Joaquin Hui Gomez
82bfe7d38e
`table_names()` enumerates with `read_dir`, which returns files as well
as directories, filtered only on the `.lance` extension — so a loose
`foo.lance` *file* at the database prefix is reported as a table.
`list_tables()` already applies the correct rule ("Only child
directories can be tables"), and
`test_listing_ignores_non_table_children` documents it, so the two
listing APIs currently disagree.
The practical damage is in `drop_all_tables()`, which is built on
`table_names`: with a loose `.lance` file present it deletes some real
tables, fails on the file with `os error 20 (NotADirectory)`, and then
fails the same way on every retry — the database can never be emptied
through the API, and the partial drop is not recoverable.
`table_names` now lists via `list_with_delimiter` and filters
`common_prefixes` through the same `table_name` helper `list_tables`
uses; the sort/`start_after`/`limit` handling is unchanged.
`drop_tables` additionally attempts every name before returning, still
reporting the first error unchanged (kept rather than an aggregate error
so the mapped exception types in the bindings stay exactly as they are).
Extended the non-table-children test to cover `table_names`, plus tests
for the drop behavior — all three fail without the fix. Full `cargo test
-p lancedb --lib --features remote` 1320 passed on the repo-pinned 1.97
toolchain; clippy/fmt clean.
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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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 |
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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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