## Summary
- bump all Lance crates to 1.0.0-beta.14 via ci/set_lance_version.py
- refresh Cargo.lock to capture new transitive requirements
- verified `cargo clippy --workspace --tests --all-features -- -D
warnings` and `cargo fmt --all`
Triggered by refs/tags/v1.0.0-beta.14
---------
Co-authored-by: Jack Ye <yezhaoqin@gmail.com>
Based on https://github.com/lancedb/lance/pull/4984
1. Bump to 1.0.0-beta.2
2. Use DirectoryNamespace in lance to perform all testing in python and
rust for much better coverage
3. Refactor `ListingDatabase` to be able to accept location and
namespace. This is because we have to leverage listing database (local
lancedb connection) for using namespace, namespace only resolves the
location and storage options but we don't want to bind all the way to
rust since user will plug-in namespace from python side. And thus
`ListingDatabase` needs to be able to accept location and namespace that
are created from namespace connection.
4. For credentials vending, we also pass storage options provider all
the way to rust layer, and the rust layer calls back to the python
function to fetch next storage option. This is exactly the same thing we
did in pylance.
## Description of changes
Fixes#2698
This PR uses
[`typing.override`](https://docs.python.org/3/library/typing.html#typing.override)
in favor of the [`overrides`](https://pypi.org/project/overrides/)
dependency when possible. As of Python 3.12, the standard library offers
`typing.override` to perform a static check on overridden methods.
### Motivation
Currently, `overrides` is incompatible with Python 3.14. As a result,
any package that attempts to import `overrides` using Python 3.14+ will
raise an `AttributeError`. An
[issue](https://github.com/mkorpela/overrides/issues/127) has been
raised and a [pull
request](https://github.com/mkorpela/overrides/pull/133) has been
submitted to the GitHub repo for the `overrides` project. But the
maintainer has been unresponsive.
To ensure readiness for Python 3.14, this package (and any other package
directly depending on `overrides`) should consider using
`typing.override` instead.
### Impact
The standard library added `typing.override` as of 3.12. As a result,
this change will affect only users of Python 3.12+. Previous versions
will continue to rely on `overrides`. Notably, the standard library
implementation is slightly different than that of `overrides`. A
thorough discussion of those differences is shown in [PEP
698](https://peps.python.org/pep-0698/), and it is also summarized
nicely by the maintainer of `overrides`
[here](https://github.com/mkorpela/overrides/issues/126#issuecomment-2401327116).
There are 2 main ways that switching from `overrides` to
`typing.override` will have an impact on developers of this repo.
1. `typing.override` does not implement any runtime checking. Instead,
it provides information to type checkers.
2. The stdlib does not provide a mixin class to enforce override
decorators on child classes. (Their reasoning for this is explained in
[the PEP](https://peps.python.org/pep-0698/).) This PR disables that
behavior entirely by replacing the `EnforceOverrides`.
Fixed the issue on lance-namespace side to avoid pinning to a specific
lance version. This should fix the issue of the increased release
artifact size and build time.
This PR integrates `lancedb` with `lance-namespace` so that users can
use LanceDB client to access Lance tables in any catalog services. In
general, we expect most of the logic to be delegated to the existing
`LanceDBConnection` and `LanceTable`, but the namespace implemenation
will control how table is created, dropped, and describe where the table
is stored with any related storage options like access credentials.
The implementation currently only supports a 1 level namespace that
directly contains tables. We will introduce nested namespace support in
a separated PR.
Users are expected to use it in the following way:
```python
>>> import lancedb
>>> import pyarrow as pa
>>> # Connect using GlueNamespace
>>> db = lancedb.connect_namespace("glue", {"catalog_id": "123456789012"})
>>> # Create a table with schema
>>> schema = pa.schema([
... pa.field("id", pa.int64()),
... pa.field("vector", pa.list_(pa.float32(), 2))
... ])
>>> table = db.create_table("my_table", schema=schema)
>>> # List tables
>>> db.table_names()
['my_table']
```
### Summary
This PR adds **SigLIP** (Sigmoid Loss Image Pretraining) as a new
embedding model in the LanceDB embedding registry. SigLIP improves
image-text alignment performance using sigmoid-based contrastive loss
and offers robust zero-shot generalization.
