diff --git a/ChatQnA/docker_compose/intel/cpu/xeon/README.md b/ChatQnA/docker_compose/intel/cpu/xeon/README.md
index f908470d71..750cca5887 100644
--- a/ChatQnA/docker_compose/intel/cpu/xeon/README.md
+++ b/ChatQnA/docker_compose/intel/cpu/xeon/README.md
@@ -1,6 +1,8 @@
 # Build Mega Service of ChatQnA on Xeon
 
-This document outlines the deployment process for a ChatQnA application utilizing the [GenAIComps](https://github.com/opea-project/GenAIComps.git) microservice pipeline on Intel Xeon server. The steps include Docker image creation, container deployment via Docker Compose, and service execution to integrate microservices such as `embedding`, `retriever`, `rerank`, and `llm`. We will publish the Docker images to Docker Hub soon, it will simplify the deployment process for this service.
+This document outlines the deployment process for a ChatQnA application utilizing the [GenAIComps](https://github.com/opea-project/GenAIComps.git) microservice pipeline on Intel Xeon server. The steps include Docker image creation, container deployment via Docker Compose, and service execution to integrate microservices such as `embedding`, `retriever`, `rerank`, and `llm`.
+
+The default pipeline deploys with vLLM as the LLM serving component and leverages rerank component. It also provides options of not using rerank in the pipeline and using TGI backend for LLM microservice, please refer to [start-all-the-services-docker-containers](#start-all-the-services-docker-containers) section in this page. Besides, refer to [Build with Pinecone VectorDB](./README_pinecone.md) and [Build with Qdrant VectorDB](./README_qdrant.md) for other deployment variants.
 
 Quick Start:
 
@@ -186,7 +188,7 @@ By default, the embedding, reranking and LLM models are set to a default value a
 
 Change the `xxx_MODEL_ID` below for your needs.
 
-For users in China who are unable to download models directly from Huggingface, you can use [ModelScope](https://www.modelscope.cn/models) or a Huggingface mirror to download models. TGI can load the models either online or offline as described below:
+For users in China who are unable to download models directly from Huggingface, you can use [ModelScope](https://www.modelscope.cn/models) or a Huggingface mirror to download models. The vLLM/TGI can load the models either online or offline as described below:
 
 1. Online
 
@@ -194,6 +196,9 @@ For users in China who are unable to download models directly from Huggingface,
    export HF_TOKEN=${your_hf_token}
    export HF_ENDPOINT="https://hf-mirror.com"
    model_name="Intel/neural-chat-7b-v3-3"
+   # Start vLLM LLM Service
+   docker run -p 8008:80 -v ./data:/data --name vllm-service -e HF_ENDPOINT=$HF_ENDPOINT -e http_proxy=$http_proxy -e https_proxy=$https_proxy --shm-size 128g opea/vllm:latest --model $model_name --host 0.0.0.0 --port 80
+   # Start TGI LLM Service
    docker run -p 8008:80 -v ./data:/data --name tgi-service -e HF_ENDPOINT=$HF_ENDPOINT -e http_proxy=$http_proxy -e https_proxy=$https_proxy --shm-size 1g ghcr.io/huggingface/text-generation-inference:2.4.0-intel-cpu --model-id $model_name
    ```
 
@@ -203,12 +208,15 @@ For users in China who are unable to download models directly from Huggingface,
 
    - Click on `Download this model` button, and choose one way to download the model to your local path `/path/to/model`.
 
-   - Run the following command to start TGI service.
+   - Run the following command to start the LLM service.
 
      ```bash
      export HF_TOKEN=${your_hf_token}
      export model_path="/path/to/model"
-     docker run -p 8008:80 -v $model_path:/data --name tgi_service --shm-size 1g ghcr.io/huggingface/text-generation-inference:2.4.0-intel-cpu --model-id /data
+     # Start vLLM LLM Service
+     docker run -p 8008:80 -v $model_path:/data --name vllm-service --shm-size 128g opea/vllm:latest --model /data --host 0.0.0.0 --port 80
+     # Start TGI LLM Service
+     docker run -p 8008:80 -v $model_path:/data --name tgi-service --shm-size 1g ghcr.io/huggingface/text-generation-inference:2.4.0-intel-cpu --model-id /data
      ```
 
 ### Setup Environment Variables
@@ -246,7 +254,7 @@ For users in China who are unable to download models directly from Huggingface,
 cd GenAIExamples/ChatQnA/docker_compose/intel/cpu/xeon/
 ```
 
-If use TGI backend.
+If use vLLM as the LLM serving backend.
 
 ```bash
 # Start ChatQnA with Rerank Pipeline
@@ -255,10 +263,10 @@ docker compose -f compose.yaml up -d
 docker compose -f compose_without_rerank.yaml up -d
 ```
 
-If use vLLM backend.
+If use TGI as the LLM serving backend.
 
 ```bash
-docker compose -f compose_vllm.yaml up -d
+docker compose -f compose_tgi.yaml up -d
 ```
 
 ### Validate Microservices
@@ -305,37 +313,34 @@ For details on how to verify the correctness of the response, refer to [how-to-v
 
 4. LLM backend Service
 
-   In first startup, this service will take more time to download the model files. After it's finished, the service will be ready.
+   In the first startup, this service will take more time to download, load and warm up the model. After it's finished, the service will be ready.
 
    Try the command below to check whether the LLM serving is ready.
 
    ```bash
-   docker logs tgi-service | grep Connected
+   # vLLM service
+   docker logs vllm-service 2>&1 | grep complete
+   # If the service is ready, you will get the response like below.
+   INFO:     Application startup complete.
    ```
 
-   If the service is ready, you will get the response like below.
-
-   ```
+   ```bash
+   # TGI service
+   docker logs tgi-service | grep Connected
+   # If the service is ready, you will get the response like below.
    2024-09-03T02:47:53.402023Z  INFO text_generation_router::server: router/src/server.rs:2311: Connected
    ```
 
    Then try the `cURL` command below to validate services.
 
    ```bash
-   # TGI service
+   # either vLLM or TGI service
    curl http://${host_ip}:9009/v1/chat/completions \
      -X POST \
      -d '{"model": "Intel/neural-chat-7b-v3-3", "messages": [{"role": "user", "content": "What is Deep Learning?"}], "max_tokens":17}' \
      -H 'Content-Type: application/json'
    ```
 
-   ```bash
-   # vLLM Service
-   curl http://${host_ip}:9009/v1/chat/completions \
-     -H "Content-Type: application/json" \
-     -d '{"model": "Intel/neural-chat-7b-v3-3", "messages": [{"role": "user", "content": "What is Deep Learning?"}]}'
-   ```
-
 5. MegaService
 
    ```bash
@@ -362,7 +367,6 @@ Or run this command to get the file on a terminal.
 
