feat(python)!: support inserting and upserting subschemas (#1965)

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
```

python/python/tests/docs/test_binary_vector.py

@@ -21,7 +21,7 @@ def test_binary_vector():
     ]
     tbl = db.create_table("my_binary_vectors", data=data)
     query = np.random.randint(0, 256, size=16)
-    tbl.search(query).to_arrow()
+    tbl.search(query).metric("hamming").to_arrow()
     # --8<-- [end:sync_binary_vector]
     db.drop_table("my_binary_vectors")
 
@@ -39,6 +39,6 @@ async def test_binary_vector_async():
     ]
     tbl = await db.create_table("my_binary_vectors", data=data)
     query = np.random.randint(0, 256, size=16)
-    await tbl.query().nearest_to(query).to_arrow()
+    await tbl.query().nearest_to(query).distance_type("hamming").to_arrow()
     # --8<-- [end:async_binary_vector]
     await db.drop_table("my_binary_vectors")

python/python/tests/docs/test_guide_index.py

@@ -118,9 +118,9 @@ def test_scalar_index():
     # --8<-- [end:search_with_scalar_index]
     # --8<-- [start:vector_search_with_scalar_index]
     data = [
-        {"book_id": 1, "vector": [1, 2]},
-        {"book_id": 2, "vector": [3, 4]},
-        {"book_id": 3, "vector": [5, 6]},
+        {"book_id": 1, "vector": [1.0, 2]},
+        {"book_id": 2, "vector": [3.0, 4]},
+        {"book_id": 3, "vector": [5.0, 6]},
     ]
 
     table = db.create_table("book_with_embeddings", data)
@@ -156,9 +156,9 @@ async def test_scalar_index_async():
     # --8<-- [end:search_with_scalar_index_async]
     # --8<-- [start:vector_search_with_scalar_index_async]
     data = [
-        {"book_id": 1, "vector": [1, 2]},
-        {"book_id": 2, "vector": [3, 4]},
-        {"book_id": 3, "vector": [5, 6]},
+        {"book_id": 1, "vector": [1.0, 2]},
+        {"book_id": 2, "vector": [3.0, 4]},
+        {"book_id": 3, "vector": [5.0, 6]},
     ]
     async_tbl = await async_db.create_table("book_with_embeddings_async", data)
     (await async_tbl.query().where("book_id != 3").nearest_to([1, 2]).to_pandas())