From bceacdc804c98e13283b6ac7ca0784cc6493d972 Mon Sep 17 00:00:00 2001
From: lvliang-intel <liang1.lv@intel.com>
Date: Wed, 18 Sep 2024 09:50:17 +0800
Subject: [PATCH] Fix README issues (#817)

Signed-off-by: lvliang-intel <liang1.lv@intel.com>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
---
 .../docker_compose/intel/cpu/xeon/README.md   | 17 ++----
 .../docker_compose/intel/hpu/gaudi/README.md  | 60 ++++++-------------
 .../docker_compose/intel/cpu/xeon/README.md   | 13 ++--
 .../docker_compose/intel/hpu/gaudi/README.md  | 13 ++--
 .../docker_compose/intel/cpu/xeon/README.md   | 13 ++--
 .../docker_compose/intel/hpu/gaudi/README.md  | 13 ++--
 .../docker_compose/intel/cpu/xeon/README.md   |  7 +--
 .../docker_compose/intel/hpu/gaudi/README.md  | 11 ++--
 .../docker_compose/intel/cpu/xeon/README.md   |  7 +--
 .../docker_compose/intel/hpu/gaudi/README.md  | 11 ++--
 .../docker_compose/intel/cpu/xeon/README.md   | 13 ++--
 .../docker_compose/intel/hpu/gaudi/README.md  | 13 ++--
 .../docker_compose/intel/cpu/xeon/README.md   | 13 ++--
 .../docker_compose/intel/hpu/gaudi/README.md  | 15 ++---
 .../docker_compose/intel/cpu/xeon/README.md   | 12 +---
 .../docker_compose/intel/cpu/xeon/README.md   | 13 ++--
 .../docker_compose/intel/hpu/gaudi/README.md  | 13 ++--
 .../docker_compose/intel/cpu/xeon/README.md   | 19 ++----
 .../docker_compose/intel/hpu/gaudi/README.md  | 18 ++----
 .../docker_compose/intel/cpu/xeon/README.md   |  7 +--
 .../docker_compose/intel/hpu/gaudi/README.md  |  9 +--
 .../docker_compose/intel/cpu/xeon/README.md   | 10 +---
 .../docker_compose/intel/cpu/xeon/README.md   | 10 +---
 .../docker_compose/intel/hpu/gaudi/README.md  | 19 ++----
 24 files changed, 106 insertions(+), 243 deletions(-)

diff --git a/ChatQnA/docker_compose/intel/cpu/xeon/README.md b/ChatQnA/docker_compose/intel/cpu/xeon/README.md
index 1f83a21c8..4868a5ec0 100644
--- a/ChatQnA/docker_compose/intel/cpu/xeon/README.md
+++ b/ChatQnA/docker_compose/intel/cpu/xeon/README.md
@@ -61,14 +61,11 @@ Port 5173 - Open to 0.0.0.0/0
 
 First of all, you need to build Docker Images locally and install the python package of it.
 
-```bash
-git clone https://github.com/opea-project/GenAIComps.git
-cd GenAIComps
-```
-
 ### 1. Build Embedding Image
 
 ```bash
+git clone https://github.com/opea-project/GenAIComps.git
+cd GenAIComps
 docker build --no-cache -t opea/embedding-tei:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f comps/embeddings/tei/langchain/Dockerfile .
 ```
 
@@ -128,7 +125,6 @@ cd ..
    git clone https://github.com/opea-project/GenAIExamples.git
    cd GenAIExamples/ChatQnA
    docker build --no-cache -t opea/chatqna:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f Dockerfile .
-   cd ../../..
    ```
 
 2. MegaService without Rerank
@@ -139,7 +135,6 @@ cd ..
    git clone https://github.com/opea-project/GenAIExamples.git
    cd GenAIExamples/ChatQnA
    docker build --no-cache -t opea/chatqna-without-rerank:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f Dockerfile.without_rerank .
-   cd ../../..
    ```
 
 ### 7. Build UI Docker Image
@@ -149,7 +144,6 @@ Build frontend Docker image via below command:
 ```bash
 cd GenAIExamples/ChatQnA/ui
 docker build --no-cache -t opea/chatqna-ui:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f ./docker/Dockerfile .
-cd ../../../..
 ```
 
 ### 8. Build Conversational React UI Docker Image (Optional)
@@ -161,7 +155,6 @@ Build frontend Docker image that enables Conversational experience with ChatQnA
 ```bash
 cd GenAIExamples/ChatQnA/ui
 docker build --no-cache -t opea/chatqna-conversation-ui:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f ./docker/Dockerfile.react .
-cd ../../../..
 ```
 
 Then run the command `docker images`, you will have the following 7 Docker Images:
@@ -188,7 +181,7 @@ 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 customers with proxy issues, the models from [ModelScope](https://www.modelscope.cn/models) are also supported in ChatQnA with TGI serving. ModelScope models are supported in two ways for TGI:
+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:
 
 1. Online
 
@@ -196,7 +189,7 @@ For customers with proxy issues, the models from [ModelScope](https://www.models
    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:2.1.0 --model-id $model_name
+   docker run -p 8008:80 -v ./data:/data --name tgi-service -e HF_ENDPOINT=$HF_ENDPOINT -e http_proxy=$http_proxy -e https_proxy=$https_proxy --shm-size 1g ghcr.io/huggingface/text-generation-inference:2.2.0 --model-id $model_name
    ```
 
 2. Offline
@@ -210,7 +203,7 @@ For customers with proxy issues, the models from [ModelScope](https://www.models
      ```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:2.1.0 --model-id /data
+     docker run -p 8008:80 -v $model_path:/data --name tgi_service --shm-size 1g ghcr.io/huggingface/text-generation-inference:2.2.0 --model-id /data
      ```
 
 ### Setup Environment Variables
diff --git a/ChatQnA/docker_compose/intel/hpu/gaudi/README.md b/ChatQnA/docker_compose/intel/hpu/gaudi/README.md
index 26b34a8e9..03f5229d4 100644
--- a/ChatQnA/docker_compose/intel/hpu/gaudi/README.md
+++ b/ChatQnA/docker_compose/intel/hpu/gaudi/README.md
@@ -6,26 +6,21 @@ This document outlines the deployment process for a ChatQnA application utilizin
 
 First of all, you need to build Docker Images locally. This step can be ignored after the Docker images published to Docker hub.
 