 ```bash
 wget https://raw.githubusercontent.com/opea-project/GenAIComps/v1.1/comps/retrievers/redis/data/nke-10k-2023.pdf
-
 ```
 
 Upload:
diff --git a/ChatQnA/docker_compose/intel/cpu/xeon/README_pinecone.md b/ChatQnA/docker_compose/intel/cpu/xeon/README_pinecone.md
index dce5b0a540..c87a0a81cf 100644
--- a/ChatQnA/docker_compose/intel/cpu/xeon/README_pinecone.md
+++ b/ChatQnA/docker_compose/intel/cpu/xeon/README_pinecone.md
@@ -1,6 +1,8 @@
 # Build Mega Service of ChatQnA on Xeon
 
-This document outlines the deployment process for a ChatQnA application utilizing the [GenAIComps](https://github.com/opea-project/GenAIComps.git) microservice pipeline on Intel Xeon server. The steps include Docker image creation, container deployment via Docker Compose, and service execution to integrate microservices such as `embedding`, `retriever`, `rerank`, and `llm`. We will publish the Docker images to Docker Hub soon, it will simplify the deployment process for this service.
+This document outlines the deployment process for a ChatQnA application utilizing the [GenAIComps](https://github.com/opea-project/GenAIComps.git) microservice pipeline on Intel Xeon server. The steps include Docker image creation, container deployment via Docker Compose, and service execution to integrate microservices such as `embedding`, `retriever`, `rerank`, and `llm`.
+
+The default pipeline deploys with vLLM as the LLM serving component and leverages rerank component.
 
 Quick Start:
 
@@ -189,7 +191,7 @@ By default, the embedding, reranking and LLM models are set to a default value a
 
 Change the `xxx_MODEL_ID` below for your needs.
 
-For users in China who are unable to download models directly from Huggingface, you can use [ModelScope](https://www.modelscope.cn/models) or a Huggingface mirror to download models. TGI can load the models either online or offline as described below:
+For users in China who are unable to download models directly from Huggingface, you can use [ModelScope](https://www.modelscope.cn/models) or a Huggingface mirror to download models. The vLLM can load the models either online or offline as described below:
 
 1. Online
 
@@ -197,7 +199,7 @@ For users in China who are unable to download models directly from Huggingface,
    export HF_TOKEN=${your_hf_token}
    export HF_ENDPOINT="https://hf-mirror.com"
    model_name="Intel/neural-chat-7b-v3-3"
-   docker run -p 8008:80 -v ./data:/data --name tgi-service -e HF_ENDPOINT=$HF_ENDPOINT -e http_proxy=$http_proxy -e https_proxy=$https_proxy --shm-size 1g ghcr.io/huggingface/text-generation-inference:2.4.0-intel-cpu --model-id $model_name
+   docker run -p 8008:80 -v ./data:/data --name vllm-service -e HF_ENDPOINT=$HF_ENDPOINT -e http_proxy=$http_proxy -e https_proxy=$https_proxy --shm-size 128g opea/vllm:latest --model $model_name --host 0.0.0.0 --port 80
    ```
 
 2. Offline
@@ -206,12 +208,12 @@ For users in China who are unable to download models directly from Huggingface,
 
    - Click on `Download this model` button, and choose one way to download the model to your local path `/path/to/model`.
 
-   - Run the following command to start TGI service.
+   - Run the following command to start the LLM service.
 
      ```bash
      export HF_TOKEN=${your_hf_token}
      export model_path="/path/to/model"
-     docker run -p 8008:80 -v $model_path:/data --name tgi_service --shm-size 1g ghcr.io/huggingface/text-generation-inference:2.4.0-intel-cpu --model-id /data
+     docker run -p 8008:80 -v $model_path:/data --name vllm-service --shm-size 128g opea/vllm:latest --model /data --host 0.0.0.0 --port 80
      ```
 
 ### Setup Environment Variables
@@ -252,7 +254,7 @@ For users in China who are unable to download models directly from Huggingface,
 cd GenAIExamples/ChatQnA/docker_compose/intel/cpu/xeon/
 ```
 
-If use TGI backend.
+If use vLLM backend.
 
 ```bash
 # Start ChatQnA with Rerank Pipeline
@@ -303,24 +305,23 @@ For details on how to verify the correctness of the response, refer to [how-to-v
 
 4. LLM backend Service
 
-   In first startup, this service will take more time to download the model files. After it's finished, the service will be ready.
+   In the first startup, this service will take more time to download, load and warm up the model. After it's finished, the service will be ready.
 
    Try the command below to check whether the LLM serving is ready.
 
    ```bash
-   docker logs tgi-service | grep Connected
+   docker logs vllm-service 2>&1 | grep complete
    ```
 
    If the service is ready, you will get the response like below.
 
    ```text
-   2024-09-03T02:47:53.402023Z  INFO text_generation_router::server: router/src/server.rs:2311: Connected
+   INFO: Application startup complete.
    ```
 
    Then try the `cURL` command below to validate services.
 
    ```bash
-   # TGI service
    curl http://${host_ip}:9009/v1/chat/completions \
      -X POST \
      -d '{"model": "Intel/neural-chat-7b-v3-3", "messages": [{"role": "user", "content": "What is Deep Learning?"}], "max_tokens":17}' \
diff --git a/ChatQnA/docker_compose/intel/cpu/xeon/README_qdrant.md b/ChatQnA/docker_compose/intel/cpu/xeon/README_qdrant.md
index 6688f25370..a77ebf0f7d 100644
--- a/ChatQnA/docker_compose/intel/cpu/xeon/README_qdrant.md
+++ b/ChatQnA/docker_compose/intel/cpu/xeon/README_qdrant.md
@@ -1,6 +1,8 @@
 # Build Mega Service of ChatQnA (with Qdrant) on Xeon
 
-This document outlines the deployment process for a ChatQnA application utilizing the [GenAIComps](https://github.com/opea-project/GenAIComps.git) microservice pipeline on Intel Xeon server. The steps include Docker image creation, container deployment via Docker Compose, and service execution to integrate microservices such as `embedding`, `retriever`, `rerank`, and `llm`. We will publish the Docker images to Docker Hub soon, it will simplify the deployment process for this service.
+This document outlines the deployment process for a ChatQnA application utilizing the [GenAIComps](https://github.com/opea-project/GenAIComps.git) microservice pipeline on Intel Xeon server. The steps include Docker image creation, container deployment via Docker Compose, and service execution to integrate microservices such as `embedding`, `retriever`, `rerank`, and `llm`.
+
+The default pipeline deploys with vLLM as the LLM serving component and leverages rerank component.
 
 ## 🚀 Apply Xeon Server on AWS
 
@@ -44,7 +46,7 @@ reranking
 =========
 Port 6046 - Open to 0.0.0.0/0
 
-tgi-service
+vllm-service
 ===========
 Port 6042 - Open to 0.0.0.0/0
 
@@ -170,7 +172,7 @@ export your_hf_api_token="Your_Huggingface_API_Token"
 **Append the value of the public IP address to the no_proxy list if you are in a proxy environment**
 
 ```
-export your_no_proxy=${your_no_proxy},"External_Public_IP",chatqna-xeon-ui-server,chatqna-xeon-backend-server,dataprep-qdrant-service,tei-embedding-service,retriever,tei-reranking-service,tgi-service
+export your_no_proxy=${your_no_proxy},"External_Public_IP",chatqna-xeon-ui-server,chatqna-xeon-backend-server,dataprep-qdrant-service,tei-embedding-service,retriever,tei-reranking-service,tgi-service,vllm-service
 ```
 
 ```bash
@@ -233,23 +235,23 @@ For details on how to verify the correctness of the response, refer to [how-to-v
        -H 'Content-Type: application/json'
    ```
 
-4. TGI Service
+4. LLM Backend Service
 
-   In first startup, this service will take more time to download the model files. After it's finished, the service will be ready.
+   In the first startup, this service will take more time to download, load and warm up the model. After it's finished, the service will be ready.
 
-   Try the command below to check whether the TGI service is ready.
+   Try the command below to check whether the LLM service is ready.
 
    ```bash
-   docker logs ${CONTAINER_ID} | grep Connected
+   docker logs vllm-service 2>&1 | grep complete
    ```
 
    If the service is ready, you will get the response like below.
 
-   ```
-   2024-09-03T02:47:53.402023Z  INFO text_generation_router::server: router/src/server.rs:2311: Connected
+   ```text
+   INFO: Application startup complete.
    ```
 
-   Then try the `cURL` command below to validate TGI.
+   Then try the `cURL` command below to validate vLLM service.
 
