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Dan RammerandClaude Opus 5
0968c21f4c
## Summary `RemoteTable::set_unenforced_primary_key` returned `NotSupported`, so the call failed against LanceDB Cloud and enterprise from every SDK — Python and TypeScript both forward to it. That also blocked sharded LSM writes, since the server rejects bucket/identity sharding on a table that declares no unenforced primary key, and the SDK had no way to declare one. No server-side support was missing. The unenforced primary key is Lance schema field metadata, and the existing `update_field_metadata` endpoint writes exactly that, so the remote table now installs the key through it. The commit layer behind that endpoint installs the position, enforces immutability and runs `verify_primary_key()` — the same code a native table reaches, so both paths agree on semantics and not just on error messages. ## Changes - `rust/lancedb/src/table/primary_key.rs`: the request validation (single column, column exists, supported dtype, key not already set) and the metadata edit (set the 1-indexed position, clear the legacy boolean key) move into `validate()` and `install_edit()`, shared by both table types so they cannot drift. Native behaviour is unchanged. - `rust/lancedb/src/remote/table.rs`: real implementation — `check_mutable()`, validate against the described schema, then one `update_field_metadata` request. - `python/python/lancedb/remote/table.py`: drop the "Not supported on LanceDB Cloud." docstring. No binding changes were needed; Python and TypeScript already forward to the Rust `BaseTable` method. ## Tests Three mocked-server tests in `rust/lancedb/src/remote/table.rs`: - the happy path, asserting the request body carries `lance-schema:unenforced-primary-key:position: "1"` and a null legacy key on the right field path; - the invalid requests a native table also rejects (empty, compound, unknown column, unsupported dtype), each failing before any write reaches the server; - the already-set case, rejected from the schema the server already reports. `cargo test --features remote -p lancedb --lib`: 1186 passed, 0 failed. `cargo clippy --features remote -p lancedb --lib --tests` is clean. ## Note for enterprise Sophon pins the lancedb submodule at `v0.39.0-beta.6`, so this reaches enterprise on the next bump. 🤖 Generated with [Claude Code](https://claude.com/claude-code) https://claude.ai/code/session_011xV8EebEZDmDDV8hVBm6MD --------- Co-authored-by: Claude Opus 5 (1M context) <noreply@anthropic.com>
The Multimodal Lakehouse for AI
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.
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