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partners[lint]: run pyupgrade to get code in line with 3.9 standards (#30781)

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Using `pyupgrade` to get all `partners` code up to 3.9 standards
(mostly, fixing old `typing` imports).
This commit is contained in:
Sydney Runkle
2025-04-11 07:18:44 -04:00
committed by GitHub
parent e72f3c26a0
commit 8c6734325b
123 changed files with 1000 additions and 1109 deletions
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117
libs/partners/qdrant/langchain_qdrant/vectorstores.py
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@@ -4,21 +4,14 @@ import functools
import os
import uuid
import warnings
from collections.abc import AsyncGenerator, Generator, Iterable, Sequence
from itertools import islice
from operator import itemgetter
from typing import (
TYPE_CHECKING,
Any,
AsyncGenerator,
Callable,
Dict,
Generator,
Iterable,
List,
Optional,
Sequence,
Tuple,
Type,
Union,
)
@@ -35,7 +28,7 @@ from qdrant_client.local.async_qdrant_local import AsyncQdrantLocal
from langchain_qdrant._utils import maximal_marginal_relevance
if TYPE_CHECKING:
DictFilter = Dict[str, Union[str, int, bool, dict, list]]
DictFilter = dict[str, Union[str, int, bool, dict, list]]
MetadataFilter = Union[DictFilter, models.Filter]
@@ -153,11 +146,11 @@ class Qdrant(VectorStore):
def add_texts(
self,
texts: Iterable[str],
metadatas: Optional[List[dict]] = None,
metadatas: Optional[list[dict]] = None,
ids: Optional[Sequence[str]] = None,
batch_size: int = 64,
**kwargs: Any,
) -> List[str]:
) -> list[str]:
"""Run more texts through the embeddings and add to the vectorstore.
Args:
@@ -188,11 +181,11 @@ class Qdrant(VectorStore):
async def aadd_texts(
self,
texts: Iterable[str],
metadatas: Optional[List[dict]] = None,
metadatas: Optional[list[dict]] = None,
ids: Optional[Sequence[str]] = None,
batch_size: int = 64,
**kwargs: Any,
) -> List[str]:
) -> list[str]:
"""Run more texts through the embeddings and add to the vectorstore.
Args:
@@ -236,7 +229,7 @@ class Qdrant(VectorStore):
score_threshold: Optional[float] = None,
consistency: Optional[models.ReadConsistency] = None,
**kwargs: Any,
) -> List[Document]:
) -> list[Document]:
"""Return docs most similar to query.
Args:
@@ -290,7 +283,7 @@ class Qdrant(VectorStore):
k: int = 4,
filter: Optional[MetadataFilter] = None,
**kwargs: Any,
) -> List[Document]:
) -> list[Document]:
"""Return docs most similar to query.
Args:
query: Text to look up documents similar to.
@@ -312,7 +305,7 @@ class Qdrant(VectorStore):
score_threshold: Optional[float] = None,
consistency: Optional[models.ReadConsistency] = None,
**kwargs: Any,
) -> List[Tuple[Document, float]]:
) -> list[tuple[Document, float]]:
"""Return docs most similar to query.
Args:
@@ -369,7 +362,7 @@ class Qdrant(VectorStore):
score_threshold: Optional[float] = None,
consistency: Optional[models.ReadConsistency] = None,
**kwargs: Any,
) -> List[Tuple[Document, float]]:
) -> list[tuple[Document, float]]:
"""Return docs most similar to query.
Args:
@@ -419,7 +412,7 @@ class Qdrant(VectorStore):
def similarity_search_by_vector(
self,
embedding: List[float],
embedding: list[float],
k: int = 4,
filter: Optional[MetadataFilter] = None,
search_params: Optional[models.SearchParams] = None,
@@ -427,7 +420,7 @@ class Qdrant(VectorStore):
score_threshold: Optional[float] = None,
consistency: Optional[models.ReadConsistency] = None,
**kwargs: Any,
) -> List[Document]:
) -> list[Document]:
"""Return docs most similar to embedding vector.
