mirror of
https://github.com/lancedb/lancedb.git
synced 2026-10-13 07:12:27 +00:00
Merge main into gatekeeper/fix-4260-1
This commit is contained in:
@@ -46,6 +46,7 @@ from .materialized_view import (
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AsyncMaterializedView,
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MaterializedView,
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MaterializedViewDefinition,
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vector_dedup as vector_dedup,
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)
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from .view import ViewDescription as ViewDescription
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from .table import AsyncTable, Table
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@@ -247,6 +247,8 @@ class Connection(object):
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filter: Optional[str] = None,
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limit: Optional[int] = None,
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with_no_data: bool = False,
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*,
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vector_source_json: Optional[str] = None,
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) -> Table: ...
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async def create_materialized_view_async(
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self,
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@@ -256,6 +258,8 @@ class Connection(object):
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filter: Optional[str] = None,
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limit: Optional[int] = None,
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with_no_data: bool = False,
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*,
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vector_source_json: Optional[str] = None,
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) -> Job: ...
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async def list_materialized_views(self) -> List[str]: ...
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async def drop_materialized_view(
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+45
-10
@@ -56,6 +56,8 @@ from .sql import QueryDescription
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from .materialized_view import (
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AsyncMaterializedView,
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MaterializedView,
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MaterializedViewSource,
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VectorDedupSource,
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SelectArg,
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normalize_select,
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)
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@@ -555,7 +557,7 @@ class DBConnection(EnforceOverrides):
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def create_materialized_view(
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self,
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name: str,
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source: str,
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source: MaterializedViewSource,
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*,
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select: SelectArg = None,
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where: Optional[str] = None,
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@@ -569,17 +571,21 @@ class DBConnection(EnforceOverrides):
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table. The view is a normal table: it can be queried, indexed and
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searched, and it appears in ``table_names``.
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The source table must have stable row ids (create it with the
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Ordinary source tables must have stable row ids (create them with the
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``new_table_enable_stable_row_ids`` storage option): they keep the
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view's provenance valid across source compactions, and cannot be
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enabled after a table exists.
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enabled after a table exists. Native dedup sources also support physical
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row IDs interpreted against their pinned snapshot.
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Parameters
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----------
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name: str
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The name of the view.
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source: str
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The name of the source table, in this database.
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source: str or VectorDedupSource
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The name of the source table, or an indexed dedup source declared
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with [vector_dedup][lancedb.vector_dedup]. Dedup sources capture a
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fixed snapshot and preserve the source table. They accept no
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additional select, where or limit options.
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select: list or dict, optional
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The view's columns: column names, ``(alias, SQL expression)``
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pairs, or a dict of the same. Omitting it selects every source
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@@ -602,7 +608,7 @@ class DBConnection(EnforceOverrides):
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def create_materialized_view_async(
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self,
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name: str,
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source: str,
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source: MaterializedViewSource,
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*,
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select: SelectArg = None,
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where: Optional[str] = None,
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@@ -1558,7 +1564,7 @@ class LanceDBConnection(DBConnection):
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def create_materialized_view(
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self,
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name: str,
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source: str,
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source: MaterializedViewSource,
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*,
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select: SelectArg = None,
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where: Optional[str] = None,
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@@ -1603,7 +1609,7 @@ class LanceDBConnection(DBConnection):
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def create_materialized_view_async(
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self,
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name: str,
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source: str,
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source: MaterializedViewSource,
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*,
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select: SelectArg = None,
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where: Optional[str] = None,
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@@ -2498,10 +2504,31 @@ class AsyncConnection(object):
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await tbl.checkout(version)
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return tbl
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async def _materialized_view_source(
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self,
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source: MaterializedViewSource,
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select: SelectArg,
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where: Optional[str],
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limit: Optional[int],
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) -> Tuple[str, Dict[str, str]]:
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if not isinstance(source, VectorDedupSource):
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return source, {}
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if select is not None or where is not None or limit is not None:
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raise ValueError(
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"vector_dedup cannot be combined with select, where or limit"
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)
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version = source.dataset_version
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if version is None:
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table = await self.open_table(source.source)
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version = await table.version()
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return source.source, {
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"vector_source_json": source._native_source_json(version)
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}
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async def create_materialized_view(
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self,
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name: str,
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source: str,
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source: MaterializedViewSource,
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*,
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select: SelectArg = None,
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where: Optional[str] = None,
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@@ -2512,6 +2539,9 @@ class AsyncConnection(object):
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See
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[DBConnection.create_materialized_view][lancedb.DBConnection.create_materialized_view].
