hybrid-search-implementation

Combining vector and keyword search for RAG retrieval leverages both semantic understanding and exact term matching. You can merge results using fusion methods like Reciprocal Rank Fusion (RRF), linear combination, or cross-encoder reranking to significantly improve search recall.

Reciprocal Rank Fusion (RRF) in hybrid search is a technique to merge result rankings from vector and keyword search engines. It calculates a unified score based on the reciprocal of each document's rank across different search strategies, requiring no manual weight tuning.

Yes, this hybrid search implementation works with PostgreSQL and Elasticsearch. It provides ready-to-use templates for both databases, allowing you to execute combined vector and keyword search queries directly within your existing data infrastructure.

Use cross-encoder reranking instead of linear combination when you need higher precision in your final search results. Cross-encoders evaluate query-document pairs together for deeper semantic matching, whereas linear combination simply blends scores from separate vector and keyword searches.

To handle RAG queries containing both product codes and abstract concepts, implement a hybrid search architecture. This ensures exact term matching captures the specific product codes while vector search retrieves the abstract conceptual information simultaneously.

To run this hybrid search pipeline, you need the asyncpg library for PostgreSQL access, elasticsearch for keyword and document indexing, and sentence-transformers to generate the vector embeddings required for semantic search queries.