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Fix project isolation: Make loadChatHistory respect active project sessions

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- Modified loadChatHistory() to check for active project before fetching all sessions
- When active project exists, use project.sessions instead of fetching from API
- Added detailed console logging to debug session filtering
- This prevents ALL sessions from appearing in every project's sidebar

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
This commit is contained in:
uroma
2026-01-22 14:43:05 +00:00
Unverified
parent b82837aa5f
commit 55aafbae9a
6463 changed files with 1115462 additions and 4486 deletions
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430
.venv/lib/python3.11/site-packages/rapidfuzz/process.pyi Normal file
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from __future__ import annotations
from collections.abc import Collection, Generator, Hashable, Iterable, Mapping, Sequence
from typing import (
Any,
Callable,
Protocol,
TypeVar,
overload,
)
from rapidfuzz.fuzz import WRatio, ratio
_StringType = Sequence[Hashable]
_StringType1 = TypeVar("_StringType1", bound=Sequence[Hashable])
_StringType2 = TypeVar("_StringType2", bound=Sequence[Hashable])
_UnprocessedType1 = TypeVar("_UnprocessedType1")
_UnprocessedType2 = TypeVar("_UnprocessedType2")
_KeyType = TypeVar("_KeyType")
_ResultType = TypeVar("_ResultType", int, float)
_StringType1_contra = TypeVar("_StringType1_contra", contravariant=True, bound=Sequence[Hashable])
_StringType2_contra = TypeVar("_StringType2_contra", contravariant=True, bound=Sequence[Hashable])
_ResultType_contra = TypeVar("_ResultType_contra", int, float, contravariant=True)
_ResultType_co = TypeVar("_ResultType_co", int, float, covariant=True)
class _Scorer(Protocol[_StringType1_contra, _StringType2_contra, _ResultType_contra, _ResultType_co]):
def __call__(
self, __s1: _StringType1_contra, __s2: _StringType2_contra, *, score_cutoff: _ResultType_contra | None
) -> _ResultType_co: ...
# mypy wants defaults to be valid for every possible parameterization of a generic function
# so add separate overloads for the default version
@overload
def extractOne(
query: Sequence[Hashable] | None,
choices: Mapping[_KeyType, _StringType2 | None],
*,
scorer: _Scorer[Sequence[Hashable], Sequence[Hashable], float, float] = WRatio,
processor: None = None,
score_cutoff: float | None = None,
score_hint: float | None = None,
scorer_kwargs: dict[str, Any] | None = None,
) -> tuple[_StringType2, float, _KeyType] | None: ...
@overload
def extractOne(
query: Sequence[Hashable] | None,
choices: Iterable[_StringType2 | None],
*,
scorer: _Scorer[Sequence[Hashable], Sequence[Hashable], float, float] = WRatio,
processor: None = None,
score_cutoff: float | None = None,
score_hint: float | None = None,
scorer_kwargs: dict[str, Any] | None = None,
) -> tuple[_StringType2, float, int] | None: ...
@overload
def extractOne(
query: _UnprocessedType1 | None,
choices: Mapping[_KeyType, _UnprocessedType2 | None],
*,
scorer: _Scorer[Sequence[Hashable], Sequence[Hashable], float, float] = WRatio,
processor: Callable[[_UnprocessedType1 | _UnprocessedType2], Sequence[Hashable]],
score_cutoff: float | None = None,
score_hint: float | None = None,
scorer_kwargs: dict[str, Any] | None = None,
) -> tuple[_UnprocessedType2, float, _KeyType] | None: ...
@overload
def extractOne(
query: _UnprocessedType1 | None,
choices: Iterable[_UnprocessedType2 | None],
*,
scorer: _Scorer[Sequence[Hashable], Sequence[Hashable], float, float] = WRatio,
processor: Callable[[_UnprocessedType1 | _UnprocessedType2], Sequence[Hashable]],
score_cutoff: float | None = None,
score_hint: float | None = None,
scorer_kwargs: dict[str, Any] | None = None,
) -> tuple[_UnprocessedType2, float, int] | None: ...
@overload
def extractOne(
query: _StringType1 | None,
choices: Mapping[_KeyType, _StringType2 | None],
*,
scorer: _Scorer[_StringType1, _StringType2, _ResultType, _ResultType],
processor: None = None,
score_cutoff: _ResultType | None = None,
score_hint: _ResultType | None = None,
scorer_kwargs: dict[str, Any] | None = None,
) -> tuple[_StringType2, _ResultType, _KeyType] | None: ...
@overload
def extractOne(
query: _StringType1 | None,
choices: Iterable[_StringType2 | None],
*,
scorer: _Scorer[_StringType1, _StringType2, _ResultType, _ResultType],
processor: None = None,
score_cutoff: _ResultType | None = None,
score_hint: _ResultType | None = None,
scorer_kwargs: dict[str, Any] | None = None,
) -> tuple[_StringType2, _ResultType, int] | None: ...