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"""
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source, native_options = await self._materialized_view_source(
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source, select, where, limit
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)
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inner = await self._inner.create_materialized_view(
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name,
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source,
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@@ -2519,13 +2549,14 @@ class AsyncConnection(object):
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filter=where,
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limit=limit,
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with_no_data=with_no_data,
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**native_options,
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)
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return AsyncMaterializedView(AsyncTable(inner))
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async def create_materialized_view_async(
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self,
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name: str,
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source: str,
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source: MaterializedViewSource,
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*,
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select: SelectArg = None,
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where: Optional[str] = None,
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@@ -2536,6 +2567,9 @@ class AsyncConnection(object):
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Wait for the returned job before opening or querying the view.
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"""
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source, native_options = await self._materialized_view_source(
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source, select, where, limit
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)
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inner = await self._inner.create_materialized_view_async(
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name,
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source,
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@@ -2543,6 +2577,7 @@ class AsyncConnection(object):
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filter=where,
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limit=limit,
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with_no_data=with_no_data,
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**native_options,
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)
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return AsyncJob(inner)
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@@ -7,6 +7,7 @@ maintained by refresh. See ``DBConnection.create_materialized_view``."""
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from __future__ import annotations
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import json
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import math
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from dataclasses import dataclass
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from typing import TYPE_CHECKING, Dict, List, Optional, Sequence, Tuple, Union
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@@ -20,6 +21,7 @@ if TYPE_CHECKING:
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from .table import AsyncTable, LanceTable
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DEFINITION_META_KEY = b"mv.definition"
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_MAX_FLOAT32 = float.fromhex("0x1.fffffep+127")
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SelectArg = Union[
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str,
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@@ -29,9 +31,104 @@ SelectArg = Union[
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]
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DEFINITION_FORMAT = 2
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@dataclass(frozen=True)
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class VectorDedupSource:
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"""A declarative source of retained original rows from an indexed snapshot.
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Construct with [vector_dedup][lancedb.vector_dedup] and pass it as the
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``source`` of ``create_materialized_view`` or
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``create_materialized_view_async``. An omitted ``dataset_version`` is
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captured once when creation is submitted, not when this object is made.
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Refreshes keep that captured version. The source table is never modified.
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"""
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source: str
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column: str
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distance_threshold: float
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dataset_version: Optional[int] = None
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def __post_init__(self):
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for name in ("source", "column"):
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value = getattr(self, name)
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if not isinstance(value, str) or not value:
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raise ValueError(f"{name} must be a non-empty string")
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if self.dataset_version is not None and (
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type(self.dataset_version) is not int
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or not 0 < self.dataset_version < 2**64
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):
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raise ValueError("dataset_version must be a positive uint64 integer")
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value = self.distance_threshold
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if isinstance(value, bool) or not isinstance(value, (int, float)):
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raise TypeError("distance_threshold must be a number")
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try:
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value = float(value)
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except OverflowError as exc:
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raise ValueError("distance_threshold must fit a finite float32") from exc
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if not math.isfinite(value) or abs(value) > _MAX_FLOAT32:
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raise ValueError("distance_threshold must fit a finite float32")
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object.__setattr__(self, "distance_threshold", value)
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def _native_source_json(self, version: int) -> str:
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return json.dumps(
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{
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"kind": "Dedup",
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"dataset_version": version,
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"column": self.column,
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"distance_threshold": repr(self.distance_threshold),
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},
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allow_nan=False,
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)
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def vector_dedup(
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source: str,
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*,
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column: str,
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distance_threshold: float,
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dataset_version: Optional[int] = None,
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) -> VectorDedupSource:
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"""Declare an indexed dedup source for a materialized view.
