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Build Mega Service of SearchQnA on Gaudi

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

🚀 Build Docker Images

First of all, you need to build Docker Images locally. This step can be ignored after the Docker images published to Docker hub.

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/web-retriever-chroma:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f comps/web_retrievers/chroma/langchain/Dockerfile .

3. 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/Dockerfile .

4. 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 .

5. 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 build --no-cache -t opea/searchqna:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f Dockerfile .

Then you need to build the last Docker image opea/searchqna:latest, which represents the Mega service through following commands:

cd GenAIExamples/SearchQnA
docker build --no-cache -t opea/searchqna:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f Dockerfile .

Then run the command docker images, you will have

  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 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_compose/intel/hpu/gaudi/
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_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"
     }'