mirror of
https://github.com/lancedb/lancedb.git
synced 2026-10-13 07:12:27 +00:00
feat(search): expose search effort with Lance beta.12
This commit is contained in:
Yang Cen
@@ -63,7 +63,7 @@ tests = [
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"polars>=0.19, <=1.32.3",
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"pyarrow<25",
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"pyarrow-stubs>=16.0",
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"pylance==14.0.0b10",
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"pylance==14.0.0b12",
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"requests>=2.31.0",
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"datafusion>=54,<55",
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"opentelemetry-sdk>=1.30.0",
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@@ -73,6 +73,7 @@ dev = [
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"pre-commit>=3.5.0",
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"pyright>=1.1.350",
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'typing-extensions>=4.0.0; python_version < "3.11"',
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"maturin>=1.10",
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]
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docs = ["mkdocs", "mkdocs-jupyter", "mkdocs-material", "mkdocstrings-python"]
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clip = ["torch", "pillow>=12.1.1", "open-clip-torch"]
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@@ -727,6 +727,7 @@ class VectorQuery:
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def use_lsm(self, enable: bool): ...
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def refine_factor(self, refine_factor: int): ...
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def nprobes(self, nprobes: int): ...
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def search_effort(self, search_effort: Optional[float]): ...
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def minimum_nprobes(self, minimum_nprobes: int): ...
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def maximum_nprobes(self, maximum_nprobes: int): ...
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def bypass_vector_index(self): ...
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@@ -752,6 +753,7 @@ class HybridQuery:
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def distance_type(self, distance_type: str): ...
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def refine_factor(self, refine_factor: int): ...
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def nprobes(self, nprobes: int): ...
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def search_effort(self, search_effort: Optional[float]): ...
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def minimum_nprobes(self, minimum_nprobes: int): ...
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def maximum_nprobes(self, maximum_nprobes: int): ...
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def bypass_vector_index(self): ...
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@@ -781,6 +783,7 @@ class PyQueryRequest:
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nprobes: Optional[int]
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minimum_nprobes: Optional[int]
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maximum_nprobes: Optional[int]
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search_effort: Optional[float]
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lower_bound: Optional[float]
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upper_bound: Optional[float]
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ef: Optional[int]
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@@ -105,6 +105,11 @@ def _query_to_namespace_request(
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if not isinstance(query, Query):
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raise TypeError(f"Expected Query, got {type(query)}")
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if query.search_effort is not None:
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raise ValueError(
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"namespace QueryTable does not support search_effort; "
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"execute the query locally"
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)
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vector_request = None
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if query.vector is not None:
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@@ -475,8 +475,8 @@ class MatchQuery(FullTextQuery):
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----------
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query : str
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The query string to match against.
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column : str
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The name of the column to match against.
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column : str, optional
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The column to match against. None leaves column selection to query planning.
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boost : float, default 1.0
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The boost factor for the query.
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The score of each matching document is multiplied by this value.
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@@ -505,7 +505,7 @@ class MatchQuery(FullTextQuery):
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"""
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query: str
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column: str
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column: Optional[str] = None
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boost: float = pydantic.Field(1.0, kw_only=True)
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fuzziness: Optional[int] = pydantic.Field(0, kw_only=True)
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max_expansions: int = pydantic.Field(50, kw_only=True)
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@@ -793,6 +793,9 @@ class Query(pydantic.BaseModel):
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# and all partitions could be searched if needed to satisfy the limit.
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maximum_nprobes: Optional[int] = None
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# Optional initial IVF effort; None preserves Auto and explicit nprobes.
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search_effort: Optional[float] = None
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# lower bound for distance search
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lower_bound: Optional[float] = None
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@@ -854,6 +857,7 @@ class Query(pydantic.BaseModel):
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query.nprobes = req.nprobes
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query.minimum_nprobes = req.minimum_nprobes
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query.maximum_nprobes = req.maximum_nprobes
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query.search_effort = req.search_effort
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query.lower_bound = req.lower_bound
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query.upper_bound = req.upper_bound
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query.ef = req.ef
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@@ -1635,6 +1639,7 @@ class LanceVectorQueryBuilder(LanceQueryBuilder):
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self._nprobes = None
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self._minimum_nprobes = None
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self._maximum_nprobes = None
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self._search_effort = None
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self._lower_bound = None
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self._upper_bound = None
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self._refine_factor = None
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@@ -1716,6 +1721,24 @@ class LanceVectorQueryBuilder(LanceQueryBuilder):
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self._nprobes = nprobes
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return self
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def search_effort(self, search_effort: Optional[float]) -> LanceVectorQueryBuilder:
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"""Set the initial IVF search effort to a finite value in [0, 1].
