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Build Mega Service of SearchQnA on Xeon

This document outlines the deployment process for a SearchQnA application utilizing the GenAIComps microservice pipeline on Intel Xeon server.

🚀 Build Docker images

1. Source Code install GenAIComps

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

2. Build Embedding Image

docker build --no-cache -t opea/embedding-tei:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f comps/embeddings/langchain/docker/Dockerfile .

3. Build Retriever Image

docker build --no-cache -t opea/web-retriever-chroma:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f comps/web_retrievers/langchain/chroma/docker/Dockerfile .

4. Build Rerank Image

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/docker/Dockerfile .

5. Build LLM Image

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 .

6. Build MegaService Docker Image

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

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

Then run the command docker images, you will have following images ready:

  1. opea/embedding-tei:latest
  2. opea/web-retriever-chroma:latest
  3. opea/reranking-tei:latest
  4. opea/llm-tgi:latest
  5. opea/searchqna:latest

🚀 Set the environment variables

Before starting the services with docker compose, you have to recheck the following environment variables.

export host_ip=<your External Public IP>    # export host_ip=$(hostname -I | awk '{print $1}')
export GOOGLE_CSE_ID=<your cse id>
export GOOGLE_API_KEY=<your google api key>
export HUGGINGFACEHUB_API_TOKEN=<your HF token>

export EMBEDDING_MODEL_ID=BAAI/bge-base-en-v1.5
export TEI_EMBEDDING_ENDPOINT=http://$host_ip:3001
export RERANK_MODEL_ID=BAAI/bge-reranker-base
export TEI_RERANKING_ENDPOINT=http://$host_ip:3004

export TGI_LLM_ENDPOINT=http://$host_ip:3006
export LLM_MODEL_ID=Intel/neural-chat-7b-v3-3

export MEGA_SERVICE_HOST_IP=${host_ip}
export EMBEDDING_SERVICE_HOST_IP=${host_ip}
export WEB_RETRIEVER_SERVICE_HOST_IP=${host_ip}
export RERANK_SERVICE_HOST_IP=${host_ip}
export LLM_SERVICE_HOST_IP=${host_ip}

export EMBEDDING_SERVICE_PORT=3002
export WEB_RETRIEVER_SERVICE_PORT=3003
export RERANK_SERVICE_PORT=3005
export LLM_SERVICE_PORT=3007

🚀 Start the MegaService

cd GenAIExamples/SearchQnA/docker/xeon/
docker compose up -d

🚀 Test MicroServices

# tei
curl http://${host_ip}:3001/embed \
    -X POST \
    -d '{"inputs":"What is Deep Learning?"}' \
    -H 'Content-Type: application/json'

# embedding microservice
curl http://${host_ip}:3002/v1/embeddings\
  -X POST \
  -d '{"text":"hello"}' \
  -H 'Content-Type: application/json'

# web retriever microservice
export your_embedding=$(python3 -c "import random; embedding = [random.uniform(-1, 1) for _ in range(768)]; print(embedding)")
curl http://${host_ip}:3003/v1/web_retrieval \
  -X POST \
  -d "{\"text\":\"What is the 2024 holiday schedule?\",\"embedding\":${your_embedding}}" \
  -H 'Content-Type: application/json'


# tei reranking service
curl http://${host_ip}:3004/rerank \
    -X POST \
    -d '{"query":"What is Deep Learning?", "texts": ["Deep Learning is not...", "Deep learning is..."]}' \
    -H 'Content-Type: application/json'

# reranking microservice
curl http://${host_ip}:3005/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'

# tgi service
curl http://${host_ip}:3006/generate \
  -X POST \
  -d '{"inputs":"What is Deep Learning?","parameters":{"max_new_tokens":17, "do_sample": true}}' \
  -H 'Content-Type: application/json'

# llm microservice
curl http://${host_ip}:3007/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'

🚀 Test MegaService

curl http://${host_ip}:3008/v1/searchqna -H "Content-Type: application/json" -d '{
     "messages": "What is the latest news? Give me also the source link.",
     "stream": "True"
     }'