Consolidate all the changes

Signed-off-by: sunshuang1866 <sunshuang1866@outlook.com>

Author: sunshuang1866

CodeGen/docker_compose/intel/cpu/xeon/README_opengauss.md

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+# Deploying CodeGen with openGauss on Intel Xeon Processors
+
+This document outlines the deployment process for a CodeGen application utilizing the [GenAIComps](https://github.com/opea-project/GenAIComps.git) microservice pipeline on Intel Xeon servers. The pipeline integrates **openGauss** as the vector database (VectorDB) and includes microservices such as `embedding`, `retriever`, and `llm`.
+
+---
+
+## Table of Contents
+
+1. [Quick Start](#quick-start)
+2. [Build Docker Images](#build-docker-images)
+3. [Validate Microservices](#validate-microservices)
+4. [Launch the UI](#launch-the-ui)
+
+---
+
+## Quick Start
+
+### 1. Set up Environment Variables
+
+To set up environment variables for deploying CodeGen services, follow these steps:
+
+1. Set the required environment variables:
+
+   ```bash
+   # Example: host_ip="192.168.1.1"
+   export host_ip="External_Public_IP"
+   export HOST_IP=$host_ip
+   export HF_TOKEN="Your_Huggingface_API_Token"
+   export GS_USER="gaussdb"
+   export GS_PASSWORD="openGauss@123"
+   export GS_DB="postgres"
+   export GS_CONNECTION_STRING="opengauss+psycopg2://${GS_USER}:${GS_PASSWORD}@${host_ip}:5432/${GS_DB}"
+   ```
+
+2. If you are in a proxy environment, also set the proxy-related environment variables:
+
+   ```bash
+   export http_proxy="Your_HTTP_Proxy"
+   export https_proxy="Your_HTTPS_Proxy"
+   # Example: no_proxy="localhost,127.0.0.1,192.168.1.1"
+   export no_proxy="Your_No_Proxy",codegen-xeon-ui-server,codegen-xeon-backend-server,dataprep-opengauss-server,tei-embedding-serving,retriever-opengauss-server,vllm-server
+   ```
+
+3. Set up other environment variables:
+
+   ```bash
+   source ../set_env_opengauss.sh
+   ```
+
+### 2. Run Docker Compose
+
+```bash
+docker compose -f compose_opengauss.yaml up -d
+```
+
+It will automatically download the Docker images from Docker Hub:
+
+```bash
+docker pull opea/codegen:latest
+docker pull opea/codegen-ui:latest
+```
+
+Note: You should build docker images from source yourself if:
+
+- You are developing off the git main branch (as the container's ports in the repo may be different from the published docker image).
+- You can't download the docker image.
+- You want to use a specific version of Docker image.
+
+Please refer to [Build Docker Images](#build-docker-images) below.
+
+### 3. Consume the CodeGen Service
+
+```bash
+curl http://${host_ip}:7778/v1/codegen \
+    -H "Content-Type: application/json" \
+    -d '{
+        "messages": "Write a Python function to calculate fibonacci numbers"
+    }'
+```
+
+---
+
+## Build Docker Images
+
+First of all, you need to build Docker Images locally.
+
+```bash
+git clone https://github.com/opea-project/GenAIComps.git
+cd GenAIComps
+```
+
+### 1. Build Retriever Image
+
+```bash
+docker build --no-cache -t opea/retriever:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f comps/retrievers/src/Dockerfile .
+```
+
+### 2. Build Dataprep Image
+
+```bash
+docker build --no-cache -t opea/dataprep:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f comps/dataprep/src/Dockerfile .
+cd ..
+```
+
+### 3. Build MegaService Docker Image
+
+```bash
+git clone https://github.com/opea-project/GenAIExamples.git
+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 .
+```
+
+### 4. Build UI Docker Image
+
+```bash
+cd GenAIExamples/CodeGen/ui