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None (the default) uses Auto, internally 0.5. Zero starts at the caller
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minimum; one searches all partitions within the caller maximum. Later
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probing may continue when too few candidates are found. Any explicit
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effort, including 0.5, overrides nprobes in either call order. Explicit
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minimum and maximum bounds still apply. This is not a recall guarantee
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and does not change refinement or HNSW ef. MemWAL LSM queries do not
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yet support this option.
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Examples
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--------
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>>> query = table.search([0.1, 0.2]).search_effort(0.75)
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"""
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self._search_effort = search_effort
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return self
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def minimum_nprobes(self, minimum_nprobes: int) -> LanceVectorQueryBuilder:
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"""Set the minimum number of probes to use.
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@@ -1858,6 +1881,7 @@ class LanceVectorQueryBuilder(LanceQueryBuilder):
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nprobes=self._nprobes,
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minimum_nprobes=self._minimum_nprobes,
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maximum_nprobes=self._maximum_nprobes,
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search_effort=self._search_effort,
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lower_bound=self._lower_bound,
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upper_bound=self._upper_bound,
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refine_factor=self._refine_factor,
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@@ -2217,6 +2241,7 @@ class LanceHybridQueryBuilder(LanceQueryBuilder):
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self._nprobes = None
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self._minimum_nprobes = None
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self._maximum_nprobes = None
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self._search_effort = None
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self._refine_factor = None
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self._distance_type = None
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self._phrase_query = None
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@@ -2524,6 +2549,26 @@ class LanceHybridQueryBuilder(LanceQueryBuilder):
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self._nprobes = nprobes
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return self
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def search_effort(self, search_effort: Optional[float]) -> LanceHybridQueryBuilder:
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"""Set the initial IVF search effort to a finite value in [0, 1].
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None (the default) uses Auto, internally 0.5. Zero starts at the caller
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minimum; one searches all partitions within the caller maximum. Later
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probing may continue when too few candidates are found. Any explicit
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effort, including 0.5, overrides nprobes in either call order. Explicit
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minimum and maximum bounds still apply. This is not a recall guarantee
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and does not change refinement or HNSW ef. MemWAL LSM queries do not
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yet support this option.
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Examples
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--------
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>>> query = table.search([0.1, 0.2]).search_effort(0.75)
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"""
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self._search_effort = search_effort
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if self._vector_query is not None:
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self._vector_query.search_effort(search_effort)
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return self
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def minimum_nprobes(self, minimum_nprobes: int) -> LanceHybridQueryBuilder:
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"""Set the minimum number of probes to use.
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@@ -2802,6 +2847,8 @@ class LanceHybridQueryBuilder(LanceQueryBuilder):
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self._vector_query.metric(self._distance_type)
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if self._nprobes is not None:
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self._vector_query.nprobes(self._nprobes)
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if self._search_effort is not None:
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self._vector_query.search_effort(self._search_effort)
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if self._minimum_nprobes is not None:
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self._vector_query.minimum_nprobes(self._minimum_nprobes)
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if self._maximum_nprobes is not None:
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@@ -3730,6 +3777,24 @@ class AsyncVectorQueryBase:
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self._inner.nprobes(nprobes)
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return self
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def search_effort(self, search_effort: Optional[float]) -> Self:
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"""Set the initial IVF search effort to a finite value in [0, 1].
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None preserves Auto (internally 0.5) and fixed nprobes. Any explicit
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effort, including 0.5, overrides nprobes in either call order; explicit
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minimum and maximum bounds remain effective. Zero starts at the caller
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minimum and one searches all partitions within the maximum. Later
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probing may continue to obtain enough candidates. This is not a recall
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guarantee and does not change refinement or HNSW ef. MemWAL LSM queries
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do not yet support this option.
