Hybrid Search
Keyword search is precise. Vector search understands meaning. Hybrid search combines both for the best of both worlds.
The problem with pure approaches: Keyword search misses synonyms ("happy" won't find "joyful"). Vector search sometimes misses exact terms (searching for "error code 404" might return generic error pages). Hybrid search combines both: keyword matching for precision + vector similarity for understanding. The key parameter is the alpha weight — the balance between keyword and semantic scores. Typical production values are 0.3-0.7 semantic weight.
When Each Approach Wins
Keyword Search Wins
Error codes, product IDs, exact names, legal clauses, API endpoints. When the exact term IS the meaning.
Semantic Search Wins
Conceptual questions, synonym-heavy queries, "how to" questions, finding related content across different phrasings.
Hybrid Wins
Real-world queries that mix specific terms with concepts: "how to fix error 404 in React" needs both keyword AND semantic understanding.
Implementation: Most vector databases support hybrid search natively. Weaviate uses BM25 + vector. Pinecone has sparse-dense vectors. The key parameter is the alpha weight — the balance between keyword and semantic scores. Typical production values are 0.3-0.7 semantic weight.
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