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The default policy keeps the smallest unassigned snapshot row ID and removes
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only its direct qualifying neighbors: an A-B-C chain without an A-C edge
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retains A and C. Rows with no pair, including null vectors, survive.
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Only same-segment/same-IVF-partition pairs are considered; quantized distances
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use reconstructed index vectors. The index must cover the selected snapshot.
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``source`` is an exact table name in the connection's root namespace, just
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as for ``create_materialized_view``. ``dataset_version=None`` captures the
|
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source's version when creation is submitted. Pass an explicit positive
|
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version to reproduce a previous snapshot. The result contains original
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source columns and rows. Do not combine this source with ``select``,
|
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``where`` or ``limit``; query the resulting table for further transforms.
|
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|
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Creation follows the usual MV contract: by default it waits for the result.
|
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Use ``with_no_data=True`` to declare only, or
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``create_materialized_view_async`` to get a job handle. On Cloud/Enterprise,
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execution uses server-side MV registry jobs; local connections run in process.
|
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|
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Examples
|
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--------
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Given a connected ``db`` and an indexed ``images.phash`` column::
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from lancedb import vector_dedup
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|
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view = db.create_materialized_view(
|
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"images_clean",
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vector_dedup("images", column="phash", distance_threshold=4),
|
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)
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cleaned = view.table
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"""
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return VectorDedupSource(source, column, distance_threshold, dataset_version)
|
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|
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|
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MaterializedViewSource = Union[str, VectorDedupSource]
|
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|
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DEFINITION_FORMAT = 4
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"""The newest stored layout this version reads: ``{"format": N, "query": "<SQL>"}``,
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format 2 being a query with ``GROUP BY``. A ``kind`` key beside it is for
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format 2 being a query with ``GROUP BY``, format 3 native pairs and format 4
|
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native dedup results. A ``kind`` key beside it is for
|
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readers older than the format number."""
|
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|
||||
|
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@@ -68,6 +68,7 @@ from lance_namespace import (
|
||||
from lancedb.materialized_view import (
|
||||
AsyncMaterializedView,
|
||||
MaterializedView,
|
||||
MaterializedViewSource,
|
||||
SelectArg,
|
||||
)
|
||||
from lancedb.table import AsyncTable, LanceTable, Table
|
||||
