506 lines
16 KiB
Markdown
506 lines
16 KiB
Markdown
# Build MegaService of ChatQnA on Gaudi
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This document outlines the deployment process for a ChatQnA 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 embedding, retriever, rerank, and llm. We will publish the Docker images to Docker Hub, it will simplify the deployment process for this service.
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Quick Start:
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1. Set up the environment variables.
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2. Run Docker Compose.
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3. Consume the ChatQnA Service.
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## Quick Start: 1.Setup Environment Variable
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To set up environment variables for deploying ChatQnA services, follow these steps:
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1. Set the required environment variables:
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```bash
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# Example: host_ip="192.168.1.1"
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export host_ip="External_Public_IP"
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# Example: no_proxy="localhost, 127.0.0.1, 192.168.1.1"
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export no_proxy="Your_No_Proxy"
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export HUGGINGFACEHUB_API_TOKEN="Your_Huggingface_API_Token"
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```
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2. If you are in a proxy environment, also set the proxy-related environment variables:
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```bash
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export http_proxy="Your_HTTP_Proxy"
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export https_proxy="Your_HTTPs_Proxy"
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```
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3. Set up other environment variables:
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```bash
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source ./set_env.sh
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```
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## Quick Start: 2.Run Docker Compose
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```bash
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docker compose up -d
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```
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It will automatically download the docker image on `docker hub`:
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```bash
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docker pull opea/chatqna:latest
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docker pull opea/chatqna-ui:latest
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```
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In following cases, you could build docker image from source by yourself.
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- Failed to download the docker image.
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- If you want to use a specific version of Docker image.
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Please refer to 'Build Docker Images' in below.
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## QuickStart: 3.Consume the ChatQnA Service
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```bash
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curl http://${host_ip}:8888/v1/chatqna \
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-H "Content-Type: application/json" \
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-d '{
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"messages": "What is the revenue of Nike in 2023?"
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}'
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```
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## 🚀 Build Docker Images
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First of all, you need to build Docker Images locally. This step can be ignored after the Docker images published to Docker hub.
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### 1. Build Retriever Image
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```bash
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docker build --no-cache -t opea/retriever-redis:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f comps/retrievers/redis/langchain/Dockerfile .
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```
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### 2. Build Dataprep Image
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```bash
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docker build --no-cache -t opea/dataprep-redis:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f comps/dataprep/redis/langchain/Dockerfile .
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```
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### 3. Build Guardrails Docker Image (Optional)
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To fortify AI initiatives in production, Guardrails microservice can secure model inputs and outputs, building Trustworthy, Safe, and Secure LLM-based Applications.
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```bash
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docker build -t opea/guardrails-tgi:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f comps/guardrails/llama_guard/langchain/Dockerfile .
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```
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### 4. Build MegaService Docker Image
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1. MegaService with Rerank
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To construct the Mega Service with Rerank, we utilize the [GenAIComps](https://github.com/opea-project/GenAIComps.git) microservice pipeline within the `chatqna.py` Python script. Build the MegaService Docker image using the command below:
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```bash
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git clone https://github.com/opea-project/GenAIExamples.git
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cd GenAIExamples/ChatQnA/docker
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docker build --no-cache -t opea/chatqna:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f Dockerfile .
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```
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2. MegaService with Guardrails
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If you want to enable guardrails microservice in the pipeline, please use the below command instead:
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```bash
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git clone https://github.com/opea-project/GenAIExamples.git
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cd GenAIExamples/ChatQnA/
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docker build --no-cache -t opea/chatqna-guardrails:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f Dockerfile.guardrails .
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```
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3. MegaService without Rerank
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To construct the Mega Service without Rerank, we utilize the [GenAIComps](https://github.com/opea-project/GenAIComps.git) microservice pipeline within the `chatqna_without_rerank.py` Python script. Build MegaService Docker image via below command:
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```bash
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git clone https://github.com/opea-project/GenAIExamples.git
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cd GenAIExamples/ChatQnA/docker
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docker build --no-cache -t opea/chatqna-without-rerank:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f Dockerfile.without_rerank .