-### 1. Source Code install GenAIComps
+### 1. Build Embedding Image
 
 ```bash
 git clone https://github.com/opea-project/GenAIComps.git
 cd GenAIComps
-```
-
-### 2. Build Embedding Image
-
-```bash
 docker build --no-cache -t opea/embedding-tei:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f comps/embeddings/tei/langchain/Dockerfile .
 ```
 
-### 3. Build Retriever Image
+### 2. Build Retriever Image
 
 ```bash
 docker build --no-cache -t opea/retriever-redis:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f comps/retrievers/redis/langchain/Dockerfile .
 ```
 
-### 4. Build Rerank Image
+### 3. Build Rerank Image
 
 > Skip for ChatQnA without Rerank pipeline
 
@@ -33,17 +28,17 @@ docker build --no-cache -t opea/retriever-redis:latest --build-arg https_proxy=$
 docker build --no-cache -t opea/reranking-tei:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f comps/reranks/tei/Dockerfile .
 ```
 
-### 5. Build LLM Image
+### 4. Build LLM Image
 
 You can use different LLM serving solutions, choose one of following four options.
 
-#### 5.1 Use TGI
+#### 4.1 Use TGI
 
 ```bash
 docker build --no-cache -t opea/llm-tgi:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f comps/llms/text-generation/tgi/Dockerfile .
 ```
 
-#### 5.2 Use VLLM
+#### 4.2 Use VLLM
 
 Build vllm docker.
 
@@ -57,7 +52,7 @@ Build microservice docker.
 docker build --no-cache -t opea/llm-vllm:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f comps/llms/text-generation/vllm/langchain/Dockerfile .
 ```
 
-#### 5.3 Use VLLM-on-Ray
+#### 4.3 Use VLLM-on-Ray
 
 Build vllm-on-ray docker.
 
@@ -71,24 +66,21 @@ Build microservice docker.
 docker build --no-cache -t opea/llm-vllm-ray:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f comps/llms/text-generation/vllm/ray/Dockerfile .
 ```
 
-### 6. Build Dataprep Image
+### 5. Build Dataprep Image
 
 ```bash
 docker build --no-cache -t opea/dataprep-redis:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f comps/dataprep/redis/langchain/Dockerfile .
 ```
 
-### 7. Build TEI Gaudi Image
+### 6. Build Guardrails Docker Image (Optional)
 
-Since a TEI Gaudi Docker image hasn't been published, we'll need to build it from the [tei-gaudi](https://github.com/huggingface/tei-gaudi) repository.
+To fortify AI initiatives in production, Guardrails microservice can secure model inputs and outputs, building Trustworthy, Safe, and Secure LLM-based Applications.
 
 ```bash
-git clone https://github.com/huggingface/tei-gaudi
-cd tei-gaudi/
-docker build --no-cache -f Dockerfile-hpu -t opea/tei-gaudi:latest .
-cd ../..
+docker build -t opea/guardrails-tgi:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f comps/guardrails/llama_guard/langchain/Dockerfile .
 ```
 
-### 8. Build MegaService Docker Image
+### 7. Build MegaService Docker Image
 
 1. MegaService with Rerank
 
@@ -98,7 +90,6 @@ cd ../..
    git clone https://github.com/opea-project/GenAIExamples.git
    cd GenAIExamples/ChatQnA/docker
    docker build --no-cache -t opea/chatqna:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f Dockerfile .
-   cd ../../..
    ```
 
 2. MegaService with Guardrails
@@ -109,7 +100,6 @@ cd ../..
    git clone https://github.com/opea-project/GenAIExamples.git
    cd GenAIExamples/ChatQnA/
    docker build --no-cache -t opea/chatqna-guardrails:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f Dockerfile.guardrails .
-   cd ../../..
    ```
 
 3. MegaService without Rerank
@@ -120,20 +110,18 @@ cd ../..
    git clone https://github.com/opea-project/GenAIExamples.git
    cd GenAIExamples/ChatQnA/docker
    docker build --no-cache -t opea/chatqna-without-rerank:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f Dockerfile.without_rerank .
-   cd ../../..
    ```
 
-### 9. Build UI Docker Image
+### 8. Build UI Docker Image
 
 Construct the frontend Docker image using the command below:
 
 ```bash
 cd GenAIExamples/ChatQnA/ui
 docker build --no-cache -t opea/chatqna-ui:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f ./docker/Dockerfile .
-cd ../../../..
 ```
 
-### 10. Build Conversational React UI Docker Image (Optional)
+### 9. Build Conversational React UI Docker Image (Optional)
 
 Build frontend Docker image that enables Conversational experience with ChatQnA megaservice via below command:
 
@@ -142,26 +130,14 @@ Build frontend Docker image that enables Conversational experience with ChatQnA
 ```bash
 cd GenAIExamples/ChatQnA/ui
 docker build --no-cache -t opea/chatqna-conversation-ui:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f ./docker/Dockerfile.react .
-cd ../../../..
-```
-
-### 11. Build Guardrails Docker Image (Optional)
-
-To fortify AI initiatives in production, Guardrails microservice can secure model inputs and outputs, building Trustworthy, Safe, and Secure LLM-based Applications.
-
-```bash
-cd GenAIComps
-docker build -t opea/guardrails-tgi:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f comps/guardrails/llama_guard/langchain/Dockerfile .
-cd ../../..
 ```
 
-Then run the command `docker images`, you will have the following 8 Docker Images:
+Then run the command `docker images`, you will have the following 7 Docker Images:
 
 - `opea/embedding-tei:latest`
 - `opea/retriever-redis:latest`
 - `opea/reranking-tei:latest`
 - `opea/llm-tgi:latest` or `opea/llm-vllm:latest` or `opea/llm-vllm-ray:latest`
-- `opea/tei-gaudi:latest`
 - `opea/dataprep-redis:latest`
 - `opea/chatqna:latest` or `opea/chatqna-guardrails:latest` or `opea/chatqna-without-rerank:latest`
 - `opea/chatqna-ui:latest`
@@ -188,7 +164,7 @@ 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 customers with proxy issues, the models from [ModelScope](https://www.modelscope.cn/models) are also supported in ChatQnA with TGI serving. ModelScope models are supported in two ways for TGI:
+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:
 
 1. Online
 
@@ -196,7 +172,7 @@ For customers with proxy issues, the models from [ModelScope](https://www.models
    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:2.1.0 --model-id $model_name
+   docker run -p 8008:80 -v ./data:/data --name tgi-service -e HF_ENDPOINT=$HF_ENDPOINT -e http_proxy=$http_proxy -e https_proxy=$https_proxy --runtime=habana -e HABANA_VISIBLE_DEVICES=all -e OMPI_MCA_btl_vader_single_copy_mechanism=none -e HUGGING_FACE_HUB_TOKEN=$HF_TOKEN -e ENABLE_HPU_GRAPH=true -e LIMIT_HPU_GRAPH=true -e USE_FLASH_ATTENTION=true -e FLASH_ATTENTION_RECOMPUTE=true --cap-add=sys_nice --ipc=host ghcr.io/huggingface/tgi-gaudi:2.0.5 --model-id $model_name --max-input-tokens 1024 --max-total-tokens 2048
    ```
 
 2. Offline
@@ -210,7 +186,7 @@ For customers with proxy issues, the models from [ModelScope](https://www.models
      ```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:2.1.0 --model-id /data
+     docker run -p 8008:80 -v $model_path:/data --name tgi_service --runtime=habana -e HABANA_VISIBLE_DEVICES=all -e OMPI_MCA_btl_vader_single_copy_mechanism=none -e HUGGING_FACE_HUB_TOKEN=$HF_TOKEN -e ENABLE_HPU_GRAPH=true -e LIMIT_HPU_GRAPH=true -e USE_FLASH_ATTENTION=true -e FLASH_ATTENTION_RECOMPUTE=true --cap-add=sys_nice --ipc=host ghcr.io/huggingface/tgi-gaudi:2.0.5 --model-id /data --max-input-tokens 1024 --max-total-tokens 2048
      ```
 
 ### Setup Environment Variables
diff --git a/CodeGen/docker_compose/intel/cpu/xeon/README.md b/CodeGen/docker_compose/intel/cpu/xeon/README.md
index e5df12937..d7dc3376e 100644
--- a/CodeGen/docker_compose/intel/cpu/xeon/README.md
+++ b/CodeGen/docker_compose/intel/cpu/xeon/README.md
@@ -14,20 +14,15 @@ After launching your instance, you can connect to it using SSH (for Linux instan
 
 Should the Docker image you seek not yet be available on Docker Hub, you can build the Docker image locally.
 
-### 1. Git Clone GenAIComps
+### 1. Build the LLM Docker Image
 
