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Bruno RamirezandClaude Sonnet 5
a9ad499781
`Table::optimize()` previously returned `NotSupported` for remote tables. This PR wires it to the LanceDB Enterprise server's `OPTIMIZE TABLE ... REWRITE DATA` SQL statement, submitted over Flight SQL. Only `OptimizeAction::Compact` without a custom `remap_options` or non-default `CompactionOptions` is supported remotely. `OptimizeAction::All`, `::Prune`, and `::Index` all return a clear `NotSupported` error instead of silently doing the wrong thing. A branch-scoped table handle submits a `WITH (branch = '<branch>')` clause; the statement doesn't validate the branch exists itself, so an unknown branch surfaces as an async job failure rather than an error from this call. The server's SQL job adapter doesn't expose compaction metrics, so `OptimizeStats::compaction` is `Some(CompactionMetrics::default())`, a placeholder rather than real numbers — it must still be `Some`, since callers (including the Python/Node bindings) unwrap it unconditionally on success. This mirrors how `refresh_materialized_view_async` already handles the same limitation for its own metrics. The Python and Node `optimize()` convenience methods call a new shared `Table::optimize_full`, which runs `Compact` and then, on a remote table, skips `Prune` and `Index` instead of attempting and failing on them — returning zeroed prune stats rather than submitting a compaction job and then raising. Python's `RemoteTable.optimize()` also now forwards `compaction_options` to the Rust layer, so a non-default value is rejected before submission instead of being silently dropped. --------- Co-authored-by: Claude Sonnet 5 <noreply@anthropic.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 |
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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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