This PR adds a `to_query_object` method to the various query builders
(except not hybrid queries yet). This makes it possible to inspect the
query that is built.
In addition this PR does some normalization between the sync and async
query paths. A few custom defaults were removed in favor of None (with
the default getting set once, in rust).
Also, the synchronous to_batches method will now actually stream results
Also, the remote API now defaults to prefiltering
This PR fixes build issues associated with `aws-lc-rs`, while
simplifying the build process. Previously, we used custom scripts for
the musl and Windows ARM builds. These were complicated and prone to
breaking. This PR switches to a setup that mirrors
https://github.com/napi-rs/package-template/blob/main/.github/workflows/CI.yml.
* linux glibc and musl builds now use the Docker images provided by the
napi project
* Windows ARM build now just cross compiles from Windows x64, which
turns out to work quite well.
- adds `loss` into the index stats for vector index
- now `optimize` can retrain the vector index
---------
Signed-off-by: BubbleCal <bubble-cal@outlook.com>
We soon won't rely on cross compiling from Linux to windows, so can
remove this check. Instead, check that we can cross compile from Windows
between architectures.
`object_store` already hard codes `rustls` as the TLS implementation, so
we have been shipping a mix of `rustls` and `openssl`. For simplicity of
builds, we should consolidate to one, and that has to be `rustls`.
vectordb is deprecated, and these platforms are particularly difficult
to maintain. Removing now to prevent further headaches.
We will keep these platforms supported on `@lancedb/lancedb`.
The `ConnectRequest` has a set of properties that only make sense for
listing databases / catalogs and a set of properties that only make
sense for remote databases.
This PR reduces all options to a single `HashMap<String, String>`. This
makes it easier to add new database / catalog implementations and makes
it clearer to users which options are applicable in which situations.
I don't believe there are any breaking changes here. The closest thing
is that I placed the `ConnectBuilder` methods `api_key`, `region`, and
`host_override` behind a `remote` feature gate. This is not strictly
needed and I could remove the feature gate but it seemed appropriate.
Since using these methods without the remote feature would have been
meaningless I don't feel this counts as a breaking change.
We could look at removing these methods entirely from the
`ConnectBuilder` (and encouraging users to use `RemoteDatabaseOptions`
instead) but I'm not sure how I feel about that.
Another approach we could take is to move these methods into a
`RemoteConnectBuilderExt` trait (and there could be a similar
`ListingConnectBuilderExt` trait to add methods for the listing database
/ catalog).
For now though my main goal is to simplify `ConnectRequest` as much as
possible (I see this being part of the key public API for database /
catalog integrations, similar to the `BaseTable`, `Catalog`, and
`Database` traits and I'd like it to be simple).
Closes#2114
Starting in #1965, we no longer pass the table schema into
`pa.Table.from_pylist()`. This means PyArrow is choosing the order of
the struct subfields, and apparently it does them in alphabetical order.
This is fine in theory, since in Lance we support providing fields in
any order. However, before we pass it to Lance, we call
`pa.Table.cast()` to align column types to the table types.
`pa.Table.cast()` is strict about field order, so we need to create a
cast target schema that aligns with the input data. We were doing this
at the top-level fields, but weren't doing this in nested fields. This
PR adds support to do this for nested ones.
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)