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```
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### 5. Build UI Docker Image
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Construct the frontend Docker image using the command below:
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```bash
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cd GenAIExamples/ChatQnA/ui
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docker build --no-cache -t opea/chatqna-ui:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f ./docker/Dockerfile .
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```
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### 6. Build Conversational React UI Docker Image (Optional)
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Build frontend Docker image that enables Conversational experience with ChatQnA megaservice via below command:
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**Export the value of the public IP address of your Gaudi node to the `host_ip` environment variable**
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```bash
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cd GenAIExamples/ChatQnA/ui
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docker build --no-cache -t opea/chatqna-conversation-ui:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f ./docker/Dockerfile.react .
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```
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### 7. Build Nginx Docker Image
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```bash
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cd GenAIComps
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docker build -t opea/nginx:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f comps/nginx/Dockerfile .
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```
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Then run the command `docker images`, you will have the following 5 Docker Images:
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- `opea/retriever-redis:latest`
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- `opea/dataprep-redis:latest`
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- `opea/chatqna:latest`
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- `opea/chatqna-ui:latest`
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- `opea/nginx:latest`
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If Conversation React UI is built, you will find one more image:
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- `opea/chatqna-conversation-ui:latest`
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If Guardrails docker image is built, you will find one more image:
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- `opea/guardrails-tgi:latest`
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## 🚀 Start MicroServices and MegaService
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### Required Models
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By default, the embedding, reranking and LLM models are set to a default value as listed below:
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| Service | Model |
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| --------- | ------------------------- |
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| Embedding | BAAI/bge-base-en-v1.5 |
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| Reranking | BAAI/bge-reranker-base |
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| LLM | Intel/neural-chat-7b-v3-3 |
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Change the `xxx_MODEL_ID` below for your needs.
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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:
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1. Online
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```bash
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export HF_TOKEN=${your_hf_token}
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export HF_ENDPOINT="https://hf-mirror.com"
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model_name="Intel/neural-chat-7b-v3-3"
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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 $model_name --max-input-tokens 1024 --max-total-tokens 2048
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```
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2. Offline
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- Search your model name in ModelScope. For example, check [this page](https://www.modelscope.cn/models/ai-modelscope/neural-chat-7b-v3-1/files) for model `neural-chat-7b-v3-1`.
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- Click on `Download this model` button, and choose one way to download the model to your local path `/path/to/model`.
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- Run the following command to start TGI service.
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```bash
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export HF_TOKEN=${your_hf_token}
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export model_path="/path/to/model"
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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
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```
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### Setup Environment Variables
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1. Set the required environment variables:
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```bash
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# Example: host_ip="192.168.1.1"
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export host_ip="External_Public_IP"
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# Example: no_proxy="localhost, 127.0.0.1, 192.168.1.1"
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export no_proxy="Your_No_Proxy"
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export HUGGINGFACEHUB_API_TOKEN="Your_Huggingface_API_Token"
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# Example: NGINX_PORT=80
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export NGINX_PORT=${your_nginx_port}
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```
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2. If you are in a proxy environment, also set the proxy-related environment variables:
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```bash
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export http_proxy="Your_HTTP_Proxy"
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export https_proxy="Your_HTTPs_Proxy"
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```
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3. Set up other environment variables:
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```bash
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source ./set_env.sh
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```
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### Start all the services Docker Containers
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```bash
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cd GenAIExamples/ChatQnA/docker_compose/intel/hpu/gaudi/
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```
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If use tgi for llm backend.
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```bash
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# Start ChatQnA with Rerank Pipeline
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docker compose -f compose.yaml up -d
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# Start ChatQnA without Rerank Pipeline
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docker compose -f compose_without_rerank.yaml up -d
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```
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If use vllm for llm backend.
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```bash
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docker compose -f compose_vllm.yaml up -d
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```
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If use vllm-on-ray for llm backend.
