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fix: preserve adaptive IVF probe defaults (#4168)

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Keep IVF probe fields unset by default so Lance supplies adaptive
behavior. Preserve `nprobes`, `minimum_nprobes`, and `maximum_nprobes`
independently through Rust, Python, namespace, and remote request
construction, with compatibility fields serialized only when explicitly
configured.

The final Rust namespace and LSM propagation update depends on the Lance
release containing lance-format/lance#9184.
This commit is contained in:
Jack Ye
2026-10-09 12:45:02 -07:00
committed by GitHub
parent ca38924882
commit f305a05fd4
24 changed files with 576 additions and 268 deletions
Download Patch File Download Diff File Expand all files Collapse all files
python/pyproject.toml
+2 -2
View File
@@ -10,7 +10,7 @@ dependencies = [
"pyarrow>=16",
"pydantic>=2.7.4,<3",
"tqdm>=4.27.0",
"lance-namespace>=0.3.2"
"lance-namespace>=0.13.0"
]
description = "lancedb"
authors = [{ name = "LanceDB Devs", email = "dev@lancedb.com" }]
@@ -63,7 +63,7 @@ tests = [
"polars>=0.19, <=1.32.3",
"pyarrow<25",
"pyarrow-stubs>=16.0",
"pylance==14.0.0b6",
"pylance==14.0.0b10",
"requests>=2.31.0",
"datafusion>=54,<55",
"opentelemetry-sdk>=1.30.0",
python/python/lancedb/_lancedb.pyi
+1
View File
@@ -778,6 +778,7 @@ class PyQueryRequest:
use_lsm: Optional[bool]
column: Optional[str]
query_vector: Optional[List[pa.Array]]
nprobes: Optional[int]
minimum_nprobes: Optional[int]
maximum_nprobes: Optional[int]
lower_bound: Optional[float]
python/python/lancedb/namespace.py
+12 -1
View File
@@ -190,8 +190,12 @@ def _query_to_namespace_request(
kwargs["with_row_id"] = query.with_row_id
if query.ef is not None:
kwargs["ef"] = query.ef
if query.nprobes is not None:
kwargs["nprobes"] = query.nprobes
if query.minimum_nprobes is not None:
kwargs["nprobes"] = query.minimum_nprobes
kwargs["minimum_nprobes"] = query.minimum_nprobes
if query.maximum_nprobes is not None:
kwargs["maximum_nprobes"] = query.maximum_nprobes
if query.refine_factor is not None:
kwargs["refine_factor"] = query.refine_factor
if query.lower_bound is not None:
@@ -205,6 +209,13 @@ def _query_to_namespace_request(
if query.distance_type is not None:
kwargs["distance_type"] = query.distance_type
if kwargs.get("maximum_nprobes") == 0:
# The generated 0.13 model rejects LanceDB's unbounded sentinel;
# model_copy preserves direct wire pass-through to the Lance receiver.
maximum_nprobes = kwargs.pop("maximum_nprobes")
return QueryTableRequest(**kwargs).model_copy(
update={"maximum_nprobes": maximum_nprobes}
)
return QueryTableRequest(**kwargs)
python/python/lancedb/query.py
+33 -19
View File
@@ -693,8 +693,9 @@ class Query(pydantic.BaseModel):
if True then apply the filter after vector / FTS search. This is ignored for
plain SQL filtering.
nprobes : Optional[int]
The number of IVF partitions to search. If this is None then a default
number of partitions will be used.
The legacy number of IVF partitions to search. Lance sets both probe
bounds to this value. If this is None then Lance's default probe settings
will be used.
- A higher number makes search more accurate but also slower.
