Build Mega Service of ChatQnA on Xeon

This document outlines the deployment process for a ChatQnA application utilizing the GenAIComps microservice pipeline on Intel Xeon server. The steps include Docker image creation, container deployment via Docker Compose, and service execution to integrate microservices such as embedding, retriever, rerank, and llm. We will publish the Docker images to Docker Hub soon, it will simplify the deployment process for this service.

Quick Start:

1. Set up the environment variables.

2. Run Docker Compose.

3. Consume the ChatQnA Service.

Quick Start: 1.Setup Environment Variable

To set up environment variables for deploying ChatQnA services, follow these steps:

1. Set the required environment variables:

   # Example: host_ip="192.168.1.1"
   export host_ip="External_Public_IP"
   # Example: no_proxy="localhost, 127.0.0.1, 192.168.1.1"
   export no_proxy="Your_No_Proxy"
   export HUGGINGFACEHUB_API_TOKEN="Your_Huggingface_API_Token"
   

2. If you are in a proxy environment, also set the proxy-related environment variables:

   export http_proxy="Your_HTTP_Proxy"
   export https_proxy="Your_HTTPs_Proxy"
   

3. Set up other environment variables:

   source ./set_env.sh
   

Quick Start: 2.Run Docker Compose

docker compose up -d

It will automatically download the docker image on docker hub:

docker pull opea/chatqna:latest
docker pull opea/chatqna-ui:latest

If you want to build docker by yourself, please refer to ‘Build Docker Images’ in below.

Note: The optional docker image opea/chatqna-without-rerank:latest has not been published yet, users need to build this docker image from source.

QuickStart: 3.Consume the ChatQnA Service

curl http://${host_ip}:8888/v1/chatqna \
    -H "Content-Type: application/json" \
    -d '{
        "messages": "What is the revenue of Nike in 2023?"
    }'

🚀 Apply Xeon Server on AWS

To apply a Xeon server on AWS, start by creating an AWS account if you don’t have one already. Then, head to the EC2 Console to begin the process. Within the EC2 service, select the Amazon EC2 M7i or M7i-flex instance type to leverage 4th Generation Intel Xeon Scalable processors that are optimized for demanding workloads.

For detailed information about these instance types, you can refer to this link. Once you’ve chosen the appropriate instance type, proceed with configuring your instance settings, including network configurations, security groups, and storage options.

After launching your instance, you can connect to it using SSH (for Linux instances) or Remote Desktop Protocol (RDP) (for Windows instances). From there, you’ll have full access to your Xeon server, allowing you to install, configure, and manage your applications as needed.

Certain ports in the EC2 instance need to opened up in the security group, for the microservices to work with the curl commands

See one example below. Please open up these ports in the EC2 instance based on the IP addresses you want to allow

redis-vector-db
===============
Port 6379 - Open to 0.0.0.0/0
Port 8001 - Open to 0.0.0.0/0

tei_embedding_service
=====================
Port 6006 - Open to 0.0.0.0/0

embedding
=========
Port 6000 - Open to 0.0.0.0/0

retriever
=========
Port 7000 - Open to 0.0.0.0/0

tei_xeon_service
================
Port 8808 - Open to 0.0.0.0/0

reranking
=========
Port 8000 - Open to 0.0.0.0/0

tgi-service or vLLM_service
===========
Port 9009 - Open to 0.0.0.0/0

llm
===
Port 9000 - Open to 0.0.0.0/0

chaqna-xeon-backend-server
==========================
Port 8888 - Open to 0.0.0.0/0

chaqna-xeon-ui-server
=====================
Port 5173 - Open to 0.0.0.0/0

🚀 Build Docker Images

First of all, you need to build Docker Images locally and install the python package of it.

1. Build Embedding Image

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 .

2. Build Retriever Image

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 .

3. Build Rerank Image

Skip for ChatQnA without Rerank pipeline

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 .

4. Build LLM Image

Use TGI as backend

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 .

Use vLLM as backend

Build vLLM docker.

git clone https://github.com/vllm-project/vllm.git
cd ./vllm/
docker build --no-cache -t opea/vllm:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f Dockerfile.cpu .
cd ..

Build microservice.

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. Build Dataprep Image

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 .
cd ..

6. Build MegaService Docker Image

1. MegaService with Rerank

   To construct the Mega Service with Rerank, we utilize the GenAIComps microservice pipeline within the chatqna.py Python script. Build MegaService Docker image via below command:

   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 .
   

2. MegaService without Rerank

   To construct the Mega Service without Rerank, we utilize the GenAIComps microservice pipeline within the chatqna_without_rerank.py Python script. Build MegaService Docker image via below command:

   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 .
   

7. Build UI Docker Image

Build frontend Docker image via below command:

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 .

8. Build Conversational React UI Docker Image (Optional)

Build frontend Docker image that enables Conversational experience with ChatQnA megaservice via below command:

Export the value of the public IP address of your Xeon server to the host_ip environment variable

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 .