    ```bash
    curl http://${host_ip}:6042/v1/chat/completions \
diff --git a/ChatQnA/docker_compose/intel/cpu/xeon/compose.yaml b/ChatQnA/docker_compose/intel/cpu/xeon/compose.yaml
index 6e94a9f998..f34868b6de 100644
--- a/ChatQnA/docker_compose/intel/cpu/xeon/compose.yaml
+++ b/ChatQnA/docker_compose/intel/cpu/xeon/compose.yaml
@@ -74,32 +74,31 @@ services:
       HF_HUB_DISABLE_PROGRESS_BARS: 1
       HF_HUB_ENABLE_HF_TRANSFER: 0
     command: --model-id ${RERANK_MODEL_ID} --auto-truncate
-  tgi-service:
-    image: ghcr.io/huggingface/text-generation-inference:2.4.0-intel-cpu
-    container_name: tgi-service
+  vllm-service:
+    image: ${REGISTRY:-opea}/vllm:${TAG:-latest}
+    container_name: vllm-service
     ports:
       - "9009:80"
     volumes:
       - "./data:/data"
-    shm_size: 1g
+    shm_size: 128g
     environment:
       no_proxy: ${no_proxy}
       http_proxy: ${http_proxy}
       https_proxy: ${https_proxy}
       HF_TOKEN: ${HUGGINGFACEHUB_API_TOKEN}
-      HF_HUB_DISABLE_PROGRESS_BARS: 1
-      HF_HUB_ENABLE_HF_TRANSFER: 0
-    command: --model-id ${LLM_MODEL_ID} --cuda-graphs 0
+      LLM_MODEL_ID: ${LLM_MODEL_ID}
+      VLLM_TORCH_PROFILER_DIR: "/mnt"
+    command: --model $LLM_MODEL_ID --host 0.0.0.0 --port 80
   chatqna-xeon-backend-server:
     image: ${REGISTRY:-opea}/chatqna:${TAG:-latest}
     container_name: chatqna-xeon-backend-server
     depends_on:
       - redis-vector-db
       - tei-embedding-service
-      - dataprep-redis-service
       - retriever
       - tei-reranking-service
-      - tgi-service
+      - vllm-service
     ports:
       - "8888:8888"
     environment:
@@ -112,7 +111,7 @@ services:
       - RETRIEVER_SERVICE_HOST_IP=retriever
       - RERANK_SERVER_HOST_IP=tei-reranking-service
       - RERANK_SERVER_PORT=${RERANK_SERVER_PORT:-80}
-      - LLM_SERVER_HOST_IP=tgi-service
+      - LLM_SERVER_HOST_IP=vllm-service
       - LLM_SERVER_PORT=${LLM_SERVER_PORT:-80}
       - LLM_MODEL=${LLM_MODEL_ID}
       - LOGFLAG=${LOGFLAG}
diff --git a/ChatQnA/docker_compose/intel/cpu/xeon/compose_pinecone.yaml b/ChatQnA/docker_compose/intel/cpu/xeon/compose_pinecone.yaml
index 022fe3b612..5378b581ef 100644
--- a/ChatQnA/docker_compose/intel/cpu/xeon/compose_pinecone.yaml
+++ b/ChatQnA/docker_compose/intel/cpu/xeon/compose_pinecone.yaml
@@ -68,22 +68,22 @@ services:
       HF_HUB_DISABLE_PROGRESS_BARS: 1
       HF_HUB_ENABLE_HF_TRANSFER: 0
     command: --model-id ${RERANK_MODEL_ID} --auto-truncate
-  tgi-service:
-    image: ghcr.io/huggingface/text-generation-inference:2.4.0-intel-cpu
-    container_name: tgi-service
+  vllm-service:
+    image: ${REGISTRY:-opea}/vllm:${TAG:-latest}
+    container_name: vllm-service
     ports:
       - "9009:80"
     volumes:
       - "./data:/data"
-    shm_size: 1g
+    shm_size: 128g
     environment:
       no_proxy: ${no_proxy}
       http_proxy: ${http_proxy}
       https_proxy: ${https_proxy}
       HF_TOKEN: ${HUGGINGFACEHUB_API_TOKEN}
-      HF_HUB_DISABLE_PROGRESS_BARS: 1
-      HF_HUB_ENABLE_HF_TRANSFER: 0
-    command: --model-id ${LLM_MODEL_ID} --cuda-graphs 0
+      LLM_MODEL_ID: ${LLM_MODEL_ID}
+      VLLM_TORCH_PROFILER_DIR: "/mnt"
+    command: --model $LLM_MODEL_ID --host 0.0.0.0 --port 80
   chatqna-xeon-backend-server:
     image: ${REGISTRY:-opea}/chatqna:${TAG:-latest}
     container_name: chatqna-xeon-backend-server
@@ -92,7 +92,7 @@ services:
       - dataprep-pinecone-service
       - retriever
       - tei-reranking-service
-      - tgi-service
+      - vllm-service
     ports:
       - "8888:8888"
     environment:
@@ -105,7 +105,7 @@ services:
       - RETRIEVER_SERVICE_HOST_IP=retriever
       - RERANK_SERVER_HOST_IP=tei-reranking-service
       - RERANK_SERVER_PORT=${RERANK_SERVER_PORT:-80}
-      - LLM_SERVER_HOST_IP=tgi-service
+      - LLM_SERVER_HOST_IP=vllm-service
       - LLM_SERVER_PORT=${LLM_SERVER_PORT:-80}
       - LOGFLAG=${LOGFLAG}
       - LLM_MODEL=${LLM_MODEL_ID}
diff --git a/ChatQnA/docker_compose/intel/cpu/xeon/compose_qdrant.yaml b/ChatQnA/docker_compose/intel/cpu/xeon/compose_qdrant.yaml
index af69531c21..c3a2d00dc8 100644
--- a/ChatQnA/docker_compose/intel/cpu/xeon/compose_qdrant.yaml
+++ b/ChatQnA/docker_compose/intel/cpu/xeon/compose_qdrant.yaml
@@ -53,7 +53,7 @@ services:
       QDRANT_HOST: qdrant-vector-db
       QDRANT_PORT: 6333
       INDEX_NAME: ${INDEX_NAME}
-      TEI_EMBEDDING_ENDPOINT: ${TEI_EMBEDDING_ENDPOINT}
+      TEI_EMBEDDING_ENDPOINT: http://tei-embedding-service:80
       LOGFLAG: ${LOGFLAG}
       RETRIEVER_COMPONENT_NAME: "OPEA_RETRIEVER_QDRANT"
     restart: unless-stopped
@@ -73,22 +73,22 @@ services:
       HF_HUB_DISABLE_PROGRESS_BARS: 1
       HF_HUB_ENABLE_HF_TRANSFER: 0
     command: --model-id ${RERANK_MODEL_ID} --auto-truncate
-  tgi-service:
-    image: ghcr.io/huggingface/text-generation-inference:2.4.0-intel-cpu
-    container_name: tgi-service
+  vllm-service:
+    image: ${REGISTRY:-opea}/vllm:${TAG:-latest}
+    container_name: vllm-service
     ports:
       - "6042:80"
     volumes:
       - "./data:/data"
-    shm_size: 1g
+    shm_size: 128g
     environment:
       no_proxy: ${no_proxy}
       http_proxy: ${http_proxy}
       https_proxy: ${https_proxy}
       HF_TOKEN: ${HUGGINGFACEHUB_API_TOKEN}
-      HF_HUB_DISABLE_PROGRESS_BARS: 1
-      HF_HUB_ENABLE_HF_TRANSFER: 0
-    command: --model-id ${LLM_MODEL_ID} --cuda-graphs 0
+      LLM_MODEL_ID: ${LLM_MODEL_ID}
