Will Jones
5c1b44020a
`cargo deny` did not check crate-level dependency declarations against `[workspace.dependencies]`, so a crate used by both the core crate and the bindings could be declared independently in each one and drift. For example `tokio` was pinned at `1.23` in `rust/lancedb` and `1.40` in `python`, and `pin-project` at `1.0.7` in the workspace table but `1.1.5` in `python`. This PR turns on cargo-deny's `bans.workspace-dependencies` lint, which fails when a dependency is used by more than one member without going through `workspace = true`, and when a `[workspace.dependencies]` entry is used by nobody. Enabling it surfaced 12 violations. Fixing them means adding `bytes`, `lancedb`, `serde`, `serde_json`, `tempfile`, `tokio`, and `uuid` to `[workspace.dependencies]`, and pointing the `arrow`, `arrow-buffer`, `async-trait`, `chrono`, and `pin-project` declarations at the entries that already existed. `Cargo.lock` is unchanged, so resolution is the same as before. The shared `chrono` entry now carries `default-features = false, features = ["clock"]`, matching what `nodejs` and `python` already asked for — cargo ignores a member's `default-features = false` unless the workspace entry sets it too. On the targets we build, `clock` covers everything `rust/lancedb` was getting from chrono's defaults. Co-authored-by: Claude Opus 5 (1M context) <noreply@anthropic.com>
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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⭐ 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
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