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rag-fusion

Raudaschl/rag-fusion

multi-query generation + Reciprocal Rank Fusion for retrieval-augmented generation

GitHub

GraphCanon updated Sep 20, 2026 · GitHub synced Sep 20, 2026

40views this month

958 stars114 forksLast push Apr 26, 2026 Python MIT

Decision brief

RAG-Fusion leverages multi-query generation and Reciprocal Rank Fusion for enhanced retrieval-augmented generation tasks, supporting evaluations via NFCorpus/BEIR.

Good fit when

For enhancing precision in retrieval-augmented generation tasks needing complex query processing
When using BEIR or similar datasets to benchmark your generation models

Avoid when

If you require real-time performance, as multi-query generation may introduce latency
In scenarios where only simple keyword-based search suffices without the need for advanced fusion techniques

Observed Jul 15, 2026 · Source: enrich:decision_facts

rag-fusion alternatives Trust report

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Maintenance and security

Full trust report

Maintenance: Slowing (146d since push); As of Sep 20, 2026
Provenance: Not a fork · Personal account; As of Sep 20, 2026
Security (OSV): No lockfile; As of Jul 11, 2026

Public GitHub metadata and optional OSV scans. Signals, not a guarantee. Trust methodology.

Install

pip install rag-fusion

PyPI

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Evidence and technical details

Sourced facts, taxonomy, compatibility claims, README excerpt, and machine-readable endpoints.

Overview

RAG-Fusion uses multi-query generation and Reciprocal Rank Fusion to improve retrieval-augmented generation applications. It provides an evaluation framework using NFCorpus/BEIR.

Capability facts

Languages: python
Source: github.language · Sep 20, 2026

Compatibility

Sourced claims from the README excerpt - not unsourced marketing copy.

OpenAI APIOpenAI API

Source: README excerpt (regex_v1, Sep 20, 2026)

2. Set up your OpenAI API key:

Source link

Python runtimePython

Source: README excerpt (regex_v1, Sep 20, 2026)

pip install openai chromadb python-dotenv tqdm tabulate rank_bm25

Source link

README

Getting Started 1. Install dependencies: 2. Set up your OpenAI API key: Then edit and replace with your actual key. 3. Run the demo: 4. Run the tests (no API key needed):

For agents

This page has a .md twin and JSON over the API.

/tools/raudaschl-rag-fusion.md GET /api/graphcanon/tools/raudaschl-rag-fusion GET /api/graphcanon/graph?tool=raudaschl-rag-fusion