# 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](https://github.com/IntelLabs/fastRAG/tree/main) for more information. This README provides set-up instructions and comprehensive details regarding the reranking microservice via fastRAG. --- ## 🚀1. Start Microservice with Python (Option 1) To start the Reranking microservice, you must first install the required python packages. ### 1.1 Install Requirements ```bash pip install -r requirements.txt ``` ### 1.2 Install fastRAG ```bash git clone https://github.com/IntelLabs/fastRAG.git cd fastRag pip install . pip install .[intel] ``` ### 1.3 Start Reranking Service with Python Script ```bash export EMBED_MODEL="Intel/bge-small-en-v1.5-rag-int8-static" python local_reranking.py ``` --- ## 🚀2. Start Microservice with Docker (Option 2) ### 2.1 Setup Environment Variables ```bash export EMBED_MODEL="Intel/bge-small-en-v1.5-rag-int8-static" ``` ### 2.2 Build Docker Image ```bash 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 ```bash 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 ``` --- ## ✅ 3. Invoke Reranking Microservice The Reranking microservice exposes following API endpoints: - Check Service Status ```bash curl http://localhost:8000/v1/health_check \ -X GET \ -H 'Content-Type: application/json' ``` - Execute reranking process by providing query and documents ```bash 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' ```