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Examples
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--------
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>>> query = table.vector_search([0.1, 0.2]).search_effort(0.75)
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"""
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self._inner.search_effort(search_effort)
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return self
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def minimum_nprobes(self, minimum_nprobes: int) -> Self:
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"""Set the minimum number of probes to use.
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@@ -4440,6 +4440,7 @@ class LanceTable(Table):
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and not self._route_pushdown_to_rust
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and self.current_branch() is None
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and query.take_offsets is None
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and query.search_effort is None
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):
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from lancedb.namespace import _execute_server_side_query
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@@ -6441,6 +6442,8 @@ class AsyncTable:
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async_query = async_query.distance_type(query.distance_type)
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if query.nprobes is not None:
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async_query = async_query.nprobes(query.nprobes)
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if query.search_effort is not None:
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async_query = async_query.search_effort(query.search_effort)
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if query.minimum_nprobes is not None and query.maximum_nprobes is not None:
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async_query = async_query.minimum_nprobes(
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query.minimum_nprobes
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@@ -6481,6 +6484,7 @@ class AsyncTable:
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)
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and not self._route_pushdown_to_rust
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and query.take_offsets is None
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and query.search_effort is None
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):
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from lancedb.namespace import _execute_server_side_query
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@@ -1209,3 +1209,15 @@ def test_local_table_to_arrow_and_to_pandas_are_unchanged(tmp_path):
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assert table.to_arrow().column("id").to_pylist() == [1, 2]
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assert table.to_pandas()["id"].tolist() == [1, 2]
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@pytest.mark.parametrize("effort", [0.0, 0.5, 1.0])
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def test_namespace_request_rejects_search_effort(effort):
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from lancedb.query import Query
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with pytest.raises(
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ValueError, match="namespace QueryTable does not support search_effort"
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):
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_query_to_namespace_request(
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["test"], Query(vector=[1.0, 0.0], search_effort=effort)
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)
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@@ -2265,3 +2265,112 @@ def test_blob_v2_to_batches_row_id(tmp_db):
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assert "_rowid" in hits.column_names
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blobs = table.fetch_blobs("blob", hits)
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assert [blobs[i].as_py() for i in range(len(blobs))] == [b"one", b"two"]
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@pytest.mark.parametrize("metric", ["l2", "cosine", "dot"])
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@pytest.mark.parametrize(
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"effort,probes", [(None, 4), (0, 1), (0.25, 2), (0.5, 4), (0.75, 8), (1, 16)]
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)
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def test_search_effort_probe_budget(tmp_path, monkeypatch, metric, effort, probes):
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import re
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from lancedb.index import IvfFlat
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monkeypatch.setenv("LANCE_AUTO_PROBE_MARGIN", "0")
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monkeypatch.setenv("LANCE_AUTO_MIN_INITIAL_NPROBES", "4")
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monkeypatch.setenv("LANCE_AUTO_MAX_INITIAL_NPROBES", "4")
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rng = np.random.default_rng(42)
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vectors = np.repeat(np.eye(16, dtype=np.float32), 32, axis=0)
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vectors += rng.normal(0, 0.001, vectors.shape).astype(np.float32)
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data = pa.table(
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{
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"id": np.arange(512),
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"vector": pa.FixedSizeListArray.from_arrays(pa.array(vectors.ravel()), 16),
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}
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)
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table = lancedb.connect(tmp_path).create_table("effort", data.slice(0, 256))
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table.add(data.slice(256))
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table.create_index(
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"vector", config=IvfFlat(distance_type=metric, num_partitions=16)
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)
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vector = np.eye(16, dtype=np.float32)[0]
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def search(value=effort):
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return table.search(vector).distance_type(metric).search_effort(value).limit(8)
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def partitions(query):
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values = re.findall(r"partitions_searched=(\d+)", query.analyze_plan())
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assert values
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return sum(map(int, values))
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assert partitions(search()) == probes
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exact = set(
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table.search(vector)
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.distance_type(metric)
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.bypass_vector_index()