@overload
def extractOne(
query: _UnprocessedType1 | None,
choices: Mapping[_KeyType, _UnprocessedType2 | None],
*,
scorer: _Scorer[_StringType1, _StringType1, _ResultType, _ResultType],
processor: Callable[[_UnprocessedType1 | _UnprocessedType2], _StringType1],
score_cutoff: _ResultType | None = None,
score_hint: _ResultType | None = None,
scorer_kwargs: dict[str, Any] | None = None,
) -> tuple[_UnprocessedType2, _ResultType, _KeyType] | None: ...
@overload
def extractOne(
query: _UnprocessedType1 | None,
choices: Iterable[_UnprocessedType2 | None],
*,
scorer: _Scorer[_StringType1, _StringType1, _ResultType, _ResultType],
processor: Callable[[_UnprocessedType1 | _UnprocessedType2], _StringType1],
score_cutoff: _ResultType | None = None,
score_hint: _ResultType | None = None,
scorer_kwargs: dict[str, Any] | None = None,
) -> tuple[_UnprocessedType2, _ResultType, int] | None: ...
# mypy wants defaults to be valid for every possible parameterization of a generic function
# so add separate overloads for the default version
@overload
def extract(
query: Sequence[Hashable] | None,
choices: Mapping[_KeyType, _StringType2 | None],
*,
scorer: _Scorer[Sequence[Hashable], Sequence[Hashable], float, float] = WRatio,
processor: None = None,
limit: int | None = 5,
score_cutoff: float | None = None,
score_hint: float | None = None,
scorer_kwargs: dict[str, Any] | None = None,
) -> list[tuple[_StringType2, float, _KeyType]]: ...
@overload
def extract(
query: Sequence[Hashable] | None,
choices: Iterable[_StringType2 | None],
*,
scorer: _Scorer[Sequence[Hashable], Sequence[Hashable], float, float] = WRatio,
processor: None = None,
limit: int | None = 5,
score_cutoff: float | None = None,
score_hint: float | None = None,
scorer_kwargs: dict[str, Any] | None = None,
) -> list[tuple[_StringType2, float, int]]: ...
@overload
def extract(
query: _UnprocessedType1 | None,
choices: Mapping[_KeyType, _UnprocessedType2 | None],
*,
scorer: _Scorer[Sequence[Hashable], Sequence[Hashable], float, float] = WRatio,
processor: Callable[[_UnprocessedType1 | _UnprocessedType2], Sequence[Hashable]],
limit: int | None = 5,
score_cutoff: float | None = None,
score_hint: float | None = None,
scorer_kwargs: dict[str, Any] | None = None,
) -> list[tuple[_UnprocessedType2, float, _KeyType]]: ...
@overload
def extract(
query: _UnprocessedType1 | None,
choices: Iterable[_UnprocessedType2 | None],
*,
scorer: _Scorer[Sequence[Hashable], Sequence[Hashable], float, float] = WRatio,
processor: Callable[[_UnprocessedType1 | _UnprocessedType2], Sequence[Hashable]],
limit: int | None = 5,
score_cutoff: float | None = None,
score_hint: float | None = None,
scorer_kwargs: dict[str, Any] | None = None,
) -> list[tuple[_UnprocessedType2, float, int]]: ...
@overload
def extract(
query: _StringType1 | None,
choices: Mapping[_KeyType, _StringType2 | None],
*,
scorer: _Scorer[_StringType1, _StringType2, _ResultType, _ResultType],
processor: None = None,
limit: int | None = 5,
score_cutoff: _ResultType | None = None,
score_hint: _ResultType | None = None,
scorer_kwargs: dict[str, Any] | None = None,
) -> list[tuple[_StringType2, _ResultType, _KeyType]]: ...
@overload
def extract(
query: _StringType1 | None,
choices: Collection[_StringType2 | None],
*,
scorer: _Scorer[_StringType1, _StringType2, _ResultType, _ResultType],
processor: None = None,
limit: int | None = 5,
score_cutoff: _ResultType | None = None,
score_hint: _ResultType | None = None,
scorer_kwargs: dict[str, Any] | None = None,
) -> list[tuple[_StringType2, _ResultType, int]]: ...
@overload
def extract(
query: _UnprocessedType1 | None,
choices: Mapping[_KeyType, _UnprocessedType2 | None],
*,
scorer: _Scorer[_StringType1, _StringType1, _ResultType, _ResultType],
processor: Callable[[_UnprocessedType1 | _UnprocessedType2], _StringType1],
limit: int | None = 5,
score_cutoff: _ResultType | None = None,
score_hint: _ResultType | None = None,
scorer_kwargs: dict[str, Any] | None = None,
) -> list[tuple[_UnprocessedType2, _ResultType, _KeyType]]: ...
