RAG-Retrieval

A unified and efficient RAG retrieval refinement and inference framework

CommonProductProgrammingNatural Language ProcessingInformation retrieval

RAG-Retrieval is a full-stack RAG refinement and inference framework that supports the inference of various RAG Reranker models, including vector models, delayed interactive models, and interactive models. It provides a lightweight Python library that allows users to call different RAG sorting models in a unified manner, simplifying the use and deployment of sorting models.

Supports various sorting models
such as Cross Encoder Reranker and LLM Reranker
Friendly to long documents
supporting maximum length truncation and the handling logic of splitting to maximize the score
Easy to extend
new sorting model integration only requires inheriting from base reranker and implementing specific functions
Provides a unified interface
simplifying the inference process of different models
Supports fine-tuning of any open-source RAG retrieval model
Provides detailed usage tutorials and test cases for easy learning and alignment with existing inference frameworks

Developers and data scientists who require efficient retrieval and sorting models
Suitable for research and application in the fields of natural language processing and information retrieval
Teams and individuals looking to simplify model deployment and inference processes

Sorting search results in search engines to improve retrieval relevance
Optimizing recommendation lists in recommendation systems to enhance user experience
Sorting candidate answers in Q&A systems to provide more accurate answers

Step 1: Visit the RAG-Retrieval GitHub page and download the code
Step 2: Manually install torch compatible with the local CUDA version according to the system environment
Step 3: Install the rag-retrieval library via pip
Step 4: Select and configure the supported Reranker models as needed
Step 5: Use the rag-retrieval library for model inference or fine-tuning

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