Then run the command docker images, you will have the following 7 Docker Images:

1. opea/dataprep-redis:latest

2. opea/embedding-tei:latest

3. opea/retriever-redis:latest

4. opea/reranking-tei:latest

5. opea/llm-tgi:latest or opea/llm-vllm:latest

6. opea/chatqna:latest or opea/chatqna-without-rerank:latest

7. opea/chatqna-ui:latest

🚀 Start Microservices

Required Models

By default, the embedding, reranking and LLM models are set to a default value as listed below:

Service	Model
Embedding	BAAI/bge-base-en-v1.5
Reranking	BAAI/bge-reranker-base
LLM	Intel/neural-chat-7b-v3-3

Change the xxx_MODEL_ID below for your needs.

For users in China who are unable to download models directly from Huggingface, you can use ModelScope or a Huggingface mirror to download models. TGI can load the models either online or offline as described below:

1. Online

   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:2.2.0 --model-id $model_name
   

2. Offline

   • Search your model name in ModelScope. For example, check this page for model neural-chat-7b-v3-1.

   • Click on Download this model button, and choose one way to download the model to your local path /path/to/model.

   • Run the following command to start TGI service.

     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:2.2.0 --model-id /data
     

Setup Environment Variables

Since the compose.yaml will consume some environment variables, you need to setup them in advance as below.

Export the value of the public IP address of your Xeon server to the host_ip environment variable

Change the External_Public_IP below with the actual IPV4 value

export host_ip="External_Public_IP"

Export the value of your Huggingface API token to the your_hf_api_token environment variable

Change the Your_Huggingface_API_Token below with tyour actual Huggingface API Token value

export your_hf_api_token="Your_Huggingface_API_Token"

Append the value of the public IP address to the no_proxy list

export your_no_proxy=${your_no_proxy},"External_Public_IP"

export no_proxy=${your_no_proxy}
export http_proxy=${your_http_proxy}
export https_proxy=${your_http_proxy}
export EMBEDDING_MODEL_ID="BAAI/bge-base-en-v1.5"
export RERANK_MODEL_ID="BAAI/bge-reranker-base"
export LLM_MODEL_ID="Intel/neural-chat-7b-v3-3"
export TEI_EMBEDDING_ENDPOINT="http://${host_ip}:6006"
export TEI_RERANKING_ENDPOINT="http://${host_ip}:8808"
export TGI_LLM_ENDPOINT="http://${host_ip}:9009"
export vLLM_LLM_ENDPOINT="http://${host_ip}:9009"
export LLM_SERVICE_PORT=9000
export REDIS_URL="redis://${host_ip}:6379"
export INDEX_NAME="rag-redis"
export HUGGINGFACEHUB_API_TOKEN=${your_hf_api_token}
export MEGA_SERVICE_HOST_IP=${host_ip}
export EMBEDDING_SERVICE_HOST_IP=${host_ip}
export RETRIEVER_SERVICE_HOST_IP=${host_ip}
export RERANK_SERVICE_HOST_IP=${host_ip}
export LLM_SERVICE_HOST_IP=${host_ip}
export BACKEND_SERVICE_ENDPOINT="http://${host_ip}:8888/v1/chatqna"
export DATAPREP_SERVICE_ENDPOINT="http://${host_ip}:6007/v1/dataprep"
export DATAPREP_GET_FILE_ENDPOINT="http://${host_ip}:6007/v1/dataprep/get_file"
export DATAPREP_DELETE_FILE_ENDPOINT="http://${host_ip}:6007/v1/dataprep/delete_file"

Note: Please replace with host_ip with you external IP address, do not use localhost.

Start all the services Docker Containers

Before running the docker compose command, you need to be in the folder that has the docker compose yaml file

cd GenAIExamples/ChatQnA/docker_compose/intel/cpu/xeon/

If use TGI backend.

# Start ChatQnA with Rerank Pipeline
docker compose -f compose.yaml up -d
# Start ChatQnA without Rerank Pipeline
docker compose -f compose_without_rerank.yaml up -d

If use vLLM backend.

docker compose -f compose_vllm.yaml up -d

Validate Microservices

1. TEI Embedding Service

   curl ${host_ip}:6006/embed \
       -X POST \
       -d '{"inputs":"What is Deep Learning?"}' \
       -H 'Content-Type: application/json'
   

2. Embedding Microservice

   curl http://${host_ip}:6000/v1/embeddings\
     -X POST \
     -d '{"text":"hello"}' \
     -H 'Content-Type: application/json'
   

3. Retriever Microservice

   To consume the retriever microservice, you need to generate a mock embedding vector by Python script. The length of 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.

   Check the vector dimension of your embedding model, set your_embedding dimension equals to it.

   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'
   

4. TEI Reranking Service

   Skip for ChatQnA without Rerank pipeline

   curl http://${host_ip}:8808/rerank \
       -X POST \
       -d '{"query":"What is Deep Learning?", "texts": ["Deep Learning is not...", "Deep learning is..."]}' \
       -H 'Content-Type: application/json'
   

5. Reranking Microservice

   Skip for ChatQnA without Rerank pipeline

   curl http://${host_ip}:8000/v1/reranking\
     -X POST \
     -d '{"initial_query":"What is Deep Learning?", "retrieved_docs": [{"text":"Deep Learning is not..."}, {"text":"Deep learning is..."}]}' \
     -H 'Content-Type: application/json'
   

6. LLM backend Service

   In first startup, this service will take more time to download the model files. After it’s finished, the service will be ready.