 ```bash
 git clone https://github.com/opea-project/GenAIComps.git
 cd GenAIComps
-```
-
-### 2. Build the LLM Docker Image
-
-```bash
 docker build -t opea/llm-tgi:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f comps/llms/text-generation/tgi/Dockerfile .
 ```
 
-### 3. Build the MegaService Docker Image
+### 2. Build the MegaService Docker Image
 
 To construct the Mega Service, we utilize the [GenAIComps](https://github.com/opea-project/GenAIComps.git) microservice pipeline within the `codegen.py` Python script. Build MegaService Docker image via the command below:
 
@@ -37,7 +32,7 @@ cd GenAIExamples/CodeGen
 docker build -t opea/codegen:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f Dockerfile .
 ```
 
-### 4. Build the UI Docker Image
+### 3. Build the UI Docker Image
 
 Build the frontend Docker image via the command below:
 
@@ -52,7 +47,7 @@ Then run the command `docker images`, you will have the following 3 Docker Image
 - `opea/codegen:latest`
 - `opea/codegen-ui:latest`
 
-### 8. Build CodeGen React UI Docker Image (Optional)
+### 4. Build CodeGen React UI Docker Image (Optional)
 
 Build react frontend Docker image via below command:
 
diff --git a/CodeGen/docker_compose/intel/hpu/gaudi/README.md b/CodeGen/docker_compose/intel/hpu/gaudi/README.md
index 6e093e5c2..74afd54ae 100644
--- a/CodeGen/docker_compose/intel/hpu/gaudi/README.md
+++ b/CodeGen/docker_compose/intel/hpu/gaudi/README.md
@@ -6,20 +6,15 @@ This document outlines the deployment process for a CodeGen application utilizin
 
 First of all, you need to build the Docker images locally. This step can be ignored after the Docker images published to the Docker Hub.
 
-### 1. Git Clone GenAIComps
+### 1. Build the LLM Docker Image
 
 ```bash
 git clone https://github.com/opea-project/GenAIComps.git
 cd GenAIComps
-```
-
-### 2. Build the LLM Docker Image
-
-```bash
 docker build -t opea/llm-tgi:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f comps/llms/text-generation/tgi/Dockerfile .
 ```
 
-### 3. Build the MegaService Docker Image
+### 2. Build the MegaService Docker Image
 
 To construct the Mega Service, we utilize the [GenAIComps](https://github.com/opea-project/GenAIComps.git) microservice pipeline within the `codegen.py` Python script. Build the MegaService Docker image via the command below:
 
@@ -29,7 +24,7 @@ cd GenAIExamples/CodeGen
 docker build -t opea/codegen:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f Dockerfile .
 ```
 
-### 4. Build the UI Docker Image
+### 3. Build the UI Docker Image
 
 Construct the frontend Docker image via the command below:
 
@@ -38,7 +33,7 @@ cd GenAIExamples/CodeGen/ui
 docker build -t opea/codegen-ui:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f ./docker/Dockerfile .
 ```
 
-### 8. Build CodeGen React UI Docker Image (Optional)
+### 4. Build CodeGen React UI Docker Image (Optional)
 
 Build react frontend Docker image via below command:
 
diff --git a/CodeTrans/docker_compose/intel/cpu/xeon/README.md b/CodeTrans/docker_compose/intel/cpu/xeon/README.md
index 3e09ade36..fd29ce210 100755
--- a/CodeTrans/docker_compose/intel/cpu/xeon/README.md
+++ b/CodeTrans/docker_compose/intel/cpu/xeon/README.md
@@ -14,20 +14,15 @@ After launching your instance, you can connect to it using SSH (for Linux instan
 
 First of all, you need to build Docker Images locally and install the python package of it. This step can be ignored after the Docker images published to Docker hub.
 
-### 1. Install GenAIComps from Source Code
+### 1. Build the LLM Docker Image
 
 ```bash
 git clone https://github.com/opea-project/GenAIComps.git
 cd GenAIComps
-```
-
-### 2. Build the LLM Docker Image
-
-```bash
 docker build -t opea/llm-tgi:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f comps/llms/text-generation/tgi/Dockerfile .
 ```
 
-### 3. Build MegaService Docker Image
+### 2. Build MegaService Docker Image
 
 ```bash
 git clone https://github.com/opea-project/GenAIExamples.git
@@ -35,14 +30,14 @@ cd GenAIExamples/CodeTrans
 docker build -t opea/codetrans:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f Dockerfile .
 ```
 
-### 4. Build UI Docker Image
+### 3. Build UI Docker Image
 
 ```bash
 cd GenAIExamples/CodeTrans/ui
 docker build -t opea/codetrans-ui:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f ./docker/Dockerfile .
 ```
 
