> ## Documentation Index
> Fetch the complete documentation index at: https://docs.lancedb.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Search

> Comprehensive guide to all search capabilities in LanceDB including vector search, full-text search, hybrid search, and more.

| Feature                                           | Description                                               |
| :------------------------------------------------ | :-------------------------------------------------------- |
| [Vector Search](/search/vector-search/)           | Semantic similarity search with multiple distance metrics |
| [Multivector Search](/search/multivector-search/) | Search using multiple vector embeddings per document      |
| [Full-Text Search](/search/full-text-search/)     | Keyword-based search with BM25 and pre-filtering          |
| [Hybrid Search](/search/hybrid-search/)           | Combines vector and full-text search with reranking       |
| [Filtering](/search/filtering/)                   | Filter results based on metadata fields                   |
| [SQL Queries](/search/sql/index)                  | 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.