+      VLLM_TORCH_PROFILER_DIR: "/mnt"
+    command: --model $LLM_MODEL_ID --host 0.0.0.0 --port 80
   chatqna-xeon-backend-server:
     image: ${REGISTRY:-opea}/chatqna:${TAG:-latest}
     container_name: chatqna-xeon-backend-server
@@ -97,7 +97,7 @@ services:
       - tei-embedding-service
       - retriever
       - tei-reranking-service
-      - tgi-service
+      - vllm-service
     ports:
       - "8912:8888"
     environment:
@@ -111,7 +111,7 @@ services:
       - RETRIEVER_SERVICE_PORT=${RETRIEVER_SERVICE_PORT:-7000}
       - RERANK_SERVER_HOST_IP=tei-reranking-service
       - RERANK_SERVER_PORT=${RERANK_SERVER_PORT:-80}
-      - LLM_SERVER_HOST_IP=tgi-service
+      - LLM_SERVER_HOST_IP=vllm-service
       - LLM_SERVER_PORT=${LLM_SERVER_PORT:-80}
       - LLM_MODEL=${LLM_MODEL_ID}
       - LOGFLAG=${LOGFLAG}
diff --git a/ChatQnA/docker_compose/intel/cpu/xeon/compose_vllm.yaml b/ChatQnA/docker_compose/intel/cpu/xeon/compose_tgi.yaml
similarity index 92%
rename from ChatQnA/docker_compose/intel/cpu/xeon/compose_vllm.yaml
rename to ChatQnA/docker_compose/intel/cpu/xeon/compose_tgi.yaml
index f34868b6de..6e94a9f998 100644
--- a/ChatQnA/docker_compose/intel/cpu/xeon/compose_vllm.yaml
+++ b/ChatQnA/docker_compose/intel/cpu/xeon/compose_tgi.yaml
@@ -74,31 +74,32 @@ services:
       HF_HUB_DISABLE_PROGRESS_BARS: 1
       HF_HUB_ENABLE_HF_TRANSFER: 0
     command: --model-id ${RERANK_MODEL_ID} --auto-truncate
-  vllm-service:
-    image: ${REGISTRY:-opea}/vllm:${TAG:-latest}
-    container_name: vllm-service
+  tgi-service:
+    image: ghcr.io/huggingface/text-generation-inference:2.4.0-intel-cpu
+    container_name: tgi-service
     ports:
       - "9009:80"
     volumes:
       - "./data:/data"
-    shm_size: 128g
+    shm_size: 1g
     environment:
       no_proxy: ${no_proxy}
       http_proxy: ${http_proxy}
       https_proxy: ${https_proxy}
       HF_TOKEN: ${HUGGINGFACEHUB_API_TOKEN}
-      LLM_MODEL_ID: ${LLM_MODEL_ID}
-      VLLM_TORCH_PROFILER_DIR: "/mnt"
-    command: --model $LLM_MODEL_ID --host 0.0.0.0 --port 80
+      HF_HUB_DISABLE_PROGRESS_BARS: 1
+      HF_HUB_ENABLE_HF_TRANSFER: 0
+    command: --model-id ${LLM_MODEL_ID} --cuda-graphs 0
   chatqna-xeon-backend-server:
     image: ${REGISTRY:-opea}/chatqna:${TAG:-latest}
     container_name: chatqna-xeon-backend-server
     depends_on:
       - redis-vector-db
       - tei-embedding-service
+      - dataprep-redis-service
       - retriever
       - tei-reranking-service
-      - vllm-service
+      - tgi-service
     ports:
       - "8888:8888"
     environment:
@@ -111,7 +112,7 @@ services:
       - RETRIEVER_SERVICE_HOST_IP=retriever
       - RERANK_SERVER_HOST_IP=tei-reranking-service
       - RERANK_SERVER_PORT=${RERANK_SERVER_PORT:-80}
-      - LLM_SERVER_HOST_IP=vllm-service
+      - LLM_SERVER_HOST_IP=tgi-service
       - LLM_SERVER_PORT=${LLM_SERVER_PORT:-80}
       - LLM_MODEL=${LLM_MODEL_ID}
       - LOGFLAG=${LOGFLAG}
diff --git a/ChatQnA/docker_compose/intel/cpu/xeon/compose_without_rerank.yaml b/ChatQnA/docker_compose/intel/cpu/xeon/compose_without_rerank.yaml
index 92d7fcf7bc..dd675dd0dd 100644
--- a/ChatQnA/docker_compose/intel/cpu/xeon/compose_without_rerank.yaml
+++ b/ChatQnA/docker_compose/intel/cpu/xeon/compose_without_rerank.yaml
@@ -58,22 +58,22 @@ services:
       LOGFLAG: ${LOGFLAG}
       RETRIEVER_COMPONENT_NAME: "OPEA_RETRIEVER_REDIS"
     restart: unless-stopped
-  tgi-service:
-    image: ghcr.io/huggingface/text-generation-inference:2.4.0-intel-cpu
-    container_name: tgi-service
+  vllm-service:
+    image: ${REGISTRY:-opea}/vllm:${TAG:-latest}
+    container_name: vllm-service
     ports:
       - "9009:80"
     volumes:
       - "./data:/data"
-    shm_size: 1g
+    shm_size: 128g
     environment:
       no_proxy: ${no_proxy}
       http_proxy: ${http_proxy}
       https_proxy: ${https_proxy}
       HF_TOKEN: ${HUGGINGFACEHUB_API_TOKEN}
-      HF_HUB_DISABLE_PROGRESS_BARS: 1
-      HF_HUB_ENABLE_HF_TRANSFER: 0
-    command: --model-id ${LLM_MODEL_ID} --cuda-graphs 0
+      LLM_MODEL_ID: ${LLM_MODEL_ID}
+      VLLM_TORCH_PROFILER_DIR: "/mnt"
+    command: --model $LLM_MODEL_ID --host 0.0.0.0 --port 80
   chatqna-xeon-backend-server:
     image: ${REGISTRY:-opea}/chatqna-without-rerank:${TAG:-latest}
     container_name: chatqna-xeon-backend-server
@@ -82,7 +82,7 @@ services:
       - tei-embedding-service
       - dataprep-redis-service
       - retriever
-      - tgi-service
+      - vllm-service
     ports:
       - "8888:8888"
     environment:
@@ -93,7 +93,7 @@ services:
       - EMBEDDING_SERVER_HOST_IP=tei-embedding-service
       - EMBEDDING_SERVER_PORT=${EMBEDDING_SERVER_PORT:-80}
       - RETRIEVER_SERVICE_HOST_IP=retriever
-      - LLM_SERVER_HOST_IP=tgi-service
+      - LLM_SERVER_HOST_IP=vllm-service
       - LLM_SERVER_PORT=${LLM_SERVER_PORT:-80}
       - LLM_MODEL=${LLM_MODEL_ID}
       - LOGFLAG=${LOGFLAG}
diff --git a/ChatQnA/tests/test_compose_on_xeon.sh b/ChatQnA/tests/test_compose_on_xeon.sh
index babca0cd43..1808dbd459 100644
--- a/ChatQnA/tests/test_compose_on_xeon.sh
+++ b/ChatQnA/tests/test_compose_on_xeon.sh
@@ -17,12 +17,12 @@ ip_address=$(hostname -I | awk '{print $1}')
 function build_docker_images() {
     cd $WORKPATH/docker_image_build
     git clone https://github.com/opea-project/GenAIComps.git && cd GenAIComps && git checkout "${opea_branch:-"main"}" && cd ../
+    git clone https://github.com/vllm-project/vllm.git
 