-### 5. Build Nginx Docker Image
+### 4. Build Nginx Docker Image
 
 ```bash
 cd GenAIComps
diff --git a/CodeTrans/docker_compose/intel/hpu/gaudi/README.md b/CodeTrans/docker_compose/intel/hpu/gaudi/README.md
index 0ce5f8879..1eb1812f2 100755
--- a/CodeTrans/docker_compose/intel/hpu/gaudi/README.md
+++ b/CodeTrans/docker_compose/intel/hpu/gaudi/README.md
@@ -6,20 +6,15 @@ This document outlines the deployment process for a CodeTrans application utiliz
 
 First of all, you need to build Docker Images locally and install the python package of it. This step can be ignored after the Docker images published to Docker hub.
 
-### 1. Source Code install GenAIComps
+### 1. Build the LLM Docker Image
 
 ```bash
 git clone https://github.com/opea-project/GenAIComps.git
 cd GenAIComps
-```
-
-### 2. Build the LLM Docker Image
-
-```bash
 docker build -t opea/llm-tgi:latest --no-cache --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f comps/llms/text-generation/tgi/Dockerfile .
 ```
 
-### 3. Build MegaService Docker Image
+### 2. Build MegaService Docker Image
 
 ```bash
 git clone https://github.com/opea-project/GenAIExamples.git
@@ -27,14 +22,14 @@ cd GenAIExamples/CodeTrans
 docker build -t opea/codetrans:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f Dockerfile .
 ```
 
-### 4. Build UI Docker Image
+### 3. Build UI Docker Image
 
 ```bash
 cd GenAIExamples/CodeTrans/ui
 docker build -t opea/codetrans-ui:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f docker/Dockerfile .
 ```
 
-### 5. Build Nginx Docker Image
+### 4. Build Nginx Docker Image
 
 ```bash
 cd GenAIComps
diff --git a/DocSum/docker_compose/intel/cpu/xeon/README.md b/DocSum/docker_compose/intel/cpu/xeon/README.md
index b5457917c..4085365be 100644
--- a/DocSum/docker_compose/intel/cpu/xeon/README.md
+++ b/DocSum/docker_compose/intel/cpu/xeon/README.md
@@ -14,14 +14,11 @@ After launching your instance, you can connect to it using SSH (for Linux instan
 
 First of all, you need to build Docker Images locally and install the python package of it.
 
-```bash
-git clone https://github.com/opea-project/GenAIComps.git
-cd GenAIComps
-```
-
 ### 1. Build LLM Image
 
 ```bash
+git clone https://github.com/opea-project/GenAIComps.git
+cd GenAIComps
 docker build -t opea/llm-docsum-tgi:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f comps/llms/summarization/tgi/langchain/Dockerfile .
 ```
 
diff --git a/DocSum/docker_compose/intel/hpu/gaudi/README.md b/DocSum/docker_compose/intel/hpu/gaudi/README.md
index 8bea9ffb3..8ef3b2916 100644
--- a/DocSum/docker_compose/intel/hpu/gaudi/README.md
+++ b/DocSum/docker_compose/intel/hpu/gaudi/README.md
@@ -6,22 +6,19 @@ This document outlines the deployment process for a Document Summarization appli
 
 First of all, you need to build Docker Images locally. This step can be ignored once the Docker images are published to Docker hub.
 
-```bash
-git clone https://github.com/opea-project/GenAIComps.git
-cd GenAIComps
-```
-
 ### 1. Pull TGI Gaudi Image
 
 As TGI Gaudi has been officially published as a Docker image, we simply need to pull it:
 
 ```bash
-docker pull ghcr.io/huggingface/tgi-gaudi:2.0.1
+docker pull ghcr.io/huggingface/tgi-gaudi:2.0.5
 ```
 
 ### 2. Build LLM Image
 
 ```bash
+git clone https://github.com/opea-project/GenAIComps.git
+cd GenAIComps
 docker build -t opea/llm-docsum-tgi:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f comps/llms/summarization/tgi/langchain/Dockerfile .
 ```
 
@@ -56,7 +53,7 @@ docker build -t opea/docsum-react-ui:latest --build-arg BACKEND_SERVICE_ENDPOINT
 
 Then run the command `docker images`, you will have the following Docker Images:
 
-1. `ghcr.io/huggingface/tgi-gaudi:2.0.1`
+1. `ghcr.io/huggingface/tgi-gaudi:2.0.5`
 2. `opea/llm-docsum-tgi:latest`
 3. `opea/docsum:latest`
 4. `opea/docsum-ui:latest`
diff --git a/FaqGen/docker_compose/intel/cpu/xeon/README.md b/FaqGen/docker_compose/intel/cpu/xeon/README.md
index f2ec2be3a..04fea0f85 100644
--- a/FaqGen/docker_compose/intel/cpu/xeon/README.md
+++ b/FaqGen/docker_compose/intel/cpu/xeon/README.md
@@ -14,14 +14,11 @@ After launching your instance, you can connect to it using SSH (for Linux instan
 
 First of all, you need to build Docker Images locally. This step can be ignored once the Docker images are published to Docker hub.
 
-```bash
-git clone https://github.com/opea-project/GenAIComps.git
-cd GenAIComps
-```
-
 ### 1. Build LLM Image
 
 ```bash
+git clone https://github.com/opea-project/GenAIComps.git
+cd GenAIComps
 docker build -t opea/llm-faqgen-tgi:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f comps/llms/faq-generation/tgi/langchain/Dockerfile .
 ```
 
diff --git a/FaqGen/docker_compose/intel/hpu/gaudi/README.md b/FaqGen/docker_compose/intel/hpu/gaudi/README.md
index 533478d15..acdded9c2 100644
--- a/FaqGen/docker_compose/intel/hpu/gaudi/README.md
+++ b/FaqGen/docker_compose/intel/hpu/gaudi/README.md
@@ -6,22 +6,19 @@ This document outlines the deployment process for a FAQ Generation application u
 
 First of all, you need to build Docker Images locally. This step can be ignored once the Docker images are published to Docker hub.
 
-```bash
-git clone https://github.com/opea-project/GenAIComps.git
-cd GenAIComps
-```
-
 ### 1. Pull TGI Gaudi Image
 
 As TGI Gaudi has been officially published as a Docker image, we simply need to pull it:
 
 ```bash
-docker pull ghcr.io/huggingface/tgi-gaudi:2.0.1
+docker pull ghcr.io/huggingface/tgi-gaudi:2.0.5
 ```
 
 ### 2. Build LLM Image
 
 ```bash
+git clone https://github.com/opea-project/GenAIComps.git
+cd GenAIComps
 docker build -t opea/llm-faqgen-tgi:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f comps/llms/faq-generation/tgi/langchain/Dockerfile .
 ```
 
@@ -56,7 +53,7 @@ docker build -t opea/faqgen-react-ui:latest --build-arg https_proxy=$https_proxy
 
 Then run the command `docker images`, you will have the following Docker Images:
 
-1. `ghcr.io/huggingface/tgi-gaudi:2.0.1`
+1. `ghcr.io/huggingface/tgi-gaudi:2.0.5`
 2. `opea/llm-faqgen-tgi:latest`
 3. `opea/faqgen:latest`
 4. `opea/faqgen-ui:latest`
diff --git a/InstructionTuning/docker_compose/intel/cpu/xeon/README.md b/InstructionTuning/docker_compose/intel/cpu/xeon/README.md
index a2c31acbd..684a88f79 100644
--- a/InstructionTuning/docker_compose/intel/cpu/xeon/README.md
+++ b/InstructionTuning/docker_compose/intel/cpu/xeon/README.md
@@ -6,23 +6,18 @@ This document outlines the deployment process for a Instruction Tuning Service u
 
 First of all, you need to build Docker Images locally. This step can be ignored after the Docker images published to Docker hub.
 
