hybrid-search-implementation

Hybrid search combines vector similarity and keyword-based retrieval to enhance information retrieval. By fusing results from both modalities, you ensure that exact term matching for specific identifiers and semantic understanding are preserved for more comprehensive RAG systems.

Reciprocal Rank Fusion (RRF) is a fusion method that combines rankings from multiple search modalities. It assigns scores based on the reciprocal of each document's rank in individual result lists, allowing you to merge vector similarity and keyword search outputs without needing raw score calibration.

Yes, you can implement hybrid search using templates for PostgreSQL with pgvector and Elasticsearch. The integration supports both vector similarity and traditional keyword retrieval within these databases to build advanced search engines and information retrieval systems.

Use cross-encoder reranking when you need higher precision through joint encoding of query and document pairs, and use linear combination when you need a faster fusion of independently scored vector and keyword results. Both methods address different scale and accuracy requirements.

Vector search alone often misses exact product codes because it prioritizes semantic similarity over exact term matching. Implementing hybrid search ensures that keyword retrieval handles specific identifiers while vector search captures broader semantic context.

Yes, sentence-transformers generate the vector embeddings, asyncpg interfaces with PostgreSQL, numpy handles numerical operations for fusion methods, and elasticsearch manages keyword indexing. These dependencies collectively support the hybrid search architecture.