@@ -632,7 +633,7 @@ class LanceNamespaceDBConnection(DBConnection):
|
||||
def create_materialized_view(
|
||||
self,
|
||||
name: str,
|
||||
source: str,
|
||||
source: MaterializedViewSource,
|
||||
*,
|
||||
select: "SelectArg" = None,
|
||||
where: Optional[str] = None,
|
||||
@@ -662,7 +663,7 @@ class LanceNamespaceDBConnection(DBConnection):
|
||||
def create_materialized_view_async(
|
||||
self,
|
||||
name: str,
|
||||
source: str,
|
||||
source: MaterializedViewSource,
|
||||
*,
|
||||
select: "SelectArg" = None,
|
||||
where: Optional[str] = None,
|
||||
@@ -1242,7 +1243,7 @@ class AsyncLanceNamespaceDBConnection:
|
||||
async def create_materialized_view(
|
||||
self,
|
||||
name: str,
|
||||
source: str,
|
||||
source: MaterializedViewSource,
|
||||
*,
|
||||
select: "SelectArg" = None,
|
||||
where: Optional[str] = None,
|
||||
@@ -1265,7 +1266,7 @@ class AsyncLanceNamespaceDBConnection:
|
||||
async def create_materialized_view_async(
|
||||
self,
|
||||
name: str,
|
||||
source: str,
|
||||
source: MaterializedViewSource,
|
||||
*,
|
||||
select: "SelectArg" = None,
|
||||
where: Optional[str] = None,
|
||||
|
||||
@@ -39,7 +39,7 @@ from ..functions import FunctionVersion, UdfDefinition
|
||||
from ..job import AsyncJob, Job
|
||||
from ..sql import Query as SqlQuery
|
||||
from ..sql import QueryDescription
|
||||
from ..materialized_view import MaterializedView, SelectArg
|
||||
from ..materialized_view import MaterializedView, MaterializedViewSource, SelectArg
|
||||
from ..secrets import EnvVarSecret, SecretInfo
|
||||
from ..view import ViewDescription
|
||||
|
||||
@@ -713,7 +713,7 @@ class RemoteDBConnection(DBConnection):
|
||||
def create_materialized_view(
|
||||
self,
|
||||
name: str,
|
||||
source: str,
|
||||
source: MaterializedViewSource,
|
||||
*,
|
||||
select: SelectArg = None,
|
||||
where: Optional[str] = None,
|
||||
@@ -745,7 +745,7 @@ class RemoteDBConnection(DBConnection):
|
||||
def create_materialized_view_async(
|
||||
self,
|
||||
name: str,
|
||||
source: str,
|
||||
source: MaterializedViewSource,
|
||||
*,
|
||||
select: SelectArg = None,
|
||||
where: Optional[str] = None,
|
||||
|
||||
@@ -31,6 +31,12 @@ class CrossEncoderReranker(Reranker):
|
||||
trust_remote_code : bool, default True
|
||||
If True, will trust the remote code to be safe. If False, will not trust
|
||||
the remote code and will not run it
|
||||
batch_size : int, default 32
|
||||
The number of query-passage pairs scored per forward pass.
|
||||
**kwargs
|
||||
Additional keyword arguments passed to
|
||||
``sentence_transformers.CrossEncoder``, for example ``max_length`` or
|
||||
``model_kwargs={"torch_dtype": "bfloat16"}``.
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
@@ -40,6 +46,8 @@ class CrossEncoderReranker(Reranker):
|
||||
device: Union[str, None] = None,
|
||||
return_score="relevance",
|
||||
trust_remote_code: bool = True,
|
||||
batch_size: int = 32,
|
||||
**kwargs,
|
||||
):
|
||||
super().__init__(return_score)
|
||||
torch = attempt_import_or_raise("torch")
|
||||
@@ -47,6 +55,8 @@ class CrossEncoderReranker(Reranker):
|
||||
self.column = column
|
||||
self.device = device
|
||||
self.trust_remote_code = trust_remote_code
|
||||
self.batch_size = batch_size
|
||||
self.kwargs = kwargs
|
||||
if self.device is None:
|
||||
self.device = "cuda" if torch.cuda.is_available() else "cpu"
|
||||
|
||||
@@ -61,6 +71,7 @@ class CrossEncoderReranker(Reranker):
|
||||
self.model_name,
|
||||
device=self.device,
|
||||
trust_remote_code=self.trust_remote_code,
|
||||
**self.kwargs,
|
||||
)
|
||||
|
||||
return cross_encoder
|
||||
@@ -71,7 +82,7 @@ class CrossEncoderReranker(Reranker):
|
||||
return result_set
|
||||
passages = result_set[self.column].to_pylist()
|
||||
cross_inp = [[query, passage] for passage in passages]
|
||||
cross_scores = self.model.predict(cross_inp)
|
||||
cross_scores = self.model.predict(cross_inp, batch_size=self.batch_size)
|
||||
result_set = result_set.append_column(
|
||||
"_relevance_score", pa.array(cross_scores, type=pa.float32())
|
||||
)
|
||||
|
||||
@@ -520,10 +520,166 @@ def test_stored_queries_and_legacy_layouts_are_read():
|
||||
"SELECT * FROM people"
|
||||
)
|
||||
|
||||
for fmt, function in [(3, "vector_duplicate_pairs"), (4, "vector_dedup")]:
|
||||
native = f"SELECT * FROM {function}('images', 3, 'phash', 4)"
