Reranking Microservice with fastRAG

fastRAG is a research framework for efficient and optimized retrieval augmented generative pipelines, incorporating state-of-the-art LLMs and Information Retrieval.

Please refer to Official fastRAG repo for more information.

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

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🚀1. Start Microservice with Python (Option 1)

To start the Reranking microservice, you must first install the required python packages.

1.1 Install Requirements

pip install -r requirements.txt

1.2 Install fastRAG

git clone https://github.com/IntelLabs/fastRAG.git
cd fastRag
pip install .
pip install .[intel]

1.3 Start Reranking Service with Python Script

export EMBED_MODEL="Intel/bge-small-en-v1.5-rag-int8-static"
python local_reranking.py

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🚀2. Start Microservice with Docker (Option 2)

2.1 Setup Environment Variables

export EMBED_MODEL="Intel/bge-small-en-v1.5-rag-int8-static"

2.2 Build Docker Image

cd ../../../
docker build -t opea/reranking-fastrag:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f comps/reranks/fastrag/Dockerfile .

2.3 Run Docker

docker run -d --name="reranking-fastrag-server" -p 8000:8000 --ipc=host -e http_proxy=$http_proxy -e https_proxy=$https_proxy -e EMBED_MODEL=$EMBED_MODEL opea/reranking-fastrag:latest

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✅ 3. Invoke Reranking Microservice

The Reranking microservice exposes following API endpoints:

• Check Service Status

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

• 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'