Documentation
README
BM25 Ranking Function
Overview
BM25 (Best Matching 25) is an improved TF-IDF ranking function that adds term frequency saturation and document length normalization. Score = Σ IDF(t) × (TF(t,d) × (k₁+1)) / (TF(t,d) + k₁ × (1 - b + b × |d|/avgdl)). Standard parameters: k₁=1.2, b=0.75. The backbone of most text search engines (Elasticsearch, Solr).
When to Use
Trigger conditions:
- Building product search with text-based relevance ranking
- Replacing basic TF-IDF with better document length normalization
- Tuning search relevance in Elasticsearch/Solr
When NOT to use:
- When semantic similarity matters more than keyword matching (use embeddings)
- For single-field exact matching (simpler methods suffice)
Algorithm
This is the opening of the README. Read the full README on GitHub.