030102-lancedb-indexlisted

# Vector Indexing & Optimization ## Overview Index types, quantization, and reindexing strategies for LanceDB vector search performance. ## Quick Reference ### Index Types | Index | Best For | Quantization | |-------|----------|-------------| | IVF_PQ | General purpose | Product Quantization | | IVF_HNSW_SQ | High recall | Scalar Quantization | | IVF_HNSW_PQ | Large datasets | Product Quantization | | IVF_FLAT | Small datasets, 100% recall | None | | IVF_RQ | Binary vectors | RaBitQ (1 bit/dim) | ### Quantization | Type | Bits/Dim | Compression | Recall Impact | |------|----------|-------------|---------------| | None | 32 | 1x | None | | SQ | 8 | 4x | Minimal | | PQ | 4-8 | 4-8x | Moderate | | RQ (RaBitQ) | 1 | 32x | Low-moderate | ### Reindexing ```python table.optimize() # Compaction + cleanup + index update ``` ## References - [Vector Index Types](references/vector-index.md) — IVF, HNSW, PQ, RQ index selection and tuning - [Indexing Concepts](references/indexing.md) — Internal mechanics, disk-based indexing - [Quantization](references/quantization.md) — Compression techniques, accuracy tradeoffs - [Reindexing](references/reindex.md) — Incremental updates, compaction, optimization