|
||||
assert read({"format": fmt, "query": native}).query == native
|
||||
|
||||
# A newer writer's layout is reported, never guessed at.
|
||||
for newer in (
|
||||
{"format": 3, "query": query},
|
||||
{"format": 5, "query": query},
|
||||
{"kind": "select_v3", "source_table": "people"},
|
||||
):
|
||||
with pytest.raises(NotImplementedError, match="cannot refresh"):
|
||||
read(newer)
|
||||
|
||||
|
||||
def indexed_dedup_source(db, name="images"):
|
||||
import pyarrow as pa
|
||||
from lancedb.index import IvfFlat
|
||||
|
||||
vectors = pa.FixedSizeListArray.from_arrays(
|
||||
pa.array(
|
||||
[v for i in range(256) for v in (float(i if i < 3 else i * 10), 0.0)],
|
||||
type=pa.float32(),
|
||||
),
|
||||
2,
|
||||
)
|
||||
table = db.create_table(name, pa.table({"id": range(256), "vector": vectors}))
|
||||
table.create_index("vector", config=IvfFlat(num_partitions=1))
|
||||
return table
|
||||
|
||||
|
||||
@pytest.mark.parametrize("stable", [False, True])
|
||||
def test_vector_dedup_source_captures_snapshot_and_preserves_source(tmp_path, stable):
|
||||
db = lancedb.connect(
|
||||
tmp_path,
|
||||
storage_options={"new_table_enable_stable_row_ids": str(stable).lower()},
|
||||
)
|
||||
source = indexed_dedup_source(db, "images.with.dot")
|
||||
version = source.version
|
||||
expression = lancedb.vector_dedup(
|
||||
source.name, column="vector", distance_threshold=1
|
||||
)
|
||||
view = db.create_materialized_view("clean", expression)
|
||||
assert "vector_dedup(" in view.definition.query
|
||||
assert f", {version}, 'vector', 1.0)" in view.definition.query
|
||||
assert view.table.count_rows() == 255
|
||||
assert [r["id"] for r in view.table.search().where("id < 3").to_list()] == [0, 2]
|
||||
assert source.version == version
|
||||
assert source.count_rows() == 256
|
||||
|
||||
source.delete("id = 0")
|
||||
view.refresh_async().wait()
|
||||
assert view.table.search().where("id = 0").to_list() == [
|
||||
{"id": 0, "vector": [0.0, 0.0]}
|
||||
]
|
||||
assert expression.dataset_version is None # reusable immutable declaration
|
||||
|
||||
|
||||
def test_vector_dedup_create_job_and_explicit_snapshot(tmp_path):
|
||||
db = lancedb.connect(tmp_path)
|
||||
source = indexed_dedup_source(db)
|
||||
version = source.version
|
||||
source.delete("id = 0")
|
||||
job = db.create_materialized_view_async(
|
||||
"clean",
|
||||
lancedb.vector_dedup(
|
||||
"images", column="vector", distance_threshold=1, dataset_version=version
|
||||
),
|
||||
)
|
||||
assert job.wait() is None
|
||||
view = db.open_materialized_view("clean")
|
||||
assert view.table.count_rows() == 255
|
||||
assert len(view.table.search().where("id = 0").to_list()) == 1
|
||||
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_async_vector_dedup_declaration_and_creation(tmp_path):
|
||||
sync_db = lancedb.connect(tmp_path)
|
||||
source = indexed_dedup_source(sync_db)
|
||||
db = await lancedb.connect_async(tmp_path)
|
||||
expression = lancedb.vector_dedup("images", column="vector", distance_threshold=1)
|
||||
view = await db.create_materialized_view("clean", expression, with_no_data=True)
