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Dropping a column a Function binding writes was refused outright, which left a bad declaration unrecoverable: the binding is immutable, there is no rebind, and so the column could never be filled again. A drop that names every output of a binding now retires it instead. A binding lives in three places -- the envelope in schema metadata, a `computed_column.*` marker on each output field, and the columns themselves -- and readers cross-check the first two, so removing one and leaving the others is a table that refuses every write. The retirement therefore commits in two steps, each landing a state that stands on its own: one `UpdateConfig` rewrites the envelope and clears the markers together, leaving the outputs as ordinary columns holding their last values, and the drop follows. Interrupted between them, the columns are still there to be dropped again. Folding the metadata into the drop's own `Project` was the obvious alternative and does not work: the transaction proto records `Project` as fields alone, so the edit would survive only in the writer's memory. Naming one output of a multi-output binding is refused and names the missing siblings, since one refresh writes them in one commit. A multi-output binding's hidden `__function_assignment_*` column goes with it. Inputs stay protected while a surviving binding reads them, and drop in the request that retires the last one.
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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