Yang Cen
6687db979d
Expose `FunctionRetryPolicy` on Function declarations and refresh overrides, together with `error_policy` and recorded attempt counts. Python uses the Rust validation rules before sending a request; local refreshes reject these Enterprise options. The policy bounds calls and backoff, supports class/message/provider-code matching, and can disable retries with `max_attempts=1`. Batch retry and skip operate on the whole failed call. Requires Enterprise server support for the new policy. Validation: rebuilt the Python extension; 47 targeted Python tests and 10 Rust tests passed. Python `make check`, Rust formatting, and `cargo clippy --features remote --tests --examples -- -D warnings` passed. The additional repository-root Ruff sweep reports existing errors under `docs/web-tests`; the Python workspace checks pass. Draft user guidance covers the supporting server requirement, safe repetition of partial work and whole-batch exhaustion. The guide sections stay hidden until release. Vale 3.24.0 passed for the added draft prose; no paired deployed SDK/server retry acceptance was run. Companion server: https://github.com/lancedb/sophon/pull/9812. BREAKING CHANGE: Rust callers constructing `FunctionRegistrationRequest` or `FunctionErrorRecord` with struct literals must supply the new `retry_policy` or `attempts` field, respectively.
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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