mirror of
https://github.com/lancedb/lancedb.git
synced 2026-10-14 15:52:26 +00:00
Jack Ye
b21fb2a4dc
The pages describing this code lived in lancedb/docs, a repository with ten stars that neither contributing guide mentioned. A user who hit a wrong page while reading the source had to find a separate, near-invisible repository to fix it. These 102 pages now sit beside the code, and a documentation fix is a pull request in the repository the reader already had open. `docs/web` is a complete Mintlify site: `cd docs/web && mint dev` serves it with no other checkout. Enterprise pages stay private and are merged in when the published site is assembled, so links into them do not resolve here — every open-source page does. `docs/web-tests` holds the tests the code examples are extracted from. They ran against a published wheel before; here they can run against this repository's own SDK, so an example that stops compiling fails the pull request that broke it rather than being noticed later. Both directories sit beside `docs/src` and are invisible to it: mkdocs reads `docs_dir: src`, so the SDK reference builds and deploys exactly as before.
27 lines
1.9 KiB
Plaintext
27 lines
1.9 KiB
Plaintext
---
|
|
title: Search
|
|
sidebarTitle: Overview
|
|
description: "Comprehensive guide to all search capabilities in LanceDB including vector search, full-text search, hybrid search, and more."
|
|
icon: "list"
|
|
---
|
|
|
|
| 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 {#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.
|
|
- Phrase FTS queries require an index created with token positions enabled.
|
|
- 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. The default top-k is 10 for search builders,
|
|
but spelling it out makes latency and result-count assumptions visible. Query builders also
|
|
support controls such as prefilter/postfilter, distance ranges, row-id inclusion, offset
|
|
pagination, and Arrow/Pandas/list result materialization.
|