/docker/Dockerfile.vllmray  \
-        -t opea/vllm_ray:habana --network=host .
+        -t opea/vllm_ray-habana:comps --network=host .
 
     ## Build OPEA microservice docker
     cd $WORKPATH
diff --git a/tests/test_llms_text-generation_vllm.sh b/tests/test_llms_text-generation_vllm.sh
index 48bee9ae8..f44e0addc 100644
--- a/tests/test_llms_text-generation_vllm.sh
+++ b/tests/test_llms_text-generation_vllm.sh
@@ -12,7 +12,7 @@ function build_docker_images() {
     cd $WORKPATH/comps/llms/text-generation/vllm
     docker build \
         -f docker/Dockerfile.hpu \
-        -t opea/vllm:hpu \
+        -t opea/vllm-hpu:comps \
         --shm-size=128g .
 
     ## Build OPEA microservice docker
@@ -35,7 +35,7 @@ function start_service() {
         --cap-add=sys_nice \
         --ipc=host \
         -e HF_TOKEN=${HUGGINGFACEHUB_API_TOKEN} \
-        opea/vllm:hpu \
+        opea/vllm-hpu:comps \
         /bin/bash -c "export VLLM_CPU_KVCACHE_SPACE=40 && python3 -m vllm.entrypoints.openai.api_server --enforce-eager --model $LLM_MODEL  --tensor-parallel-size 1 --host 0.0.0.0 --port 80 --block-size 128 --max-num-seqs 256 --max-seq_len-to-capture 2048"
 
     export vLLM_ENDPOINT="http://${ip_address}:${port_number}"
diff --git a/tests/test_lvms_tgi_llava_next.sh b/tests/test_lvms_tgi_llava_next.sh
index 970e3004f..66700ef2b 100644
--- a/tests/test_lvms_tgi_llava_next.sh
+++ b/tests/test_lvms_tgi_llava_next.sh
@@ -11,16 +11,16 @@ function build_docker_images() {
     cd $WORKPATH
     echo $(pwd)
     git clone https://github.com/yuanwu2017/tgi-gaudi.git && cd tgi-gaudi && git checkout v2.0.4
-    docker build -t opea/llava-tgi:latest .
+    docker build -t opea/llava-tgi:comps .
     cd ..
-    docker build --no-cache -t opea/lvm-tgi:latest -f comps/lvms/Dockerfile_tgi .
+    docker build --no-cache -t opea/lvm-tgi:comps -f comps/lvms/Dockerfile_tgi .
 }
 
 function start_service() {
     unset http_proxy
     model="llava-hf/llava-v1.6-mistral-7b-hf"
-    docker run -d --name="test-comps-lvm-llava-tgi" -e http_proxy=$http_proxy -e https_proxy=$https_proxy -p 8399:80 --runtime=habana -e PT_HPU_ENABLE_LAZY_COLLECTIVES=true -e SKIP_TOKENIZER_IN_TGI=true -e HABANA_VISIBLE_DEVICES=all  -e OMPI_MCA_btl_vader_single_copy_mechanism=none --cap-add=sys_nice --ipc=host opea/llava-tgi:latest --model-id $model --max-input-tokens 4096 --max-total-tokens 8192
-    docker run -d --name="test-comps-lvm-tgi" -e LVM_ENDPOINT=http://$ip_address:8399 -e http_proxy=$http_proxy -e https_proxy=$https_proxy -p 9399:9399 --ipc=host opea/lvm-tgi:latest
+    docker run -d --name="test-comps-lvm-llava-tgi" -e http_proxy=$http_proxy -e https_proxy=$https_proxy -p 8399:80 --runtime=habana -e PT_HPU_ENABLE_LAZY_COLLECTIVES=true -e SKIP_TOKENIZER_IN_TGI=true -e HABANA_VISIBLE_DEVICES=all  -e OMPI_MCA_btl_vader_single_copy_mechanism=none --cap-add=sys_nice --ipc=host opea/llava-tgi:comps --model-id $model --max-input-tokens 4096 --max-total-tokens 8192
+    docker run -d --name="test-comps-lvm-tgi" -e LVM_ENDPOINT=http://$ip_address:8399 -e http_proxy=$http_proxy -e https_proxy=$https_proxy -p 9399:9399 --ipc=host opea/lvm-tgi:comps
     sleep 3m
 }
 