-### 1. Source Code install GenAIComps
-
-```bash
-git clone https://github.com/opea-project/GenAIComps.git
-cd GenAIComps
-```
-
-### 2. Build Docker Image
+### 1. Build Docker Image
 
 Build docker image with below command:
 
 ```bash
+git clone https://github.com/opea-project/GenAIComps.git
+cd GenAIComps
 export HF_TOKEN=${your_huggingface_token}
 docker build -t opea/finetuning:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy --build-arg HF_TOKEN=$HF_TOKEN -f comps/finetuning/Dockerfile .
 ```
 
-### 3. Run Docker with CLI
+### 2. Run Docker with CLI
 
 Start docker container with below command:
 
diff --git a/InstructionTuning/docker_compose/intel/hpu/gaudi/README.md b/InstructionTuning/docker_compose/intel/hpu/gaudi/README.md
index 30c72cc4e..550e8e213 100644
--- a/InstructionTuning/docker_compose/intel/hpu/gaudi/README.md
+++ b/InstructionTuning/docker_compose/intel/hpu/gaudi/README.md
@@ -6,22 +6,17 @@ This document outlines the deployment process for a Instruction Tuning Service u
 
 First of all, you need to build Docker Images locally. This step can be ignored after the Docker images published to Docker hub.
 
-### 1. Source Code install GenAIComps
-
-```bash
-git clone https://github.com/opea-project/GenAIComps.git
-cd GenAIComps
-```
-
-### 2. Build Docker Image
+### 1. Build Docker Image
 
 Build docker image with below command:
 
 ```bash
+git clone https://github.com/opea-project/GenAIComps.git
+cd GenAIComps
 docker build -t opea/finetuning-gaudi:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f comps/finetuning/Dockerfile.intel_hpu .
 ```
 
-### 3. Run Docker with CLI
+### 2. Run Docker with CLI
 
 Start docker container with below command:
 
diff --git a/MultimodalQnA/docker_compose/intel/cpu/xeon/README.md b/MultimodalQnA/docker_compose/intel/cpu/xeon/README.md
index e31150969..9b3a3edaa 100644
--- a/MultimodalQnA/docker_compose/intel/cpu/xeon/README.md
+++ b/MultimodalQnA/docker_compose/intel/cpu/xeon/README.md
@@ -100,18 +100,13 @@ Note: Please replace with `host_ip` with you external IP address, do not use loc
 
 ## 🚀 Build Docker Images
 
-First of all, you need to build Docker Images locally and install the python package of it.
-
-```bash
-git clone https://github.com/opea-project/GenAIComps.git
-cd GenAIComps
-```
-
 ### 1. Build embedding-multimodal-bridgetower Image
 
 Build embedding-multimodal-bridgetower docker image
 
 ```bash
+git clone https://github.com/opea-project/GenAIComps.git
+cd GenAIComps
 docker build --no-cache -t opea/embedding-multimodal-bridgetower:latest --build-arg EMBEDDER_PORT=$EMBEDDER_PORT --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f comps/embeddings/multimodal/bridgetower/Dockerfile .
 ```
 
@@ -340,6 +335,6 @@ curl http://${host_ip}:8888/v1/multimodalqna \
 
 ```bash
 curl http://${host_ip}:8888/v1/multimodalqna \
-	-H "Content-Type: application/json" \
-	-d '{"messages": [{"role": "user", "content": [{"type": "text", "text": "hello, "}, {"type": "image_url", "image_url": {"url": "https://www.ilankelman.org/stopsigns/australia.jpg"}}]}, {"role": "assistant", "content": "opea project! "}, {"role": "user", "content": "chao, "}], "max_tokens": 10}'
+    -H "Content-Type: application/json" \
+    -d '{"messages": [{"role": "user", "content": [{"type": "text", "text": "hello, "}, {"type": "image_url", "image_url": {"url": "https://www.ilankelman.org/stopsigns/australia.jpg"}}]}, {"role": "assistant", "content": "opea project! "}, {"role": "user", "content": "chao, "}], "max_tokens": 10}'
 ```
diff --git a/MultimodalQnA/docker_compose/intel/hpu/gaudi/README.md b/MultimodalQnA/docker_compose/intel/hpu/gaudi/README.md
index 5505aa9bd..be71659d5 100644
--- a/MultimodalQnA/docker_compose/intel/hpu/gaudi/README.md
+++ b/MultimodalQnA/docker_compose/intel/hpu/gaudi/README.md
@@ -1,4 +1,4 @@
-# Build Mega Service of MultimodalRAGWithVideos on Gaudi
+# Build Mega Service of MultimodalQnA on Gaudi
 
 This document outlines the deployment process for a MultimodalQnA application utilizing the [GenAIComps](https://github.com/opea-project/GenAIComps.git) microservice pipeline on Intel Gaudi server. The steps include Docker image creation, container deployment via Docker Compose, and service execution to integrate microservices such as `multimodal_embedding` that employs [BridgeTower](https://huggingface.co/BridgeTower/bridgetower-large-itm-mlm-gaudi) model as embedding model, `multimodal_retriever`, `lvm`, and `multimodal-data-prep`. We will publish the Docker images to Docker Hub soon, it will simplify the deployment process for this service.
 
@@ -52,16 +52,13 @@ Note: Please replace with `host_ip` with you external IP address, do not use loc
 
 First of all, you need to build Docker Images locally and install the python package of it.
 
