BM25
BM25 (Best Matching 25) is a ranking function used by search engines to estimate the relevance of documents to a search query. It enhances basic TF-IDF by incorporating document length normalization ($b$) and term frequency saturation ($k_1$), preventing document lengths from biasing similarity scores.
HNSW
Hierarchical Navigable Small World (HNSW) is a graph-based data structure for Approximate Nearest Neighbor (ANN) search. It builds a multi-layered hierarchy of proximity graphs. The top layer has long-range links for fast global routing, while the bottom layer has short-range links for local accuracy.
Hybrid Search
Hybrid Search combines sparse lexical retrieval (BM25) and dense semantic retrieval (Vector Search) to provide optimal query relevance. It executes both keyword matching and vector similarity lookups in parallel, and merges the resulting rank lists using Reciprocal Rank Fusion (RRF) to leverage the strengths of both retrieval styles.
Vector Search
Vector search finds semantically similar text by comparing query embeddings with document embeddings in high-dimensional vector space. It uses metrics like Cosine Similarity or Inner Product, allowing search engines to match queries based on semantic meaning rather than exact keywords.