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```bash
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docker compose -f compose_vllm_ray.yaml up -d
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```
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If you want to enable guardrails microservice in the pipeline, please follow the below command instead:
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```bash
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cd GenAIExamples/ChatQnA/docker_compose/intel/hpu/gaudi/
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docker compose -f compose_guardrails.yaml up -d
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```
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> **_NOTE:_** Users need at least two Gaudi cards to run the ChatQnA successfully.
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### Validate MicroServices and MegaService
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Follow the instructions to validate MicroServices.
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For validation details, please refer to [how-to-validate_service](./how_to_validate_service.md).
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1. TEI Embedding Service
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```bash
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curl ${host_ip}:8090/embed \
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-X POST \
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-d '{"inputs":"What is Deep Learning?"}' \
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-H 'Content-Type: application/json'
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```
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2. Retriever Microservice
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To consume the retriever microservice, you need to generate a mock embedding vector by Python script. The length of embedding vector
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is determined by the embedding model.
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Here we use the model `EMBEDDING_MODEL_ID="BAAI/bge-base-en-v1.5"`, which vector size is 768.
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Check the vecotor dimension of your embedding model, set `your_embedding` dimension equals to it.
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```bash
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export your_embedding=$(python3 -c "import random; embedding = [random.uniform(-1, 1) for _ in range(768)]; print(embedding)")
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curl http://${host_ip}:7000/v1/retrieval \
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-X POST \
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-d "{\"text\":\"test\",\"embedding\":${your_embedding}}" \
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-H 'Content-Type: application/json'
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```
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3. TEI Reranking Service
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> Skip for ChatQnA without Rerank pipeline
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```bash
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curl http://${host_ip}:8808/rerank \
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-X POST \
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-d '{"query":"What is Deep Learning?", "texts": ["Deep Learning is not...", "Deep learning is..."]}' \
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-H 'Content-Type: application/json'
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```
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4. LLM backend Service
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In first startup, this service will take more time to download the model files. After it's finished, the service will be ready.
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Try the command below to check whether the LLM serving is ready.
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```bash
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docker logs ${CONTAINER_ID} | grep Connected
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```
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If the service is ready, you will get the response like below.
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```
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2024-09-03T02:47:53.402023Z INFO text_generation_router::server: router/src/server.rs:2311: Connected
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```
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Then try the `cURL` command below to validate services.
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```bash
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#TGI Service
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curl http://${host_ip}:8005/generate \
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-X POST \
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-d '{"inputs":"What is Deep Learning?","parameters":{"max_new_tokens":64, "do_sample": true}}' \
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-H 'Content-Type: application/json'
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```
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```bash
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#vLLM Service
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curl http://${host_ip}:8007/v1/completions \
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-H "Content-Type: application/json" \
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-d '{
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"model": "${LLM_MODEL_ID}",
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"prompt": "What is Deep Learning?",
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"max_tokens": 32,
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"temperature": 0
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}'
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```
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```bash
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#vLLM-on-Ray Service
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curl http://${host_ip}:8006/v1/chat/completions \
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-H "Content-Type: application/json" \
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-d '{"model": "${LLM_MODEL_ID}", "messages": [{"role": "user", "content": "What is Deep Learning?"}]}'
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```
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5. MegaService
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```bash
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curl http://${host_ip}:8888/v1/chatqna -H "Content-Type: application/json" -d '{
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"messages": "What is the revenue of Nike in 2023?"
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}'
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```
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6. Nginx Service
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```bash
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curl http://${host_ip}:${NGINX_PORT}/v1/chatqna \
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-H "Content-Type: application/json" \
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-d '{"messages": "What is the revenue of Nike in 2023?"}'
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```
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7. Dataprep Microservice（Optional）
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If you want to update the default knowledge base, you can use the following commands:
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Update Knowledge Base via Local File Upload:
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```bash
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curl -X POST "http://${host_ip}:6007/v1/dataprep" \
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-H "Content-Type: multipart/form-data" \
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-F "files=@./nke-10k-2023.pdf"
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```
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This command updates a knowledge base by uploading a local file for processing. Update the file path according to your environment.
