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3 Commits
| Author | SHA1 | Date | |
|---|---|---|---|
| 238028767a | |||
| 7357d63e87 | |||
| 624a75edf7 |
@@ -541,7 +541,17 @@ where(predicate): this
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A filter statement to be applied to this query.
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The filter should be supplied as an SQL query string. For example:
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Filters are applied before full-text and vector searches by default; no
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separate prefilter call is needed. For vector searches only, use
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[VectorQuery#postfilter](VectorQuery.md#postfilter) to apply the filter after the search.
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The filter should be supplied as an SQL query string.
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Filtering performance can often be improved by creating a scalar index
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on the filter column(s).
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Calling this multiple times combines the filters with a logical AND rather
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than replacing the previous filter.
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#### Parameters
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@@ -551,18 +561,20 @@ The filter should be supplied as an SQL query string. For example:
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`this`
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#### Example
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#### Examples
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```ts
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x > 10
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y > 0 AND y < 100
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x > 5 OR y = 'test'
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const results = await table
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.search("puppy", "fts")
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.where("meta = 'foo'")
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.limit(10)
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.toArray();
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```
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Filtering performance can often be improved by creating a scalar index
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on the filter column(s).
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Calling this multiple times combines the filters with a logical AND rather
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than replacing the previous filter.
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```ts
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query.where("x > 10");
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query.where("y > 0 AND y < 100");
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query.where("x > 5 OR y = 'test'");
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```
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#### Inherited from
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@@ -560,6 +560,9 @@ postfilter(): VectorQuery
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If this is called then filtering will happen after the vector search instead of
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before.
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This method is only available for vector search queries. Full-text search
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filters are always applied before the search.
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By default filtering will be performed before the vector search. This is how
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filtering is typically understood to work. This prefilter step does add some
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additional latency. Creating a scalar index on the filter column(s) can
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@@ -790,7 +793,17 @@ where(predicate): this
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A filter statement to be applied to this query.
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The filter should be supplied as an SQL query string. For example:
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Filters are applied before full-text and vector searches by default; no
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separate prefilter call is needed. For vector searches only, use
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[VectorQuery#postfilter](VectorQuery.md#postfilter) to apply the filter after the search.
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The filter should be supplied as an SQL query string.
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Filtering performance can often be improved by creating a scalar index
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on the filter column(s).
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Calling this multiple times combines the filters with a logical AND rather
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than replacing the previous filter.
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#### Parameters
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@@ -800,18 +813,20 @@ The filter should be supplied as an SQL query string. For example:
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`this`
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#### Example
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#### Examples
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```ts
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x > 10
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y > 0 AND y < 100
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x > 5 OR y = 'test'
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const results = await table
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.search("puppy", "fts")
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.where("meta = 'foo'")
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.limit(10)
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.toArray();
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```
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Filtering performance can often be improved by creating a scalar index
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on the filter column(s).
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Calling this multiple times combines the filters with a logical AND rather
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than replacing the previous filter.
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```ts
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query.where("x > 10");
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query.where("y > 0 AND y < 100");
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query.where("x > 5 OR y = 'test'");
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```
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#### Inherited from
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@@ -47,5 +47,22 @@ test("filtering examples", async () => {
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.limit(5)
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.toArray();
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// --8<-- [end:orderby_search]
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const ftsTable = await db.createTable("myFts", [
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{ text: "Frodo was a happy puppy", category: "pet" },
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{ text: "A puppy training guide", category: "guide" },
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{ text: "There are several kittens playing", category: "pet" },
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]);
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await ftsTable.createIndex("text", { config: lancedb.Index.fts() });
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// --8<-- [start:fts_prefilter]
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const ftsResults = await ftsTable
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.search("puppy", "fts")
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.where("category = 'pet'")
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.limit(10)
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.toArray();
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// --8<-- [end:fts_prefilter]
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expect(ftsResults).toHaveLength(1);
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expect(ftsResults[0].category).toBe("pet");
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});
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});
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+24
-5
@@ -363,17 +363,33 @@ export class StandardQueryBase<
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/**
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* A filter statement to be applied to this query.
