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Xuanwo
a2a0d7f632
`build-no-lock` restores Rust build outputs twice: toolchain setup enables its own cache by default before Cargo.lock removal, then the workflow restores an explicit cache afterward. Both restore into the same Cargo and target paths; the implicit action also allows PR cache saves. Disable only toolchain setup's implicit cache. The explicit restore remains after lock removal and retains its main-only save condition. This configuration is inherited unchanged by #4996. Its [failed job](https://github.com/lancedb/lancedb/actions/runs/43229196192/job/131220088196) reported runner ENOSPC while the second cache action was active, before dependency installation or Cargo. Earlier hosted logs confirm two restores into overlapping paths, but the failed job's full logs are unavailable, so the exact disk peak and ENOSPC cause remain unproven. A bounded probe using the resolved composite-action definitions verifies two restores before this change versus one afterward, preserves ordering and main-only saves, and confirms all other workflow configuration is unchanged. The [hosted run](https://github.com/lancedb/lancedb/actions/runs/43230840086/job/131225407007) confirms one successful 1.62 GB cache restore after lock removal and no PR cache save. It reached Cargo and linking without ENOSPC, but hit the inherited 30-minute deadline with the unchanged one-job driver. Free disk was 85 GiB before the build and 79 GiB at cancellation; these endpoint observations do not establish peak disk use or a general ENOSPC guarantee. The complementary two-job repair in #4996 now includes this prerequisite for combined hosted acceptance; this PR remains limited to cache ownership.
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
Star LanceDB to get updates!
⭐ 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.
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