Will JonesandClaude Opus 5
65e9a4b873
An embedder can extend the SQL dialect with its own statements, but has had nowhere to say what a statement *is*: its grammar, the label it reports to an audit log, and the access it needs are three separate facts, and describing them separately means a host ends up with parallel downcast chains that must be kept in step by hand. Adding a statement to only two of the three is a silent gap rather than a compile error. `lancedb::sql` gains the seam that keeps them together: - `CustomSqlHandler` contributes a grammar; `route_custom_sql` picks the one that owns a statement and leaves the rest to DataFusion. - `SqlStatement` pairs a planned node with its audit label and the `AccessRequirement`s it needs. The vocabulary names what is reached for -- read, write, own, create, database, namespace, system -- rather than a privilege, so no access-control model has to live in the dialect. - `StatementRegistry` holds both, with the consultation order it is given. Registration is front-insertion so an extension can get ahead of a catch-all that would otherwise swallow its keyword. - `WriteObserver` reports a committed write without the statement knowing how its host represents that. - `DmlResult` carries what a DML statement did as a one-row batch. The feature adds no dependency that is not already required, so it is on by default; the flag is there so an embedder that does not want the surface can opt out. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
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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⭐ 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:
- 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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