@@ -773,20 +774,23 @@ class Query(pydantic.BaseModel):
# distance type to use for vector search
distance_type: Optional[str] = None
# legacy number of IVF partitions to search
#
# Lance sets both probe bounds to this value. Explicit bounds can override it.
nprobes: Optional[int] = None
# which columns to return in the results (dict values may be str or Expr)
columns: QueryProjection = None
# minimum number of IVF partitions to search
#
# If None then a default value (20) will be used.
# If None then Lance's default will be used.
minimum_nprobes: Optional[int] = None
# maximum number of IVF partitions to search
#
# If None then a default value (20) will be used.
#
# If 0 then no limit will be applied and all partitions could be searched
# if needed to satisfy the limit.
# If None then Lance's default will be used. If 0 then no limit will be applied
# and all partitions could be searched if needed to satisfy the limit.
maximum_nprobes: Optional[int] = None
# lower bound for distance search
@@ -847,6 +851,7 @@ class Query(pydantic.BaseModel):
query.vector_column = req.column
query.vector = req.query_vector
query.distance_type = req.distance_type
query.nprobes = req.nprobes
query.minimum_nprobes = req.minimum_nprobes
query.maximum_nprobes = req.maximum_nprobes
query.lower_bound = req.lower_bound
@@ -1627,6 +1632,7 @@ class LanceVectorQueryBuilder(LanceQueryBuilder):
super().__init__(table)
self._query = query
self._distance_type = None
self._nprobes = None
self._minimum_nprobes = None
self._maximum_nprobes = None
self._lower_bound = None
@@ -1691,9 +1697,9 @@ class LanceVectorQueryBuilder(LanceQueryBuilder):
See discussion in [Querying an ANN Index](https://lancedb.com/docs/indexing/)
for tuning advice.
This method sets both the minimum and maximum number of probes to the same
value. See `minimum_nprobes` and `maximum_nprobes` for more fine-grained
control.
The value is retained as `nprobes` through client and server request
construction. Lance sets both probe bounds to this value. Explicit minimum
or maximum settings can override their respective bounds.
Parameters
----------
@@ -1707,8 +1713,7 @@ class LanceVectorQueryBuilder(LanceQueryBuilder):
"""
if nprobes <= 0:
raise ValueError("Invalid input, nprobes must be greater than 0")
self._minimum_nprobes = nprobes
self._maximum_nprobes = nprobes
self._nprobes = nprobes
return self
def minimum_nprobes(self, minimum_nprobes: int) -> LanceVectorQueryBuilder:
@@ -1850,6 +1855,7 @@ class LanceVectorQueryBuilder(LanceQueryBuilder):
limit=self._limit,
distance_type=self._distance_type,
columns=self._columns,
nprobes=self._nprobes,
minimum_nprobes=self._minimum_nprobes,
maximum_nprobes=self._maximum_nprobes,
lower_bound=self._lower_bound,
@@ -2208,6 +2214,7 @@ class LanceHybridQueryBuilder(LanceQueryBuilder):
self._fts_columns = fts_columns
self._norm = None
self._reranker = None
self._nprobes = None
self._minimum_nprobes = None
self._maximum_nprobes = None
self._refine_factor = None
@@ -2499,6 +2506,9 @@ class LanceHybridQueryBuilder(LanceQueryBuilder):
Higher values will yield better recall (more likely to find vectors if
they exist) at the expense of latency.
The value is retained as `nprobes` until Lance sets both probe bounds to it.
Explicit minimum or maximum settings can override their respective bounds.
Parameters
----------
nprobes: int
@@ -2511,8 +2521,7 @@ class LanceHybridQueryBuilder(LanceQueryBuilder):
"""
if nprobes <= 0:
raise ValueError("Invalid input, nprobes must be greater than 0")
self._minimum_nprobes = nprobes
self._maximum_nprobes = nprobes
self._nprobes = nprobes
return self
def minimum_nprobes(self, minimum_nprobes: int) -> LanceHybridQueryBuilder:
@@ -2791,6 +2800,8 @@ class LanceHybridQueryBuilder(LanceQueryBuilder):
self._fts_query.phrase_query(True)
if self._distance_type:
self._vector_query.metric(self._distance_type)
if self._nprobes is not None:
self._vector_query.nprobes(self._nprobes)
if self._minimum_nprobes is not None:
self._vector_query.minimum_nprobes(self._minimum_nprobes)
if self._maximum_nprobes is not None:
@@ -3690,7 +3701,7 @@ class AsyncVectorQueryBase:
def nprobes(self, nprobes: int) -> Self:
"""
Set the number of partitions to search (probe)
Set the legacy IVF probe parameter
The number of probes must be greater than 0.
