hybrid-search-implementation

Hybrid search improves retrieval recall by running vector similarity and keyword matching in parallel, fusing the candidates to capture both semantic meaning and exact term matches within RAG pipelines.

RRF, or Reciprocal Rank Fusion, merges vector and keyword result ranks mathematically, while cross-encoder fusion evaluates candidate pairs jointly for deeper semantic precision, offering different balances between recall and precision.

Implement hybrid search by configuring a vector store and a keyword index to run concurrently, then apply fusion methods like Linear or Cascade filtering to blend results for improved search engine relevance.

Use cascade filtering in a vector search architecture when you need to progressively narrow down candidates sequentially, whereas linear fusion applies fixed weights to combine vector and keyword scores simultaneously.

Yes, a hybrid search architecture requires a separate vector store for semantic similarity and a keyword index for exact term matching, running both in parallel before applying fusion methods to yield top matches.

Vector search misses exact keyword matches because it relies on semantic similarity rather than term precision, requiring hybrid search with a keyword index and fusion methods to retrieve highly relevant results.