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.

Prerequisites

We use Ollama as our LLM service for AIPC.

Please follow the instructions to set up Ollama on your PC. This will set the entrypoint needed for the Ollama to suit the ChatQnA examples.

Set Up Ollama LLM Service

Install Ollama Service

Install Ollama service with one command:

curl -fsSL https://ollama.com/install.sh | sh

Set Ollama Service Configuration

Ollama Service Configuration file is /etc/systemd/system/ollama.service. Edit the file to set OLLAMA_HOST environment. Replace <host_ip> with your host IPV4 (please use external public IP). For example the host_ip is 10.132.x.y, then `Environment=”OLLAMA_HOST=10.132.x.y:11434”’.

Environment="OLLAMA_HOST=host_ip:11434"

Set https_proxy environment for Ollama

If your system access network through proxy, add https_proxy in Ollama Service Configuration file

Environment="https_proxy=Your_HTTPS_Proxy"

Restart Ollama services

$ sudo systemctl daemon-reload
$ sudo systemctl restart ollama.service

Check the service started

netstat -tuln | grep  11434

The output are:

tcp        0      0 10.132.x.y:11434      0.0.0.0:*               LISTEN

Pull Ollama LLM model

Run the command to download LLM models. The <host_ip> is the one set in Ollama Service Configuration

export host_ip=<host_ip>
export OLLAMA_HOST=http://${host_ip}:11434
ollama pull llama3

After downloaded the models, you can list the models by ollama list.

The output should be similar to the following:

NAME            ID              SIZE    MODIFIED
llama3:latest   365c0bd3c000    4.7 GB  5 days ago

Consume Ollama LLM Service

Access ollama service to verify that the ollama is functioning correctly.

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

The outputs are similar to these:

{"model":"llama3","created_at":"2024-10-11T07:58:38.949268562Z","response":"Deep","done":false}
{"model":"llama3","created_at":"2024-10-11T07:58:39.017625351Z","response":" learning","done":false}
{"model":"llama3","created_at":"2024-10-11T07:58:39.102848076Z","response":" is","done":false}
{"model":"llama3","created_at":"2024-10-11T07:58:39.171037991Z","response":" a","done":false}
{"model":"llama3","created_at":"2024-10-11T07:58:39.243757952Z","response":" subset","done":false}
{"model":"llama3","created_at":"2024-10-11T07:58:39.328708084Z","response":" of","done":false}
{"model":"llama3","created_at":"2024-10-11T07:58:39.413844974Z","response":" machine","done":false}
{"model":"llama3","created_at":"2024-10-11T07:58:39.486239329Z","response":" learning","done":false}
{"model":"llama3","created_at":"2024-10-11T07:58:39.555960842Z","response":" that","done":false}
{"model":"llama3","created_at":"2024-10-11T07:58:39.642418238Z","response":" involves","done":false}
{"model":"llama3","created_at":"2024-10-11T07:58:39.714137478Z","response":" the","done":false}
{"model":"llama3","created_at":"2024-10-11T07:58:39.798776679Z","response":" use","done":false}
{"model":"llama3","created_at":"2024-10-11T07:58:39.883747938Z","response":" of","done":false}
...

🚀 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 LLM Image

docker build --no-cache -t opea/llm-ollama:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f comps/llms/text-generation/ollama/langchain/Dockerfile .

3. 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 ..

4. 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 .

5. 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 .

Then run the command docker images, you will have the following 5 Docker Images:

  1. opea/dataprep-redis:latest

  2. opea/retriever-redis:latest

  3. opea/llm-ollama:latest

  4. opea/chatqna:latest

  5. opea/chatqna-ui: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

export your_no_proxy=${your_no_proxy},"External_Public_IP"

  • Linux PC

export no_proxy=${your_no_proxy}
export http_proxy=${your_http_proxy}
export https_proxy=${your_http_proxy}
export EMBEDDING_MODEL_ID="BAAI/bge-base-en-v1.5"
export RERANK_MODEL_ID="BAAI/bge-reranker-base"
export TEI_EMBEDDING_ENDPOINT="http://${host_ip}:6006"
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=${host_ip}
export EMBEDDING_SERVER_HOST_IP=${host_ip}
export RETRIEVER_SERVICE_HOST_IP=${host_ip}
export RERANK_SERVER_HOST_IP=${host_ip}
export LLM_SERVER_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://${host_ip}:6007/v1/dataprep/delete_file"
export FRONTEND_SERVICE_IP=${host_ip}
export FRONTEND_SERVICE_PORT=5173
export BACKEND_SERVICE_NAME=chatqna
export BACKEND_SERVICE_IP=${host_ip}
export BACKEND_SERVICE_PORT=8888

export OLLAMA_ENDPOINT=http://${host_ip}:11434
export OLLAMA_MODEL="llama3"

  • Windows PC

set EMBEDDING_MODEL_ID=BAAI/bge-base-en-v1.5
set RERANK_MODEL_ID=BAAI/bge-reranker-base
set TEI_EMBEDDING_ENDPOINT=http://%host_ip%:6006
set REDIS_URL=redis://%host_ip%:6379
set INDEX_NAME=rag-redis
set HUGGINGFACEHUB_API_TOKEN=%your_hf_api_token%
set MEGA_SERVICE_HOST_IP=%host_ip%
set EMBEDDING_SERVER_HOST_IP=%host_ip%
set RETRIEVER_SERVICE_HOST_IP=%host_ip%
set RERANK_SERVER_HOST_IP=%host_ip%
set LLM_SERVER_HOST_IP=%host_ip%
set BACKEND_SERVICE_ENDPOINT=http://%host_ip%:8888/v1/chatqna
set DATAPREP_SERVICE_ENDPOINT=http://%host_ip%:6007/v1/dataprep
set DATAPREP_GET_FILE_ENDPOINT="http://%host_ip%:6007/v1/dataprep/get_file"
set DATAPREP_DELETE_FILE_ENDPOINT="http://%host_ip%:6007/v1/dataprep/delete_file"
set FRONTEND_SERVICE_IP=%host_ip%
set FRONTEND_SERVICE_PORT=5173
set BACKEND_SERVICE_NAME=chatqna
set BACKEND_SERVICE_IP=%host_ip%
set BACKEND_SERVICE_PORT=8888

set OLLAMA_ENDPOINT=http://host.docker.internal:11434
set OLLAMA_MODEL="llama3"

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

Let ollama service runs (if you have started ollama service in Prerequisites, skip this step)

# e.g. ollama run llama3
OLLAMA_HOST=${host_ip}:11434 ollama run $OLLAMA_MODEL
# for windows
# ollama run %OLLAMA_MODEL%

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", "prompt":"What is Deep Learning?"}'

  5. LLM Microservice

    curl http://${host_ip}:9000/v1/chat/completions\
  -X POST \
  -d '{"query":"What is Deep Learning?","max_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'

  6. MegaService

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

  7. 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}:5173.