@@ -3702,16 +3713,19 @@ class AsyncVectorQueryBase:
The partition whose centroids are closest to the query vector will be
exhaustiely searched to find matches. This parameter controls how many
partitions should be searched.
partitions will be searched.
Increasing this value will increase the recall of your query but will
also increase the latency of your query. The default value is 20. This
default is good for many cases but the best value to use will depend on
your data and the recall that you need to achieve.
also increase the latency of your query. If this method is not called,
Lance's adaptive probe defaults are used.
For best results we recommend tuning this parameter with a benchmark against
your actual data to find the smallest possible value that will still give
you the desired recall.
LanceDB retains this as `nprobes` through local and remote request
construction. Lance sets both probe bounds to this value. Explicit minimum
or maximum settings can override their respective bounds.
"""
self._inner.nprobes(nprobes)
return self
python/python/lancedb/table.py
+3 -2
View File
@@ -6439,9 +6439,10 @@ class AsyncTable:
)
if query.distance_type is not None:
async_query = async_query.distance_type(query.distance_type)
if query.nprobes is not None:
async_query = async_query.nprobes(query.nprobes)
if query.minimum_nprobes is not None and query.maximum_nprobes is not None:
# Set both to the minimum first to avoid min > max error.
async_query = async_query.nprobes(
async_query = async_query.minimum_nprobes(
query.minimum_nprobes
).maximum_nprobes(query.maximum_nprobes)
elif query.minimum_nprobes is not None:
python/python/tests/test_hybrid_query.py
+14
View File
@@ -355,6 +355,20 @@ def test_hybrid_query_minimum_nprobes_zero_raises(sync_table: Table):
)
def test_hybrid_query_nprobes_remains_independent(sync_table: Table):
query = sync_table.search(query_type="hybrid").nprobes(20)
assert query._nprobes == 20
assert query._minimum_nprobes is None
assert query._maximum_nprobes is None
query = query.minimum_nprobes(5).nprobes(20)
assert query._nprobes == 20
assert query._minimum_nprobes == 5
assert query._maximum_nprobes is None
def test_hybrid_query_distance_range(sync_table: Table):
reranker = RRFReranker(return_score="all")
result = (
python/python/tests/test_namespace.py
+25 -1
View File
@@ -15,7 +15,8 @@ import pyarrow as pa
import lancedb
from lance_namespace.errors import NamespaceNotEmptyError, TableNotFoundError
from lancedb.listing import AsyncListing, Listing
from lancedb.namespace import _MAX_QUERY_K
from lancedb.namespace import _MAX_QUERY_K, _query_to_namespace_request
from lancedb.query import Query
from lancedb.table import AsyncTable, LanceTable
@@ -1111,6 +1112,29 @@ class TestPushdownOperations:
]
assert all(r.k <= 2**31 - 1 for r in namespace_client.requests)
def test_probe_fields_pass_through_independently(self):
request = _query_to_namespace_request(
["geneva", "hist"],
Query(
vector=[1.0, 2.0],
nprobes=20,
minimum_nprobes=3,
maximum_nprobes=10,
),
)
assert request.nprobes == 20
assert request.minimum_nprobes == 3
assert request.maximum_nprobes == 10
request = _query_to_namespace_request(
["geneva", "hist"],
Query(vector=[1.0, 2.0], nprobes=5, maximum_nprobes=0),
)
assert request.nprobes == 5
assert request.maximum_nprobes == 0
