feat: add maximum and minimum nprobes properties (#2430)

This exposes the maximum_nprobes and minimum_nprobes feature that was
added in https://github.com/lancedb/lance/pull/3903

<!-- This is an auto-generated comment: release notes by coderabbit.ai
-->
## Summary by CodeRabbit

- **New Features**
- Added support for specifying minimum and maximum probe counts in
vector search queries, allowing finer control over search behavior.
- Users can now independently set minimum and maximum probes for vector
and hybrid queries via new methods and parameters in Python, Node.js,
and Rust APIs.

- **Bug Fixes**
- Improved parameter validation to ensure correct usage of minimum and
maximum probe values.

- **Tests**
- Expanded test coverage to validate correct handling, serialization,
and error cases for the new probe parameters.
<!-- end of auto-generated comment: release notes by coderabbit.ai -->

python/python/tests/test_query.py

@@ -439,6 +439,33 @@ def test_query_builder_with_filter(table):
     assert all(np.array(rs[0]["vector"]) == [3, 4])
 
 
+def test_invalid_nprobes_sync(table):
+    with pytest.raises(ValueError, match="minimum_nprobes must be greater than 0"):
+        LanceVectorQueryBuilder(table, [0, 0], "vector").minimum_nprobes(0).to_list()
+    with pytest.raises(
+        ValueError, match="maximum_nprobes must be greater than minimum_nprobes"
+    ):
+        LanceVectorQueryBuilder(table, [0, 0], "vector").maximum_nprobes(5).to_list()
+    with pytest.raises(
+        ValueError, match="minimum_nprobes must be less or equal to maximum_nprobes"
+    ):
+        LanceVectorQueryBuilder(table, [0, 0], "vector").minimum_nprobes(100).to_list()
+
+
+@pytest.mark.asyncio
+async def test_invalid_nprobes_async(table_async: AsyncTable):
+    with pytest.raises(ValueError, match="minimum_nprobes must be greater than 0"):
+        await table_async.vector_search([0, 0]).minimum_nprobes(0).to_list()
+    with pytest.raises(
+        ValueError, match="maximum_nprobes must be greater than minimum_nprobes"
+    ):
+        await table_async.vector_search([0, 0]).maximum_nprobes(5).to_list()
+    with pytest.raises(
+        ValueError, match="minimum_nprobes must be less or equal to maximum_nprobes"
+    ):
+        await table_async.vector_search([0, 0]).minimum_nprobes(100).to_list()
+
+
 def test_query_builder_with_prefilter(table):
     df = (
         LanceVectorQueryBuilder(table, [0, 0], "vector")
@@ -585,6 +612,21 @@ async def test_query_async(table_async: AsyncTable):
         table_async.query().nearest_to(pa.array([1, 2])).nprobes(10),
         expected_num_rows=2,
     )
+    await check_query(
+        table_async.query().nearest_to(pa.array([1, 2])).minimum_nprobes(10),
+        expected_num_rows=2,
+    )
+    await check_query(
+        table_async.query().nearest_to(pa.array([1, 2])).maximum_nprobes(30),
+        expected_num_rows=2,
+    )
+    await check_query(
+        table_async.query()
+        .nearest_to(pa.array([1, 2]))
+        .minimum_nprobes(10)
+        .maximum_nprobes(20),
+        expected_num_rows=2,
+    )
     await check_query(
         table_async.query().nearest_to(pa.array([1, 2])).bypass_vector_index(),
         expected_num_rows=2,
@@ -911,7 +953,39 @@ def test_query_serialization_sync(table: lancedb.table.Table):
 
     q = table.search([5.0, 6.0]).nprobes(10).refine_factor(5).to_query_object()
     check_set_props(
-        q, vector_column="vector", vector=[5.0, 6.0], nprobes=10, refine_factor=5
+        q,
+        vector_column="vector",
+        vector=[5.0, 6.0],
+        minimum_nprobes=10,
+        maximum_nprobes=10,
+        refine_factor=5,
+    )
+
+    q = table.search([5.0, 6.0]).minimum_nprobes(10).to_query_object()
+    check_set_props(
+        q,
+        vector_column="vector",
+        vector=[5.0, 6.0],
+        minimum_nprobes=10,
+        maximum_nprobes=None,
+    )
+
+    q = table.search([5.0, 6.0]).nprobes(50).to_query_object()
+    check_set_props(
+        q,
+        vector_column="vector",
+        vector=[5.0, 6.0],
+        minimum_nprobes=50,
+        maximum_nprobes=50,
+    )
+
+    q = table.search([5.0, 6.0]).maximum_nprobes(10).to_query_object()
+    check_set_props(
+        q,
+        vector_column="vector",
+        vector=[5.0, 6.0],
+        maximum_nprobes=10,
+        minimum_nprobes=None,
     )
 