|
||||
assert await view.table.count_rows() == 0
|
||||
# The snapshot is captured at declaration even if refresh comes later.
|
||||
source.delete("id = 0")
|
||||
job = await view.refresh_async()
|
||||
await job.wait()
|
||||
assert await view.table.count_rows() == 255
|
||||
another = await db.create_materialized_view_async(
|
||||
"later", expression, with_no_data=True
|
||||
)
|
||||
await another.wait()
|
||||
later = await db.open_materialized_view("later")
|
||||
assert (await later.definition()).query != (await view.definition()).query
|
||||
assert await later.table.count_rows() == 0
|
||||
|
||||
|
||||
@pytest.mark.parametrize(
|
||||
"options", [{"select": ["id"]}, {"select": []}, {"where": "id > 0"}, {"limit": 1}]
|
||||
)
|
||||
def test_vector_dedup_rejects_extra_clauses_before_opening_source(tmp_path, options):
|
||||
db = lancedb.connect(tmp_path)
|
||||
expression = lancedb.vector_dedup("missing", column="vector", distance_threshold=1)
|
||||
with pytest.raises(ValueError, match="cannot be combined"):
|
||||
db.create_materialized_view("bad", expression, **options)
|
||||
assert db.list_tables().tables == []
|
||||
|
||||
|
||||
@pytest.mark.parametrize("version", [0, -1, True, 1.5, "2", 2**64])
|
||||
def test_vector_dedup_rejects_invalid_snapshot(version):
|
||||
with pytest.raises(ValueError, match="dataset_version"):
|
||||
lancedb.vector_dedup(
|
||||
"images", column="vector", distance_threshold=1, dataset_version=version
|
||||
)
|
||||
|
||||
|
||||
@pytest.mark.parametrize(
|
||||
"threshold", [float("nan"), float("inf"), -float("inf"), 1e100, 10**1000, True, "4"]
|
||||
)
|
||||
def test_vector_dedup_rejects_invalid_threshold(threshold):
|
||||
with pytest.raises((ValueError, TypeError), match="distance_threshold"):
|
||||
lancedb.vector_dedup("images", column="vector", distance_threshold=threshold)
|
||||
|
||||
|
||||
def test_vector_dedup_descriptor_is_frozen():
|
||||
from dataclasses import FrozenInstanceError
|
||||
|
||||
expression = lancedb.vector_dedup("images", column="vector", distance_threshold=1)
|
||||
with pytest.raises(FrozenInstanceError):
|
||||
expression.column = "other"
|
||||
|
||||
|
||||
@pytest.mark.parametrize("with_no_data", [False, True])
|
||||
def test_remote_vector_dedup_uses_mv_endpoint_without_sql_client(with_no_data):
|
||||
with mock_remote_materialized_view_create() as (host, requests):
|
||||
db = lancedb.connect(
|
||||
"db://dev",
|
||||
api_key="fake",
|
||||
host_override=host,
|
||||
sql_host_override="grpc://127.0.0.1:1",
|
||||
client_config={"retry_config": {"retries": 0}},
|
||||
)
|
||||
job = db.create_materialized_view_async(
|
||||
"clean",
|
||||
lancedb.vector_dedup(
|
||||
"images.with'dot",
|
||||
column="vector'field",
|
||||
distance_threshold=4,
|
||||
dataset_version=7,
|
||||
),
|
||||
with_no_data=with_no_data,
|
||||
)
|
||||
assert job.id == "mv-create-123"
|
||||
assert requests == [
|
||||
(
|
||||
"/v1/materialized_view/clean/create",
|
||||
{
|
||||
"query": (
|
||||
"SELECT * FROM vector_dedup('`images.with''dot`', "
|
||||
"7, 'vector''field', 4.0)"
|
||||
),
|
||||
"with_no_data": with_no_data,
|
||||
},
|
||||
)
|
||||
]
|
||||
|
||||
@@ -481,6 +481,14 @@ def test_cross_encoder_reranker(tmp_path):
|
||||
_run_test_reranker(reranker, table, "single player experience", None, schema)
|
||||
|
||||
|
||||
def test_cross_encoder_reranker_kwargs(tmp_path):
|
||||
pytest.importorskip("sentence_transformers")
|
||||
