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>
opea-project · Sep 18, 2024 · bceacdc · bceacdc
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diff --git 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:
@@ -188,15 +181,15 @@ 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
 
    ```bash
    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
@@ -6,44 +6,39 @@ 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
 
 ```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
 
 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`
@@ -188,15 +164,15 @@ 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
 
    ```bash
    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
@@ -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
@@ -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
@@ -14,35 +14,30 @@ 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
 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