adenzhou1350
4bdc8c3c00
Addresses the client-side waiting portion of #4402. When create_index returns a job ID, wait_timeout now waits for that submission rather than an already-ready index with the same name. It propagates JobFailed / JobCancelled and bounds job polling by the requested timeout. Older servers without job IDs keep name-based waiting, calls without wait_timeout remain fire-and-forget, and the existing two-hour maximum is preserved. The job wrapper is shared with create_index_async; no synchronous server-side config validation is added. Rebased on main81d52a98, preserving the new materialized-view SQL constructor. Validation on Linux / Rust 1.97, exact head61122f92: - All five new native HTTP-mock tests pass (DONE/FAILED/CANCELLED, pending deadline, legacy response, no wait, maximum timeout). On unchanged current-main production the job-tracking and deadline regressions fail by returning early against the old index. - All 274 remote-table tests pass, no failures/skips. - cargo fmt --all -- --check and Clippy --lib --tests -- -D warnings pass. Tests and Clippy used --offline --profile ci --locked -p lancedb --no-default-features --features remote. The affected package was rebuilt from each selected source root; dependency caches were reused. This is Rust mocked-HTTP coverage, not live-cloud, Python/Node binding, full-workspace or performance qualification.
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:
- 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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