LeonSGPandXuanwo
3321860e9f
Arrow map scalars returned by a table query cannot currently be passed back to `update`: the generic Arrow scalar conversion turns them into a list of tuples, which the SQL literal encoder cannot handle. Empty map scalars become an untyped SQL list and also fail to update a map column. Add a dedicated `MapScalar` conversion that renders its keys and values as a SQL map while preserving the existing null conversion. The regression tests exercise real synchronous and asynchronous table updates, quoted keys, nullable values, empty typed maps, and nested maps. Validation: the complete documented unit-test scope passed locally (1,515 passed, 37 skipped; unchanged baseline: 1,507 passed, 37 skipped). Full doctests passed (71 passed, 6 skipped), and repository-wide Python formatting/lint, Rust formatting/clippy, and Python 3.13 type checking passed. The locally built native wheel also passed all eight regression cases in a separate Python 3.13 environment. Full Linux/S3 integration and all platform matrices remain for CI. The SQL map constructor's existing backend constraints still apply: duplicate keys are rejected, and a nested map containing both nonempty and empty inner maps can fail SQL type unification. This change does not claim arbitrary Arrow map roundtrip support. AI assistance: implementation and local verification used Codex with gpt-6.1-sol agents; the diff and evidence were reviewed before submission. --------- Co-authored-by: Xuanwo <github@xuanwo.io>
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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