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.limit(8)
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.to_arrow()["id"]
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.to_pylist()
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)
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actual = set(search().to_arrow()["id"].to_pylist())
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assert len(actual & exact) / 8 >= 0.5
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expected = 1 if effort is None else probes
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assert partitions(search().nprobes(1)) == expected
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assert (
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partitions(
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table.search(vector)
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.distance_type(metric)
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.nprobes(1)
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.search_effort(effort)
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.limit(8)
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)
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== expected
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)
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assert partitions(search().nprobes(1).search_effort(None)) == 1
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assert partitions(search().maximum_nprobes(2)) <= 2
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assert partitions(search().minimum_nprobes(3).maximum_nprobes(3)) == 3
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assert partitions(search(0).nprobes(16).maximum_nprobes(0)) == 1
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if effort is None:
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assert search().to_arrow() == search(0.5).to_arrow()
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filtered = search(0).maximum_nprobes(2).where("id % 8 = 0", prefilter=True)
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assert partitions(filtered) <= 2
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assert all(row["id"] % 8 == 0 for row in filtered.to_list())
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@pytest.mark.parametrize(
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"effort", [-0.1, 1.1, float("nan"), float("inf"), -float("inf")]
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)
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def test_search_effort_invalid_sync(table, effort):
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with pytest.raises(ValueError, match="search_effort must be finite and in"):
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table.search([0, 0]).search_effort(effort).to_list()
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@pytest.mark.asyncio
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@pytest.mark.parametrize("hybrid", [False, True])
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@pytest.mark.parametrize("effort", [None, 0, 0.25, 0.5, 0.75, 1])
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async def test_search_effort_async_roundtrip(table_async, hybrid, effort):
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query = table_async.vector_search([0, 0])
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if hybrid:
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query = query.nearest_to_text("dog")
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query = query.nprobes(5).search_effort(effort).minimum_nprobes(2).maximum_nprobes(3)
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request = query.to_query_object()
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assert request.search_effort == effort
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assert request.nprobes == 5
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assert request.minimum_nprobes == 2
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assert request.maximum_nprobes == 3
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assert query.search_effort(None).to_query_object().search_effort is None
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query.search_effort(effort)
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if not hybrid:
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assert len(await query.limit(1).to_list()) == 1
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@pytest.mark.asyncio
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@pytest.mark.parametrize("hybrid", [False, True])
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@pytest.mark.parametrize(
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"effort", [-0.1, 1.1, float("nan"), float("inf"), -float("inf")]
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)
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async def test_search_effort_invalid_async(table_async, hybrid, effort):
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query = table_async.vector_search([0, 0])
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if hybrid:
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query = query.nearest_to_text("dog")
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with pytest.raises(ValueError, match="search_effort must be finite and in"):
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query.search_effort(effort)
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+31
-3
@@ -71,7 +71,7 @@ impl<'a, 'py> FromPyObject<'a, 'py> for PyLanceDB<FtsQuery> {
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match class_name(&ob)?.as_str() {
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"MatchQuery" => {
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let query = ob.getattr("query")?.extract()?;
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let column = ob.getattr("column")?.extract()?;
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let column = ob.getattr("column")?.extract::<Option<String>>()?;
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let boost = ob.getattr("boost")?.extract()?;
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let fuzziness = ob.getattr("fuzziness")?.extract()?;
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let max_expansions = ob.getattr("max_expansions")?.extract()?;
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@@ -88,7 +88,7 @@ impl<'a, 'py> FromPyObject<'a, 'py> for PyLanceDB<FtsQuery> {
|
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|
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let mut query =
|
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MatchQuery::new(query)
|
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.with_column(Some(column))
|
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.with_column(column)
|
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.with_boost(boost)
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.with_fuzziness(fuzziness)
|
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.with_max_expansions(max_expansions)
|
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@@ -199,7 +199,7 @@ impl<'py> IntoPyObject<'py> for PyLanceDB<FtsQuery> {
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}
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namespace
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.getattr(intern!(py, "MatchQuery"))?