   Try the command below to check whether the LLM serving is ready.

   docker logs ${CONTAINER_ID} | grep Connected
   

   If the service is ready, you will get the response like below.

   2024-09-03T02:47:53.402023Z  INFO text_generation_router::server: router/src/server.rs:2311: Connected
   

   Then try the cURL command below to validate services.

   # TGI service
   curl http://${host_ip}:9009/generate \
     -X POST \
     -d '{"inputs":"What is Deep Learning?","parameters":{"max_new_tokens":17, "do_sample": true}}' \
     -H 'Content-Type: application/json'
   

   # vLLM Service
   curl http://${host_ip}:9009/v1/completions \
     -H "Content-Type: application/json" \
     -d '{"model": "Intel/neural-chat-7b-v3-3", "prompt": "What is Deep Learning?", "max_tokens": 32, "temperature": 0}'
   

7. LLM Microservice

   This service depends on above LLM backend service startup. It will be ready after long time, to wait for them being ready in first startup.

   curl http://${host_ip}:9000/v1/chat/completions\
     -X POST \
     -d '{"query":"What is Deep Learning?","max_new_tokens":17,"top_k":10,"top_p":0.95,"typical_p":0.95,"temperature":0.01,"repetition_penalty":1.03,"streaming":true}' \
     -H 'Content-Type: application/json'
   

8. MegaService

   curl http://${host_ip}:8888/v1/chatqna -H "Content-Type: application/json" -d '{
        "messages": "What is the revenue of Nike in 2023?"
        }'
   

9. Dataprep Microservice（Optional）

   If you want to update the default knowledge base, you can use the following commands:

   Update Knowledge Base via Local File nke-10k-2023.pdf. Or click here to download the file via any web browser. Or run this command to get the file on a terminal.

   wget https://raw.githubusercontent.com/opea-project/GenAIComps/main/comps/retrievers/redis/data/nke-10k-2023.pdf
   

   Upload:

   curl -X POST "http://${host_ip}:6007/v1/dataprep" \
        -H "Content-Type: multipart/form-data" \
        -F "files=@./nke-10k-2023.pdf"
   

   This command updates a knowledge base by uploading a local file for processing. Update the file path according to your environment.

   Add Knowledge Base via HTTP Links:

   curl -X POST "http://${host_ip}:6007/v1/dataprep" \
        -H "Content-Type: multipart/form-data" \
        -F 'link_list=["https://opea.dev"]'
   

   This command updates a knowledge base by submitting a list of HTTP links for processing.

   Also, you are able to get the file list that you uploaded:

   curl -X POST "http://${host_ip}:6007/v1/dataprep/get_file" \
        -H "Content-Type: application/json"
   

   Then you will get the response JSON like this. Notice that the returned name/id of the uploaded link is https://xxx.txt.

   [
     {
       "name": "nke-10k-2023.pdf",
       "id": "nke-10k-2023.pdf",
       "type": "File",
       "parent": ""
     },
     {
       "name": "https://opea.dev.txt",
       "id": "https://opea.dev.txt",
       "type": "File",
       "parent": ""
     }
   ]
   

   To delete the file/link you uploaded:

   The file_path here should be the id get from /v1/dataprep/get_file API.

   # delete link
   curl -X POST "http://${host_ip}:6007/v1/dataprep/delete_file" \
        -d '{"file_path": "https://opea.dev.txt"}' \
        -H "Content-Type: application/json"
   
   # delete file
   curl -X POST "http://${host_ip}:6007/v1/dataprep/delete_file" \
        -d '{"file_path": "nke-10k-2023.pdf"}' \
        -H "Content-Type: application/json"
   
   # delete all uploaded files and links
   curl -X POST "http://${host_ip}:6007/v1/dataprep/delete_file" \
        -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.yaml file as shown below:

  chaqna-gaudi-ui-server:
    image: opea/chatqna-ui:latest
    ...
    ports:
      - "80:5173"

🚀 Launch the Conversational UI (Optional)

To access the Conversational UI (react based) frontend, modify the UI service in the compose.yaml file. Replace chaqna-xeon-ui-server service with the chatqna-xeon-conversation-ui-server service as per the config below:

chaqna-xeon-conversation-ui-server:
  image: opea/chatqna-conversation-ui:latest
  container_name: chatqna-xeon-conversation-ui-server
  environment:
    - APP_BACKEND_SERVICE_ENDPOINT=${BACKEND_SERVICE_ENDPOINT}
    - APP_DATA_PREP_SERVICE_URL=${DATAPREP_SERVICE_ENDPOINT}
  ports:
    - "5174:80"
  depends_on:
    - chaqna-xeon-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:

  chaqna-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):