This PR aims to fix#2047 by doing the following things:
- Add a distance_type parameter to the sync query builders of Python
SDK.
- Make metric an alias to distance_type.
Fixes#2031
When we do hybrid search, we normalize the scores. We do this
calculation in-place, because the Rerankers expect the `_distance` and
`_score` columns to be the normalized ones. So I've changed the logic so
that we restore the original distance and scores by matching on row ids.
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.)
related to #2014
this fixes:
- linear reranker may lost some results if the merging consumes all
vector results earlier than fts results
- linear reranker inverts the fts score but only vector distance can be
inverted
---------
Signed-off-by: BubbleCal <bubble-cal@outlook.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
```
it reports error `AttributeError: 'builtins.FTSQuery' object has no
attribute 'select_columns'`
because we missed `select_columns` method in rust
Signed-off-by: BubbleCal <bubble-cal@outlook.com>