|
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.call((query.terms, query.column.unwrap()), Some(&kwargs))
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.call((query.terms, query.column), Some(&kwargs))
|
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}
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FtsQuery::Phrase(query) => {
|
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let kwargs = PyDict::new(py);
|
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@@ -339,6 +339,7 @@ pub struct PyQueryRequest {
|
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pub nprobes: Option<usize>,
|
||||
pub minimum_nprobes: Option<usize>,
|
||||
pub maximum_nprobes: Option<usize>,
|
||||
pub search_effort: Option<f64>,
|
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pub lower_bound: Option<f32>,
|
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pub upper_bound: Option<f32>,
|
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pub ef: Option<usize>,
|
||||
@@ -371,6 +372,7 @@ impl From<AnyQuery> for PyQueryRequest {
|
||||
nprobes: None,
|
||||
minimum_nprobes: None,
|
||||
maximum_nprobes: None,
|
||||
search_effort: None,
|
||||
lower_bound: None,
|
||||
upper_bound: None,
|
||||
ef: None,
|
||||
@@ -399,6 +401,7 @@ impl From<AnyQuery> for PyQueryRequest {
|
||||
nprobes: vector_query.nprobes,
|
||||
minimum_nprobes: vector_query.minimum_nprobes,
|
||||
maximum_nprobes: vector_query.maximum_nprobes,
|
||||
search_effort: vector_query.search_effort,
|
||||
lower_bound: vector_query.lower_bound,
|
||||
upper_bound: vector_query.upper_bound,
|
||||
ef: vector_query.ef,
|
||||
@@ -998,6 +1001,15 @@ impl VectorQuery {
|
||||
Ok(())
|
||||
}
|
||||
|
||||
pub fn search_effort(&mut self, search_effort: Option<f64>) -> PyResult<()> {
|
||||
self.inner = self
|
||||
.inner
|
||||
.clone()
|
||||
.search_effort(search_effort)
|
||||
.infer_error()?;
|
||||
Ok(())
|
||||
}
|
||||
|
||||
pub fn minimum_nprobes(&mut self, minimum_nprobes: u32) -> PyResult<()> {
|
||||
self.inner = self
|
||||
.inner
|
||||
@@ -1191,6 +1203,18 @@ impl HybridQuery {
|
||||
self.inner_vec.nprobes(nprobe)
|
||||
}
|
||||
|
||||
pub fn search_effort(&mut self, search_effort: Option<f64>) -> PyResult<()> {
|
||||
self.inner_vec.search_effort(search_effort)
|
||||
}
|
||||
|
||||
pub fn minimum_nprobes(&mut self, minimum_nprobes: u32) -> PyResult<()> {
|
||||
self.inner_vec.minimum_nprobes(minimum_nprobes)
|
||||
}
|
||||
|
||||
pub fn maximum_nprobes(&mut self, maximum_nprobes: u32) -> PyResult<()> {
|
||||
self.inner_vec.maximum_nprobes(maximum_nprobes)
|
||||
}
|
||||
|
||||
pub fn ef(&mut self, ef: u32) {
|
||||
self.inner_vec.ef(ef);
|
||||
}
|
||||
@@ -1235,6 +1259,10 @@ impl HybridQuery {
|
||||
req.query_vector = vec_req.query_vector;
|
||||
req.column = vec_req.column;
|
||||
req.distance_type = vec_req.distance_type;
|
||||
req.nprobes = vec_req.nprobes;
|
||||
req.minimum_nprobes = vec_req.minimum_nprobes;
|
||||
req.maximum_nprobes = vec_req.maximum_nprobes;
|
||||
req.search_effort = vec_req.search_effort;
|
||||
req.ef = vec_req.ef;
|
||||
req.refine_factor = vec_req.refine_factor;
|
||||
req.lower_bound = vec_req.lower_bound;
|
||||
|
||||
Generated
