Wyatt AltandClaude Fable 5.1
1f95398c34
A view could not carry a column it does not compute: the definition planned every output as a SQL expression, and the refresh engine treated any commit it did not make as drift and rebuilt. Servers fill such columns on tables with a separate job, as computed columns bound to a registered function, and want the same column on a view. This lets a declaration add computed columns, placed at their positions in the select list and validated by the existing computed-column contract, with the view created in one commit. Refresh writes those columns NULL on every path and never reads them, so a rewritten row comes back unfilled, and a commit that rewrites only computed columns is recognised as a fill rather than drift, so the next refresh carries on incrementally. A source column a computed column reads without the view projecting it is held as an internal projection, so the select list stays the view's column list. Nothing in the stored definition changes; an older reader fails closed on the schema check. Two smaller changes ride along because the feature needs them: an identity projection keeps its source column's nullability, with the schema check accepting a nullable physical field for a non-null planned one so existing views keep refreshing; and `prepare_declaration` takes `Option` projections, so an empty list declares no projection rather than `SELECT *`. --------- Co-authored-by: Claude Fable 5.1 <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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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.
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