Hello LanceDB team,
while developing using `lancedb` as a library I encountered a typing
problem affecting IDE hints and completions during development.
---
## Current Situation
Currently, the abstract base class `lancedb.query:LanceQueryBuilder`
uses method chaining to build up the search parameters, where the
methods have `LanceQueryBuilder` as a return type hint.
This leads to two issues:
1. Implementing subclasses of `LanceQueryBuilder` need to override
methods to modify the return type hint, even when they don't need to
change its implementation, just to ensure adequate IDE hints and
completions.
2. When using method chaining the first method directly inherited from
the abstract `LanceQueryBuilder` causes the inferred type to switch back
to `LanceQueryBuilder`. So even when the type starts from
`lancdb.table:LanceTable.search(query_type="vector", ...)` and therefor
correctly is inferred as `LanceVectorQueryBuilder`, after calling e.g.
`LanceVectorQueryBuilder.limit(...)` it is seen as the abstract
`LanceQueryBuilder` from that point on.
### Example of current situation
## Proposed changes
I propose to change the return type hints of the corresponding methods
(including classmethod `create()`) in the abstract base class
`LanceQueryBuilder` from `LanceQueryBuilder` to `Self`.
`Self` is already imported in the module:
```py
if sys.version_info >= (3, 11):
from typing import Self
else:
from typing_extensions import Self
```
### Further possible changes
Additionally, the implementing subclasses could also change the return
type hints to `Self` to potentially allow for further inheritance
easily.
> [!NOTE]
> **However this is not part of this pull request as of writing.**
### Example after proposed changes
---
Best regards
Martin
Previously, users could only specify new data types in `alterColumns` as
strings:
```ts
await tbl.alterColumns([
path: "price",
dataType: "float"
]);
```
But this has some problems:
1. It wasn't clear what were valid types
2. It was impossible to specify nested types, like lists and vector
columns.
This PR changes it to take an Arrow data type, similar to how the Python
API works. This allows casting vector types:
```ts
await tbl.alterColumns([
{
path: "vector",
dataType: new arrow.FixedSizeList(
2,
new arrow.Field("item", new arrow.Float16(), false),
),
},
]);
```
Closes#2185
Hello LanceDB team,
---
I have fixed a discrepancy in the class docstring of
`lancedb.embeddings.base:EmbeddingFunction` and made consistency
alignments to that docstring.
### Changes made
1. The docstring referred to the abstract method
`get_source_embeddings()`.
This method does not exist in the repository at the current state.
I have changed the mention to refer to the actual abstract method
`compute_source_embeddings()`.
2. Also, I aligned the consistency within the ordered list which is
describing the methods to be implemented by concrete embedding
functions.
---
Thank you for developing this useful library. 👍
Best regards
Martin
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.
Prior to this commit, issuing drop_all_tables on a listing database with
an external manifest store would delete physical tables but leave
references behind in the manifest store. The table drop would succeed,
but subsequent creation of a table with the same name would fail with a
conflict.
With this patch, the external manifest store is updated to account for
the dropped tables so that dropped table names can be reused.
@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>
Datafusion makes the batch size available as part of the `SessionState`.
We should use that to set the `max_batch_length` property in the
`QueryExecutionOptions`.
This PR makes it possible to create a table using an asynchronous stream
of input data. Currently only a synchronous iterator is supported. There
are a number of follow-ups not yet tackled:
* Support for embedding functions (the embedding functions wrapper needs
to be re-written to be async, should be an easy lift)
* Support for async input into the remote table (the make_ipc_batch
needs to change to accept async input, leaving undone for now because I
think we want to support actual streaming uploads into the remote table
soon)
* Support for async input into the add function (pretty essential, but
it is a fairly distinct code path, so saving for a different PR)
It seems that `RecordBatch::with_schema` is unable to remove schema
metadata from a batch. It fails with the error `target schema is not
superset of current schema`.
I'm not sure how the `test_metadata_erased` test is passing. Strangely,
the metadata was not present by the time the batch arrived at the
metadata eraser. I think maybe the schema metadata is only present in
the batch if there is a filter.
I've created a new unit test that makes sure the metadata is erased if
we have a filter also
In earlier PRs (#1886, #1191) we made the default limit 10 regardless of
the query type. This was confusing for users and in many cases a
breaking change. Users would have queries that used to return all
results, but instead only returned the first 10, causing silent bugs.
Part of the cause was consistency: the Python sync API seems to have
always had a limit of 10, while newer APIs (Python async and Nodejs)
didn't.
This PR sets the default limit only for searches (vector search, FTS),
while letting scans (even with filters) be unbounded. It does this
consistently for all SDKs.
Fixes#1983Fixes#1852Fixes#2141