Args:
@@ -477,7 +470,7 @@ class Qdrant(VectorStore):
@sync_call_fallback
async def asimilarity_search_by_vector(
self,
embedding: List[float],
embedding: list[float],
k: int = 4,
filter: Optional[MetadataFilter] = None,
search_params: Optional[models.SearchParams] = None,
@@ -485,7 +478,7 @@ class Qdrant(VectorStore):
score_threshold: Optional[float] = None,
consistency: Optional[models.ReadConsistency] = None,
**kwargs: Any,
) -> List[Document]:
) -> list[Document]:
"""Return docs most similar to embedding vector.
Args:
@@ -535,7 +528,7 @@ class Qdrant(VectorStore):
def similarity_search_with_score_by_vector(
self,
embedding: List[float],
embedding: list[float],
k: int = 4,
filter: Optional[MetadataFilter] = None,
search_params: Optional[models.SearchParams] = None,
@@ -543,7 +536,7 @@ class Qdrant(VectorStore):
score_threshold: Optional[float] = None,
consistency: Optional[models.ReadConsistency] = None,
**kwargs: Any,
) -> List[Tuple[Document, float]]:
) -> list[tuple[Document, float]]:
"""Return docs most similar to embedding vector.
Args:
@@ -622,7 +615,7 @@ class Qdrant(VectorStore):
@sync_call_fallback
async def asimilarity_search_with_score_by_vector(
self,
embedding: List[float],
embedding: list[float],
k: int = 4,
filter: Optional[MetadataFilter] = None,
search_params: Optional[models.SearchParams] = None,
@@ -630,7 +623,7 @@ class Qdrant(VectorStore):
score_threshold: Optional[float] = None,
consistency: Optional[models.ReadConsistency] = None,
**kwargs: Any,
) -> List[Tuple[Document, float]]:
) -> list[tuple[Document, float]]:
"""Return docs most similar to embedding vector.
Args:
@@ -725,7 +718,7 @@ class Qdrant(VectorStore):
score_threshold: Optional[float] = None,
consistency: Optional[models.ReadConsistency] = None,
**kwargs: Any,
) -> List[Document]:
) -> list[Document]:
"""Return docs selected using the maximal marginal relevance.
Maximal marginal relevance optimizes for similarity to query AND diversity
@@ -789,7 +782,7 @@ class Qdrant(VectorStore):
score_threshold: Optional[float] = None,
consistency: Optional[models.ReadConsistency] = None,
**kwargs: Any,
) -> List[Document]:
) -> list[Document]:
"""Return docs selected using the maximal marginal relevance.
Maximal marginal relevance optimizes for similarity to query AND diversity
@@ -844,7 +837,7 @@ class Qdrant(VectorStore):
def max_marginal_relevance_search_by_vector(
self,
embedding: List[float],
embedding: list[float],
k: int = 4,
fetch_k: int = 20,
lambda_mult: float = 0.5,
@@ -853,7 +846,7 @@ class Qdrant(VectorStore):
score_threshold: Optional[float] = None,
consistency: Optional[models.ReadConsistency] = None,
**kwargs: Any,
) -> List[Document]:
) -> list[Document]:
"""Return docs selected using the maximal marginal relevance.
Maximal marginal relevance optimizes for similarity to query AND diversity
@@ -907,7 +900,7 @@ class Qdrant(VectorStore):
@sync_call_fallback
async def amax_marginal_relevance_search_by_vector(
self,
embedding: List[float],
embedding: list[float],
k: int = 4,
fetch_k: int = 20,
lambda_mult: float = 0.5,
@@ -916,7 +909,7 @@ class Qdrant(VectorStore):
score_threshold: Optional[float] = None,
consistency: Optional[models.ReadConsistency] = None,
**kwargs: Any,
) -> List[Document]:
) -> list[Document]:
"""Return docs selected using the maximal marginal relevance.
Maximal marginal relevance optimizes for similarity to query AND diversity
among selected documents.
