030101-lancedb-searchlisted

# Vector Search Core ## Overview Core concepts for vector search with LanceDB: distance metrics, ANN indexing, embedding models, and search patterns. ## Quick Reference ### Distance Metrics | Metric | Use Case | Behavior | |--------|----------|----------| | `l2` | General purpose | Smaller = more similar | | `cosine` | Text/document similarity | Smaller = more similar | | `dot` | Normalized vectors | Larger = more similar | | `hamming` | Binary vectors | Smaller = more similar | ### Search Modes - **Exact search**: Brute force, 100% recall, no index needed - **ANN search**: Approximate, fast, requires index, configurable recall - **Hybrid**: Vector + FTS combined with reranking ### Embedding Functions - OpenAI: `text-embedding-ada-002`, `text-embedding-3-small`, `text-embedding-3-large` - Sentence Transformers: local models via `sentence-transformers` - Custom: create your own embedding function ## References - [Vector Fundamentals](references/vector-fundamentals.md) — Distance metrics, ANN vs exact, nprobes - [Vector Search Patterns](references/vector-search-patterns.md) — Prefiltering, binary search, batch, brute-force - [Embeddings](references/embeddings.md) — Embedding function registry, dimension selection