-```bash
-git clone https://github.com/opea-project/GenAIComps.git
-cd GenAIComps
-```
-
 ### 1. Build embedding-multimodal-bridgetower Image
 
 Build embedding-multimodal-bridgetower docker image
 
 ```bash
+git clone https://github.com/opea-project/GenAIComps.git
+cd GenAIComps
 docker build --no-cache -t opea/embedding-multimodal-bridgetower:latest --build-arg EMBEDDER_PORT=$EMBEDDER_PORT --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f comps/embeddings/multimodal/bridgetower/Dockerfile .
 ```
 
@@ -82,7 +79,7 @@ docker build --no-cache -t opea/retriever-multimodal-redis:latest --build-arg ht
 Build TGI Gaudi image
 
 ```bash
-docker pull ghcr.io/huggingface/tgi-gaudi:2.0.4
+docker pull ghcr.io/huggingface/tgi-gaudi:2.0.5
 ```
 
 Build lvm-tgi microservice image
@@ -105,7 +102,6 @@ To construct the Mega Service, we utilize the [GenAIComps](https://github.com/op
 git clone https://github.com/opea-project/GenAIExamples.git
 cd GenAIExamples/MultimodalQnA
 docker build --no-cache -t opea/multimodalqna:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f Dockerfile .
-cd ../..
 ```
 
 ### 6. Build UI Docker Image
@@ -115,14 +111,13 @@ Build frontend Docker image via below command:
 ```bash
 cd  GenAIExamples/MultimodalQnA/ui/
 docker build --no-cache -t opea/multimodalqna-ui:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f ./docker/Dockerfile .
-cd ../../../
 ```
 
 Then run the command `docker images`, you will have the following 8 Docker Images:
 
 1. `opea/dataprep-multimodal-redis:latest`
 2. `opea/lvm-tgi:latest`
-3. `ghcr.io/huggingface/tgi-gaudi:2.0.4`
+3. `ghcr.io/huggingface/tgi-gaudi:2.0.5`
 4. `opea/retriever-multimodal-redis:latest`
 5. `opea/embedding-multimodal:latest`
 6. `opea/embedding-multimodal-bridgetower:latest`
diff --git a/ProductivitySuite/docker_compose/intel/cpu/xeon/README.md b/ProductivitySuite/docker_compose/intel/cpu/xeon/README.md
index 1741b9b94..dd1f59f27 100644
--- a/ProductivitySuite/docker_compose/intel/cpu/xeon/README.md
+++ b/ProductivitySuite/docker_compose/intel/cpu/xeon/README.md
@@ -6,14 +6,11 @@ This document outlines the deployment process for OPEA Productivity Suite utiliz
 
 First of all, you need to build Docker Images locally and install the python package of it.
 
-```bash
-git clone https://github.com/opea-project/GenAIComps.git
-cd GenAIComps
-```
-
 ### 1. Build Embedding Image
 
 ```bash
+git clone https://github.com/opea-project/GenAIComps.git
+cd GenAIComps
 docker build --no-cache -t opea/embedding-tei:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f comps/embeddings/tei/langchain/Dockerfile .
 ```
 
@@ -69,7 +66,6 @@ The Productivity Suite is composed of multiple GenAIExample reference solutions
 git clone https://github.com/opea-project/GenAIExamples.git
 cd GenAIExamples/ChatQnA/
 docker build --no-cache -t opea/chatqna:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f Dockerfile .
-cd ../../..
 ```
 
 #### 8.2 Build DocSum Megaservice Docker Images
@@ -77,7 +73,6 @@ cd ../../..
 ```bash
 cd GenAIExamples/DocSum
 docker build --no-cache -t opea/docsum:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f Dockerfile .
-cd ../../..
 ```
 
 #### 8.3 Build CodeGen Megaservice Docker Images
@@ -85,7 +80,6 @@ cd ../../..
 ```bash
 cd GenAIExamples/CodeGen
 docker build --no-cache -t opea/codegen:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f Dockerfile .
-cd ../../..
 ```
 
 #### 8.4 Build FAQGen Megaservice Docker Images
@@ -93,7 +87,6 @@ cd ../../..
 ```bash
 cd GenAIExamples/FaqGen
 docker build --no-cache -t opea/faqgen:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f Dockerfile .
-cd ../../..
 ```
 
 ### 9. Build UI Docker Image
@@ -105,7 +98,6 @@ Build frontend Docker image that enables via below command:
 ```bash
 cd GenAIExamples/ProductivitySuite/ui
 docker build --no-cache -t ProductivitySuite/docker_compose/intel/cpu/xeon/compose.yaml docker/Dockerfile.react .
-cd ../../../..
 ```
 
 ## 🚀 Start Microservices
diff --git a/RerankFinetuning/docker_compose/intel/cpu/xeon/README.md b/RerankFinetuning/docker_compose/intel/cpu/xeon/README.md
index ffd5947c4..700358ac9 100644
--- a/RerankFinetuning/docker_compose/intel/cpu/xeon/README.md
+++ b/RerankFinetuning/docker_compose/intel/cpu/xeon/README.md
@@ -6,23 +6,18 @@ This document outlines the deployment process for a rerank model finetuning serv
 
 First of all, you need to build Docker Images locally. This step can be ignored after the Docker images published to Docker hub.
 
-### 1. Source Code install GenAIComps
-
-```bash
-git clone https://github.com/opea-project/GenAIComps.git
-cd GenAIComps
-```
-
-### 2. Build Docker Image
+### 1. Build Docker Image
 
 Build docker image with below command:
 
 ```bash
+git clone https://github.com/opea-project/GenAIComps.git
+cd GenAIComps
 export HF_TOKEN=${your_huggingface_token}
 docker build -t opea/finetuning:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy --build-arg HF_TOKEN=$HF_TOKEN -f comps/finetuning/Dockerfile .
 ```
 
-### 3. Run Docker with CLI
+### 2. Run Docker with CLI
 
 Start docker container with below command:
 
diff --git a/RerankFinetuning/docker_compose/intel/hpu/gaudi/README.md b/RerankFinetuning/docker_compose/intel/hpu/gaudi/README.md
index 07983cc58..eebe6a374 100644
--- a/RerankFinetuning/docker_compose/intel/hpu/gaudi/README.md
+++ b/RerankFinetuning/docker_compose/intel/hpu/gaudi/README.md
@@ -6,22 +6,17 @@ This document outlines the deployment process for a rerank model finetuning serv
 
 First of all, you need to build Docker Images locally. This step can be ignored after the Docker images published to Docker hub.
 
