hybrid-search-implementation

Hybrid search combines vector and keyword search to improve RAG retrieval accuracy by fusing semantic understanding with exact term matching. You can implement this using Reciprocal Rank Fusion, linear combination, or cross-encoder reranking techniques.

Reciprocal Rank Fusion (RRF) is a hybrid search fusion method that merges result rankings from vector and keyword search methods. It calculates a combined score based on the reciprocal of each document's rank in the individual result lists, enhancing overall retrieval accuracy.

Yes, this hybrid search implementation works with both Elasticsearch and PostgreSQL. It provides specific templates for both platforms, allowing you to fuse vector and keyword search results directly within your existing asyncpg or Elasticsearch database environments.

Semantic search misses exact product codes because it relies on vector embeddings for contextual meaning rather than exact term matching. Hybrid search solves this by combining vector search with keyword search, ensuring specific alphanumeric codes are successfully retrieved.

The best way to fuse vector and keyword search results is using hybrid search strategies like Reciprocal Rank Fusion, linear combination, or cross-encoder reranking. These methods leverage both semantic understanding and exact matching to yield highly accurate domain-specific retrieval.