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README_tei.md

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🌟 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 enables us to host our own reranker endpoint seamlessly.

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

📦 1. Start Microservice with Docker

🔹 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 -p 12005:80 -v $volume:/data --runtime=habana -e HABANA_VISIBLE_DEVICES=all -e OMPI_MCA_btl_vader_single_copy_mechanism=none -e MAX_WARMUP_SEQUENCE_LENGTH=512 --cap-add=sys_nice --ipc=host ghcr.io/huggingface/tei-gaudi:latest --model-id $RERANK_MODEL_ID --hf-api-token $HF_TOKEN --auto-truncate

  • 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 12005:80 -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 --auto-truncate

  1. Verify the TEI Service:

    Run the following command to check if the service is up and running.

      export host_ip=$(hostname -I | awk '{print $1}')
  curl $host_ip:12005/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:

     export TEI_RERANKING_PORT=12005
 export HF_TOKEN=${your_hf_api_token}
 export host_ip=$(hostname -I | awk '{print $1}')
 export TEI_RERANKING_ENDPOINT="http://${host_ip}:${TEI_RERANKING_PORT}"

docker run -d --name="reranking-tei-server" -e LOGFLAG=True  -p 10700:8000 --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=12005
 export RERANK_PORT=10700
 export host_ip=$(hostname -I | awk '{print $1}')
 export TEI_RERANKING_ENDPOINT="http://${host_ip}:${TEI_RERANKING_PORT}"
 export TAG=comps

  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-gaudi -d

  • For Xeon CPU:

     docker compose up reranking-tei -d

📦 3. Consume Reranking Service

🔹 3.1 Check Service Status

  • Verify the reranking service is running:

      curl http://localhost:10700/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:10700/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:10700/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.