AI and LLMs · Comparison
Keyword vs vector vs hybrid search
Keyword search matches the words people type, vector search matches what they mean, and hybrid search runs both and merges the results.
3 options · 7 questions side by side · updated
| Compare | Keyword search | Vector search | Hybrid search |
|---|---|---|---|
| Matches on | Shared words, after stemming | Similar meaning | Both |
| Good at | Names, codes and exact phrases | Paraphrases, synonyms, other languages | Mixed, real-world queries |
| Weak at | Different wording for the same idea | Exact terms and rare words | Little, beyond extra complexity |
| Needs | A full-text index | An embedding model and a vector index | Both, plus a merge step |
| Cost | Cheap, no model calls | An embedding call per document and query | Both costs combined |
| Typical tools | SQLite FTS5, PostgreSQL, Meilisearch | pgvector, sqlite-vec, Pinecone | Elasticsearch, Weaviate, PostgreSQL with both |
| Example strength | 'SKU-4471' finds that exact product | 'shoes for rainy days' finds waterproof boots | Handles both kinds in one search box |
How to choose between Keyword search, Vector search and Hybrid search
- Pick keyword search for catalogues, codes and names, or as a cheap, predictable baseline.
- Pick vector search when people describe what they want in their own words.
- Pick hybrid search for RAG and general site search, where both kinds of query turn up.
The options
- Keyword searchDatabases and storageSearching inside text by words rather than exact matches, with results ranked by how well they match, the way a search box should work.
- Vector searchFinding items by meaning rather than exact words, by storing embeddings and returning the ones closest to the embedding of a query.
- Hybrid searchRunning keyword search and vector search on the same query and merging the two result lists, so you catch both exact matches and matches by meaning.
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