Documentation Index
Fetch the complete documentation index at: https://lancedb-bcbb4faf-mintlify-f5da8d82.mintlify.app/llms.txt
Use this file to discover all available pages before exploring further.
| Feature | Description |
|---|---|
| Vector Search | Semantic similarity search with multiple distance metrics |
| Multivector Search | Search using multiple vector embeddings per document |
| Full-Text Search | Keyword-based search with BM25 and pre-filtering |
| Hybrid Search | Combines vector and full-text search with reranking |
| Filtering | Filter results based on metadata fields |
| SQL Queries | SQL query capabilities for data exploration and analytics |
Before you search
- Vector search can run without an ANN index as an exhaustive scan. That’s useful while prototyping, but build a vector index before relying on low-latency searches over larger tables.
- Full-text and hybrid text search require an FTS index on the text column you query. If a table has multiple FTS indexes, specify the target column. FTS also supports phrase, boolean, boosted, multi-match, and fuzzy query forms when you need more than plain terms.
- Multivector search currently uses cosine similarity and accepts either one query vector or a matrix of query vectors; every query vector must match the inner dimension of the multivector column.
- Set an explicit
.limit(...)for production queries. Query builders also support controls such as prefilter/postfilter, distance ranges, row-id inclusion, offset pagination, and Arrow/Pandas/list result materialization.