## Summary
- raise a clear `TypeError` when `Vector` is used without a dimension
- preserve normal `Vector(dim)` behavior across Pydantic v1 and v2
- add a regression test that defines a model without importing PyArrow
## Root cause
Pydantic interpreted the bare `Vector` factory as a callable field type
and inspected its postponed annotations in the user model's namespace.
Because that namespace did not define LanceDB's internal `pa` alias,
model construction failed with the misleading `NameError: name 'pa' is
not defined` instead of explaining that `Vector` must be parameterized.
The factory now exposes Pydantic's v1 and v2 schema hooks and rejects
bare use before signature introspection with guidance to use
`Vector(dim)`.
## Validation
- `uvx --from 'ruff==0.15.20' ruff check .`
- `uvx --from 'ruff==0.15.20' ruff format --check
python/python/lancedb/pydantic.py python/python/tests/test_pydantic.py`
- `cd python && uv run --extra tests pytest
python/tests/test_pydantic.py::test_bare_vector_raises_clear_error -q`
- `cd python && uv run --extra tests pytest
python/tests/test_pydantic.py -q`
- compatibility checks with Pydantic 1.10.22, 2.11.4, and 2.13.4
Fixes#2384
<!-- lance-gatekeeper-fix:v1 agent=71e7473e18c91db5137a3c0d3bb73640
generation=1 -->
Co-authored-by: Gatefixer <313497061+lancedb-gatefixer[bot]@users.noreply.github.com>
Closes#3502
## Problem
A bare, unparameterised `typing.List` / `typing.Tuple` field crashes
`to_arrow_schema` with an opaque `AttributeError: __args__`:
```python
from typing import Tuple
from lancedb.pydantic import LanceModel
class Doc(LanceModel):
items: Tuple
Doc.to_arrow_schema() # AttributeError: __args__
```
In `_py_type_to_arrow_type`, the branch `elif getattr(py_type,
"__origin__", None) in (list, tuple)` is taken for a bare generic (its
`__origin__` is `list / tuple`), but the next line reads
`py_type.__args__[0]`, and a bare generic has no `__args__`. Other
unsupported types (e.g. `Dict[str, int]`) correctly raise a clear
`TypeError`, so this case is inconsistent.
Fix
Guard the element-type lookup with `getattr(py_type, "__args__", None)`
and raise a clear `TypeError` when it is missing, matching the existing
behavior for other unsupported types. Bare builtin list / tuple are
unaffected (their `__origin__` is `None`, so they already fall through
to the existing `TypeError`).
Testing
- Added `test_bare_generic_raises_type_error` covering both `List` and
`Tuple`.
- ruff format and ruff check clean.
## 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.
This request improves support for `pydantic` integration by adding
`to_pydantic` method to asynchronous queries and handling models that
use `alias` in field definitions. Fixes#2436 and closes#2437 .
<!-- This is an auto-generated comment: release notes by coderabbit.ai
-->
## Summary by CodeRabbit
- **New Features**
- Added support for converting asynchronous query results to Pydantic
models.
- **Bug Fixes**
- Simplified conversion of query results to Pydantic models for improved
reliability.
- Improved handling of field aliases and computed fields when mapping
query results to Pydantic models.
- **Tests**
- Added tests to verify correct mapping of aliased and computed fields
in both synchronous and asynchronous scenarios.
<!-- end of auto-generated comment: release notes by coderabbit.ai -->
This PR adds ColPali support with ColPaliEmbeddings class (tagged
"colpali") using ColQwen2.5 for multi-vector text/image embeddings. Also
added MultiVector Pydantic type to handle the vector lists.
I've added some integration test for the embedding model and some unit
test for the new Pydantic type. Could be a template for other ColPali
variants as well. or until transformers🤗 starts supporting it.
Still `TODO`:
- [ ] Documentation
- [ ] Add an example
_Could also allow Image as query, but didn't work well when testing it._
[ColPali-Engine](https://github.com/illuin-tech/colpali) version:
0.3.9.dev17+g3faee24
<!-- This is an auto-generated comment: release notes by coderabbit.ai
-->
## Summary by CodeRabbit
- **New Features**
- Introduced support for ColPali-based multimodal multi-vector
embeddings for both text and images.
- Added a new embedding class for generating multi-vector embeddings,
configurable for various model and processing options.
- Added a new Pydantic type for multi-vector embeddings, supporting
validation and schema generation for lists of fixed-dimension vectors.
- **Bug Fixes**
- Ensured proper asynchronous index creation in query tests for improved
reliability.
- **Tests**
- Added integration tests for ColPali embeddings, including
text-to-image search and validation of multi-vector fields.
- Added comprehensive tests for the new multi-vector Pydantic type,
covering schema, validation, and default value behavior.
- **Chores**
- Updated optional dependencies to include the ColPali engine.
- Added utility to check for availability of flash attention support.
<!-- end of auto-generated comment: release notes by coderabbit.ai -->
This changes `lancedb` from a "pure python" setuptools project to a
maturin project and adds a rust lancedb dependency.
The async python client is extremely minimal (only `connect` and
`Connection.table_names` are supported). The purpose of this PR is to
get the infrastructure in place for building out the rest of the async
client.
Although this is not technically a breaking change (no APIs are
changing) it is still a considerable change in the way the wheels are
built because they now include the native shared library.
lancedb-gatefixer[bot]
Armaan Sandhu
Zelys
Lei Xu
Mykola Skrynnyk
Magnus
Philip Meier
Weston Pace