     echo "Build all the images with --no-cache, check docker_image_build.log for details..."
-    service_list="chatqna chatqna-ui dataprep-redis retriever nginx"
+    service_list="chatqna chatqna-ui dataprep-redis retriever vllm nginx"
     docker compose -f build.yaml build ${service_list} --no-cache > ${LOG_PATH}/docker_image_build.log
 
-    docker pull ghcr.io/huggingface/text-generation-inference:2.4.0-intel-cpu
     docker pull ghcr.io/huggingface/text-embeddings-inference:cpu-1.5
 
     docker images && sleep 1s
@@ -33,21 +33,19 @@ function start_services() {
 
     export EMBEDDING_MODEL_ID="BAAI/bge-base-en-v1.5"
     export RERANK_MODEL_ID="BAAI/bge-reranker-base"
-    export LLM_MODEL_ID="meta-llama/Meta-Llama-3-8B-Instruct"
+    export LLM_MODEL_ID="Intel/neural-chat-7b-v3-3"
     export INDEX_NAME="rag-redis"
     export HUGGINGFACEHUB_API_TOKEN=${HUGGINGFACEHUB_API_TOKEN}
 
     # Start Docker Containers
-    sed -i "s|container_name: chatqna-xeon-backend-server|container_name: chatqna-xeon-backend-server\n    volumes:\n      - \"${WORKPATH}\/docker_image_build\/GenAIComps:\/home\/user\/GenAIComps\"|g" compose.yaml
     docker compose -f compose.yaml up -d > ${LOG_PATH}/start_services_with_compose.log
-
     n=0
-    until [[ "$n" -ge 500 ]]; do
-        docker logs tgi-service > ${LOG_PATH}/tgi_service_start.log
-        if grep -q Connected ${LOG_PATH}/tgi_service_start.log; then
+    until [[ "$n" -ge 100 ]]; do
+        docker logs vllm-service > ${LOG_PATH}/vllm_service_start.log 2>&1
+        if grep -q complete ${LOG_PATH}/vllm_service_start.log; then
             break
         fi
-        sleep 1s
+        sleep 5s
         n=$((n+1))
     done
 }
@@ -59,38 +57,24 @@ function validate_service() {
     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")
-    elif [[ $SERVICE_NAME == *"dataprep_get"* ]]; then
-        HTTP_RESPONSE=$(curl --silent --write-out "HTTPSTATUS:%{http_code}" -X POST -H 'Content-Type: application/json' "$URL")
-    elif [[ $SERVICE_NAME == *"dataprep_del"* ]]; then
-        HTTP_RESPONSE=$(curl --silent --write-out "HTTPSTATUS:%{http_code}" -X POST -d '{"file_path": "all"}' -H 'Content-Type: application/json' "$URL")
-    else
-        HTTP_RESPONSE=$(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 $HTTP_RESPONSE | sed -e 's/HTTPSTATUS\:.*//g')
+    local HTTP_STATUS=$(curl -s -o /dev/null -w "%{http_code}" -X POST -d "$INPUT_DATA" -H 'Content-Type: application/json' "$URL")
+    if [ "$HTTP_STATUS" -eq 200 ]; then
+        echo "[ $SERVICE_NAME ] HTTP status is 200. Checking content..."
 
-    docker logs ${DOCKER_NAME} >> ${LOG_PATH}/${SERVICE_NAME}.log
+        local CONTENT=$(curl -s -X POST -d "$INPUT_DATA" -H 'Content-Type: application/json' "$URL" | tee ${LOG_PATH}/${SERVICE_NAME}.log)
 
-    # check response status
-    if [ "$HTTP_STATUS" -ne "200" ]; then
-        echo "[ $SERVICE_NAME ] HTTP status is not 200. Received status was $HTTP_STATUS"
-        exit 1
+        if echo "$CONTENT" | grep -q "$EXPECTED_RESULT"; then
+            echo "[ $SERVICE_NAME ] Content is as expected."
+        else
+            echo "[ $SERVICE_NAME ] Content does not match the expected result: $CONTENT"
+            docker logs ${DOCKER_NAME} >> ${LOG_PATH}/${SERVICE_NAME}.log
+            exit 1
+        fi
     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"
+        echo "[ $SERVICE_NAME ] HTTP status is not 200. Received status was $HTTP_STATUS"
+        docker logs ${DOCKER_NAME} >> ${LOG_PATH}/${SERVICE_NAME}.log
         exit 1
-    else
-        echo "[ $SERVICE_NAME ] Content is as expected."
     fi
-
     sleep 1s
 }
 
@@ -100,48 +84,19 @@ function validate_microservices() {
     # tei for embedding service
     validate_service \
         "${ip_address}:6006/embed" \
-        "[[" \
+        "\[\[" \
         "tei-embedding" \
         "tei-embedding-server" \
         '{"inputs":"What is Deep Learning?"}'
 
     sleep 1m # retrieval can't curl as expected, try to wait for more time
 
-    # test /v1/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." > $LOG_PATH/dataprep_file.txt
-    validate_service \
-        "http://${ip_address}:6007/v1/dataprep" \
-        "Data preparation succeeded" \
-        "dataprep_upload_file" \
-        "dataprep-redis-server"
-
-    # test /v1/dataprep upload link
-    validate_service \
-        "http://${ip_address}:6007/v1/dataprep" \
-        "Data preparation succeeded" \
-        "dataprep_upload_link" \
-        "dataprep-redis-server"
-
-    # test /v1/dataprep/get_file
-    validate_service \
-        "http://${ip_address}:6007/v1/dataprep/get_file" \
-        '{"name":' \
-        "dataprep_get" \
-        "dataprep-redis-server"
-
-    # test /v1/dataprep/delete_file
-    validate_service \
-        "http://${ip_address}:6007/v1/dataprep/delete_file" \
-        '{"status":true}' \
-        "dataprep_del" \
-        "dataprep-redis-server"
-
     # retrieval microservice
     test_embedding=$(python3 -c "import random; embedding = [random.uniform(-1, 1) for _ in range(768)]; print(embedding)")
     validate_service \
         "${ip_address}:7000/v1/retrieval" \
-        "retrieved_docs" \
-        "retrieval-microservice" \
+        " " \
+        "retrieval" \
         "retriever-redis-server" \
         "{\"text\":\"What is the revenue of Nike in 2023?\",\"embedding\":${test_embedding}}"
 
@@ -153,29 +108,27 @@ function validate_microservices() {
         "tei-reranking-server" \
         '{"query":"What is Deep Learning?", "texts": ["Deep Learning is not...", "Deep learning is..."]}'
 
-    # tgi for llm service
+    # vllm for llm service
     validate_service \
-        "${ip_address}:9009/generate" \
-        "generated_text" \
-        "tgi-llm" \
-        "tgi-service" \
-        '{"inputs":"What is Deep Learning?","parameters":{"max_new_tokens":17, "do_sample": true}}'
-
+        "${ip_address}:9009/v1/chat/completions" \
+        "content" \
+        "vllm-llm" \
+        "vllm-service" \
+        '{"model": "Intel/neural-chat-7b-v3-3", "messages": [{"role": "user", "content": "What is Deep Learning?"}], "max_tokens": 17}'
 }
 
 function validate_megaservice() {
     # Curl the Mega Service
     validate_service \
         "${ip_address}:8888/v1/chatqna" \
-        "data: " \
-        "chatqna-megaservice" \
+        "data" \
+        "mega-chatqna" \
         "chatqna-xeon-backend-server" \
         '{"messages": "What is the revenue of Nike in 2023?"}'
 