Fixes#2498
### What’s Implemented
#### 1. `SigLIP` Embedding Class
* Added `SigLIP` support under `python/lancedb/embeddings/siglip.py`
* Implements:
* `compute_source_embeddings`
* `_batch_generate_embeddings`
* Normalization logic
* Batch-wise progress logging for image embedding
#### 2. Registry Integration
* Registered `SigLIP` in `embeddings/__init__.py`
* `SigLIP` now usable via `connect(..., embedding="siglip")`
#### 3. Evaluation Benchmark Support
* Added SigLIP to `test_embeddings_slow.py` for side-by-side
benchmarking with OpenCLIP and ImageBind
### New Test Methods
#### `test_siglip`
* End-to-end test to verify embeddings table creation and vector shape
for SigLIP
#### `test_siglip_vs_openclip_vs_imagebind_benchmark_full`
* Benchmarks:
* **Recall\@1 / 5 / 10**
* **mAP (Mean Average Precision)**
* **Embedding & Search Latency**
* Dimensionality reporting
### Notes
* SigLIP outputs 768D embeddings (vs 512D for OpenCLIP)
* Benchmark shows competitive performance despite higher dimensionality
* I'm still new to contributing to open-source and learning as I go.
Please feel free to suggest any improvements — I'm happy to make
changes!
This patch fix can not build on python3.9 dev
the reason is that for ibm-watsonx-ai the min version is py3.10
more can check on `pyoven` https://pyoven.org/package/ibm-watsonx-ai/
also fix tiny md lint
---------
Signed-off-by: yihong0618 <zouzou0208@gmail.com>
Other embedding integrations such as Cohere and OpenAI already send
requests in batches. We should do that for Ollama too to improve
throughput.
The Ollama [`.embed`
API](63ca747622/ollama/_client.py (L359-L378))
was added in version 0.3.0 (almost a year ago) so I updated the version
requirement in pyproject.
<!-- This is an auto-generated comment: release notes by coderabbit.ai
-->
## Summary by CodeRabbit
- **Bug Fixes**
- Improved compatibility with newer versions of the "ollama" package by
requiring version 0.3.0 or higher.
- Enhanced embedding generation to process batches of texts more
efficiently and reliably.
- **Refactor**
- Improved type consistency and clarity for embedding-related methods.
<!-- end of auto-generated comment: release notes by coderabbit.ai -->
Adds example for querying a dataset with SQL
<!-- This is an auto-generated comment: release notes by coderabbit.ai
-->
## Summary by CodeRabbit
- **Documentation**
- Added new guides on querying LanceDB tables using SQL with DuckDB and
Apache Datafusion.
- Included detailed instructions for integrating LanceDB with Datafusion
in Python.
- Updated navigation to include Datafusion and SQL querying
documentation.
- Improved formatting in TypeScript and vectordb update examples for
consistency.
- **Tests**
- Added a new test demonstrating SQL querying on Lance tables via
DataFusion integration.
<!-- end of auto-generated comment: release notes by coderabbit.ai -->
---------
Co-authored-by: Weston Pace <weston.pace@gmail.com>
Closes#2191
<!-- This is an auto-generated comment: release notes by coderabbit.ai
-->
## Summary by CodeRabbit
- **Chores**
- Updated the required version of the pyarrow package to version 16 or
higher.
- Adjusted automated testing workflows to install pyarrow version 16 for
compatibility checks.
<!-- 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 is an auto-generated comment: release notes by coderabbit.ai
-->
## Summary by CodeRabbit
- **New Features**
- Added the ability to prewarm (load into memory) table indexes via new
methods in Python, Node.js, and Rust APIs, potentially reducing
cold-start query latency.
- **Bug Fixes**
- Ensured prewarming an index does not interfere with subsequent search
operations.
- **Tests**
- Introduced new test cases to verify full-text search index creation,
prewarming, and search functionalities in both Python and Node.js.
- **Chores**
- Updated dependencies for improved compatibility and performance.
<!-- end of auto-generated comment: release notes by coderabbit.ai -->
---------
Co-authored-by: Lu Qiu <luqiujob@gmail.com>
This change allows to centrally manage the plance depndency without
everybody needing to monitor for compatibility manually.
<!-- This is an auto-generated comment: release notes by coderabbit.ai
-->
## Summary by CodeRabbit
- **New Features**
- Introduced an optional dependency that enhances development support.
