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lancedb-gatefixer[bot]
85b0eba176
## Summary - infer fixed-size float32 Arrow vectors from `VectorField` metadata when a model uses the standard `list[float]` annotation - document the mypy-compatible embedding-field declaration while preserving the existing `Vector(dim)` runtime API - add regression coverage for schema conversion and model construction ## Root cause `Vector(dim)` is a runtime constrained-type factory, but Python's static typing rules do not permit call expressions in annotation positions. Mypy's suggested `Vector[...]` spelling also cannot represent a dimension computed at runtime and fails because `Vector` is a function. `VectorField` already carries the embedding function and its dimension, but schema conversion previously ignored that metadata for a statically valid `list[float]` annotation. The runtime factory remains available for dimension-constrained Pydantic validation. Embedding-backed models that require mypy compatibility can now use `list[float] = embedding.VectorField()`; LanceDB derives the same nullable fixed-size float32 Arrow type from the field metadata. ## Validation - `cd python && uv run --extra tests pytest python/tests/test_pydantic.py -q` (23 passed) - focused mypy check reproducing the original error and accepting the new `LanceModel` declaration - isolated schema checks with Pydantic 1.10.24 and 2.11.7 - `ruff format .` - `ruff check .` Fixes #1982 <!-- lance-gatekeeper-fix:v1 agent=877facbc659cdc9424bd367e12426366 generation=1 --> Co-authored-by: Gatefixer <313497061+lancedb-gatefixer[bot]@users.noreply.github.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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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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