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Add Knowledge Base via HTTP Links:
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```bash
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curl -X POST "http://${host_ip}:6007/v1/dataprep" \
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-H "Content-Type: multipart/form-data" \
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-F 'link_list=["https://opea.dev"]'
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```
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This command updates a knowledge base by submitting a list of HTTP links for processing.
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Also, you are able to get the file/link list that you uploaded:
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```bash
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curl -X POST "http://${host_ip}:6007/v1/dataprep/get_file" \
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-H "Content-Type: application/json"
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```
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Then you will get the response JSON like this. Notice that the returned `name`/`id` of the uploaded link is `https://xxx.txt`.
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```json
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[
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{
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"name": "nke-10k-2023.pdf",
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"id": "nke-10k-2023.pdf",
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"type": "File",
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"parent": ""
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},
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{
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"name": "https://opea.dev.txt",
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"id": "https://opea.dev.txt",
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"type": "File",
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"parent": ""
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}
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]
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```
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To delete the file/link you uploaded:
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```bash
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# delete link
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curl -X POST "http://${host_ip}:6007/v1/dataprep/delete_file" \
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-d '{"file_path": "https://opea.dev.txt"}' \
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-H "Content-Type: application/json"
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# delete file
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curl -X POST "http://${host_ip}:6007/v1/dataprep/delete_file" \
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-d '{"file_path": "nke-10k-2023.pdf"}' \
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-H "Content-Type: application/json"
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# delete all uploaded files and links
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curl -X POST "http://${host_ip}:6007/v1/dataprep/delete_file" \
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-d '{"file_path": "all"}' \
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-H "Content-Type: application/json"
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```
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8. Guardrails (Optional)
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```bash
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curl http://${host_ip}:9090/v1/guardrails\
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-X POST \
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-d '{"text":"How do you buy a tiger in the US?","parameters":{"max_new_tokens":32}}' \
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-H 'Content-Type: application/json'
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```
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## 🚀 Launch the UI
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### Launch with origin port
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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.yaml` file as shown below:
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```yaml
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chatqna-gaudi-ui-server:
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image: opea/chatqna-ui:latest
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...
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ports:
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- "80:5173"
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```
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### Launch with Nginx
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If you want to launch the UI using Nginx, open this URL: `http://${host_ip}:${NGINX_PORT}` in your browser to access the frontend.
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## 🚀 Launch the Conversational UI (Optional)
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To access the Conversational UI (react based) frontend, modify the UI service in the `compose.yaml` file. Replace `chatqna-gaudi-ui-server` service with the `chatqna-gaudi-conversation-ui-server` service as per the config below:
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```yaml
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chatqna-gaudi-conversation-ui-server:
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image: opea/chatqna-conversation-ui:latest
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container_name: chatqna-gaudi-conversation-ui-server
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environment:
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- APP_BACKEND_SERVICE_ENDPOINT=${BACKEND_SERVICE_ENDPOINT}
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- APP_DATA_PREP_SERVICE_URL=${DATAPREP_SERVICE_ENDPOINT}
|
||
ports:
|
||
- "5174:80"
|
||
depends_on:
|
||
- chatqna-gaudi-backend-server
|
||
ipc: host
|
||
restart: always
|
||
```
|
||
|
||
Once the services are up, open the following URL in your browser: http://{host_ip}:5174. By default, the UI runs on port 80 internally. If you prefer to use a different host port to access the frontend, you can modify the port mapping in the `compose.yaml` file as shown below:
|
||
|
||
```yaml
|
||
chatqna-gaudi-conversation-ui-server:
|
||
image: opea/chatqna-conversation-ui:latest
|
||
...
|
||
ports:
|
||
- "80:80"
|
||
```
|
||
|
||
|
||
|
||
Here is an example of running ChatQnA:
|
||
|
||
|
||
|
||
Here is an example of running ChatQnA with Conversational UI (React):
|
||
|
||
|