assert request.to_dict()["maximum_nprobes"] == 0
@pytest.mark.asyncio
class TestAsyncPushdownOperations:
python/python/tests/test_query.py
+88 -28
View File
@@ -995,12 +995,22 @@ def test_invalid_nprobes_sync(table):
ValueError,
match="maximum_nprobes must be greater than or equal to minimum_nprobes",
):
LanceVectorQueryBuilder(table, [0, 0], "vector").maximum_nprobes(5).to_list()
(
LanceVectorQueryBuilder(table, [0, 0], "vector")
.minimum_nprobes(10)
.maximum_nprobes(5)
.to_list()
)
with pytest.raises(
ValueError,
match="minimum_nprobes must be less than or equal to maximum_nprobes",
match="maximum_nprobes must be greater than or equal to minimum_nprobes",
):
LanceVectorQueryBuilder(table, [0, 0], "vector").minimum_nprobes(100).to_list()
(
LanceVectorQueryBuilder(table, [0, 0], "vector")
.maximum_nprobes(5)
.minimum_nprobes(100)
.to_list()
)
def test_nprobes_works_sync(table):
@@ -1039,12 +1049,22 @@ async def test_invalid_nprobes_async(table_async: AsyncTable):
ValueError,
match="maximum_nprobes must be greater than or equal to minimum_nprobes",
):
await table_async.vector_search([0, 0]).maximum_nprobes(5).to_list()
await (
table_async.vector_search([0, 0])
.minimum_nprobes(10)
.maximum_nprobes(5)
.to_list()
)
with pytest.raises(
ValueError,
match="minimum_nprobes must be less than or equal to maximum_nprobes",
):
await table_async.vector_search([0, 0]).minimum_nprobes(100).to_list()
await (
table_async.vector_search([0, 0])
.maximum_nprobes(5)
.minimum_nprobes(100)
.to_list()
)
def test_query_builder_with_prefilter(table):
@@ -1635,8 +1655,9 @@ def test_query_serialization_sync(table: lancedb.table.Table):
q,
vector_column="vector",
vector=[5.0, 6.0],
minimum_nprobes=10,
maximum_nprobes=10,
nprobes=10,
minimum_nprobes=None,
maximum_nprobes=None,
refine_factor=5,
)
@@ -1654,8 +1675,19 @@ def test_query_serialization_sync(table: lancedb.table.Table):
q,
vector_column="vector",
vector=[5.0, 6.0],
minimum_nprobes=50,
maximum_nprobes=50,
nprobes=50,
minimum_nprobes=None,
maximum_nprobes=None,
)
q = table.search([5.0, 6.0]).minimum_nprobes(5).nprobes(50).to_query_object()
check_set_props(
q,
vector_column="vector",
vector=[5.0, 6.0],
nprobes=50,
minimum_nprobes=5,
maximum_nprobes=None,
)
q = table.search([5.0, 6.0]).maximum_nprobes(10).to_query_object()
@@ -1667,6 +1699,15 @@ def test_query_serialization_sync(table: lancedb.table.Table):
minimum_nprobes=None,
)
q = table.search([5.0, 6.0]).nprobes(5).maximum_nprobes(0).to_query_object()
check_set_props(
q,
vector_column="vector",
vector=[5.0, 6.0],
nprobes=5,
maximum_nprobes=0,
)
q = table.search([5.0, 6.0]).distance_range(0.0, 1.0).to_query_object()
check_set_props(
q, vector_column="vector", vector=[5.0, 6.0], lower_bound=0.0, upper_bound=1.0
@@ -1716,8 +1757,8 @@ async def test_query_serialization_async(table_async: AsyncTable):
limit=10,
vector=sample_vector,
postfilter=False,
minimum_nprobes=20,
maximum_nprobes=20,
minimum_nprobes=None,
maximum_nprobes=None,
with_row_id=False,
bypass_vector_index=False,
)
@@ -1727,8 +1768,8 @@ async def test_query_serialization_async(table_async: AsyncTable):
q,
vector=sample_vector,
postfilter=False,
minimum_nprobes=20,
maximum_nprobes=20,
minimum_nprobes=None,
maximum_nprobes=None,
with_row_id=False,
bypass_vector_index=False,
limit=10,
