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.

git clone https://github.com/opea-project/GenAIComps.git
cd GenAIComps

1. Build Embedding Image

docker build --no-cache -t opea/embedding-tei:latest -f comps/embeddings/tei/langchain/Dockerfile .

2. Build Retriever Image

docker build --no-cache -t opea/retriever-redis:latest -f comps/retrievers/redis/langchain/Dockerfile .

3. Build Rerank Image

docker build --no-cache -t opea/reranking-tei:latest -f comps/reranks/tei/Dockerfile .

4. Build LLM Image

We use Ollama as our LLM service for AIPC. Please pre-download Ollama on your PC.

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

5. Build Dataprep Image

docker build --no-cache -t opea/dataprep-redis:latest -f comps/dataprep/redis/langchain/Dockerfile .
cd ..

6. 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:

git clone https://github.com/opea-project/GenAIExamples.git
cd GenAIExamples/ChatQnA
docker build --no-cache -t opea/chatqna:latest -f Dockerfile .
cd ../../..

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 -f ./docker/Dockerfile .
cd ../../../..

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-ollama:latest

6. opea/chatqna:latest

7. 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 TEI_RERANKING_ENDPOINT="http://${host_ip}:8808"
export REDIS_URL="redis://${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 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 TEI_RERANKING_ENDPOINT=http://%host_ip%:8808
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_SERVICE_HOST_IP=%host_ip%
set RETRIEVER_SERVICE_HOST_IP=%host_ip%
set RERANK_SERVICE_HOST_IP=%host_ip%
set LLM_SERVICE_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 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 GenAIExamples/ChatQnA/docker_compose/intel/cpu/aipc/
docker compose up -d

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

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 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'
   

4. 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'
   

5. Reranking Microservice

   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. Ollama Service

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

7. LLM Microservice

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

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 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.

🚀 Launch the UI

To access the frontend, open the following URL in your browser: http://{host_ip}:5173.