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*
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* The filter should be supplied as an SQL query string. For example:
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* @example
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* x > 10
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* y > 0 AND y < 100
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* x > 5 OR y = 'test'
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* Filters are applied before full-text and vector searches by default; no
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* separate prefilter call is needed. For vector searches only, use
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* {@link VectorQuery#postfilter} to apply the filter after the search.
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*
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* The filter should be supplied as an SQL query string.
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*
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* Filtering performance can often be improved by creating a scalar index
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* on the filter column(s).
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*
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* Calling this multiple times combines the filters with a logical AND rather
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* than replacing the previous filter.
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*
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* @example Filter a full-text search
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* ```ts
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* const results = await table
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* .search("puppy", "fts")
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* .where("meta = 'foo'")
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* .limit(10)
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* .toArray();
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* ```
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*
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* @example SQL filter expressions
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* ```ts
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* query.where("x > 10");
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* query.where("y > 0 AND y < 100");
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* query.where("x > 5 OR y = 'test'");
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* ```
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*/
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where(predicate: string): this {
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this.doCall((inner: NativeQueryType) => inner.onlyIf(predicate));
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@@ -669,6 +685,9 @@ export class VectorQuery extends StandardQueryBase<NativeVectorQuery> {
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* If this is called then filtering will happen after the vector search instead of
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* before.
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*
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* This method is only available for vector search queries. Full-text search
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* filters are always applied before the search.
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*
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* By default filtering will be performed before the vector search. This is how
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* filtering is typically understood to work. This prefilter step does add some
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* additional latency. Creating a scalar index on the filter column(s) can
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@@ -707,6 +707,9 @@ class LanceDBConnection(DBConnection):
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self._namespace_client_properties = namespace_client_properties
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if _inner is not None:
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self._conn = _inner
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# Native-derived wrappers resolve this in their async reconstruction
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# path so construction never synchronously re-enters LOOP.
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self._read_consistency_interval = read_consistency_interval
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self._cached_namespace_client = None
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return
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@@ -756,11 +759,14 @@ class LanceDBConnection(DBConnection):
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# storage_options. Also, this class really shouldn't be holding any state
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# beyond _conn.
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self._conn = AsyncConnection(LOOP.run(do_connect()))
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# Keep property access synchronous so debugger introspection cannot wait on
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# the background loop while that thread is suspended at a breakpoint.
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self._read_consistency_interval = read_consistency_interval
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self._cached_namespace_client: Optional[LanceNamespace] = None
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@property
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def read_consistency_interval(self) -> Optional[timedelta]:
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return LOOP.run(self._conn.get_read_consistency_interval())
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return self._read_consistency_interval
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@property
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def session(self) -> Optional[Session]:
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@@ -771,8 +777,16 @@ class LanceDBConnection(DBConnection):
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return self._conn.uri
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@classmethod
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def from_inner(cls, inner: LanceDbConnection):
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return cls(None, _inner=inner)
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def from_inner(
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cls,
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inner: LanceDbConnection,
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read_consistency_interval: Optional[timedelta],
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):
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return cls(
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None,
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read_consistency_interval=read_consistency_interval,
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_inner=inner,
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)
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def __repr__(self) -> str:
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return f"{self.__class__.__name__}(uri={self._conn.uri!r})"
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@@ -226,7 +226,7 @@ class PermutationBuilder:
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async def do_execute():
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inner_tbl = await self._async.execute()
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return LanceTable.from_inner(inner_tbl)
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return await LanceTable.from_inner(inner_tbl)
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return LOOP.run(do_execute())
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@@ -2182,11 +2182,15 @@ class LanceTable(Table):
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return self.name
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@classmethod
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def from_inner(cls, tbl: LanceDBTable):
