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.
This helps scientists and researchers find more relevant information from their document collections. You input your original search query, and it generates multiple refined queries to cast a wider net. The output is a re-ranked list of documents, providing more comprehensive results than traditional searches. This is for anyone who struggles to find that crucial piece of information hidden deep within their documents using standard search tools.
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.
This project helps operations engineers, data scientists, or research analysts quickly get precise answers from large collections of documents. You input a question, and it sifts through your documents, like news articles or technical reports, to deliver highly relevant text snippets or facts, even for complex questions requiring multiple steps of reasoning. It's designed for users who need to find specific information efficiently within their internal knowledge bases or public datasets.
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