diff --git a/tests/test_prompt_registry_mongo.sh b/tests/test_prompt_registry_mongo.sh
index e91bf225c..1f410d07f 100644
--- a/tests/test_prompt_registry_mongo.sh
+++ b/tests/test_prompt_registry_mongo.sh
@@ -17,12 +17,12 @@ function build_docker_images() {
     echo $(pwd)
     docker run -d -p 27017:27017 --name=test-comps-mongo mongo:latest
 
-    docker build --no-cache -t opea/promptregistry-mongo-server:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f comps/prompt_registry/mongo/docker/Dockerfile .
+    docker build --no-cache -t opea/promptregistry-mongo-server:comps --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f comps/prompt_registry/mongo/docker/Dockerfile .
 }
 
 function start_service() {
 
-    docker run -d --name="test-comps-promptregistry-mongo-server" -p 6012:6012 -e http_proxy=$http_proxy -e https_proxy=$https_proxy -e no_proxy=$no_proxy -e MONGO_HOST=${MONGO_HOST} -e MONGO_PORT=${MONGO_PORT} -e DB_NAME=${DB_NAME} -e COLLECTION_NAME=${COLLECTION_NAME} opea/promptregistry-mongo-server:latest
+    docker run -d --name="test-comps-promptregistry-mongo-server" -p 6012:6012 -e http_proxy=$http_proxy -e https_proxy=$https_proxy -e no_proxy=$no_proxy -e MONGO_HOST=${MONGO_HOST} -e MONGO_PORT=${MONGO_PORT} -e DB_NAME=${DB_NAME} -e COLLECTION_NAME=${COLLECTION_NAME} opea/promptregistry-mongo-server:comps
 
     sleep 10s
 }
diff --git a/tests/test_retrievers_langchain_pgvector.sh b/tests/test_retrievers_langchain_pgvector.sh
index 4c5b08963..653ddafab 100755
--- a/tests/test_retrievers_langchain_pgvector.sh
+++ b/tests/test_retrievers_langchain_pgvector.sh
@@ -17,7 +17,7 @@ function start_service() {
     export POSTGRES_PASSWORD=testpwd
     export POSTGRES_DB=vectordb
 
-    docker run --name test-vectorstore-postgres -e POSTGRES_USER=${POSTGRES_USER} -e POSTGRES_HOST_AUTH_METHOD=trust -e POSTGRES_DB=${POSTGRES_DB} -e POSTGRES_PASSWORD=${POSTGRES_PASSWORD} -d -v $WORKPATH/comps/vectorstores/langchain/pgvector/init.sql:/docker-entrypoint-initdb.d/init.sql -p 5432:5432 pgvector/pgvector:0.7.0-pg16
+    docker run --name test-comps-vectorstore-postgres -e POSTGRES_USER=${POSTGRES_USER} -e POSTGRES_HOST_AUTH_METHOD=trust -e POSTGRES_DB=${POSTGRES_DB} -e POSTGRES_PASSWORD=${POSTGRES_PASSWORD} -d -v $WORKPATH/comps/vectorstores/langchain/pgvector/init.sql:/docker-entrypoint-initdb.d/init.sql -p 5432:5432 pgvector/pgvector:0.7.0-pg16
     sleep 10s
 