-### 1. Source Code install GenAIComps
-
-```bash
-git clone https://github.com/opea-project/GenAIComps.git
-cd GenAIComps
-```
-
-### 2. Build Docker Image
+### 1. Build Docker Image
 
 Build docker image with below command:
 
 ```bash
+git clone https://github.com/opea-project/GenAIComps.git
+cd GenAIComps
 docker build -t opea/finetuning-gaudi:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f comps/finetuning/Dockerfile.intel_hpu .
 ```
 
-### 3. Run Docker with CLI
+### 2. Run Docker with CLI
 
 Start docker container with below command:
 
diff --git a/SearchQnA/docker_compose/intel/cpu/xeon/README.md b/SearchQnA/docker_compose/intel/cpu/xeon/README.md
index 79e24e1e6..f31975ac6 100644
--- a/SearchQnA/docker_compose/intel/cpu/xeon/README.md
+++ b/SearchQnA/docker_compose/intel/cpu/xeon/README.md
@@ -4,38 +4,33 @@ This document outlines the deployment process for a SearchQnA application utiliz
 
 ## 🚀 Build Docker images
 
-### 1. Source Code install GenAIComps
+### 1. Build Embedding Image
 
 ```bash
 git clone https://github.com/opea-project/GenAIComps.git
 cd GenAIComps
-```
-
-### 2. Build Embedding Image
-
-```bash
 docker build --no-cache -t opea/embedding-tei:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f comps/embeddings/tei/langchain/Dockerfile .
 ```
 
-### 3. Build Retriever Image
+### 2. Build Retriever Image
 
 ```bash
 docker build --no-cache -t opea/web-retriever-chroma:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f comps/web_retrievers/chroma/langchain/Dockerfile .
 ```
 
-### 4. Build Rerank Image
+### 3. Build Rerank Image
 
 ```bash
 docker build --no-cache -t opea/reranking-tei:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f comps/reranks/tei/Dockerfile .
 ```
 
-### 5. Build LLM Image
+### 4. Build LLM Image
 
 ```bash
 docker build --no-cache -t opea/llm-tgi:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f comps/llms/text-generation/tgi/Dockerfile .
 ```
 
-### 6. Build MegaService Docker Image
+### 5. Build MegaService Docker Image
 
 To construct the Mega Service, we utilize the [GenAIComps](https://github.com/opea-project/GenAIComps.git) microservice pipeline within the `searchqna.py` Python script. Build the MegaService Docker image using the command below:
 
@@ -43,17 +38,15 @@ To construct the Mega Service, we utilize the [GenAIComps](https://github.com/op
 git clone https://github.com/opea-project/GenAIExamples.git
 cd GenAIExamples/SearchQnA
 docker build --no-cache -t opea/searchqna:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f Dockerfile .
-cd ../../..
 ```
 
-### 7. Build UI Docker Image
+### 6. Build UI Docker Image
 
 Build frontend Docker image via below command:
 
 ```bash
 cd GenAIExamples/SearchQnA/ui
 docker build --no-cache -t opea/opea/searchqna-ui:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f ./docker/Dockerfile .
-cd ../../../..
 ```
 
 Then run the command `docker images`, you will have following images ready:
diff --git a/SearchQnA/docker_compose/intel/hpu/gaudi/README.md b/SearchQnA/docker_compose/intel/hpu/gaudi/README.md
index 9a7ce6a2e..b34398c35 100644
--- a/SearchQnA/docker_compose/intel/hpu/gaudi/README.md
+++ b/SearchQnA/docker_compose/intel/hpu/gaudi/README.md
@@ -6,38 +6,33 @@ This document outlines the deployment process for a SearchQnA application utiliz
 
 First of all, you need to build Docker Images locally. This step can be ignored after the Docker images published to Docker hub.
 
-### 1. Source Code install GenAIComps
+### 1. Build Embedding Image
 
 ```bash
 git clone https://github.com/opea-project/GenAIComps.git
 cd GenAIComps
-```
-
-### 2. Build Embedding Image
-
-```bash
 docker build --no-cache -t opea/embedding-tei:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f comps/embeddings/tei/langchain/Dockerfile .
 ```
 
-### 3. Build Retriever Image
+### 2. Build Retriever Image
 
 ```bash
 docker build --no-cache -t opea/web-retriever-chroma:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f comps/web_retrievers/chroma/langchain/Dockerfile .
 ```
 
-### 4. Build Rerank Image
+### 3. Build Rerank Image
 
 ```bash
 docker build --no-cache -t opea/reranking-tei:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f comps/reranks/tei/Dockerfile .
 ```
 
-### 5. Build LLM Image
+### 4. Build LLM Image
 
 ```bash
 docker build --no-cache -t opea/llm-tgi:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f comps/llms/text-generation/tgi/Dockerfile .
 ```
 
-### 6. Build TEI Gaudi Image
+### 5. Build TEI Gaudi Image
 
 Since a TEI Gaudi Docker image hasn't been published, we'll need to build it from the [tei-guadi](https://github.com/huggingface/tei-gaudi) repository.
 
@@ -48,7 +43,7 @@ docker build --no-cache -f Dockerfile-hpu -t opea/tei-gaudi:latest .
 cd ../..
 ```
 
-### 7. Build MegaService Docker Image
+### 6. Build MegaService Docker Image
 
 To construct the Mega Service, we utilize the [GenAIComps](https://github.com/opea-project/GenAIComps.git) microservice pipeline within the `searchqna.py` Python script. Build the MegaService Docker image using the command below:
 
@@ -56,7 +51,6 @@ To construct the Mega Service, we utilize the [GenAIComps](https://github.com/op
 git clone https://github.com/opea-project/GenAIExamples.git
 cd GenAIExamples/SearchQnA/docker
 docker build --no-cache -t opea/searchqna:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f Dockerfile .
-cd ../../..
 ```
 
 Then you need to build the last Docker image `opea/searchqna:latest`, which represents the Mega service through following commands:
diff --git a/Translation/docker_compose/intel/cpu/xeon/README.md b/Translation/docker_compose/intel/cpu/xeon/README.md
index 17e9902e6..31e6e9654 100644
--- a/Translation/docker_compose/intel/cpu/xeon/README.md
+++ b/Translation/docker_compose/intel/cpu/xeon/README.md
@@ -14,14 +14,11 @@ After launching your instance, you can connect to it using SSH (for Linux instan
 
 First of all, you need to build Docker Images locally and install the python package of it.
 
-```bash
-git clone https://github.com/opea-project/GenAIComps.git
-cd GenAIComps
-```
-
 ### 1. Build LLM Image
 
 ```bash
+git clone https://github.com/opea-project/GenAIComps.git
+cd GenAIComps
 docker build -t opea/llm-tgi:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f comps/llms/text-generation/tgi/Dockerfile .
 ```
 
diff --git a/Translation/docker_compose/intel/hpu/gaudi/README.md b/Translation/docker_compose/intel/hpu/gaudi/README.md
index 7fa067c7d..1f8f82837 100644
--- a/Translation/docker_compose/intel/hpu/gaudi/README.md
+++ b/Translation/docker_compose/intel/hpu/gaudi/README.md
@@ -6,14 +6,11 @@ This document outlines the deployment process for a Translation application util
 
 First of all, you need to build Docker Images locally. This step can be ignored after the Docker images published to Docker hub.
 
-```bash
-git clone https://github.com/opea-project/GenAIComps.git
-cd GenAIComps
-```
-
 ### 1. Build LLM Image
 