reranker = CrossEncoderReranker(batch_size=4, max_length=64)
|
||||
assert reranker.model.max_length == 64
|
||||
table, schema = get_test_table(tmp_path)
|
||||
_run_test_reranker(reranker, table, "single player experience", None, schema)
|
||||
|
||||
|
||||
def test_colbert_reranker(tmp_path):
|
||||
pytest.importorskip("rerankers")
|
||||
reranker = ColbertReranker()
|
||||
|
||||
@@ -407,7 +407,8 @@ impl Connection {
|
||||
})
|
||||
}
|
||||
|
||||
#[pyo3(signature = (name, source, projections=None, filter=None, limit=None, with_no_data=false))]
|
||||
#[allow(clippy::too_many_arguments)]
|
||||
#[pyo3(signature = (name, source, projections=None, filter=None, limit=None, with_no_data=false, *, vector_source_json=None))]
|
||||
pub fn create_materialized_view(
|
||||
self_: PyRef<'_, Self>,
|
||||
name: String,
|
||||
@@ -416,10 +417,17 @@ impl Connection {
|
||||
filter: Option<String>,
|
||||
limit: Option<u64>,
|
||||
with_no_data: bool,
|
||||
vector_source_json: Option<String>,
|
||||
) -> PyResult<Bound<'_, PyAny>> {
|
||||
let inner = self_.get_inner()?.clone();
|
||||
future_into_py(self_.py(), async move {
|
||||
let mut builder = inner.create_materialized_view(name, source);
|
||||
if let Some(source) = vector_source_json {
|
||||
builder = builder.vector_source(
|
||||
serde_json::from_str(&source)
|
||||
.map_err(|e| PyValueError::new_err(e.to_string()))?,
|
||||
);
|
||||
}
|
||||
if let Some(projections) = projections {
|
||||
builder = builder.select(projections);
|
||||
}
|
||||
@@ -435,7 +443,8 @@ impl Connection {
|
||||
})
|
||||
}
|
||||
|
||||
#[pyo3(signature = (name, source, projections=None, filter=None, limit=None, with_no_data=false))]
|
||||
#[allow(clippy::too_many_arguments)]
|
||||
#[pyo3(signature = (name, source, projections=None, filter=None, limit=None, with_no_data=false, *, vector_source_json=None))]
|
||||
pub fn create_materialized_view_async(
|
||||
self_: PyRef<'_, Self>,
|
||||
name: String,
|
||||
@@ -444,10 +453,17 @@ impl Connection {
|
||||
filter: Option<String>,
|
||||
limit: Option<u64>,
|
||||
with_no_data: bool,
|
||||
vector_source_json: Option<String>,
|
||||
) -> PyResult<Bound<'_, PyAny>> {
|
||||
let inner = self_.get_inner()?.clone();
|
||||
future_into_py(self_.py(), async move {
|
||||
let mut builder = inner.create_materialized_view(name, source);
|
||||
if let Some(source) = vector_source_json {
|
||||
builder = builder.vector_source(
|
||||
serde_json::from_str(&source)
|
||||
.map_err(|e| PyValueError::new_err(e.to_string()))?,
|
||||
);
|
||||
}
|
||||
if let Some(projections) = projections {
|
||||
builder = builder.select(projections);
|
||||
}
|
||||
|
||||
Vendored
+1
-1
@@ -1,6 +1,6 @@
|
||||
[package]
|
||||
name = "lance-arrow"
|
||||
version = "13.0.0-beta.14"
|
||||
version = "13.0.0-beta.15"
|
||||
edition = "2024"
|
||||
authors = ["Lance Devs <dev@lance.org>"]
|
||||
license = "Apache-2.0"
|
||||
|
||||
+2
-2
@@ -1,5 +1,5 @@
|
||||
This directory contains `rust/lance-arrow` from Lance tag `v13.0.0-beta.14`
|
||||
(commit `b80ac1d9f3c24b47bd8e297d03053ea35d216a9f`), licensed under Apache-2.0.
|
||||
This directory contains `rust/lance-arrow` from Lance tag `v13.0.0-beta.15`
|
||||
(commit `db53dcf367088b32ae3a7461407d43593759ae3e`), licensed under Apache-2.0.
|
||||
|
||||
LanceDB carries a local fix in `src/lib.rs` for sliced child validity bitmaps
|
||||
until a Lance release includes the fix. The patch is selected in the root
|
||||
|
||||
Reference in New Issue
Block a user