## What
Decode the path component of `file://` image URIs before passing it to
Pillow.
## Why
`Path.as_uri()` percent-encodes characters such as spaces. Passing
`parsed.path` directly to Pillow therefore tries to open a literal `%20`
path and fails.
## Testing
- Added a regression test that opens an image whose local filename
contains a space.
- Verified the focused URI conversion behavior against the changed
method.
- Ruff check, formatting check, and `compileall` on both changed files.
Co-authored-by: Xuanwo <github@xuanwo.io>
Adds [typos](https://github.com/crate-ci/typos) as a CI check and
pre-commit hook, the same way Lance does it, so misspellings like the
ones fixed in #4146 get caught automatically going forward.
This also fixes the misspellings `typos` found across the repo (Rust,
Python, TypeScript source, comments, and generated docs), and adds a
small `.typos.toml` with `extend-words` entries for terms that are
correct but look like typos: `AKS` (Azure Kubernetes Service), `RabitQ`
(a real quantization algorithm name), `mmaped` (the actual name of a
`candle-core` API we call), and `Writeable` (from Python's
`_typeshed.WriteableBuffer`). Third-party license files are excluded.
Fixes#4147
Co-authored-by: Claude Sonnet 5 <noreply@anthropic.com>
## What
`JinaEmbeddings._generate_image_input_dict()` crashes with
`AttributeError: 'function' object has no attribute 'urlparse'` on any
image given as a URL string, local path string, or `pathlib.Path` — i.e.
every documented `jina-clip-v1` image-embedding use case except raw
`bytes`.
## Why
```python
from urllib.parse import urlparse
...
parsed = urlparse.urlparse(image)
```
`urlparse` is imported as a function, then called as if it were the
`urllib.parse` module (`urlparse.urlparse(...)`). The module-level
`is_valid_url()` a few lines above does it correctly (`urlparse(text)`),
which is why this reads as a typo rather than intentional. Fixed to
`urlparse(str(image))` — `str()` is needed because `urlparse()` only
accepts `str`/`bytes` and raises a different `AttributeError` on a raw
`Path`.
## Testing
Added `test_jina_generate_image_input_dict_local_path`, which fails with
the original `AttributeError` before the fix and passes after, covering
both a `str` path and a `pathlib.Path`. Verified locally (built the Rust
extension, ran red→green, then the full `test_embeddings.py` file: 15
passed / 8 skipped, no regressions) and with `ruff check`/`ruff format`.
---
Disclosure: this PR was drafted with AI assistance (Claude); I reviewed,
tested, and take responsibility for the change.
---------
Co-authored-by: Claude Opus 4.8 <noreply@anthropic.com>
## Summary
- pass an Instructor-compatible `[instruction, text]` pair when
detecting embedding dimensions
- add a regression test that verifies the dimension probe uses the
configured source instruction
## Root cause
`InstructorEmbeddingFunction.ndims()` encoded a bare string even though
Instructor models require instruction/text pairs. With affected
`sentence-transformers` versions, the bare input omitted
`instruction_mask` and raised `KeyError` while defining the LanceDB
schema.
## Validation
- `uv run --extra tests pytest python/tests/test_embeddings.py -q` (`14
passed, 9 skipped`)
- `uv run --project python --extra tests --extra dev ruff format --check
python/python/lancedb/embeddings/instructor.py
python/python/tests/test_embeddings.py`
- `uv run --project python --extra tests --extra dev ruff check .`
Fixes#2041
<!-- lance-gatekeeper-fix:v1 agent=4b05e0d9f3eef17bccfb446e788294f4
generation=1 -->
Co-authored-by: Gatefixer <313497061+lancedb-gatefixer[bot]@users.noreply.github.com>
## Summary
- replace the synthetic registry-variable metadata test with the OpenAI
embedding function reported in #2387
- verify the resolved API key survives table metadata reconstruction
- assert the OpenAI client receives the resolved key while serialized
metadata retains the variable reference
## Root cause
LanceDB 0.22.0 reconstructed embedding functions from table metadata
with the model constructor, bypassing EmbeddingFunction.create and
leaving the literal $var:api_key placeholder in OpenAI configuration.
The production path was corrected for duplicate #2181 by #2640; this
change gives that fix direct, network-free OpenAI regression coverage
for #2387.
## Validation
- uv run --extra tests pytest python/tests/test_embeddings.py -q (13
passed, 9 skipped)
- uv run --project python --extra dev ruff check .
- uv run --project python --extra dev ruff format --check
python/python/tests/test_embeddings.py
- git diff --check
Fixes#2387
<!-- lance-gatekeeper-fix:v1 agent=d453b1b9b2a298a776f2e4ea1b1449b5
generation=1 -->
Co-authored-by: Gatefixer <313497061+lancedb-gatefixer[bot]@users.noreply.github.com>
This PR fixes a serialization error when using Ollama embeddings in
`create_table`.