@@ -1739,8 +1780,9 @@ async def test_query_serialization_async(table_async: AsyncTable):
q,
vector=sample_vector,
postfilter=False,
minimum_nprobes=50,
maximum_nprobes=50,
nprobes=50,
minimum_nprobes=None,
maximum_nprobes=None,
with_row_id=False,
bypass_vector_index=False,
limit=10,
@@ -1759,8 +1801,8 @@ async def test_query_serialization_async(table_async: AsyncTable):
filter="id = 1",
postfilter=True,
vector=sample_vector,
minimum_nprobes=20,
maximum_nprobes=20,
minimum_nprobes=None,
maximum_nprobes=None,
with_row_id=False,
bypass_vector_index=False,
)
@@ -1774,8 +1816,9 @@ async def test_query_serialization_async(table_async: AsyncTable):
check_set_props(
q,
vector=sample_vector,
minimum_nprobes=10,
maximum_nprobes=10,
nprobes=10,
minimum_nprobes=None,
maximum_nprobes=None,
refine_factor=5,
postfilter=False,
with_row_id=False,
@@ -1788,7 +1831,24 @@ async def test_query_serialization_async(table_async: AsyncTable):
q,
vector=sample_vector,
minimum_nprobes=5,
maximum_nprobes=20,
maximum_nprobes=None,
postfilter=False,
with_row_id=False,
bypass_vector_index=False,
limit=10,
)
q = (
(await table_async.search([5.0, 6.0]))
.nprobes(5)
.maximum_nprobes(0)
.to_query_object()
)
check_set_props(
q,
vector=sample_vector,
nprobes=5,
maximum_nprobes=0,
postfilter=False,
with_row_id=False,
bypass_vector_index=False,
@@ -1806,8 +1866,8 @@ async def test_query_serialization_async(table_async: AsyncTable):
lower_bound=0.0,
upper_bound=1.0,
postfilter=False,
minimum_nprobes=20,
maximum_nprobes=20,
minimum_nprobes=None,
maximum_nprobes=None,
with_row_id=False,
bypass_vector_index=False,
limit=10,
@@ -1819,8 +1879,8 @@ async def test_query_serialization_async(table_async: AsyncTable):
distance_type="cosine",
vector=sample_vector,
postfilter=False,
minimum_nprobes=20,
maximum_nprobes=20,
minimum_nprobes=None,
maximum_nprobes=None,
with_row_id=False,
bypass_vector_index=False,
limit=10,
@@ -1832,8 +1892,8 @@ async def test_query_serialization_async(table_async: AsyncTable):
ef=7,
vector=sample_vector,
postfilter=False,
minimum_nprobes=20,
maximum_nprobes=20,
minimum_nprobes=None,
maximum_nprobes=None,
with_row_id=False,
bypass_vector_index=False,
limit=10,
@@ -1845,8 +1905,8 @@ async def test_query_serialization_async(table_async: AsyncTable):
bypass_vector_index=True,
vector=sample_vector,
postfilter=False,
minimum_nprobes=20,
maximum_nprobes=20,
minimum_nprobes=None,
maximum_nprobes=None,
with_row_id=False,
limit=10,
)
python/python/tests/test_remote_db.py
+23 -14
View File
@@ -2164,9 +2164,6 @@ def test_query_sync_minimal():
"ef": None,
"vector": [1.0, 2.0, 3.0],
"vector_column": "vector",
"nprobes": 20,
"minimum_nprobes": 20,
"maximum_nprobes": 20,
"version": None,
}
@@ -2338,8 +2335,6 @@ def test_query_sync_maximal():
"refine_factor": 10,
"vector": [1.0, 2.0, 3.0],
"nprobes": 5,
"minimum_nprobes": 5,
"maximum_nprobes": 5,
"lower_bound": None,
"upper_bound": None,
"ef": None,
@@ -2435,7 +2430,7 @@ async def test_query_async_nprobes_zero(hybrid):
assert requests == ["/v1/table/test/describe/"]
def test_query_sync_nprobes():
def test_query_sync_probe_bounds():
def handler(body):
assert body == {
"k": 10,
@@ -2447,7 +2442,6 @@ def test_query_sync_nprobes():
"upper_bound": None,
"ef": None,
"vector": [1.0, 2.0, 3.0],
"nprobes": 5,
"minimum_nprobes": 5,
"maximum_nprobes": 15,