+docker build --no-cache -t opea/codegen-ui:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f docker/Dockerfile .
+```
+
+Then run the command `docker images`, you will have the following Docker Images:
+
+1. `opea/dataprep:latest`
+2. `opea/retriever:latest`
+3. `opea/codegen:latest`
+4. `opea/codegen-ui:latest`
+
+---
+
+## Required Models
+
+By default, the embedding and LLM models are set to default values as listed below:
+
+| Service   | Model                          |
+| --------- | ------------------------------ |
+| Embedding | BAAI/bge-base-en-v1.5          |
+| LLM       | Qwen/Qwen2.5-Coder-7B-Instruct |
+
+Change the `xxx_MODEL_ID` in the environment for your needs.
+
+---
+
+## Validate Microservices
+
+Note: When verifying the microservices by curl or API from a remote client, please make sure the **ports** of the microservices are opened in the firewall of the cloud node.
+
+### 1. TEI Embedding Service
+
+```bash
+curl ${host_ip}:8090/embed \
+    -X POST \
+    -d '{"inputs":"What is Deep Learning?"}' \
+    -H 'Content-Type: application/json'
+```
+
+### 2. Retriever Microservice
+
+To consume the retriever microservice, you need to generate a mock embedding vector by Python script. The length of the embedding vector is determined by the embedding model. Here we use the model `EMBEDDING_MODEL_ID="BAAI/bge-base-en-v1.5"`, which vector size is 768.
+
+```bash
+export your_embedding=$(python3 -c "import random; embedding = [random.uniform(-1, 1) for _ in range(768)]; print(embedding)")
+curl http://${host_ip}:7000/v1/retrieval \
+  -X POST \
+  -d "{\"text\":\"test\",\"embedding\":${your_embedding}}" \
+  -H 'Content-Type: application/json'
+```
+
+### 3. LLM Backend Service
+
+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 vllm-server 2>&1 | grep complete
+```
+
+If the service is ready, you will get the response like below:
+
+```text
+INFO: Application startup complete.
+```
+
+Then try the `cURL` command below to validate services:
+
+```bash
+curl http://${host_ip}:8028/v1/chat/completions \
+  -X POST \
+  -d '{"model": "Qwen/Qwen2.5-Coder-7B-Instruct", "messages": [{"role": "user", "content": "Write a hello world in Python"}], "max_tokens":50}' \
+  -H 'Content-Type: application/json'
+```
+
+### 4. MegaService
+
+```bash
+curl http://${host_ip}:7778/v1/codegen \
+    -H "Content-Type: application/json" \
+    -d '{
+        "messages": "Write a Python function to sort a list"
+    }'
+```
+
+### 5. Dataprep Microservice (Optional)
+
+If you want to update the default knowledge base, you can use the following commands:
+
+**Upload a file:**
+
+```bash
+curl -X POST "http://${host_ip}:6007/v1/dataprep/ingest" \
+     -H "Content-Type: multipart/form-data" \
+     -F "files=@./your_code_file.py"
+```
+
+**Add Knowledge Base via HTTP Links:**
+
+```bash
+curl -X POST "http://${host_ip}:6007/v1/dataprep/ingest" \
+     -H "Content-Type: multipart/form-data" \
+     -F 'link_list=["https://example.com/code"]'
+```
+
+**Delete uploaded files:**
+
+```bash
+curl -X POST "http://${host_ip}:6007/v1/dataprep/delete" \
+     -d '{"file_path": "all"}' \
+     -H "Content-Type: application/json"
+```
+
+---
+
+## Launch the UI
+
+To access the frontend, open the following URL in your browser: `http://{host_ip}:5173`
+
+By default, the UI runs on port 5173 internally. If you prefer to use a different host port to access the frontend, you can modify the port mapping in the `compose_opengauss.yaml` file as shown below:
+
+```yaml
+codegen-xeon-ui-server:
+  image: opea/codegen-ui:latest
+  ...
+  ports:
+    - "80:5173"
+```