The use of `@cached_property` for the Ollama client was causing issues
during serialization/pickling, which is required by certain LanceDB
operations (like when using multiprocessing or certain storage
backends). Switching to a standard `@property` ensures the client is
instantiated when needed without being stored in a way that breaks
serialization.
Verified with the following script:
```python
import lancedb
from lancedb.embeddings import get_registry
import pickle
registry = get_registry().get(\"ollama\")
model = registry(name=\"llama3\")
# This would fail before the fix
pickled = pickle.dumps(model)
unpickled = pickle.loads(pickled)
```
Fixes#2629 (or similar serialization issues reported).
---------
Co-authored-by: Unmilan Mukherjee <Missing-Identity@users.noreply.github.com>
## Summary
- When an embedding function returns an empty list (e.g. `[]`) for an
input row — as can happen when a model produces no output for a blank
string — `_append_vector_columns` crashed with `ArrowInvalid: Length of
item not correct: expected N but got array of size 0` because PyArrow
cannot fit a zero-length value into a fixed-size list element.
- The fix adds a validation step in `gen()`, inside
`_append_vector_columns`, that replaces any vector whose length does not
match the expected `ndims` (including empty lists and `None`) with
`None` before `pa.array()` is called.
- `None` is a valid null in a PyArrow fixed-size list array, so the bad
entry flows into `_handle_bad_vectors` and is handled according to the
caller-supplied `on_bad_vectors` policy (`error` / `drop` / `fill` /
`null`) instead of causing an unconditional crash.
## Test plan
- [ ] Added `test_embedding_with_empty_output_vectors` in
`python/python/tests/test_embeddings.py` that uses an embedding function
returning `[]` for empty-string inputs, calls `table.add(...,
on_bad_vectors="drop")`, and asserts no crash and that bad rows are
correctly dropped.
- [ ] Existing `test_embedding_with_bad_results` continues to pass (NaN
vectors still handled correctly).
- [ ] Verified manually that `pa.array([[1.,2.,3.,4.], []],
type=pa.list_(pa.float32(), 4))` raises `ArrowInvalid` without the fix,
and succeeds with `None` in place of `[]`.
Fixes#1672
---------
Co-authored-by: Will Jones <willjones127@gmail.com>
Co-authored-by: Claude Sonnet 4.6 <noreply@anthropic.com>
The test added in #3190 unconditionally imports `PIL`, which is an
optional dependency. This causes CI failures in environments where
Pillow isn't installed (`ModuleNotFoundError: No module named 'PIL'`).
Use `pytest.importorskip` to skip gracefully when Pillow is unavailable.
Fixes CI failure on main.
Co-authored-by: Claude Sonnet 4.6 <noreply@anthropic.com>
url_retrieve() calls urllib.request.urlopen() but only urllib.error was
imported, causing AttributeError for any HTTP URL input. This affects
open-clip, siglip, and jinaai embedding functions when processing image
URLs.
The bug has existed since the embeddings API refactor (#580) but was
masked because most users pass local file paths or bytes rather than
HTTP URLs.
## Summary
Fixes#1679
This PR prevents the OpenAI embedding function from retrying when
receiving a 401 Unauthorized error. Authentication errors are permanent
failures that won't be fixed by retrying, yet the current implementation
retries all exceptions up to 7 times by default.
## Changes
- Modified `retry_with_exponential_backoff` in `utils.py` to check for
non-retryable errors before retrying
- Added `_is_non_retryable_error` helper function that detects:
- Exceptions with name `AuthenticationError` (OpenAI's 401 error)
- Exceptions with `status_code` attribute of 401 or 403
- Enhanced OpenAI embeddings to explicitly catch and re-raise
`AuthenticationError` with better logging
- Added unit test `test_openai_no_retry_on_401` to verify authentication
errors don't trigger retries
## Test Plan
- Added test that verifies:
1. A function raising `AuthenticationError` is only called once
2. No retry delays occur (sleep is never called)
- Existing tests continue to pass
- Formatting applied via `make format`
## Example Behavior
**Before**: With an invalid API key, users would see 7 retry attempts
over ~2 minutes:
```
WARNING:root:Error occurred: Error code: 401 - {'error': {'message': 'Incorrect API key provided...'}}
Retrying in 3.97 seconds (retry 1 of 7)
WARNING:root:Error occurred: Error code: 401...
Retrying in 7.94 seconds (retry 2 of 7)
...
```
**After**: With an invalid API key, the error is raised immediately:
```
ERROR:root:Authentication failed: Invalid API key provided
AuthenticationError: Error code: 401 - {'error': {'message': 'Incorrect API key provided...'}}
```
This provides better UX and prevents unnecessary API calls that would
fail anyway.
---------
Co-authored-by: Will Jones <willjones127@gmail.com>
@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>
BREAKING CHANGE: embedding function implementations in Node need to now
call `resolveVariables()` in their constructors and should **not**
implement `toJSON()`.
