BM25

Probabilistic ranking function for estimating document relevance to search queries. Industry standard for keyword search, combining term frequency, rarity, and length normalization into a single scoring model.

A two-stage retrieval process where initial candidates from vector search are reordered using more sophisticated models like cross-encoders. Reranking significantly improves result quality by applying computationally expensive models to a small set of candidates, commonly used in RAG systems and search applications.

Information retrieval using high-dimensional sparse vectors where most values are zero, typically based on term frequency methods like BM25. Sparse retrieval excels at exact keyword matching and is interpretable, often combined with dense retrieval in hybrid search systems for robust performance.

Search technique combining BM25 lexical search and semantic vector search using Reciprocal Rank Fusion (RRF) to merge results, balancing precision of keyword matching with contextual understanding of neural embeddings.

Scoring function used in late interaction models like ColBERT that computes query-document relevance by finding maximum similarity between each query token and document tokens, then summing.

Hybrid search algorithm combining results from multiple ranking systems by computing reciprocal ranks, commonly used to merge dense vector search with sparse keyword search for improved retrieval.