Joaquin Hui
b505dc1315
`table_names()` lists any `*.lance` directory, but `open_table()` maps every `DatasetNotFound` to `TableNotFound`, so a corrupt or partially-written table looks identical to one that never existed (#3127). This takes the issue's Option 2: on `DatasetNotFound`, check the parent listing for the table's `.lance` entry — the same predicate `table_names()` uses — and return a new `TableCorrupted` error when the directory is present. The check runs only on the error path, and any failure in the recheck falls back to the previous `TableNotFound` behavior. Tests cover the reporter's empty-dir repro, a deleted-manifest case, true absence (still `TableNotFound`), and an end-to-end list-then-open assertion; the three new corrupt-case tests fail without the src change. `cargo test -p lancedb --lib` 732 passed, clippy/fmt clean, `cargo check --workspace --all-targets` clean (both language bindings end in wildcard error arms). Two notes for review: `Error` isn't `#[non_exhaustive]`, so the new variant is technically semver-breaking for exhaustive matchers (pre-1.0, and the alternative — changing `TableNotFound`'s shape — breaks more); and on the Python side corrupt tables now surface as `RuntimeError` rather than `ValueError`, which is the intended distinction but worth a maintainer's eye. `open_from_namespace` was left unchanged since namespace listings come from a server-side registry, not directory globbing. Closes #3127
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
Star LanceDB to get updates!
⭐ 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.
Contributors
Stay in Touch With Us