+34
-8
@@ -1900,6 +1900,7 @@ clip = [
|
||||
{ name = "torch" },
|
||||
]
|
||||
dev = [
|
||||
{ name = "maturin" },
|
||||
{ name = "pre-commit" },
|
||||
{ name = "pyright" },
|
||||
{ name = "ruff" },
|
||||
@@ -1981,6 +1982,7 @@ requires-dist = [
|
||||
{ name = "ibm-watsonx-ai", marker = "python_full_version >= '3.10' and extra == 'embeddings'", specifier = ">=1.1.2" },
|
||||
{ name = "instructorembedding", marker = "extra == 'embeddings'", specifier = ">=1.0.1" },
|
||||
{ name = "lance-namespace", specifier = ">=0.13.0" },
|
||||
{ name = "maturin", marker = "extra == 'dev'", specifier = ">=1.10" },
|
||||
{ name = "mkdocs", marker = "extra == 'docs'" },
|
||||
{ name = "mkdocs-jupyter", marker = "extra == 'docs'" },
|
||||
{ name = "mkdocs-material", marker = "extra == 'docs'" },
|
||||
@@ -2005,7 +2007,7 @@ requires-dist = [
|
||||
{ name = "pyarrow-stubs", marker = "extra == 'tests'", specifier = ">=16.0" },
|
||||
{ name = "pydantic", specifier = ">=2.7.4,<3" },
|
||||
{ name = "pylance", marker = "extra == 'pylance'", specifier = ">=5.0.0b5" },
|
||||
{ name = "pylance", marker = "extra == 'tests'", specifier = "==14.0.0b10" },
|
||||
{ name = "pylance", marker = "extra == 'tests'", specifier = "==14.0.0b12" },
|
||||
{ name = "pyright", marker = "extra == 'dev'", specifier = ">=1.1.350" },
|
||||
{ name = "pytest", marker = "extra == 'tests'", specifier = ">=7.0" },
|
||||
{ name = "pytest-asyncio", marker = "extra == 'tests'", specifier = ">=0.21" },
|
||||
@@ -2156,6 +2158,30 @@ wheels = [
|
||||
{ url = "https://files.pythonhosted.org/packages/41/09/5b161152e2d90f7b87f781c2e1267494aef9c32498df793f73ad0a0a494a/matplotlib_inline-0.2.2-py3-none-any.whl", hash = "sha256:3c821cf1c209f59fb2d2d64abbf5b23b67bcb2210d663f9918dd851c6da1fcf6", size = 9534, upload-time = "2026-05-08T17:33:32.055Z" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "maturin"
|
||||
version = "1.15.0"
|
||||
source = { registry = "https://pypi.org/simple" }
|
||||
dependencies = [
|
||||
{ name = "tomli", marker = "python_full_version < '3.11'" },
|
||||
]
|
||||
sdist = { url = "https://files.pythonhosted.org/packages/b9/c8/22e5e21b2679c9bce6415ca578034ca2cc9316be0642ae21e051a2d5198c/maturin-1.15.0.tar.gz", hash = "sha256:94b26cc8e8aba61a5f2099715fe640e18c5f678e9a500408b38761263954228a", size = 385504, upload-time = "2026-08-24T12:11:22.665Z" }
|
||||
wheels = [
|
||||
{ url = "https://files.pythonhosted.org/packages/14/69/5c01b461044eb1f45ddcce006706eb88110c793cdb11c7ae0b5e08492e94/maturin-1.15.0-py3-none-linux_armv6l.whl", hash = "sha256:6bf6dc62e22d4dcfd5a51244ff0d58975fa4979c48209fe84159617648956d82", size = 10206220, upload-time = "2026-08-24T12:10:53.327Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/eb/1f/2b431554e11687cdb1077e0cdadcc118c53f611086b3af00c8545a67c6a5/maturin-1.15.0-py3-none-macosx_10_12_x86_64.macosx_11_0_arm64.macosx_10_12_universal2.whl", hash = "sha256:cd35772633f489841132bc8e71d6fc7f842df30b9c05cd5cdf1ee1ddcb744cc7", size = 19416513, upload-time = "2026-08-24T12:10:56.126Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/51/36/e23a21cb34a648b711036b9b2fe1d4f3f4ee24f8db54215d73f1a9a3a3ec/maturin-1.15.0-py3-none-macosx_10_12_x86_64.whl", hash = "sha256:c40b4eae7bf5ef1f4b1af8d623fe4105016f93578fb15b764e741d08ec3b92dd", size = 10014962, upload-time = "2026-08-24T12:10:58.486Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/8c/80/33b15cb2d8f30f12c807955e8f2fd775027692904e30ec0784744ce8cd83/maturin-1.15.0-py3-none-manylinux_2_12_i686.manylinux2010_i686.musllinux_1_1_i686.whl", hash = "sha256:7eb066372f541f8eb4909c79c5d9bd0b9e8125980bdf1ec9e8aba23c6c8d6c55", size = 10196223, upload-time = "2026-08-24T12:11:00.696Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/fe/91/b495e19e2f5c503b540452b2039115e7b2363867e8c5ad4179eb752fa92c/maturin-1.15.0-py3-none-manylinux_2_12_x86_64.manylinux2010_x86_64.musllinux_1_1_x86_64.whl", hash = "sha256:653020a63525bb224e5ab0adf02e17a2e08bc86dbea7fc1399c9a56d7529b99e", size = 10541186, upload-time = "2026-08-24T12:11:02.857Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/5d/2b/2abff58037188d852b124871b1f0d720e1c2bfb3d4f1b03d87c52cd66488/maturin-1.15.0-py3-none-manylinux_2_17_aarch64.manylinux2014_aarch64.musllinux_1_1_aarch64.whl", hash = "sha256:0ebf9767892725083138e671c34482c660317a2f3d6a29fc0e0f34e9d8c99136", size = 10083468, upload-time = "2026-08-24T12:11:05.012Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/3f/07/b7e9f8be99a6627849e81ac7b6694876bce8f50a92995fe17e3cf2610f0a/maturin-1.15.0-py3-none-manylinux_2_17_armv7l.manylinux2014_armv7l.musllinux_1_1_armv7l.whl", hash = "sha256:7ab7eebffd7b8debca2265985de4eaeb332141276d24b9560b5ad484d4b3add1", size = 10047786, upload-time = "2026-08-24T12:11:07.042Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/ca/ab/167e3cb7accee11b507dbe53e0e87aeccb376d44ae66284c96ee4df3a9fd/maturin-1.15.0-py3-none-manylinux_2_17_ppc64le.manylinux2014_ppc64le.musllinux_1_1_ppc64le.whl", hash = "sha256:126e12e618b4db42f68c779a56d41f82a390145ba36ac3f621d057eb34f5ad9d", size = 13315332, upload-time = "2026-08-24T12:11:09.433Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/14/4d/801379f646cbc6b00998e5289b0630a886be3a4ee4c75b6bc9b87478a7f1/maturin-1.15.0-py3-none-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:4f9d33e6c3f9615c8caceecbbbd440f8eb25a3ddeb687077682cd5eca2e9ae15", size = 10807183, upload-time = "2026-08-24T12:11:11.73Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/89/27/2e612e1cbd1580e9e94d4722c227b5180dca27b32b955a34b79918aa1292/maturin-1.15.0-py3-none-manylinux_2_31_riscv64.musllinux_1_1_riscv64.whl", hash = "sha256:bf29beddd0c6708f112db51d5275fc28b28b9e9c9c5faae387eaef662918b176", size = 10413274, upload-time = "2026-08-24T12:11:14.04Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/70/d8/202a7b4d75a51f20f84ec9ce3b7345b12b822207072164e2b1c6ef665125/maturin-1.15.0-py3-none-win32.whl", hash = "sha256:da649988be98e87e009e51b1bf0d301b6a301bc0cecbdd60d40d8ba60748d1ca", size = 8928744, upload-time = "2026-08-24T12:11:16.269Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/40/dc/4e90da594986ba78dd3bc8a5921ecdcdb11085b22b02a412caab3b225601/maturin-1.15.0-py3-none-win_amd64.whl", hash = "sha256:552c2be4afd43fe8d5c9f3ec8d4c4756d973b8dcbe94c14084390301f50243e1", size = 10335085, upload-time = "2026-08-24T12:11:18.326Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/8b/10/15d4314edf130955edf2dc237aa393a8a7c10f2b9b57b89fa2f61f915659/maturin-1.15.0-py3-none-win_arm64.whl", hash = "sha256:c7dc0c66c78d3debdd9c5aa807e861fbcbf07f3505d34b125df74c03986b0f48", size = 9713795, upload-time = "2026-08-24T12:11:20.83Z" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "mdit-py-plugins"