@@ -970,7 +963,7 @@ class Qdrant(VectorStore):
def max_marginal_relevance_search_with_score_by_vector(
self,
embedding: List[float],
embedding: list[float],
k: int = 4,
fetch_k: int = 20,
lambda_mult: float = 0.5,
@@ -979,7 +972,7 @@ class Qdrant(VectorStore):
score_threshold: Optional[float] = None,
consistency: Optional[models.ReadConsistency] = None,
**kwargs: Any,
) -> List[Tuple[Document, float]]:
) -> list[tuple[Document, float]]:
"""Return docs selected using the maximal marginal relevance.
Maximal marginal relevance optimizes for similarity to query AND diversity
among selected documents.
@@ -1058,7 +1051,7 @@ class Qdrant(VectorStore):
@sync_call_fallback
async def amax_marginal_relevance_search_with_score_by_vector(
self,
embedding: List[float],
embedding: list[float],
k: int = 4,
fetch_k: int = 20,
lambda_mult: float = 0.5,
@@ -1067,7 +1060,7 @@ class Qdrant(VectorStore):
score_threshold: Optional[float] = None,
consistency: Optional[models.ReadConsistency] = None,
**kwargs: Any,
) -> List[Tuple[Document, float]]:
) -> list[tuple[Document, float]]:
"""Return docs selected using the maximal marginal relevance.
Maximal marginal relevance optimizes for similarity to query AND diversity
among selected documents.
@@ -1128,7 +1121,7 @@ class Qdrant(VectorStore):
for i in mmr_selected
]
def delete(self, ids: Optional[List[str]] = None, **kwargs: Any) -> Optional[bool]:
def delete(self, ids: Optional[list[str]] = None, **kwargs: Any) -> Optional[bool]:
"""Delete by vector ID or other criteria.
Args:
@@ -1147,7 +1140,7 @@ class Qdrant(VectorStore):
@sync_call_fallback
async def adelete(
self, ids: Optional[List[str]] = None, **kwargs: Any
self, ids: Optional[list[str]] = None, **kwargs: Any
) -> Optional[bool]:
"""Delete by vector ID or other criteria.
@@ -1174,10 +1167,10 @@ class Qdrant(VectorStore):
@classmethod
def from_texts(
cls: Type[Qdrant],
texts: List[str],
cls: type[Qdrant],
texts: list[str],
embedding: Embeddings,
metadatas: Optional[List[dict]] = None,
metadatas: Optional[list[dict]] = None,
ids: Optional[Sequence[str]] = None,
location: Optional[str] = None,
url: Optional[str] = None,
@@ -1351,7 +1344,7 @@ class Qdrant(VectorStore):
@classmethod
def from_existing_collection(
cls: Type[Qdrant],
cls: type[Qdrant],
embedding: Embeddings,
path: Optional[str] = None,
collection_name: Optional[str] = None,
@@ -1408,10 +1401,10 @@ class Qdrant(VectorStore):
@classmethod
@sync_call_fallback
async def afrom_texts(
cls: Type[Qdrant],
texts: List[str],
cls: type[Qdrant],
texts: list[str],
embedding: Embeddings,
metadatas: Optional[List[dict]] = None,
metadatas: Optional[list[dict]] = None,
ids: Optional[Sequence[str]] = None,
location: Optional[str] = None,
url: Optional[str] = None,
@@ -1585,8 +1578,8 @@ class Qdrant(VectorStore):
@classmethod
def construct_instance(
cls: Type[Qdrant],
texts: List[str],
cls: type[Qdrant],
texts: list[str],
embedding: Embeddings,
location: Optional[str] = None,
url: Optional[str] = None,
@@ -1744,8 +1737,8 @@ class Qdrant(VectorStore):
@classmethod
async def aconstruct_instance(
cls: Type[Qdrant],
texts: List[str],
cls: type[Qdrant],
texts: list[str],
embedding: Embeddings,
location: Optional[str] = None,
url: Optional[str] = None,
@@ -1936,7 +1929,7 @@ class Qdrant(VectorStore):
query: str,
k: int = 4,
**kwargs: Any,
) -> List[Tuple[Document, float]]:
) -> list[tuple[Document, float]]:
"""Return docs and relevance scores in the range [0, 1].
0 is dissimilar, 1 is most similar.
@@ -1959,7 +1952,7 @@ class Qdrant(VectorStore):
query: str,
k: int = 4,
**kwargs: Any,
) -> List[Tuple[Document, float]]:
) -> list[tuple[Document, float]]:
"""Return docs and relevance scores in the range [0, 1].
