In Depth
Hybrid search combines lexical search (keyword matching using algorithms like BM25) with semantic search (meaning-based matching using embeddings) to achieve better retrieval than either approach alone. Keyword search excels at exact matches, proper nouns, and technical terms, while semantic search handles synonyms, paraphrases, and conceptual similarity. Hybrid search gets the best of both worlds.
Implementation typically involves running both search methods in parallel and combining their results using reciprocal rank fusion (RRF) or learned score combination. Modern vector databases like Weaviate, Pinecone, and Qdrant support hybrid search natively, allowing users to configure the balance between keyword and semantic signals.
Hybrid search is particularly important for RAG systems, where retrieval quality directly impacts the AI's response quality. Pure semantic search can miss exact terms (like product codes or error messages), while pure keyword search misses conceptually relevant content. Hybrid search followed by a reranking step represents the current best practice for high-quality retrieval in production AI systems.