dorianbrown/rank_bm25

Health v2 · maintenance only

Adoption not part of the score

1378 stars · 109 forks observed · 2026-08-28

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

A Python library implementing several BM25 ranking algorithms (Okapi BM25, BM25L, BM25+) for scoring and ranking documents against a query. It is a lightweight, in-memory 'two line search engine' that requires users to handle their own tokenization and preprocessing.

Use cases

• rank documents by relevance to a query in python
• build a simple keyword search engine
• score a corpus with BM25
• implement lexical retrieval for RAG pipelines
• compare BM25 variants like BM25L and BM25+

When to choose

• you need a simple, dependency-light BM25 implementation for small to medium corpora
• you want to prototype lexical search or hybrid retrieval quickly
• you want control over your own tokenization and preprocessing

When to avoid

• you need high-performance retrieval over large-scale production corpora
• you need built-in indexing, persistence, or text preprocessing
• you need semantic/vector search rather than lexical ranking

Facets

library · maturity stable

search-engine nlp python bm25 document-ranking information-retrieval text-search search natural-language-processing

Member repositories

For agents

markdown · JSON · MCP: product_card(name="dorianbrown/rank_bm25")