     # tei endpoint
@@ -41,7 +41,7 @@ function validate_microservice() {
         -X POST \
         -d "{\"text\":\"test\",\"embedding\":${test_embedding}}" \
         -H 'Content-Type: application/json'
-    docker logs test-vectorstore-postgres
+    docker logs test-comps-vectorstore-postgres
     docker logs test-comps-retriever-tei-endpoint
 }
 
@@ -51,7 +51,7 @@ function stop_docker() {
         docker stop $cid_retrievers && docker rm $cid_retrievers && sleep 1s
     fi
 
-    cid_redis=$(docker ps -aq --filter "name=test-vectorstore-postgres")
+    cid_redis=$(docker ps -aq --filter "name=test-comps-vectorstore-postgres")
     if [[ ! -z "$cid_redis" ]]; then
         docker stop $cid_redis && docker rm $cid_redis && sleep 1s
     fi
diff --git a/tests/test_vectorstores_pgvector.sh b/tests/test_vectorstores_pgvector.sh
deleted file mode 100755
index 1b43a6930..000000000
--- a/tests/test_vectorstores_pgvector.sh
+++ /dev/null
@@ -1,63 +0,0 @@
-#!/bin/bash
-# Copyright (C) 2024 Intel Corporation
-# SPDX-License-Identifier: Apache-2.0
-
-set -xe
-
-WORKPATH=$(dirname "$PWD")
-ip_address=$(hostname -I | awk '{print $1}')
-function build_docker_images() {
-    cd $WORKPATH
-
-    # piull pgvector image
-    docker pull pgvector/pgvector:0.7.0-pg16
-
-    # build dataprep image for pgvector
-    docker build -t opea/dataprep-pgvector:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f $WORKPATH/comps/dataprep/pgvector/langchain/docker/Dockerfile .
-}
-
-function start_service() {
-    export POSTGRES_USER=testuser
-    export POSTGRES_PASSWORD=testpwd
-    export POSTGRES_DB=vectordb
-
-    docker run --name vectorstore-postgres -e POSTGRES_USER=${POSTGRES_USER} -e POSTGRES_HOST_AUTH_METHOD=trust -e POSTGRES_DB=${POSTGRES_DB} -e POSTGRES_PASSWORD=${POSTGRES_PASSWORD} -p 5432:5432 -d -v $WORKPATH/comps/vectorstores/langchain/pgvector/init.sql:/docker-entrypoint-initdb.d/init.sql pgvector/pgvector:0.7.0-pg16
-
-    sleep 10s
-
-    docker run -d --name="dataprep-pgvector" -p 6007:6007 --ipc=host -e http_proxy=$http_proxy -e https_proxy=$https_proxy -e PG_CONNECTION_STRING=postgresql+psycopg2://${POSTGRES_USER}:${POSTGRES_PASSWORD}@$ip_address:5432/${POSTGRES_DB} opea/dataprep-pgvector:latest
-}
-
-function validate_microservice() {
-    url="http://$ip_address:6007/v1/dataprep"
-    touch $WORKPATH/tests/test.txt
-    echo 'The OPEA platform includes: Detailed framework of composable building blocks for state-of-the-art generative AI systems including LLMs, data stores, and prompt engines' > $WORKPATH/tests/test.txt
-
-    curl --location --request POST "${url}" \
-    --form 'files=@"'${WORKPATH}'/tests/test.txt"' \
-    --proxy http://proxy-chain.intel.com:912
-}
-
-function stop_docker() {
-    cid=$(docker ps -aq --filter "name=vectorstore-postgres*")
-    if [[ ! -z "$cid" ]]; then docker stop $cid && docker rm $cid && sleep 1s; fi
-
-    cid=$(docker ps -aq --filter "name=dataprep-pgvector*")
-    if [[ ! -z "$cid" ]]; then docker stop $cid && docker rm $cid && sleep 1s; fi
-}
-
-function main() {
-
-    stop_docker
-
-    build_docker_images
-    start_service
-
-    validate_microservice
-
-    #stop_docker
-    #echo y | docker system prune
-
-}
-
-main