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feat(search): expose search effort (#4520)
Lance supports `search_effort`, but LanceDB cannot pass it through its query APIs. Expose the parameter in Rust, Python sync/async and TypeScript, including remote requests and hybrid query conversion, using the [Lance v14.0.0-beta.13](https://github.com/lance-format/lance/releases/tag/v14.0.0-beta.13) dependency already on main. The default is `None` (Auto, internally 0.5). Any explicit effort, including 0.5, overrides fixed `nprobes` in either setter order; explicit minimum/maximum bounds remain effective. Invalid values are rejected in the Rust core. Hybrid conversion now retains the vector probe parameters and handles an inferred FTS column without panicking. Namespace queries with explicit effort execute locally because the namespace wire model cannot carry it. MemWAL LSM queries reject it explicitly because Lance's LSM scanner does not yet expose this option. Maturin is included in the dev extra so the documented build command works in the project environment. BREAKING CHANGE: The public Rust `VectorQueryRequest` struct now includes `search_effort: Option<f64>`. Downstream code that initializes every field explicitly must add `search_effort: None` or use `..Default::default()`. Exhaustive destructuring patterns must include the new field or `..`. Query-builder calls and struct literals that already use `..Default::default()` require no source changes. The new effort setters in Python and TypeScript are additive. This PR does not change the persisted file format. Using explicit effort with remote tables requires server support for `search_effort` and its precedence over `nprobes`; the client wire tests do not establish that a deployed server supports it. Server-side support is tracked in lancedb/sophon#8607. Validation on AWS after merging current main (Lance beta.13 / SDK beta.5): - 14 Rust regressions and the API doctest pass. - 60 parameter regressions, the full 47-test namespace suite, and three API doctests pass. The parameter selection includes some namespace cases. - 40 TypeScript query tests and 54 remote tests pass; build, lint and generated docs pass. - Rust Clippy, Python package lint/format, Pyright and lockfile checks pass. Repository-wide Ruff reports 49 lint errors and 11 formatting failures in unchanged `docs/web-tests` files; those files are byte-identical to main and outside this change. All 1,103 Git-tracked files match the AWS validation checkout. No benchmark scripts or result artifacts are included, and no performance improvement is claimed. Uses lance-format/lance#9798. Server-side support is in lancedb/sophon#8607.
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 +1721,27 @@ 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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>>> import lancedb
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>>> db = lancedb.connect("memory://")
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>>> table = db.create_table("effort_vectors", [{"vector": [0.1, 0.2]}])
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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 +1884,7 @@ 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 +2244,7 @@ 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 +2552,32 @@ 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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>>> import lancedb
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>>> db = lancedb.connect("memory://")
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>>> table = db.create_table(
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... "effort_hybrid", [{"vector": [0.1, 0.2], "text": "dog"}]
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... )
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>>> query = (
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... table.search(query_type="hybrid").vector([0.1, 0.2])
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... .text("dog").search_effort(0.75)
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... )
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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) -> LanceHybridQueryBuilder:
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"""Set the minimum number of probes to use.
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@@ -2802,6 +2856,8 @@ 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 +3786,32 @@ 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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>>> import asyncio
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>>> import lancedb
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>>> async def build_query():
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... db = await lancedb.connect_async("memory://")
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... table = await db.create_table(
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... "effort_async", [{"vector": [0.1, 0.2]}]
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... )
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... return table.vector_search([0.1, 0.2]).search_effort(0.75)
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>>> query = asyncio.run(build_query())
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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 +2265,127 @@ 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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"text": ["dog"] * 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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vector_plan = query.analyze_plan().split("FTS Search Plan:", 1)[0]
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values = re.findall(r"partitions_searched=(\d+)", vector_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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if metric == "l2" and effort == 0.5:
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table.create_index("text", config=FTS())
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hybrid = (
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table.search(query_type="hybrid")
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.vector(vector)
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.text("dog")
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.nprobes(1)
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.search_effort(0.5)
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.limit(8)
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)
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assert partitions(hybrid) == 4
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assert partitions(hybrid.search_effort(None)) == 1
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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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|
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|
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@pytest.mark.asyncio
|
||||
@pytest.mark.parametrize("hybrid", [False, True])
|
||||
@pytest.mark.parametrize(
|
||||
"effort", [-0.1, 1.1, float("nan"), float("inf"), -float("inf")]
|
||||
)
|
||||
async def test_search_effort_invalid_async(table_async, hybrid, effort):
|
||||
query = table_async.vector_search([0, 0])
|
||||
if hybrid:
|
||||
query = query.nearest_to_text("dog")
|
||||
with pytest.raises(ValueError, match="search_effort must be finite and in"):
|
||||
query.search_effort(effort)
|
||||
|
||||
+31
-3
@@ -71,7 +71,7 @@ impl<'a, 'py> FromPyObject<'a, 'py> for PyLanceDB<FtsQuery> {
|
||||
match class_name(&ob)?.as_str() {
|
||||
"MatchQuery" => {
|
||||
let query = ob.getattr("query")?.extract()?;
|
||||
let column = ob.getattr("column")?.extract()?;
|
||||
let column = ob.getattr("column")?.extract::<Option<String>>()?;
|
||||
let boost = ob.getattr("boost")?.extract()?;
|
||||
let fuzziness = ob.getattr("fuzziness")?.extract()?;
|
||||
let max_expansions = ob.getattr("max_expansions")?.extract()?;
|
||||
@@ -88,7 +88,7 @@ impl<'a, 'py> FromPyObject<'a, 'py> for PyLanceDB<FtsQuery> {
|
||||
|
||||
let mut query =
|
||||
MatchQuery::new(query)
|
||||
.with_column(Some(column))
|
||||
.with_column(column)
|
||||
.with_boost(boost)
|
||||
.with_fuzziness(fuzziness)
|
||||
.with_max_expansions(max_expansions)
|
||||
@@ -199,7 +199,7 @@ impl<'py> IntoPyObject<'py> for PyLanceDB<FtsQuery> {
|
||||
}
|
||||
namespace
|
||||
.getattr(intern!(py, "MatchQuery"))?
|
||||
.call((query.terms, query.column.unwrap()), Some(&kwargs))
|
||||
.call((query.terms, query.column), Some(&kwargs))
|
||||
}
|
||||
FtsQuery::Phrase(query) => {
|
||||
let kwargs = PyDict::new(py);
|
||||
@@ -339,6 +339,7 @@ pub struct PyQueryRequest {
|
||||
pub nprobes: Option<usize>,
|
||||
pub minimum_nprobes: Option<usize>,
|
||||
pub maximum_nprobes: Option<usize>,
|
||||
pub search_effort: Option<f64>,
|
||||
pub lower_bound: Option<f32>,
|
||||
pub upper_bound: Option<f32>,
|
||||
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