This tries to address the handling of secrets. In Node, they are
currently lost. In Python, they are currently leaked into the table
schema metadata.
This PR introduces an in-memory variable store on the function registry.
It also allows embedding function definitions to label certain config
values as "sensitive", and the preprocessing logic will raise an error
if users try to pass in hard-coded values.
Closes#2110Closes#521
---------
Co-authored-by: Weston Pace <weston.pace@gmail.com>
BREAKING CHANGE: For a field "vector", list of integers will now be
converted to binary (uint8) vectors instead of f32 vectors. Use float
values instead for f32 vectors.
* Adds proper support for inserting and upserting subsets of the full
schema. I thought I had previously implemented this in #1827, but it
turns out I had not tested carefully enough.
* Refactors `_santize_data` and other utility functions to be simpler
and not require `numpy` or `combine_chunks()`.
* Added a new suite of unit tests to validate sanitization utilities.
## Examples
```python
import pandas as pd
import lancedb
db = lancedb.connect("memory://demo")
intial_data = pd.DataFrame({
"a": [1, 2, 3],
"b": [4, 5, 6],
"c": [7, 8, 9]
})
table = db.create_table("demo", intial_data)
# Insert a subschema
new_data = pd.DataFrame({"a": [10, 11]})
table.add(new_data)
table.to_pandas()
```
```
a b c
0 1 4.0 7.0
1 2 5.0 8.0
2 3 6.0 9.0
3 10 NaN NaN
4 11 NaN NaN
```
```python
# Upsert a subschema
upsert_data = pd.DataFrame({
"a": [3, 10, 15],
"b": [6, 7, 8],
})
table.merge_insert(on="a").when_matched_update_all().when_not_matched_insert_all().execute(upsert_data)
table.to_pandas()
```
```
a b c
0 1 4.0 7.0
1 2 5.0 8.0
2 3 6.0 9.0
3 10 7.0 NaN
4 11 NaN NaN
5 15 8.0 NaN
```
* Test that we can insert subschemas (omit nullable columns) in Python.
* More work is needed to support this in Node. See:
https://github.com/lancedb/lancedb/issues/1832
* Test that we can insert data with nullable schema but no nulls in
non-nullable schema.
* Add `"null"` option for `on_bad_vectors` where we fill with null if
the vector is bad.
* Make null values not considered bad if the field itself is nullable.
## user story
fixes https://github.com/lancedb/lancedb/issues/1480https://github.com/invl/retry has not had an update in 8 years, one if
its sub-dependencies via requirements.txt
(https://github.com/pytest-dev/py) is no longer maintained and has a
high severity vulnerability (CVE-2022-42969).
retry is only used for a single function in the python codebase for a
deprecated helper function `with_embeddings`, which was created for an
older tutorial (https://github.com/lancedb/lancedb/pull/12) [but is now
deprecated](https://lancedb.github.io/lancedb/embeddings/legacy/).
## changes
i backported a limited range of functionality of the `@retry()`
decorator directly into lancedb so that we no longer have a dependency
to the `retry` package.
## tests
```
/Users/james/src/lancedb/python $ ruff check .
All checks passed!
/Users/james/src/lancedb/python $ pytest python/tests/test_embeddings.py
python/tests/test_embeddings.py .......s.... [100%]
================================================================ 11 passed, 1 skipped, 2 warnings in 7.08s ================================================================
```
The `ratelimiter` package hasn't been updated in ages and is no longer
maintained. This PR removes the dependency on `ratelimiter` and replaces
it with a custom rate limiter implementation.
---------
Co-authored-by: Will Jones <willjones127@gmail.com>
The LanceDB embeddings registry allows users to annotate the pydantic
model used as table schema with the desired embedding function, e.g.:
```python
class Schema(LanceModel):
id: str
vector: Vector(openai.ndims()) = openai.VectorField()
text: str = openai.SourceField()
```
Tables created like this does not require embeddings to be calculated by
the user explicitly, e.g. this works:
```python
table.add([{"id": "foo", "text": "rust all the things"}])
```
However, trying to construct pydantic model instances without vector
doesn't because it's a required field.
Instead, you need add a default value:
```python
class Schema(LanceModel):
id: str
vector: Vector(openai.ndims()) = openai.VectorField(default=None)
text: str = openai.SourceField()
```
then this completes without errors:
```python
table.add([Schema(id="foo", text="rust all the things")])
```
However, all of the vectors are filled with zeros. Instead in
add_vector_col we have to add an additional check so that the embedding
generation is called.
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.
Hongzhu YiandXuanwo
Will JonesandClaude Sonnet 5
Andrew ChenandClaude Opus 4.8
lancedb-gatefixer[bot]
Unmilan MukherjeeandUnmilan Mukherjee
lennylxx
Varun ChawlaandWill Jones
Le Duc ManhandWill Jones
msu-reevoandWill Jones
Lei Xu
fzowl
James Wu
rjrobbenandrjrobben
Antonio Molner DomenechandWill Jones
Chang She