xhluca/bm25s

Health v2 · maintenance only

Adoption not part of the score

1774 stars · 101 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

BM25S is a pure Python library implementing BM25 lexical ranking using Numpy/Scipy sparse matrices with an optional Numba backend, achieving speeds comparable to or exceeding Elasticsearch without Java or servers. It supports BM25 variants (Okapi, BM25L, BM25+), tokenization, stemming, and Hugging Face Hub index sharing.

Use cases

• rank documents by relevance to a query in Python
• add fast lexical search to a RAG pipeline without Elasticsearch
• replace rank-bm25 with a much faster BM25 implementation
• build keyword retrieval over a corpus with minimal dependencies
• score and retrieve top-k documents for hybrid search
• share and load prebuilt BM25 indices via Hugging Face Hub

When to choose

• you need fast BM25 scoring entirely in Python with no Java or web server
• you want a lightweight, low-memory lexical retriever for RAG or hybrid search
• you need BM25 variants like BM25L or BM25+ out of the box

When to avoid

• you need a full search engine with distributed indexing, faceting, or analytics
• you require advanced analyzers or multilingual tokenization matching Elasticsearch quality
• your corpus is too large to fit in memory as sparse matrices

Facets

library · maturity active

search-engine nlp rag machine-learning python cross-platform bm25 lexical-search information-retrieval sparse-matrices numpy numba text-ranking search natural-language-processing retrieval-augmented-generation

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