6a431ff0a0
## Summary Adds `Table::set_unenforced_primary_key` — records a single column as the table's unenforced primary key in Lance schema field metadata. "Unenforced" means LanceDB does not check uniqueness on write; the key is metadata that `merge_insert` consumes. - Single-column only; the column must exist and have a supported dtype (Int32, Int64, Utf8, LargeUtf8, Binary, LargeBinary, FixedSizeBinary). The API accepts an iterable for binding ergonomics but requires exactly one column — compound keys are rejected. - The primary key is immutable: calling this on a table that already has an unenforced primary key is rejected. Concurrent writers racing to set the key fail at commit time rather than silently overriding it. - `RemoteTable` returns `NotSupported`. - Bindings: Python (`AsyncTable`, `LanceTable`, `RemoteTable`) and TypeScript (`Table.setUnenforcedPrimaryKey`). ## Context Split out from #3354 per review feedback, so the unenforced primary key and the `merge_insert` sharding spec land as separate reviewable PRs. No Lance dependency bump — `main` is already on v7.0.0-beta.10, which includes the field-metadata round-trip fix the API relies on. Enforcing primary-key immutability at the Lance commit layer (so the cross-column concurrent race is also rejected) is a companion Lance change: lance-format/lance#6810.
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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