lancedb-gatefixer[bot]
fc5170cbed
Function descriptions include `source_digest`, but both the Rust response model and Python `FunctionVersion` discarded it. Clients therefore could not compare a catalog entry with `udf_def.registration_request.artifact.digest`. Preserve the digest through Rust serialization and expose it on Python `FunctionVersion`, with a documented Rust accessor. Missing or null digests decode as `None`, keeping older responses compatible. Regression tests cover serialization, immutability, registration jobs, get, and synchronous/asynchronous list responses while keeping the image digest independent. Validation: - Confirmed that the original Python model silently discarded a server-provided source digest. - Rebuilt the Python extension in the uv-managed environment; both focused Function test files passed (106 tests). - Focused Rust Function model tests passed (10 tests). - Full workspace `cargo check --quiet --features remote --tests --examples` and `cargo clippy --quiet --features remote --tests --examples` passed. - Rust formatting and Ruff checks/formatting for changed Python files passed. - Repository-wide Ruff reports 138 existing violations in unchanged docs/CI files. Fixes #4723 <!-- lance-gatekeeper-fix:v1 agent=c145f25136f7b908217e04bde8a2548b generation=1 --> Co-authored-by: Gatefixer <313497061+lancedb-gatefixer[bot]@users.noreply.github.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:
- 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
Stay in Touch With Us