Jack Ye
e4d0f9e2da
Dropping a view unbinds its name and leaves the definition dataset to a server-side cleanup job, so the two are separate events a caller may want to wait on. `drop_view_async` returns that job — the same shape `drop_materialized_view_async` and `drop_function_async` already use: a `202` carries the job id, a `200` (nothing was bound to the name) yields an already-finished job with no id, and any other success status is an error rather than a silent no-op. ## `drop_view` waits `drop_view` now awaits the job before returning, so a caller who does not want to think about cleanup gets the stronger guarantee: when it returns, the definition really is deleted. That is deliberately **different** from `drop_materialized_view` and `drop_function`, which return as soon as the name is unbound and document that content may still be deleting. The view API is the newer one, and waiting is the semantic worth having; the other two are left alone rather than changing behaviour already released. ## Surfaces `Database` trait, the remote client, `Connection`, and the Python and Node bindings — matching where `drop_materialized_view_async` is already exposed. Four client tests cover the accepted case reporting its job id, the nothing-bound case reporting a finished job, a `202` without a usable `job_id`, and an unexpected success status.
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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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.
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