TaylorandBrendan Clement 9e65bd2c27 docs: use Enterprise terminology across website and SDK documentation (#4787)
Use “LanceDB Enterprise” throughout the staged website guides, SDK
reference sources, generated API pages, and user-facing SDK messages. A
separate branded “LanceDB Distributed” name adds terminology without
clarifying behavior; describe where operations run directly.

- Update `docs/web`, including the architecture diagram title and alt
text. Use Enterprise in headings, table labels, and prose; simplify
redundant product/execution-model wording and name the product only once
in the diagram alt text.
- Update Python docstrings and the Python/Java reference pages,
TypeScript comments and native declaration comments, and Rust API
documentation. Regenerate the TypeScript reference, including 24
replacements across six pages.
- Update SDK warnings/errors and existing test expectations to match,
along with contributor guidance and Vale rules. Remove the obsolete rule
distinguishing Enterprise from Distributed.
- Keep lowercase “distributed” for technical descriptions. API
identifiers, anchors, asset paths, and execution behavior are unchanged.
No branded-name exceptions are proposed.

Validation:
- Python and Node native builds succeeded; focused Python tests passed
(36 passed, 4 skipped), and focused Node tests passed (14).
- TypeDoc generation and the complete MkDocs reference build passed.
MkDocs retains an existing unresolved
`lancedb.functions.SecretReference` cross-reference.
- Rust formatting, Node lint/format, changed-file Python lint/format, 32
Vale tests, and whitespace checks passed. Repository-wide Python lint
has 48 existing findings in six untouched files.
- Combined documentation assembly passed. Mintlify validation and
link/anchor/redirect checks previously passed for both site builds; no
code snippets changed.

Companion PRs: [saved editions, terminology guide, and shared
rules](https://github.com/lancedb/docs/pull/413), [Enterprise overview
and writing rules](https://github.com/lancedb/sophon/pull/9511).

---------

Co-authored-by: Brendan Clement <brendan.clement@lancedb.com>
2026-11-02 10:25:14 -08:00
2026-10-30 12:54:54 -07:00
2023-03-17 18:15:19 -07:00
2025-03-10 09:01:23 -07:00

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LanceDB

The Multimodal Lakehouse for AI

How to Install ✦ Detailed Documentation ✦ Tutorials and Recipes ✦ Contributors

The ultimate multimodal data platform for AI/ML applications.

LanceDB is designed for fast, scalable, and production-ready vector search. It is built on top of the Lance columnar format. You can store, index, and search over petabytes of multimodal data and vectors with ease. LanceDB is a central location where developers can build, train and analyze their AI workloads.


Demo: Multimodal Search by Keyword, Vector or with SQL

LanceDB Multimodal Search

Star LanceDB to get updates!

⭐ Click here ⭐ to see how fast we're growing!

Key Features:

  • Fast Vector Search: Search billions of vectors in milliseconds with state-of-the-art indexing.
  • Comprehensive Search: Support for vector similarity search, full-text search and SQL.
  • Multimodal Support: Store, query and filter vectors, metadata and multimodal data (text, images, videos, point clouds, and more).
  • Advanced Features: Zero-copy, automatic versioning, manage versions of your data without needing extra infrastructure. GPU support in building vector index.

Products:

  • LanceDB Embedded (open source): 100% open source, runs in your application's process; your data can live on local disk or in object storage. No vendor lock-in.
  • LanceDB Enterprise: Production-scale distributed search, indexing, and maintenance. Plus, complete data sovereignty and security.

Ecosystem:

  • Columnar Storage: Built on the Lance columnar format for efficient storage and analytics.
  • Seamless Integration: Python, Node.js, Rust, and REST APIs for easy integration. Native Python and Javascript/Typescript support.
  • Rich Ecosystem: Integrations with LangChain 🦜️🔗, LlamaIndex 🦙, Apache-Arrow, Pandas, Polars, DuckDB and more on the way.

How to Install:

Follow the Quickstart doc to set up LanceDB locally.

API & SDK: We also support Python, Typescript and Rust SDKs

Interface Documentation
Python SDK https://lancedb.github.io/lancedb/python/python/
Typescript SDK https://lancedb.github.io/lancedb/js/globals/
Rust SDK https://docs.rs/lancedb/latest/lancedb/index.html
REST API https://docs.lancedb.com/api-reference/rest

Join Us and Contribute

We welcome contributions from everyone! Whether you're a developer, researcher, or just someone who wants to help out.

If you have any suggestions or feature requests, please feel free to open an issue on GitHub or discuss it on our Discord server.

Check out the GitHub Issues if you would like to work on the features that are planned for the future. If you have any suggestions or feature requests, please feel free to open an issue on GitHub.

Contributors

Stay in Touch With Us


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Description
Developer-friendly OSS embedded retrieval library for multimodal AI. Search More; Manage Less.
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