     q = table.search([5.0, 6.0]).distance_range(0.0, 1.0).to_query_object()
@@ -963,7 +1037,8 @@ async def test_query_serialization_async(table_async: AsyncTable):
         limit=10,
         vector=sample_vector,
         postfilter=False,
-        nprobes=20,
+        minimum_nprobes=20,
+        maximum_nprobes=20,
         with_row_id=False,
         bypass_vector_index=False,
     )
@@ -973,7 +1048,20 @@ async def test_query_serialization_async(table_async: AsyncTable):
         q,
         vector=sample_vector,
         postfilter=False,
-        nprobes=20,
+        minimum_nprobes=20,
+        maximum_nprobes=20,
+        with_row_id=False,
+        bypass_vector_index=False,
+        limit=10,
+    )
+
+    q = (await table_async.search([5.0, 6.0])).nprobes(50).to_query_object()
+    check_set_props(
+        q,
+        vector=sample_vector,
+        postfilter=False,
+        minimum_nprobes=50,
+        maximum_nprobes=50,
         with_row_id=False,
         bypass_vector_index=False,
         limit=10,
@@ -992,7 +1080,8 @@ async def test_query_serialization_async(table_async: AsyncTable):
         filter="id = 1",
         postfilter=True,
         vector=sample_vector,
-        nprobes=20,
+        minimum_nprobes=20,
+        maximum_nprobes=20,
         with_row_id=False,
         bypass_vector_index=False,
     )
@@ -1006,7 +1095,8 @@ async def test_query_serialization_async(table_async: AsyncTable):
     check_set_props(
         q,
         vector=sample_vector,
-        nprobes=10,
+        minimum_nprobes=10,
+        maximum_nprobes=10,
         refine_factor=5,
         postfilter=False,
         with_row_id=False,
@@ -1014,6 +1104,18 @@ async def test_query_serialization_async(table_async: AsyncTable):
         limit=10,
     )
 
+    q = (await table_async.search([5.0, 6.0])).minimum_nprobes(5).to_query_object()
+    check_set_props(
+        q,
+        vector=sample_vector,
+        minimum_nprobes=5,
+        maximum_nprobes=20,
+        postfilter=False,
+        with_row_id=False,
+        bypass_vector_index=False,
+        limit=10,
+    )
+
     q = (
         (await table_async.search([5.0, 6.0]))
         .distance_range(0.0, 1.0)
@@ -1025,7 +1127,8 @@ async def test_query_serialization_async(table_async: AsyncTable):
         lower_bound=0.0,
         upper_bound=1.0,
         postfilter=False,
-        nprobes=20,
+        minimum_nprobes=20,
+        maximum_nprobes=20,
         with_row_id=False,
         bypass_vector_index=False,
         limit=10,
@@ -1037,7 +1140,8 @@ async def test_query_serialization_async(table_async: AsyncTable):
         distance_type="cosine",
         vector=sample_vector,
         postfilter=False,
-        nprobes=20,
+        minimum_nprobes=20,
+        maximum_nprobes=20,
         with_row_id=False,
         bypass_vector_index=False,
         limit=10,
@@ -1049,7 +1153,8 @@ async def test_query_serialization_async(table_async: AsyncTable):
         ef=7,
         vector=sample_vector,
         postfilter=False,
-        nprobes=20,
+        minimum_nprobes=20,
+        maximum_nprobes=20,
         with_row_id=False,
         bypass_vector_index=False,
         limit=10,
@@ -1061,7 +1166,8 @@ async def test_query_serialization_async(table_async: AsyncTable):
         bypass_vector_index=True,
         vector=sample_vector,
         postfilter=False,
-        nprobes=20,
+        minimum_nprobes=20,
+        maximum_nprobes=20,
         with_row_id=False,
         limit=10,
     )