 }
 
 function validate_frontend() {
-    echo "[ TEST INFO ]: --------- frontend test started ---------"
     cd $WORKPATH/ui/svelte
     local conda_env_name="OPEA_e2e"
     export PATH=${HOME}/miniforge3/bin/:$PATH
@@ -184,8 +137,8 @@ function validate_frontend() {
     else
         conda create -n ${conda_env_name} python=3.12 -y
     fi
+
     source activate ${conda_env_name}
-    echo "[ TEST INFO ]: --------- conda env activated ---------"
 
     sed -i "s/localhost/$ip_address/g" playwright.config.ts
 
@@ -206,7 +159,7 @@ function validate_frontend() {
 
 function stop_docker() {
     cd $WORKPATH/docker_compose/intel/cpu/xeon
-    docker compose stop && docker compose rm -f
+    docker compose -f compose.yaml down
 }
 
 function main() {
@@ -223,11 +176,8 @@ function main() {
         python3 $WORKPATH/tests/chatqna_benchmark.py
     elif [ "${mode}" == "" ]; then
         validate_microservices
-        echo "==== microservices validated ===="
         validate_megaservice
-        echo "==== megaservice validated ===="
-        validate_frontend
-        echo "==== frontend validated ===="
+        # validate_frontend
     fi
 
     stop_docker
diff --git a/ChatQnA/tests/test_compose_pinecone_on_xeon.sh b/ChatQnA/tests/test_compose_pinecone_on_xeon.sh
index 35c58f6754..d525cdb80a 100755
--- a/ChatQnA/tests/test_compose_pinecone_on_xeon.sh
+++ b/ChatQnA/tests/test_compose_pinecone_on_xeon.sh
@@ -17,12 +17,12 @@ ip_address=$(hostname -I | awk '{print $1}')
 function build_docker_images() {
     cd $WORKPATH/docker_image_build
     git clone https://github.com/opea-project/GenAIComps.git && cd GenAIComps && git checkout "${opea_branch:-"main"}" && cd ../
+    git clone https://github.com/vllm-project/vllm.git
 
     echo "Build all the images with --no-cache, check docker_image_build.log for details..."
-    service_list="chatqna chatqna-ui dataprep-pinecone retriever nginx"
+    service_list="chatqna chatqna-ui dataprep-pinecone retriever vllm nginx"
     docker compose -f build.yaml build ${service_list} --no-cache > ${LOG_PATH}/docker_image_build.log
 
-    docker pull ghcr.io/huggingface/text-generation-inference:2.4.0-intel-cpu
     docker pull ghcr.io/huggingface/text-embeddings-inference:cpu-1.5
 
     docker images && sleep 1s
@@ -33,7 +33,7 @@ function start_services() {
     export no_proxy=${no_proxy},${ip_address}
     export EMBEDDING_MODEL_ID="BAAI/bge-base-en-v1.5"
     export RERANK_MODEL_ID="BAAI/bge-reranker-base"
-    export LLM_MODEL_ID="meta-llama/Meta-Llama-3-8B-Instruct"
+    export LLM_MODEL_ID="Intel/neural-chat-7b-v3-3"
     export PINECONE_API_KEY=${PINECONE_KEY_LANGCHAIN_TEST}
     export PINECONE_INDEX_NAME="langchain-test"
     export INDEX_NAME="langchain-test"
@@ -44,12 +44,12 @@ function start_services() {
     docker compose -f compose_pinecone.yaml up -d > ${LOG_PATH}/start_services_with_compose.log
 
     n=0
-    until [[ "$n" -ge 500 ]]; do
-        docker logs tgi-service > ${LOG_PATH}/tgi_service_start.log
-        if grep -q Connected ${LOG_PATH}/tgi_service_start.log; then
+    until [[ "$n" -ge 100 ]]; do
+        docker logs vllm-service > ${LOG_PATH}/vllm_service_start.log 2>&1
+        if grep -q complete ${LOG_PATH}/vllm_service_start.log; then
             break
         fi
-        sleep 1s
+        sleep 5s
         n=$((n+1))
     done
 }
@@ -146,15 +146,14 @@ function validate_microservices() {
         '{"query":"What is Deep Learning?", "texts": ["Deep Learning is not...", "Deep learning is..."]}'
 
 
-    # tgi for llm service
+    # vllm for llm service
     echo "Validating llm service"
     validate_service \
-        "${ip_address}:9009/generate" \
-        "generated_text" \
-        "tgi-llm" \
-        "tgi-service" \
-        '{"inputs":"What is Deep Learning?","parameters":{"max_new_tokens":17, "do_sample": true}}'
-
+        "${ip_address}:9009/v1/chat/completions" \
+        "content" \
+        "vllm-llm" \
+        "vllm-service" \
+        '{"model": "Intel/neural-chat-7b-v3-3", "messages": [{"role": "user", "content": "What is Deep Learning?"}], "max_tokens": 17}'
 }
 
 function validate_megaservice() {
diff --git a/ChatQnA/tests/test_compose_qdrant_on_xeon.sh b/ChatQnA/tests/test_compose_qdrant_on_xeon.sh
index ee4b4efb0a..299a7def1a 100644
--- a/ChatQnA/tests/test_compose_qdrant_on_xeon.sh
+++ b/ChatQnA/tests/test_compose_qdrant_on_xeon.sh
@@ -17,9 +17,10 @@ ip_address=$(hostname -I | awk '{print $1}')
 function build_docker_images() {
     cd $WORKPATH/docker_image_build
     git clone https://github.com/opea-project/GenAIComps.git && cd GenAIComps && git checkout "${opea_branch:-"main"}" && cd ../
+    git clone https://github.com/vllm-project/vllm.git
 
     echo "Build all the images with --no-cache, check docker_image_build.log for details..."
-    service_list="chatqna chatqna-ui dataprep-qdrant retriever nginx"
+    service_list="chatqna chatqna-ui dataprep-qdrant retriever vllm nginx"
     docker compose -f build.yaml build ${service_list} --no-cache > ${LOG_PATH}/docker_image_build.log
 
     docker images && sleep 1s
@@ -40,8 +41,8 @@ function start_services() {
     docker compose -f compose_qdrant.yaml up -d > ${LOG_PATH}/start_services_with_compose.log
     n=0
     until [[ "$n" -ge 100 ]]; do
-        docker logs tgi-service > tgi_service_start.log
-        if grep -q Connected tgi_service_start.log; then
+        docker logs vllm-service > ${LOG_PATH}/vllm_service_start.log 2>&1
+        if grep -q complete ${LOG_PATH}/vllm_service_start.log; then
             break
         fi
         sleep 5s
@@ -49,7 +50,7 @@ function start_services() {
     done
 }
 
-function validate_services() {
+function validate_service() {
     local URL="$1"
     local EXPECTED_RESULT="$2"
     local SERVICE_NAME="$3"
@@ -91,7 +92,7 @@ function validate_microservices() {
     # Check if the microservices are running correctly.
 
