rag-fusion and OpenRag
RAG-Fusion's multi-query generation and reciprocal rank fusion technique represents a retrieval strategy that could be integrated as one component within OpenRag's broader multi-strategy architecture, making them complements rather than competitors.
About rag-fusion
Raudaschl/rag-fusion
RAG-Fusion: multi-query generation + Reciprocal Rank Fusion for better retrieval-augmented generation. Includes evaluation harness with NFCorpus/BEIR.
Multi-query generation is powered by OpenAI's GPT to explore different facets of user intent, with results aggregated via Reciprocal Rank Fusion for re-ranking across multiple retrieval perspectives. The implementation combines vector search (ChromaDB) with optional BM25 keyword search in a hybrid architecture, and includes a quantitative evaluation harness against NFCorpus/BEIR benchmarks showing +22% NDCG and +40% recall gains over baseline vector search through diverse prompt variants and result fusion strategies.
About OpenRag
incidentfox/OpenRag
Multi-strategy RAG system achieving 74% Recall@10 on MultiHop-RAG. Combines RAPTOR hierarchical retrieval, knowledge graphs, HyDE, BM25, and Cohere neural reranking.
Implements a FastAPI server with pluggable retrieval strategies (semantic search, HyDE query expansion, BM25 hybrid matching, multi-hop decomposition) that run in parallel before Cohere neural reranking, with built-in persistence for RAPTOR hierarchies and a comprehensive benchmark suite supporting MultiHop-RAG and CRAG datasets. Ablation studies show the neural reranker alone contributes +9.3% recall improvement, while local cross-encoder alternatives are available for privacy-sensitive deployments.
Related comparisons
Scores updated daily from GitHub, PyPI, and npm data. How scores work