@overload
def extract(
query: _UnprocessedType1 | None,
choices: Collection[_UnprocessedType2 | None],
*,
scorer: _Scorer[_StringType1, _StringType1, _ResultType, _ResultType],
processor: Callable[[_UnprocessedType1 | _UnprocessedType2], _StringType1],
limit: int | None = 5,
score_cutoff: _ResultType | None = None,
score_hint: _ResultType | None = None,
scorer_kwargs: dict[str, Any] | None = None,
) -> list[tuple[_UnprocessedType2, _ResultType, int]]: ...
# mypy wants defaults to be valid for every possible parameterization of a generic function
# so add separate overloads for the default version
@overload
def extract_iter(
query: Sequence[Hashable] | None,
choices: Mapping[_KeyType, _StringType2 | None],
*,
scorer: _Scorer[Sequence[Hashable], Sequence[Hashable], float, float] = WRatio,
processor: None = None,
score_cutoff: float | None = None,
score_hint: float | None = None,
scorer_kwargs: dict[str, Any] | None = None,
) -> Generator[tuple[_StringType2, float, _KeyType], None, None]: ...
@overload
def extract_iter(
query: Sequence[Hashable] | None,
choices: Iterable[_StringType2 | None],
*,
scorer: _Scorer[Sequence[Hashable], Sequence[Hashable], float, float] = WRatio,
processor: None = None,
score_cutoff: float | None = None,
score_hint: float | None = None,
scorer_kwargs: dict[str, Any] | None = None,
) -> Generator[tuple[_StringType2, float, int], None, None]: ...
@overload
def extract_iter(
query: _UnprocessedType1 | None,
choices: Mapping[_KeyType, _UnprocessedType2 | None],
*,
scorer: _Scorer[Sequence[Hashable], Sequence[Hashable], float, float] = WRatio,
processor: Callable[[_UnprocessedType1 | _UnprocessedType2], Sequence[Hashable]],
score_cutoff: float | None = None,
score_hint: float | None = None,
scorer_kwargs: dict[str, Any] | None = None,
) -> Generator[tuple[_UnprocessedType2, float, _KeyType], None, None]: ...
@overload
def extract_iter(
query: _UnprocessedType1 | None,
choices: Iterable[_UnprocessedType2 | None],
*,
scorer: _Scorer[Sequence[Hashable], Sequence[Hashable], float, float] = WRatio,
processor: Callable[[_UnprocessedType1 | _UnprocessedType2], Sequence[Hashable]],
score_cutoff: float | None = None,
score_hint: float | None = None,
scorer_kwargs: dict[str, Any] | None = None,
) -> Generator[tuple[_UnprocessedType2, float, int], None, None]: ...
@overload
def extract_iter(
query: _StringType1 | None,
choices: Mapping[_KeyType, _StringType2 | None],
*,
scorer: _Scorer[_StringType1, _StringType2, _ResultType, _ResultType],
processor: None = None,
score_cutoff: _ResultType | None = None,
score_hint: _ResultType | None = None,
scorer_kwargs: dict[str, Any] | None = None,
) -> Generator[tuple[_StringType2, _ResultType, _KeyType], None, None]: ...
@overload
def extract_iter(
query: _StringType1 | None,
choices: Iterable[_StringType2 | None],
*,
scorer: _Scorer[_StringType1, _StringType2, _ResultType, _ResultType],
processor: None = None,
score_cutoff: _ResultType | None = None,
score_hint: _ResultType | None = None,
scorer_kwargs: dict[str, Any] | None = None,
) -> Generator[tuple[_StringType2, _ResultType, int], None, None]: ...
@overload
def extract_iter(
query: _UnprocessedType1 | None,
choices: Mapping[_KeyType, _UnprocessedType2 | None],
*,
scorer: _Scorer[_StringType1, _StringType1, _ResultType, _ResultType],
processor: Callable[[_UnprocessedType1 | _UnprocessedType2], _StringType1],
score_cutoff: _ResultType | None = None,
score_hint: _ResultType | None = None,
scorer_kwargs: dict[str, Any] | None = None,
) -> Generator[tuple[_UnprocessedType2, _ResultType, _KeyType], None, None]: ...