     # tei for embedding service
-    validate_services \
+    validate_service \
         "${ip_address}:6040/embed" \
         "[[" \
         "tei-embedding" \
@@ -100,14 +101,14 @@ function validate_microservices() {
 
     # test /v1/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." > $LOG_PATH/dataprep_file.txt
-    validate_services \
+    validate_service \
         "${ip_address}:6043/v1/dataprep" \
         "Data preparation succeeded" \
         "dataprep_upload_file" \
         "dataprep-qdrant-server"
 
     # test upload link
-    validate_services \
+    validate_service \
         "${ip_address}:6043/v1/dataprep" \
         "Data preparation succeeded" \
         "dataprep_upload_link" \
@@ -115,7 +116,7 @@ function validate_microservices() {
 
     # retrieval microservice
     test_embedding=$(python3 -c "import random; embedding = [random.uniform(-1, 1) for _ in range(768)]; print(embedding)")
-    validate_services \
+    validate_service \
         "${ip_address}:6045/v1/retrieval" \
         "retrieved_docs" \
         "retrieval" \
@@ -123,25 +124,25 @@ function validate_microservices() {
         "{\"text\":\"What is Deep Learning?\",\"embedding\":${test_embedding}}"
 
     # tei for rerank microservice
-    validate_services \
+    validate_service \
         "${ip_address}:6041/rerank" \
         '{"index":1,"score":' \
         "tei-rerank" \
         "tei-reranking-server" \
         '{"query":"What is Deep Learning?", "texts": ["Deep Learning is not...", "Deep learning is..."]}'
 
-    # tgi for llm service
-    validate_services \
-        "${ip_address}:6042/generate" \
-        "generated_text" \
-        "tgi-llm" \
-        "tgi-service" \
-        '{"inputs":"What is Deep Learning?","parameters":{"max_new_tokens":17, "do_sample": true}}'
+    # vllm for llm service
+    validate_service \
+        "${ip_address}:6042/v1/chat/completions" \
+        "content" \
+        "vllm-llm" \
+        "vllm-service" \
+        '{"model": "Intel/neural-chat-7b-v3-3", "messages": [{"role": "user", "content": "What is Deep Learning?"}], "max_tokens": 17}'
 }
 
 function validate_megaservice() {
     # Curl the Mega Service
-    validate_services \
+    validate_service \
         "${ip_address}:8912/v1/chatqna" \
         "data: " \
         "mega-chatqna" \
@@ -174,7 +175,7 @@ function validate_frontend() {
 
 function stop_docker() {
     cd $WORKPATH/docker_compose/intel/cpu/xeon
-    docker compose -f compose_qdrant.yaml stop && docker compose -f compose_qdrant.yaml rm -f
+    docker compose -f compose_qdrant.yaml down
 }
 
 function main() {
diff --git a/ChatQnA/tests/test_compose_vllm_on_xeon.sh b/ChatQnA/tests/test_compose_tgi_on_xeon.sh
similarity index 50%
rename from ChatQnA/tests/test_compose_vllm_on_xeon.sh
rename to ChatQnA/tests/test_compose_tgi_on_xeon.sh
index 6d95c68a91..ca2a7c3fda 100644
--- a/ChatQnA/tests/test_compose_vllm_on_xeon.sh
+++ b/ChatQnA/tests/test_compose_tgi_on_xeon.sh
@@ -17,13 +17,12 @@ ip_address=$(hostname -I | awk '{print $1}')
 function build_docker_images() {
     cd $WORKPATH/docker_image_build
     git clone https://github.com/opea-project/GenAIComps.git && cd GenAIComps && git checkout "${opea_branch:-"main"}" && cd ../
-    git clone https://github.com/vllm-project/vllm.git
 
     echo "Build all the images with --no-cache, check docker_image_build.log for details..."
-    service_list="chatqna chatqna-ui dataprep-redis retriever vllm nginx"
+    service_list="chatqna chatqna-ui dataprep-redis retriever nginx"
     docker compose -f build.yaml build ${service_list} --no-cache > ${LOG_PATH}/docker_image_build.log
 
-    docker pull ghcr.io/huggingface/tgi-gaudi:2.0.6
+    docker pull ghcr.io/huggingface/text-generation-inference:2.4.0-intel-cpu
     docker pull ghcr.io/huggingface/text-embeddings-inference:cpu-1.5
 
     docker images && sleep 1s
@@ -39,11 +38,13 @@ function start_services() {
     export HUGGINGFACEHUB_API_TOKEN=${HUGGINGFACEHUB_API_TOKEN}
 
     # Start Docker Containers
-    docker compose -f compose_vllm.yaml up -d > ${LOG_PATH}/start_services_with_compose.log
+    sed -i "s|container_name: chatqna-xeon-backend-server|container_name: chatqna-xeon-backend-server\n    volumes:\n      - \"${WORKPATH}\/docker_image_build\/GenAIComps:\/home\/user\/GenAIComps\"|g" compose.yaml
+    docker compose -f compose_tgi.yaml up -d > ${LOG_PATH}/start_services_with_compose.log
+
     n=0
     until [[ "$n" -ge 100 ]]; do
-        docker logs vllm-service > ${LOG_PATH}/vllm_service_start.log
-        if grep -q Connected ${LOG_PATH}/vllm_service_start.log; then
+        docker logs tgi-service > ${LOG_PATH}/tgi_service_start.log
+        if grep -q Connected ${LOG_PATH}/tgi_service_start.log; then
             break
         fi
         sleep 5s
@@ -51,31 +52,45 @@ function start_services() {
     done
 }
 
-function validate_services() {
+function validate_service() {
     local URL="$1"
     local EXPECTED_RESULT="$2"
     local SERVICE_NAME="$3"
     local DOCKER_NAME="$4"
     local INPUT_DATA="$5"
 
-    local HTTP_STATUS=$(curl -s -o /dev/null -w "%{http_code}" -X POST -d "$INPUT_DATA" -H 'Content-Type: application/json' "$URL")
-    if [ "$HTTP_STATUS" -eq 200 ]; then
-        echo "[ $SERVICE_NAME ] HTTP status is 200. Checking content..."
+    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")
+    elif [[ $SERVICE_NAME == *"dataprep_get"* ]]; then
+        HTTP_RESPONSE=$(curl --silent --write-out "HTTPSTATUS:%{http_code}" -X POST -H 'Content-Type: application/json' "$URL")
+    elif [[ $SERVICE_NAME == *"dataprep_del"* ]]; then
+        HTTP_RESPONSE=$(curl --silent --write-out "HTTPSTATUS:%{http_code}" -X POST -d '{"file_path": "all"}' -H 'Content-Type: application/json' "$URL")
+    else
+        HTTP_RESPONSE=$(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')
 
-        local CONTENT=$(curl -s -X POST -d "$INPUT_DATA" -H 'Content-Type: application/json' "$URL" | tee ${LOG_PATH}/${SERVICE_NAME}.log)
+    docker logs ${DOCKER_NAME} >> ${LOG_PATH}/${SERVICE_NAME}.log
 
-        if echo "$CONTENT" | grep -q "$EXPECTED_RESULT"; then
-            echo "[ $SERVICE_NAME ] Content is as expected."
-        else
-            echo "[ $SERVICE_NAME ] Content does not match the expected result: $CONTENT"
-            docker logs ${DOCKER_NAME} >> ${LOG_PATH}/${SERVICE_NAME}.log
-            exit 1
-        fi
-    else
+    # check response status
+    if [ "$HTTP_STATUS" -ne "200" ]; then
         echo "[ $SERVICE_NAME ] HTTP status is not 200. Received status was $HTTP_STATUS"
-        docker logs ${DOCKER_NAME} >> ${LOG_PATH}/${SERVICE_NAME}.log
         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
 }
 
@@ -83,53 +98,83 @@ function validate_microservices() {
     # Check if the microservices are running correctly.
 
