9d8699f99e
## Summary Fixes #1846. Python `Enum` fields raised `TypeError: Converting Pydantic type to Arrow Type: unsupported type <enum 'SomethingTypes'>` when converting a Pydantic model to an Arrow schema. The fix adds Enum detection in `_pydantic_type_to_arrow_type`. When an Enum subclass is encountered, the value type of its members is inspected and mapped to the appropriate Arrow type: - `str`-valued enums (e.g. `class Status(str, Enum)`) → `pa.utf8()` - `int`-valued enums (e.g. `class Priority(int, Enum)`) → `pa.int64()` - Other homogeneous value types → the Arrow type for that Python type - Mixed-value or empty enums → `pa.utf8()` (safe fallback) This covers the common `(str, Enum)` and `(int, Enum)` mixin patterns used in practice. ## Changes - `python/python/lancedb/pydantic.py`: add Enum branch in `_pydantic_type_to_arrow_type` - `python/python/tests/test_pydantic.py`: add `test_enum_types` covering `str`, `int`, and `Optional` Enum fields ## Note on #2395 PR #2395 handles `StrEnum` (Python 3.11+) specifically, using a dictionary-encoded type. This PR handles the broader `(str, Enum)` / `(int, Enum)` mixin pattern that works across all Python versions and stores values as their natural Arrow type. AI assistance was used in developing this fix.
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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