RAG-Fusion enhances Retrieval-Augmented Generation (RAG) by employing multiple query generation and Reciprocal Rank Fusion to improve search results. RAG-Fusion addresses the limitations of single-query RAG systems by generating diverse search queries and fusing results using Reciprocal Rank Fusion (RRF). This approach aims to capture a wider range of semantic nuances and improve retrieval accuracy, particularly for complex queries.
RAG-Fusion stands out with its ability to combine the strengths of keyword-based search (BM25) and semantic search using vector embeddings. The use of Reciprocal Rank Fusion allows for a seamless integration of these two approaches. The inclusion of a diverse prompt further enhances the system's ability to explore different aspects of the user's query. The project offers a comprehensive evaluation harness using a standard benchmark dataset.
- Query Generation: Leverages OpenAI's GPT to create multiple variations of a user's query.
- Vector Search: Utilizes ChromaDB for efficient vector-based document retrieval.
- Reciprocal Rank Fusion: Combines ranked results from multiple searches using RRF.
- Evaluation Harness: Includes a quantitative evaluation system using established benchmarks.
- Multiple Methods: Supports evaluation with various retrieval methods (BM25, vector, Hybrid, RAG-Fusion etc.)
- Test Suite: Provides unit tests to ensure code quality and stability.
- Clear Documentation: Includes detailed instructions for installation, setup, and usage.
RAG-Fusion is an active project with a clear roadmap and demonstrated improvements over baseline RAG systems. Active development is indicated by recent commits and ongoing evaluation efforts. The inclusion of a comprehensive evaluation harness and detailed documentation contributes to its usability and reliability. The strong performance on benchmark datasets signals a promising direction.
RAG-Fusion benefits researchers and developers working on knowledge retrieval and question answering systems. It is particularly useful for applications requiring accurate and comprehensive information retrieval, such as medical or legal research. By combining multiple search strategies, RAG-Fusion provides a substantial improvement in retrieval quality compared to traditional RAG approaches.