Jack Ye
f7ed6c6b93
The manual said the SQL dialect's function and type reference was out of scope. It is not: the engine is DataFusion 54 with its default function set registered, so the dialect is documented already and the useful thing is to say which version and point at it. The types are worth being concrete about rather than deferring. Lance's DataFusion table provider returns the dataset's Arrow schema directly — there is no SQL type layer over storage — so DataFusion's type reference describes Lance columns exactly, and a vector column is a FixedSizeList that behaves like one. Two consequences of that mapping are documented because they surprise people. The pseudo-columns the provider can append are real and typed — `_rowid`, `_rowaddr` and `_rowoffset` are nullable UInt64, and the nullability is a reused selection bitmap rather than a missing value, so a null there means nothing. And a blob column's Arrow type depends on how it is read: descriptions by default, binary on request, which changes the schema a query sees. `fts()` is recorded as what it is, a DataFusion TableProvider over the dataset. That explains the parts that otherwise look arbitrary: why it is a table function rather than a predicate, why every column of the table is available in its result, and why projections push into the scan.
The Multimodal AI Lakehouse
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
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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:
- Open Source & Local: 100% open source, runs locally or in your cloud. No vendor lock-in.
- Cloud and Enterprise: Production-scale vector search with no servers to manage. 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
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