"version": None,
@@ -2464,7 +2458,28 @@ def test_query_sync_nprobes():
)
def test_query_sync_no_max_nprobes():
def test_query_sync_nprobes_passthrough():
def handler(body):
assert body["nprobes"] == 20
assert "minimum_nprobes" not in body
assert "maximum_nprobes" not in body
return pa.table({"id": [1]})
with query_test_table(handler) as table:
table.search([1, 2, 3]).nprobes(20).to_list()
def test_query_sync_nprobes_with_unbounded_maximum():
def handler(body):
assert body["nprobes"] == 20
assert body["maximum_nprobes"] == 0
return pa.table({"id": [1]})
with query_test_table(handler) as table:
table.search([1, 2, 3]).nprobes(20).maximum_nprobes(0).to_list()
def test_query_sync_minimum_nprobes_only():
def handler(body):
assert body == {
"k": 10,
@@ -2476,9 +2491,7 @@ def test_query_sync_no_max_nprobes():
"upper_bound": None,
"ef": None,
"vector": [1.0, 2.0, 3.0],
"nprobes": 5,
"minimum_nprobes": 5,
"maximum_nprobes": 0,
"version": None,
}
@@ -2488,7 +2501,6 @@ def test_query_sync_no_max_nprobes():
(
table.search([1, 2, 3], vector_column_name="vector2", fast_search=True)
.minimum_nprobes(5)
.maximum_nprobes(0)
.to_list()
)
@@ -2643,9 +2655,6 @@ def test_query_sync_hybrid():
"refine_factor": None,
"vector": [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
"vector_column": "vector",
"nprobes": 20,
"minimum_nprobes": 20,
"maximum_nprobes": 20,
"lower_bound": None,
"upper_bound": None,
"ef": None,
python/src/query.rs
+5 -7
View File
@@ -336,9 +336,8 @@ pub struct PyQueryRequest {
pub use_lsm: Option<bool>,
pub column: Option<String>,
pub query_vector: Option<PyQueryVectors>,
pub nprobes: Option<usize>,
pub minimum_nprobes: Option<usize>,
// None means user did not set it and default should be used (currently 20)
// Some(0) means user set it to None and there is no limit
pub maximum_nprobes: Option<usize>,
pub lower_bound: Option<f32>,
pub upper_bound: Option<f32>,
@@ -369,6 +368,7 @@ impl From<AnyQuery> for PyQueryRequest {
use_lsm: query_request.use_lsm,
column: None,
query_vector: None,
nprobes: None,
minimum_nprobes: None,
maximum_nprobes: None,
lower_bound: None,
@@ -396,11 +396,9 @@ impl From<AnyQuery> for PyQueryRequest {
use_lsm: vector_query.base.use_lsm,
column: vector_query.column,
query_vector: Some(PyQueryVectors(vector_query.query_vector)),
minimum_nprobes: Some(vector_query.minimum_nprobes),
maximum_nprobes: match vector_query.maximum_nprobes {
None => Some(0),
Some(value) => Some(value),
},
nprobes: vector_query.nprobes,
minimum_nprobes: vector_query.minimum_nprobes,
maximum_nprobes: vector_query.maximum_nprobes,
lower_bound: vector_query.lower_bound,
upper_bound: vector_query.upper_bound,
ef: vector_query.ef,
python/uv.lock
Generated
+15 -15
View File
@@ -1849,19 +1849,19 @@ wheels = [
[[package]]
name = "lance-namespace"
version = "0.11.1"
version = "0.13.0"
source = { registry = "https://pypi.org/simple" }
dependencies = [
{ name = "lance-namespace-urllib3-client" },
]
sdist = { url = "https://files.pythonhosted.org/packages/bf/93/da5f7fcac690db9b282a3439ed9e34960c147619a0d6e1f4eb8cd240e7a5/lance_namespace-0.11.1.tar.gz", hash = "sha256:f67cfbbe0647b7cb42f23b673e7edf8a75b7d8a047265a916492f8d247ee1bc2", size = 11631, upload-time = "2026-08-18T17:40:06.294Z" }
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