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from .db import LanceDBConnection
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async def from_inner(cls, tbl: LanceDBTable):
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from .db import AsyncConnection, LanceDBConnection
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async_tbl = AsyncTable(tbl)
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conn = LanceDBConnection.from_inner(tbl.database())
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inner_conn = tbl.database()
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read_consistency_interval = await AsyncConnection(
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inner_conn
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).get_read_consistency_interval()
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conn = LanceDBConnection.from_inner(inner_conn, read_consistency_interval)
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return cls(
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conn,
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async_tbl.name,
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@@ -77,6 +77,23 @@ def test_sync_repr_does_not_use_background_loop(tmp_path, monkeypatch):
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assert repr(table) == f"LanceTable(name='test', _conn={db!r})"
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def test_read_consistency_interval_does_not_use_background_loop(tmp_path, monkeypatch):
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from lancedb.background_loop import LOOP
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from lancedb.db import LanceDBConnection
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consistency_interval = timedelta(seconds=5)
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db = lancedb.connect(tmp_path, read_consistency_interval=consistency_interval)
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db_from_inner = LanceDBConnection.from_inner(db._inner, consistency_interval)
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def fail_run(*args, **kwargs):
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raise AssertionError("properties should not use the Python background loop")
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monkeypatch.setattr(LOOP, "run", fail_run)
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assert db.read_consistency_interval == consistency_interval
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assert db_from_inner.read_consistency_interval == consistency_interval
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def test_ingest_pd(tmp_path):
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db = lancedb.connect(tmp_path)
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@@ -6,6 +6,7 @@ import math
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import pytest
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from lancedb import DBConnection, Table, connect
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from lancedb.background_loop import LOOP
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from lancedb.permutation import Permutation, Permutations, permutation_builder
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@@ -31,6 +32,25 @@ def test_split_random_ratios(mem_db):
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assert 65 <= split_1_count <= 75 # ~70% ± tolerance
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def test_execute_does_not_reenter_background_loop(tmp_path, monkeypatch):
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import threading
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db = connect(tmp_path)
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tbl = db.create_table("test_table", pa.table({"x": range(10)}))
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original_run = LOOP.run
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def fail_on_reentry(future):
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assert threading.current_thread() is not LOOP.thread
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return original_run(future)
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monkeypatch.setattr(LOOP, "run", fail_on_reentry)
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permutation_tbl = permutation_builder(tbl).execute()
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assert permutation_tbl.count_rows() == 10
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assert permutation_tbl._conn.read_consistency_interval is None
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def test_split_random_counts(mem_db):
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"""Test random splitting with absolute counts."""
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tbl = mem_db.create_table(
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@@ -6,6 +6,7 @@ import os
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import sys
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import threading
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import warnings
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from concurrent.futures import ThreadPoolExecutor
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from datetime import date, datetime, timedelta
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from time import sleep
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from typing import List
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@@ -2124,6 +2125,27 @@ def test_delete(mem_db: DBConnection):
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assert table.to_arrow()["id"].to_pylist() == [1]
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def test_concurrent_deletes_are_thread_safe(mem_db: DBConnection):
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num_workers = 8
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table = mem_db.create_table(
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"my_table", data=[{"id": row_id} for row_id in range(num_workers)]
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)
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barrier = threading.Barrier(num_workers)
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def delete(row_id: int):
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barrier.wait()
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return table.delete(f"id = {row_id}")
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with ThreadPoolExecutor(max_workers=num_workers) as pool:
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results = list(pool.map(delete, range(num_workers)))
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assert all(result.num_deleted_rows == 1 for result in results)
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assert sorted(result.version for result in results) == list(
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range(2, num_workers + 2)
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)
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assert table.count_rows() == 0
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def test_delete_expr(mem_db: DBConnection):
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table = mem_db.create_table(
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"my_table",
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@@ -745,6 +745,9 @@ impl Table {
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#[allow(private_interfaces)]
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pub fn delete(self_: PyRef<'_, Self>, condition: PredicateArg) -> PyResult<Bound<'_, PyAny>> {
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// Do not hold the Python borrow across the await. The cloned Rust table
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// handle is thread-safe and allows deletes on the same Python table to
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// run concurrently without PyO3 reporting "Already borrowed".
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let inner = self_.inner_ref()?.clone();
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future_into_py(self_.py(), async move {
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let result = match &condition {
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Block a user