0 is dissimilar, 1 is most similar.
@@ -1980,10 +1973,10 @@ class Qdrant(VectorStore):
def _build_payloads(
cls,
texts: Iterable[str],
metadatas: Optional[List[dict]],
metadatas: Optional[list[dict]],
content_payload_key: str,
metadata_payload_key: str,
) -> List[dict]:
) -> list[dict]:
payloads = []
for i, text in enumerate(texts):
if text is None:
@@ -2017,7 +2010,7 @@ class Qdrant(VectorStore):
metadata=metadata,
)
def _build_condition(self, key: str, value: Any) -> List[models.FieldCondition]:
def _build_condition(self, key: str, value: Any) -> list[models.FieldCondition]:
out = []
if isinstance(value, dict):
@@ -2053,7 +2046,7 @@ class Qdrant(VectorStore):
]
)
def _embed_query(self, query: str) -> List[float]:
def _embed_query(self, query: str) -> list[float]:
"""Embed query text.
Used to provide backward compatibility with `embedding_function` argument.
@@ -2073,7 +2066,7 @@ class Qdrant(VectorStore):
raise ValueError("Neither of embeddings or embedding_function is set")
return embedding.tolist() if hasattr(embedding, "tolist") else embedding
async def _aembed_query(self, query: str) -> List[float]:
async def _aembed_query(self, query: str) -> list[float]:
"""Embed query text asynchronously.
Used to provide backward compatibility with `embedding_function` argument.
@@ -2093,7 +2086,7 @@ class Qdrant(VectorStore):
raise ValueError("Neither of embeddings or embedding_function is set")
return embedding.tolist() if hasattr(embedding, "tolist") else embedding
def _embed_texts(self, texts: Iterable[str]) -> List[List[float]]:
def _embed_texts(self, texts: Iterable[str]) -> list[list[float]]:
"""Embed search texts.
Used to provide backward compatibility with `embedding_function` argument.
@@ -2120,7 +2113,7 @@ class Qdrant(VectorStore):
return embeddings
async def _aembed_texts(self, texts: Iterable[str]) -> List[List[float]]:
async def _aembed_texts(self, texts: Iterable[str]) -> list[list[float]]:
"""Embed search texts.
Used to provide backward compatibility with `embedding_function` argument.
@@ -2150,10 +2143,10 @@ class Qdrant(VectorStore):
def _generate_rest_batches(
self,
texts: Iterable[str],
metadatas: Optional[List[dict]] = None,
metadatas: Optional[list[dict]] = None,
ids: Optional[Sequence[str]] = None,
batch_size: int = 64,
) -> Generator[Tuple[List[str], List[models.PointStruct]], None, None]:
) -> Generator[tuple[list[str], list[models.PointStruct]], None, None]:
texts_iterator = iter(texts)
metadatas_iterator = iter(metadatas or [])
ids_iterator = iter(ids or [uuid.uuid4().hex for _ in iter(texts)])
@@ -2190,10 +2183,10 @@ class Qdrant(VectorStore):
async def _agenerate_rest_batches(
self,
texts: Iterable[str],
metadatas: Optional[List[dict]] = None,
metadatas: Optional[list[dict]] = None,
ids: Optional[Sequence[str]] = None,
batch_size: int = 64,
) -> AsyncGenerator[Tuple[List[str], List[models.PointStruct]], None]:
) -> AsyncGenerator[tuple[list[str], list[models.PointStruct]], None]:
texts_iterator = iter(texts)
metadatas_iterator = iter(metadatas or [])
ids_iterator = iter(ids or [uuid.uuid4().hex for _ in iter(texts)])
@@ -2241,7 +2234,7 @@ class Qdrant(VectorStore):
host: Optional[str] = None,
path: Optional[str] = None,
**kwargs: Any,
) -> Tuple[QdrantClient, Optional[AsyncQdrantClient]]:
) -> tuple[QdrantClient, Optional[AsyncQdrantClient]]:
if api_key is None:
api_key = os.getenv("QDRANT_API_KEY")