Ryan GreenandClaude Opus 5
bd94fc3572
Support adding column with pyarrow schema in remote client. Previously this raised an error. This achieves parity with local client. Server-side implementation is already complete. `RemoteTable::add_columns` matched only `SqlExpressions` and refused everything else, so `add_columns(pa.Field | List[pa.Field] | pa.Schema)` reached a remote table as `NotSupported`. Every layer above was already in place: the Python and Node surfaces accept a schema, both bindings build `NewColumnTransform::AllNulls` from it, and the Function-binding guard already reads that variant's column names. Only the arm that turns it into a request was missing. Send the schema as an Arrow IPC schema message under `application/vnd.apache.arrow.stream`, which is what the server takes. It is also the only encoding that round-trips a field whole: the JSON type representations either hide decimal precision in a length field or drop a timestamp's unit and timezone. With no JSON envelope the branch rides the query string, as it does for the other binary-bodied endpoints. The response handling is now shared by both arms rather than living inside the SQL one, so the new path gets the same schema-cache invalidation, write-version tracking, and old-server empty-body fallback. Also widen the sync `RemoteTable.add_columns` annotation to match `Table.add_columns`. It delegates to the async table, so a schema already worked at runtime; only the signature and its docs disagreed. Co-authored-by: Claude Opus 5 (1M context) <noreply@anthropic.com>
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 |
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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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