Users can now benefit from improved static analysis capabilities when
installing the recommended version (0.23.2 or later).
<!-- end of auto-generated comment: release notes by coderabbit.ai -->
We attempted to make pylance optional in
https://github.com/lancedb/lancedb/pull/2156 but it appears this did not
quite work. Users are unable to use lancedb from a fresh install. This
reverts the optional-ness so we can get back in a working state while we
fix the issue.
@wjones127 is there a standard way you guys setup your virtualenv? I can
either relist all the dependencies in the pyright precommit section, or
specify a venv, or the user has to be in the virtual environment when
they run git commit. If the venv location was standardized or a python
manager like `uv` was used it would be easier to avoid duplicating the
pyright dependency list.
Per your suggestion, in `pyproject.toml` I added in all the passing
files to the `includes` section.
For ruff I upgraded the version and removed "TCH" which doesn't exist as
an option.
I added a `pyright_report.csv` which contains a list of all files sorted
by pyright errors ascending as a todo list to work on.
I fixed about 30 issues in `table.py` stemming from str's being passed
into methods that required a string within a set of string Literals by
extracting them into `types.py`
Can you verify in the rust bridge that the schema should be a property
and not a method here? If it's a method, then there's another place in
the code where `inner.schema` should be `inner.schema()`
``` python
class RecordBatchStream:
@property
def schema(self) -> pa.Schema: ...
```
Also unless the `_lancedb.pyi` file is wrong, then there is no
`__anext__` here for `__inner` when it's not an `AsyncGenerator` and
only `next` is defined:
``` python
async def __anext__(self) -> pa.RecordBatch:
return await self._inner.__anext__()
if isinstance(self._inner, AsyncGenerator):
batch = await self._inner.__anext__()
else:
batch = await self._inner.next()
if batch is None:
raise StopAsyncIteration
return batch
```
in the else statement, `_inner` is a `RecordBatchStream`
```python
class RecordBatchStream:
@property
def schema(self) -> pa.Schema: ...
async def next(self) -> Optional[pa.RecordBatch]: ...
```
---------
Co-authored-by: Will Jones <willjones127@gmail.com>
This also changes the pylance pin from `==0.23.2` to `~=0.23.2` which
should allow the pylance dependency to float a little. The pylance
dependency is actually not used for much anymore and so it should be
tolerant of patch changes.
This includes several improvements and fixes to the Python Async query
builders:
1. The API reference docs show all the methods for each builder
2. The hybrid query builder now has all the same setter methods as the
vector search one, so you can now set things like `.distance_type()` on
a hybrid query.
3. Re-rankers are now properly hooked up and tested for FTS and vector
search. Previously the re-rankers were accidentally bypassed in unit
tests, because the builders overrode `.to_arrow()`, but the unit test
called `.to_batches()` which was only defined in the base class. Now all
builders implement `.to_batches()` and leave `.to_arrow()` to the base
class.
4. The `AsyncQueryBase` and `AsyncVectoryQueryBase` setter methods now
return `Self`, which provides the appropriate subclass as the type hint
return value. Previously, `AsyncQueryBase` had them all hard-coded to
`AsyncQuery`, which was unfortunate. (This required bringing in
`typing-extensions` for older Python version, but I think it's worth
it.)
Users who call the remote SDK from code that uses futures (either
`ThreadPoolExecutor` or `asyncio`) can get odd errors like:
```
Traceback (most recent call last):
File "/usr/lib/python3.12/asyncio/events.py", line 88, in _run
self._context.run(self._callback, *self._args)
RuntimeError: cannot enter context: <_contextvars.Context object at 0x7cfe94cdc900> is already entered
```
This PR fixes that by executing all LanceDB futures in a dedicated
thread pool running on a background thread. That way, it doesn't
interact with their threadpool.
* Replaces Python implementation of Remote SDK with Rust one.
* Drops dependency on `attrs` and `cachetools`. Makes `requests` an
optional dependency used only for embeddings feature.
* Adds dependency on `nest-asyncio`. This was required to get hybrid
search working.
* Deprecate `request_thread_pool` parameter. We now use the tokio
threadpool.
* Stop caching the `schema` on a remote table. Schema is mutable and
there's no mechanism in place to invalidate the cache.
* Removed the client-side resolution of the vector column. We should
already be resolving this server-side.
