What you can build with it

Purpose

RAG-Fusion generates query variants and merges retrieval rankings with Reciprocal Rank Fusion to find material a single query may miss.

Evidence 1 ↗ · Evidence 2 ↗

Jev’s role

Jev is an optional reranker in the evaluation pipeline; its experiments require JEV_API_KEY. The original demo remains vector-only.

Evidence 1 ↗ · Evidence 2 ↗

How to use it

Compare retrieval strategies on NFCorpus with the evaluation harness; the hybrid pipeline combines BM25, vector search, fusion and reranking.

Evidence 1 ↗ · Evidence 2 ↗

Limits to consider

Query rewrites add latency. The README flags precision-sensitive workloads as poor fits and warns that an older results table predates a parser fix.

Evidence 1 ↗ · Evidence 2 ↗

Repository description

RAG-Fusion: multi-query generation + Reciprocal Rank Fusion for better retrieval-augmented generation. Includes evaluation harness with NFCorpus/BEIR.

Project facts

Relationship to Jev
Integration
Evidence
Documented
Language
Python
License
MIT
Origin
Original repository
Repository status
Not archived
Created
2023-09-25
Last code update
2026-09-27
GitHub stars
959
Evidence checked
Sep 30, 2026
Metadata checked
Sep 29, 2026
Check status
Up to date

Stars measure the whole repository, including work unrelated to Jev.

Evidence and scope

Documented records the linked documentation or source. JevHunt has not independently run or benchmarked this project.

Evidence commit: a9742c4aae95b097c1f715cf2350b569c9407872

Discovered through: github-search.

How we review →

Suggest a correction · Claim this listing · Submit a project