Xuanwo
3c4aaa8e62
Catalog lookup failures lose their structured resource identity at the Rust/Python SDK boundaries, leaving callers with generic errors or empty correlation fields. Decode the existing Flight SQL error metadata and preserve resource kind/name, namespace, error code, and request/job IDs in the existing error types. Direct `get_function` calls unwrap named Function or namespace failures. Generic SQL job failures remain `JobFailedError` with the typed resource error as their cause; native deferred commands correctly leave durable job IDs null. Existing unstructured SQL classifications, including `FailedPrecondition`, remain supported. Validation: focused Rust/Python regressions and a native-extension run against a real Sophon process passed all five Function/Secret resource-error cases. The final one-commit candidate passes `cargo check -p lancedb-python` and formatting; earlier runtime evidence applies after removal of an unused tuple member. Independent deployed acceptance remains pending for ENT-3373. Server companion: https://github.com/lancedb/sophon/pull/9804. Integration validation: all 14 DocLinks pass on the merge candidate with #4869. The subsequent main documentation routing change was inspected; it does not alter the typed-error paths. The inherited pin-utils cargo-deny finding is tracked in a separate dependency repair.
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:
- LanceDB Embedded (open source): 100% open source, runs in your application's process; your data can live on local disk or in object storage. No vendor lock-in.
- LanceDB Enterprise: Production-scale distributed search, indexing, and maintenance. Plus, 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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