|
||||
version = "0.6.1"
|
||||
@@ -3912,7 +3938,7 @@ crypto = [
|
||||
|
||||
[[package]]
|
||||
name = "pylance"
|
||||
version = "14.0.0b10"
|
||||
version = "14.0.0b12"
|
||||
source = { registry = "https://pypi.fury.io/lance-format/" }
|
||||
dependencies = [
|
||||
{ name = "lance-namespace" },
|
||||
@@ -3922,12 +3948,12 @@ dependencies = [
|
||||
{ name = "pyarrow" },
|
||||
]
|
||||
wheels = [
|
||||
{ url = "https://pypi.fury.io/lance-format/-/ver_4hvE/pylance-14.0.0b10-cp310-abi3-macosx_11_0_arm64.whl", hash = "sha256:415b6b1b9fe735fbae7c755eb9774ae2fe0feddc90bf80bee24087cbfbfdf8c1" },
|
||||
{ url = "https://pypi.fury.io/lance-format/-/ver_pM1km/pylance-14.0.0b10-cp310-abi3-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:87317cad22f4d35fea11eb70aa5c0d71a0e1ea3b96de38f3deca933daa749fff" },
|
||||
{ url = "https://pypi.fury.io/lance-format/-/ver_24jdyj/pylance-14.0.0b10-cp310-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:6da554a2fc2197ba254676b12824d8120b48d65937697e0661a61302458a4740" },
|
||||
{ url = "https://pypi.fury.io/lance-format/-/ver_XclaZ/pylance-14.0.0b10-cp310-abi3-manylinux_2_28_aarch64.whl", hash = "sha256:36c80a801675bb9dcbd681fa99d0cdd5ca642dfb3ca08d71ac980278a2f7748b" },
|
||||
{ url = "https://pypi.fury.io/lance-format/-/ver_L0yh/pylance-14.0.0b10-cp310-abi3-manylinux_2_28_x86_64.whl", hash = "sha256:e50ad0b866faefb2fa3ae87328b93fa0d06b2bdd5bb5d0c1a6c74b907cc2fd93" },
|
||||
{ url = "https://pypi.fury.io/lance-format/-/ver_2gbNr8/pylance-14.0.0b10-cp310-abi3-win_amd64.whl", hash = "sha256:3d86451fe00f71510005bb700737fb1a4f9361554541cbf802f3c5072554d014" },
|
||||
{ url = "https://pypi.fury.io/lance-format/-/ver_1Ixasc/pylance-14.0.0b12-cp310-abi3-macosx_11_0_arm64.whl", hash = "sha256:84350a3c5c1eea3398f4c4fad7cfe85b831d831a5e43b5ca2a783dbdb0e11969" },
|
||||
{ url = "https://pypi.fury.io/lance-format/-/ver_sCEkG/pylance-14.0.0b12-cp310-abi3-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:76b386c182a6a8e7b86fbef28f84d1dd39e4626ac8681a0cf36f40a45dd7cb87" },
|
||||
{ url = "https://pypi.fury.io/lance-format/-/ver_1vdT1C/pylance-14.0.0b12-cp310-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:6aa548c7937fcdfa8f1943a2d59041a42f8b500199880dbb509c7195fa253381" },
|
||||
{ url = "https://pypi.fury.io/lance-format/-/ver_2fXu1V/pylance-14.0.0b12-cp310-abi3-manylinux_2_28_aarch64.whl", hash = "sha256:79f82d6a6c50f37aab338470e3733436e6a27fbb5972d29fcf453ece56d3b3c6" },
|
||||
{ url = "https://pypi.fury.io/lance-format/-/ver_24UavN/pylance-14.0.0b12-cp310-abi3-manylinux_2_28_x86_64.whl", hash = "sha256:de0305999257002785f60169e6926896584d66f1da050bb5afd1d48c6e3d9bb9" },
|
||||
{ url = "https://pypi.fury.io/lance-format/-/ver_XNynX/pylance-14.0.0b12-cp310-abi3-win_amd64.whl", hash = "sha256:4d0cdb689e898ce71e8e434d00a3b97639cea8fb97da21f1c921543be363268f" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
|
||||
Reference in New Issue
Block a user