     # tei for embedding service
-    validate_services \
+    validate_service \
         "${ip_address}:6006/embed" \
-        "\[\[" \
+        "[[" \
         "tei-embedding" \
         "tei-embedding-server" \
         '{"inputs":"What is Deep Learning?"}'
 
     sleep 1m # retrieval can't curl as expected, try to wait for more time
 
+    # test /v1/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." > $LOG_PATH/dataprep_file.txt
+    validate_service \
+        "http://${ip_address}:6007/v1/dataprep" \
+        "Data preparation succeeded" \
+        "dataprep_upload_file" \
+        "dataprep-redis-server"
+
+    # test /v1/dataprep upload link
+    validate_service \
+        "http://${ip_address}:6007/v1/dataprep" \
+        "Data preparation succeeded" \
+        "dataprep_upload_link" \
+        "dataprep-redis-server"
+
+    # test /v1/dataprep/get_file
+    validate_service \
+        "http://${ip_address}:6007/v1/dataprep/get_file" \
+        '{"name":' \
+        "dataprep_get" \
+        "dataprep-redis-server"
+
+    # test /v1/dataprep/delete_file
+    validate_service \
+        "http://${ip_address}:6007/v1/dataprep/delete_file" \
+        '{"status":true}' \
+        "dataprep_del" \
+        "dataprep-redis-server"
+
     # retrieval microservice
     test_embedding=$(python3 -c "import random; embedding = [random.uniform(-1, 1) for _ in range(768)]; print(embedding)")
-    validate_services \
+    validate_service \
         "${ip_address}:7000/v1/retrieval" \
-        " " \
-        "retrieval" \
+        "retrieved_docs" \
+        "retrieval-microservice" \
         "retriever-redis-server" \
         "{\"text\":\"What is the revenue of Nike in 2023?\",\"embedding\":${test_embedding}}"
 
     # tei for rerank microservice
-    validate_services \
+    validate_service \
         "${ip_address}:8808/rerank" \
         '{"index":1,"score":' \
         "tei-rerank" \
         "tei-reranking-server" \
         '{"query":"What is Deep Learning?", "texts": ["Deep Learning is not...", "Deep learning is..."]}'
 
-    # vllm for llm service
-    validate_services \
-        "${ip_address}:9009/v1/completions" \
-        "text" \
-        "vllm-llm" \
-        "vllm-service" \
-        '{"model": "Intel/neural-chat-7b-v3-3", "prompt": "What is Deep Learning?", "max_tokens": 32, "temperature": 0}'
+    # tgi for llm service
+    validate_service \
+        "${ip_address}:9009/v1/chat/completions" \
+        "content" \
+        "tgi-llm" \
+        "tgi-service" \
+        '{"model": "Intel/neural-chat-7b-v3-3", "messages": [{"role": "user", "content": "What is Deep Learning?"}], "max_tokens": 17}'
 }
 
 function validate_megaservice() {
     # Curl the Mega Service
-    validate_services \
+    validate_service \
         "${ip_address}:8888/v1/chatqna" \
-        "data" \
-        "mega-chatqna" \
+        "data: " \
+        "chatqna-megaservice" \
         "chatqna-xeon-backend-server" \
         '{"messages": "What is the revenue of Nike in 2023?"}'
 
 }
 
 function validate_frontend() {
+    echo "[ TEST INFO ]: --------- frontend test started ---------"
     cd $WORKPATH/ui/svelte
     local conda_env_name="OPEA_e2e"
     export PATH=${HOME}/miniforge3/bin/:$PATH
@@ -138,8 +183,8 @@ function validate_frontend() {
     else
         conda create -n ${conda_env_name} python=3.12 -y
     fi
-
     source activate ${conda_env_name}
+    echo "[ TEST INFO ]: --------- conda env activated ---------"
 
     sed -i "s/localhost/$ip_address/g" playwright.config.ts
 
@@ -160,7 +205,7 @@ function validate_frontend() {
 
 function stop_docker() {
     cd $WORKPATH/docker_compose/intel/cpu/xeon
-    docker compose -f compose_vllm.yaml down
+    docker compose -f compose_tgi.yaml down
 }
 
 function main() {
@@ -177,8 +222,11 @@ function main() {
         python3 $WORKPATH/tests/chatqna_benchmark.py
     elif [ "${mode}" == "" ]; then
         validate_microservices
+        echo "==== microservices validated ===="
         validate_megaservice
-        # validate_frontend
+        echo "==== megaservice validated ===="
+        validate_frontend
+        echo "==== frontend validated ===="
     fi
 
     stop_docker
diff --git a/ChatQnA/tests/test_compose_without_rerank_on_xeon.sh b/ChatQnA/tests/test_compose_without_rerank_on_xeon.sh
index 230b8a5d60..7d2858e41a 100644
--- a/ChatQnA/tests/test_compose_without_rerank_on_xeon.sh
+++ b/ChatQnA/tests/test_compose_without_rerank_on_xeon.sh
@@ -17,12 +17,12 @@ ip_address=$(hostname -I | awk '{print $1}')
 function build_docker_images() {
     cd $WORKPATH/docker_image_build
     git clone https://github.com/opea-project/GenAIComps.git && cd GenAIComps && git checkout "${opea_branch:-"main"}" && cd ../
+    git clone https://github.com/vllm-project/vllm.git
 
     echo "Build all the images with --no-cache, check docker_image_build.log for details..."
-    service_list="chatqna-without-rerank chatqna-ui dataprep-redis retriever nginx"
+    service_list="chatqna-without-rerank chatqna-ui dataprep-redis retriever vllm nginx"
     docker compose -f build.yaml build ${service_list} --no-cache > ${LOG_PATH}/docker_image_build.log
 
-    docker pull ghcr.io/huggingface/tgi-gaudi:2.0.6
     docker pull ghcr.io/huggingface/text-embeddings-inference:cpu-1.5
 
     docker images && sleep 1s
@@ -40,8 +40,8 @@ function start_services() {
     docker compose -f compose_without_rerank.yaml up -d > ${LOG_PATH}/start_services_with_compose.log
     n=0
     until [[ "$n" -ge 100 ]]; do
-        docker logs tgi-service > ${LOG_PATH}/tgi_service_start.log
-        if grep -q Connected ${LOG_PATH}/tgi_service_start.log; then
+        docker logs vllm-service > ${LOG_PATH}/vllm_service_start.log 2>&1
+        if grep -q complete ${LOG_PATH}/vllm_service_start.log; then
             break
         fi
         sleep 5s
@@ -142,13 +142,13 @@ function validate_microservices() {
         "retriever-redis-server" \
         "{\"text\":\"What is the revenue of Nike in 2023?\",\"embedding\":${test_embedding}}"
 
-    # tgi for llm service
+    # vllm for llm service
     validate_service \
-        "${ip_address}:9009/generate" \
-        "generated_text" \
-        "tgi-llm" \
-        "tgi-service" \
-        '{"inputs":"What is Deep Learning?","parameters":{"max_new_tokens":17, "do_sample": true}}'
+        "${ip_address}:9009/v1/chat/completions" \
+        "content" \
+        "vllm-llm" \
+        "vllm-service" \
+        '{"model": "Intel/neural-chat-7b-v3-3", "messages": [{"role": "user", "content": "What is Deep Learning?"}], "max_tokens": 17}'
 }
 
 function validate_megaservice() {
@@ -194,7 +194,7 @@ function validate_frontend() {
 
 function stop_docker() {
     cd $WORKPATH/docker_compose/intel/cpu/xeon/
-    docker compose -f compose_without_rerank.yaml stop && docker compose -f compose_without_rerank.yaml rm -f
+    docker compose -f compose_without_rerank.yaml down
 }
 
 function main() {