@overload
def extract_iter(
query: _UnprocessedType1 | None,
choices: Iterable[_UnprocessedType2 | None],
*,
scorer: _Scorer[_StringType1, _StringType1, _ResultType, _ResultType],
processor: Callable[[_UnprocessedType1 | _UnprocessedType2], _StringType1],
score_cutoff: _ResultType | None = None,
score_hint: _ResultType | None = None,
scorer_kwargs: dict[str, Any] | None = None,
) -> Generator[tuple[_UnprocessedType2, _ResultType, int], None, None]: ...
try:
import numpy.typing as npt
@overload
def cdist(
queries: Iterable[Sequence[Hashable] | None],
choices: Iterable[Sequence[Hashable] | None],
*,
scorer: _Scorer[Sequence[Hashable], Sequence[Hashable], float, float] = ratio,
processor: None = None,
score_cutoff: float | None = None,
score_hint: float | None = None,
score_multiplier: float = 1,
dtype: npt.DTypeLike | None = None,
workers: int = 1,
scorer_kwargs: dict[str, Any] | None = None,
) -> npt.NDArray[Any]: ...
@overload
def cdist(
queries: Iterable[_UnprocessedType1 | None],
choices: Iterable[_UnprocessedType2 | None],
*,
scorer: _Scorer[Sequence[Hashable], Sequence[Hashable], float, float] = ratio,
processor: Callable[[_UnprocessedType1 | _UnprocessedType2], Sequence[Hashable]],
score_cutoff: float | None = None,
score_hint: float | None = None,
score_multiplier: float = 1,
dtype: npt.DTypeLike | None = None,
workers: int = 1,
scorer_kwargs: dict[str, Any] | None = None,
) -> npt.NDArray[Any]: ...
@overload
def cdist(
queries: Iterable[_StringType1 | None],
choices: Iterable[_StringType2 | None],
*,
scorer: _Scorer[_StringType1, _StringType2, _ResultType, _ResultType],
processor: None = None,
score_cutoff: _ResultType | None = None,
score_hint: _ResultType | None = None,
score_multiplier: _ResultType = 1,
dtype: npt.DTypeLike | None = None,
workers: int = 1,
scorer_kwargs: dict[str, Any] | None = None,
) -> npt.NDArray[Any]: ...
@overload
def cdist(
queries: Iterable[_UnprocessedType1 | None],
choices: Iterable[_UnprocessedType2 | None],
*,
scorer: _Scorer[_StringType1, _StringType1, _ResultType, _ResultType],
processor: Callable[[_UnprocessedType1 | _UnprocessedType2], _StringType1],
score_cutoff: _ResultType | None = None,
score_hint: _ResultType | None = None,
score_multiplier: _ResultType = 1,
dtype: npt.DTypeLike | None = None,
workers: int = 1,
scorer_kwargs: dict[str, Any] | None = None,
) -> npt.NDArray[Any]: ...
@overload
def cpdist(
queries: Iterable[Sequence[Hashable] | None],
choices: Iterable[Sequence[Hashable] | None],
*,
scorer: _Scorer[Sequence[Hashable], Sequence[Hashable], float, float] = ratio,
processor: None = None,
score_cutoff: float | None = None,
score_hint: float | None = None,
score_multiplier: float = 1,
dtype: npt.DTypeLike | None = None,
workers: int = 1,
scorer_kwargs: dict[str, Any] | None = None,
) -> npt.NDArray[Any]: ...
@overload
def cpdist(
queries: Iterable[_UnprocessedType1 | None],
choices: Iterable[_UnprocessedType2 | None],
*,
scorer: _Scorer[Sequence[Hashable], Sequence[Hashable], float, float] = ratio,
processor: Callable[[_UnprocessedType1 | _UnprocessedType2], Sequence[Hashable]],
score_cutoff: float | None = None,
score_hint: float | None = None,
score_multiplier: float = 1,
dtype: npt.DTypeLike | None = None,
workers: int = 1,
scorer_kwargs: dict[str, Any] | None = None,
) -> npt.NDArray[Any]: ...
@overload
def cpdist(
queries: Iterable[_StringType1 | None],
choices: Iterable[_StringType2 | None],
*,
scorer: _Scorer[_StringType1, _StringType2, _ResultType, _ResultType],
processor: None = None,
score_cutoff: _ResultType | None = None,
score_hint: _ResultType | None = None,
score_multiplier: _ResultType = 1,
dtype: npt.DTypeLike | None = None,
workers: int = 1,
scorer_kwargs: dict[str, Any] | None = None,
) -> npt.NDArray[Any]: ...
@overload
def cpdist(
queries: Iterable[_UnprocessedType1 | None],
choices: Iterable[_UnprocessedType2 | None],
*,
scorer: _Scorer[_StringType1, _StringType1, _ResultType, _ResultType],
processor: Callable[[_UnprocessedType1 | _UnprocessedType2], _StringType1],
score_cutoff: _ResultType | None = None,
score_hint: _ResultType | None = None,
score_multiplier: _ResultType = 1,
dtype: npt.DTypeLike | None = None,
workers: int = 1,
scorer_kwargs: dict[str, Any] | None = None,
) -> npt.NDArray[Any]: ...
except ImportError:
pass