binary vectors and hamming distance can work on only IVF_FLAT, so
introduce them all in this PR.
---------
Signed-off-by: BubbleCal <bubble-cal@outlook.com>
Hi lancedb team,
This PR adds the `bypass_vector_index` logic to the sync API, as
described in [Issue
#535](https://github.com/lancedb/lancedb/issues/535). (Closes#535).
Iv'e implemented it only for the regular vector search. If you think it
should also be supported for FTS, Hybrid, or Empty queries and for the
cloud solution, please let me know, and I’ll be happy to extend it.
Since there’s no `CONTRIBUTING.md` or contribution guidelines, I opted
for the simplest implementation to get this started.
Looking forward to your feedback!
Thanks!
---------
Co-authored-by: Will Jones <willjones127@gmail.com>
* Add `See Also` section to `cleanup_old_files` and `compact_files` so
they know it's linked to `optimize`.
* Fixes link to `compact_files` arguments
* Improves formatting of note.
### Changes to sync API
* Updated `LanceTable` and `LanceDBConnection` reprs
* Add `storage_options`, `data_storage_version`, and
`enable_v2_manifest_paths` to sync create table API.
* Add `storage_options` to `open_table` in sync API.
* Add `list_indices()` and `index_stats()` to sync API
* `create_table()` will now create only 1 version when data is passed.
Previously it would always create two versions: 1 to create an empty
table and 1 to add data to it.
### Changes to async API
* Add `embedding_functions` to async `create_table()` API.
* Added `head()` to async API
### Refactors
* Refactor index parameters into dataclasses so they are easier to use
from Python
* Moved most tests to use an in-memory DB so we don't need to create so
many temp directories
Closes#1792Closes#1932
---------
Co-authored-by: Weston Pace <weston.pace@gmail.com>
User reported on Discord, when using
`table.vector_search([np.float16(1.0), np.float16(2.0), ...])`, it
yields `TypeError: 'numpy.float16' object is not iterable`
db.connect with azure storage account name is supported in async connect
but not sync connect.
Add this functionality
---------
Co-authored-by: Will Jones <willjones127@gmail.com>
Previously, whenever `Table.add()` was called, we would write and
re-open the underlying dataset. This was bad for performance, as it
reset the table cache and initiated a lot of IO. It also could be the
source of bugs, since we didn't necessarily pass all the necessary
connection options down when re-opening the table.
Closes#1655
Closes#1791Closes#1764Closes#1897 (Makes this unnecessary)
BREAKING CHANGE: when using azure connection string `az://...` the call
to connect will fail if the azure storage credentials are not set. this
is breaking from the previous behaviour where the call would fail after
connect, when user invokes methods on the connection.
This currently only works for local tables (remote tables cannot be
queried)
This is also exclusive to the sync interface. However, since the pyarrow
dataset interface is synchronous I am not sure if there is much value in
making an async-wrapping variant.
In addition, I added a `to_batches` method to the base query in the sync
API. This already exists in the async API. In the sync API this PR only
adds support for vector queries and scalar queries and not for hybrid or
FTS queries.
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.
