README_tei.md

🌟 Reranking Microservice with TEI

Text Embeddings Inference (TEI) is a comprehensive toolkit designed for efficient deployment and serving of open source text embeddings models. It enable us to host our own reranker endpoint seamlessly.

This README provides set-up instructions and comprehensive details regarding the reranking microservice via TEI.

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📦 1. Start Microservice with docker run

🔹 1.1 Start Reranking Service with TEI

1. Start the TEI service:

• For Gaudi HPU:

    export HF_TOKEN=${your_hf_api_token}
    export RERANK_MODEL_ID="BAAI/bge-reranker-base"
    export volume=$PWD/data
  
    docker run -d -p 6060:80 -v $volume:/data -e http_proxy=$http_proxy -e https_proxy=$https_proxy --pull always ghcr.io/huggingface/text-embeddings-inference:cpu-1.5 --model-id $RERANK_MODEL_ID --hf-api-token $HF_TOKEN

• For Xeon CPU:

    export HF_TOKEN=${your_hf_api_token}
    export RERANK_MODEL_ID="BAAI/bge-reranker-base"
    export volume=$PWD/data
  
    docker run -d -p 6060:80 -v $volume:/data -e http_proxy=$http_proxy -e https_proxy=$https_proxy --pull always ghcr.io/huggingface/tei-gaudi:1.5.2 --model-id $RERANK_MODEL_ID --hf-api-token $HF_TOKEN

2. Verify the TEI Service: Run the following command to check if the service is up and running.

  export ip_address=$(hostname -I | awk '{print $1}')
  curl ip_address:6060/rerank \
      -X POST \
      -d '{"query":"What is Deep Learning?", "texts": ["Deep Learning is not...", "Deep learning is..."]}' \
      -H 'Content-Type: application/json'

🔹 1.2 Build Docker Image and Run Docker with CLI

1. Build the Docker image for the reranking microservice:

    docker build --no-cache \
      -t opea/reranking:comps \
      --build-arg https_proxy=$https_proxy \
      --build-arg http_proxy=$http_proxy \
      --build-arg SERVICE=tei \
      -f comps/rerankings/src/Dockerfile .

2. Run the reranking microservice and connect it to the TEI service:

   docker run -d --name="reranking-tei-server" -e LOGFLAG=True  -p 8000:8000 --ipc=host -e http_proxy=$http_proxy -e https_proxy=$https_proxy -e TEI_RERANKING_ENDPOINT=$TEI_RERANKING_ENDPOINT -e HF_TOKEN=$HF_TOKEN  -e RERANK_COMPONENT_NAME="OPEA_TEI_RERANKING"  opea/reranking:comps
   

📦 2. Start Microservice with docker compose

Deploy both the TEI Reranking Service and the Reranking Microservice using Docker Compose.

🔹 Steps:

1. Set environment variables:

    export RERANK_MODEL_ID="BAAI/bge-reranker-base"
    export TEI_RERANKING_PORT=12003
    export RERANK_PORT=8000
    export TEI_RERANKING_ENDPOINT="http://${host_ip}:${TEI_RERANKING_PORT}"
    export TAG=comps
    export host_ip=${host_ip}

2. Navigate to the Docker Compose directory:

   cd comps/rerankings/deployment/docker_compose/

3. Start the services:

• For Gaudi HPU:

   docker compose up reranking-tei -d

• For Xeon CPU:

   docker compose up reranking-tei-gaudi -d

📦 3. Consume Reranking Service

🔹 3.1 Check Service Status

Verify the reranking service is running:

curl http://localhost:8000/v1/health_check \
-X GET \
-H 'Content-Type: application/json'

🔹 3.2 Use the Reranking Service API

• Execute reranking process by providing query and documents

  curl http://localhost: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'

  • You can add the parameter top_n to specify the return number of the reranker model, default value is 1.

  curl http://localhost:8000/v1/reranking \
    -X POST \
    -d '{"initial_query":"What is Deep Learning?", "retrieved_docs": [{"text":"Deep Learning is not..."}, {"text":"Deep learning is..."}], "top_n":2}' \
    -H 'Content-Type: application/json'

✨ Tips for Better Understanding:

1. Port Mapping: Ensure the ports are correctly mapped to avoid conflicts with other services.

2. Model Selection: Choose a model appropriate for your use case, like "BAAI/bge-reranker-base".

3. Environment Variables: Use http_proxy and https_proxy for proxy setup if necessary.

4. Data Volume: The -v ./data:/data flag ensures the data directory is correctly mounted.