 ```bash
+git clone https://github.com/opea-project/GenAIComps.git
+cd GenAIComps
 docker build -t opea/llm-tgi:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f comps/llms/text-generation/tgi/Dockerfile .
 ```
 
@@ -32,7 +29,7 @@ docker build -t opea/translation:latest --build-arg https_proxy=$https_proxy --b
 Construct the frontend Docker image using the command below:
 
 ```bash
-cd GenAIExamples/Translation//
+cd GenAIExamples/Translation
 docker build -t opea/translation-ui:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f ./docker/Dockerfile .
 ```
 
diff --git a/VideoQnA/docker_compose/intel/cpu/xeon/README.md b/VideoQnA/docker_compose/intel/cpu/xeon/README.md
index 958aaffc7..cae0423ca 100644
--- a/VideoQnA/docker_compose/intel/cpu/xeon/README.md
+++ b/VideoQnA/docker_compose/intel/cpu/xeon/README.md
@@ -48,14 +48,11 @@ Port 5173 - Open to 0.0.0.0/0
 
 First of all, you need to build Docker Images locally and install the python package of it.
 
-```bash
-git clone https://github.com/opea-project/GenAIComps.git
-cd GenAIComps
-```
-
 ### 1. Build Embedding Image
 
 ```bash
+git clone https://github.com/opea-project/GenAIComps.git
+cd GenAIComps
 docker build -t opea/embedding-multimodal-clip:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f comps/embeddings/multimodal_clip/Dockerfile .
 ```
 
@@ -84,7 +81,6 @@ docker build -t opea/lvm-video-llama:latest --build-arg https_proxy=$https_proxy
 
 ```bash
 docker build -t opea/dataprep-multimodal-vdms:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f comps/dataprep/vdms/multimodal_langchain/Dockerfile .
-cd ..
 ```
 
 ### 6. Build MegaService Docker Image
@@ -104,7 +100,7 @@ docker build -t opea/videoqna:latest --build-arg https_proxy=$https_proxy --buil
 Build frontend Docker image via below command:
 
 ```bash
-cd ui
+cd GenAIExamples/VideoQnA/ui/
 docker build -t opea/videoqna-ui:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f ./docker/Dockerfile .
 ```
 
diff --git a/VisualQnA/docker_compose/intel/cpu/xeon/README.md b/VisualQnA/docker_compose/intel/cpu/xeon/README.md
index 9f8b65de0..3a6058e0c 100644
--- a/VisualQnA/docker_compose/intel/cpu/xeon/README.md
+++ b/VisualQnA/docker_compose/intel/cpu/xeon/README.md
@@ -36,16 +36,12 @@ Port 5173 - Open to 0.0.0.0/0
 
 First of all, you need to build Docker Images locally and install the python package of it.
 
-```bash
-git clone https://github.com/opea-project/GenAIComps.git
-cd GenAIComps
-```
-
 ### 1. Build LVM and NGINX Docker Images
 
 ```bash
+git clone https://github.com/opea-project/GenAIComps.git
+cd GenAIComps
 docker build --no-cache -t opea/lvm-tgi:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f comps/lvms/tgi-llava/Dockerfile .
-
 docker build --no-cache -t opea/nginx:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f comps/nginx/Dockerfile .
 ```
 
@@ -57,7 +53,6 @@ To construct the Mega Service, we utilize the [GenAIComps](https://github.com/op
 git clone https://github.com/opea-project/GenAIExamples.git
 cd GenAIExamples/VisualQnA
 docker build --no-cache -t opea/visualqna:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f Dockerfile .
-cd ../..
 ```
 
 ### 3. Build UI Docker Image
@@ -67,7 +62,6 @@ Build frontend Docker image via below command:
 ```bash
 cd GenAIExamples/VisualQnA/ui
 docker build --no-cache -t opea/visualqna-ui:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f docker/Dockerfile .
-cd ../../..
 ```
 
 ### 4. Pull TGI Xeon Image
diff --git a/VisualQnA/docker_compose/intel/hpu/gaudi/README.md b/VisualQnA/docker_compose/intel/hpu/gaudi/README.md
index 4a0b5f0bf..2a8f3a276 100644
--- a/VisualQnA/docker_compose/intel/hpu/gaudi/README.md
+++ b/VisualQnA/docker_compose/intel/hpu/gaudi/README.md
@@ -6,28 +6,22 @@ This document outlines the deployment process for a VisualQnA application utiliz
 
 First of all, you need to build Docker Images locally. This step can be ignored after the Docker images published to Docker hub.
 
-### 1. Source Code install GenAIComps
+### 1. Build LVM and NGINX Docker Images
 
 ```bash
 git clone https://github.com/opea-project/GenAIComps.git
 cd GenAIComps
-```
-
-### 2. Build LVM and NGINX Docker Images
-
-```bash
 docker build --no-cache -t opea/lvm-tgi:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f comps/lvms/tgi-llava/Dockerfile .
-
 docker build --no-cache -t opea/nginx:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f comps/nginx/Dockerfile .
 ```
 
-### 3. Pull TGI Gaudi Image
+### 2. Pull TGI Gaudi Image
 
 ```bash
-docker pull ghcr.io/huggingface/tgi-gaudi:2.0.4
+docker pull ghcr.io/huggingface/tgi-gaudi:2.0.5
 ```
 
-### 4. Build MegaService Docker Image
+### 3. Build MegaService Docker Image
 
 To construct the Mega Service, we utilize the [GenAIComps](https://github.com/opea-project/GenAIComps.git) microservice pipeline within the `visuralqna.py` Python script. Build the MegaService Docker image using the command below:
 
@@ -38,19 +32,18 @@ docker build --no-cache -t opea/visualqna:latest --build-arg https_proxy=$https_
 cd ../..
 ```
 
-### 5. Build UI Docker Image
+### 4. Build UI Docker Image
 
 Build frontend Docker image via below command:
 
 ```bash
 cd GenAIExamples/VisualQnA/ui
 docker build --no-cache -t opea/visualqna-ui:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f ./docker/Dockerfile .
-cd ../../..
 ```
 
 Then run the command `docker images`, you will have the following 5 Docker Images:
 
-1. `ghcr.io/huggingface/tgi-gaudi:2.0.4`
+1. `ghcr.io/huggingface/tgi-gaudi:2.0.5`
 2. `opea/lvm-tgi:latest`
 3. `opea/visualqna:latest`
 4. `opea/visualqna-ui:latest`