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Build Mega Service of ChatQnA on AIPC

This document outlines the deployment process for a ChatQnA application utilizing the GenAIComps microservice pipeline on AIPC. The steps include Docker image creation, container deployment via Docker Compose, and service execution to integrate microservices such as embedding, retriever, rerank, and llm.

🚀 Build Docker Images

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

mkdir ~/OPEA -p
cd ~/OPEA
git clone https://github.com/opea-project/GenAIComps.git
cd GenAIComps

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

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

1. 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 .

2. 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 ..

3. Build MegaService Docker Image

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

cd ~/OPEA
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 .

4. Build UI Docker Image

Build frontend Docker image via below command:

cd ~/OPEA/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 .

5. Build Nginx Docker Image

cd GenAIComps
docker build -t opea/nginx:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f comps/nginx/Dockerfile .

Then run the command docker images, you will have the following 6 Docker Images:

  1. opea/dataprep-redis:latest
  2. opea/retriever-redis:latest
  3. opea/chatqna:latest
  4. opea/chatqna-ui:latest
  5. opea/nginx:latest

🚀 Start Microservices

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 AIPC to the host_ip environment variable

Change the External_Public_IP below with the actual IPV4 value

export host_ip="External_Public_IP"

For Linux users, please run hostname -I | awk '{print $1}'. For Windows users, please run ipconfig | findstr /i "IPv4" to get the 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 if you are in a proxy environment

export your_no_proxy=${your_no_proxy},"External_Public_IP",chatqna-aipc-backend-server,tei-embedding-service,retriever,tei-reranking-service,redis-vector-db,dataprep-redis-service
  • Linux PC
export no_proxy=${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 INDEX_NAME="rag-redis"
export HUGGINGFACEHUB_API_TOKEN=${your_hf_api_token}
export OLLAMA_HOST=${host_ip}
export OLLAMA_MODEL="llama3.2"
  • Windows PC
set EMBEDDING_MODEL_ID=BAAI/bge-base-en-v1.5
set RERANK_MODEL_ID=BAAI/bge-reranker-base
set INDEX_NAME=rag-redis
set HUGGINGFACEHUB_API_TOKEN=%your_hf_api_token%
set OLLAMA_HOST=host.docker.internal
set OLLAMA_MODEL="llama3.2"

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 ~/OPEA/GenAIExamples/ChatQnA/docker_compose/intel/cpu/aipc/
docker compose up -d

Validate Microservices

Follow the instructions to validate MicroServices. For details on how to verify the correctness of the response, refer to how-to-validate_service.

  1. TEI Embedding Service

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

  2. Retriever Microservice
    To validate the retriever microservice, you need to generate a mock embedding vector of length 768 in Python script:

    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":"What is the revenue of Nike in 2023?","embedding":"'"${your_embedding}"'"}' \
  -H 'Content-Type: application/json'

  3. TEI Reranking Service

    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'

  4. Ollama Service

    curl http://${host_ip}:11434/api/generate -d '{"model": "llama3.2", "prompt":"What is Deep Learning?"}'

  5. MegaService

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

  6. Upload RAG Files through Dataprep Microservice (Optional)

    To chat with retrieved information, you need to upload a file using Dataprep service.

    Here is an example of Nike 2023 pdf file.

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

# upload pdf file with dataprep
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.

Alternatively, you can 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.

To check the uploaded files, you are able to get the file list that uploaded:

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

the output is: [{"name":"nke-10k-2023.pdf","id":"nke-10k-2023.pdf","type":"File","parent":""}]

🚀 Launch the UI

To access the frontend, open the following URL in your browser: http://{host_ip}:80.