What is BM25 and why use it for RAG?

BM25 (Okapi BM25) is a probabilistic ranking function that scores documents by term frequency, inverse document frequency, and document length. It is the lexical baseline most production RAG systems combine with semantic search in a hybrid retriever because it excels at exact keyword and rare-term matches.

What do k1 and b control?

k1 controls term-frequency saturation — higher values let repeated terms keep adding score. b controls length normalization between 0 and 1 — at b=1 longer documents are penalized fully, at b=0 length is ignored. The common defaults are k1=1.5 and b=0.75.

How is BM25 different from cosine similarity on embeddings?

BM25 is purely lexical — it matches the actual words shared between query and document. Embedding cosine similarity is semantic and can match paraphrases with no shared words. They fail in opposite ways, which is why hybrid retrieval fuses both.

Is anything sent to a server?

No. Tokenization, IDF computation, and scoring all run in your browser. Your query and documents never leave the page, so it is safe to test with private content.

BM25 Relevance Scorer

BM25 relevance scorer

BM25 is the lexical ranking function behind Elasticsearch, OpenSearch, and most production search engines, and it remains the default keyword half of nearly every hybrid RAG retriever. This tool runs a faithful Okapi BM25 implementation entirely in your browser so you can paste a query and a set of candidate chunks and see exactly how a lexical retriever would rank them — useful for sanity- checking whether your retrieval problem even needs embeddings.

How it works

The query and every document are lowercased and split into word tokens. The tool counts how many documents contain each query term to compute the inverse document frequency, IDF(t) = ln(1 + (N - n + 0.5) / (n + 0.5)), where N is the document count and n is the number of documents containing the term. Each document’s score is the sum over query terms of IDF(t) · f · (k1 + 1) / (f + k1 · (1 - b + b · len/avglen)), where f is the term frequency in that document, len is the document length in tokens, and avglen is the average document length. The two knobs, k1 and b, let you tune term saturation and length normalization respectively.

Tips and notes

Hybrid beats either alone. In practice teams run BM25 and embedding search in parallel and fuse the rankings (reciprocal rank fusion), because BM25 nails exact identifiers and rare terms while embeddings catch paraphrase.
Start with the defaults. k1=1.5 and b=0.75 are the standard starting values; only tune after you have a labeled relevance set to measure against.
Short queries reward IDF. A rare query term that appears in only one document will dominate the ranking — that is the intended behavior, not a bug.
Everything stays local. Because nothing is uploaded, you can evaluate proprietary documents and customer queries without privacy risk.