What is Hybrid Search?
Hybrid search is a retrieval technique that combines vector-based semantic search with traditional keyword-based search methods to provide more comprehensive and accurate results than either approach alone. The system executes both search types simultaneously, retrieves candidates from each method, and merges them using ranking or scoring algorithms to produce a final result set that captures both semantic relevance and exact keyword matches.
The approach addresses the limitations inherent in using only one retrieval method. Semantic search using embeddings handles synonyms, paraphrases, and conceptual similarity well but may miss results that contain exact terms or rare keywords that don't appear frequently in training data. Keyword search (often using BM25 or similar algorithms) excels at precise matching and domain-specific terminology but fails to understand semantic relationships. Hybrid search gets the best of both worlds.
Many modern vector databases like Qdrant, Weaviate, and Pinecone now offer hybrid search as a built-in feature, handling the complexity of combining different search modalities. The merging strategies vary from simple score combination to sophisticated re-ranking models that evaluate candidates holistically. Hybrid search has become a recommended practice for production RAG systems, providing more robust retrieval across diverse query types without requiring users to understand which search method would work best for their particular query.