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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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82
libs/partners/qdrant/langchain_qdrant/qdrant.py
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@@ -1,20 +1,14 @@
from __future__ import annotations
import uuid
from collections.abc import Generator, Iterable, Sequence
from enum import Enum
from itertools import islice
from operator import itemgetter
from typing import (
Any,
Callable,
Dict,
Generator,
Iterable,
List,
Optional,
Sequence,
Tuple,
Type,
Union,
)
@@ -275,10 +269,10 @@ class QdrantVectorStore(VectorStore):
@classmethod
def from_texts(
cls: Type[QdrantVectorStore],
texts: List[str],
cls: type[QdrantVectorStore],
texts: list[str],
embedding: Optional[Embeddings] = None,
metadatas: Optional[List[dict]] = None,
metadatas: Optional[list[dict]] = None,
ids: Optional[Sequence[str | int]] = None,
collection_name: Optional[str] = None,
location: Optional[str] = None,
@@ -299,9 +293,9 @@ class QdrantVectorStore(VectorStore):
retrieval_mode: RetrievalMode = RetrievalMode.DENSE,
sparse_embedding: Optional[SparseEmbeddings] = None,
sparse_vector_name: str = SPARSE_VECTOR_NAME,
collection_create_options: Dict[str, Any] = {},
vector_params: Dict[str, Any] = {},
sparse_vector_params: Dict[str, Any] = {},
collection_create_options: dict[str, Any] = {},
vector_params: dict[str, Any] = {},
sparse_vector_params: dict[str, Any] = {},
batch_size: int = 64,
force_recreate: bool = False,
validate_embeddings: bool = True,
@@ -363,7 +357,7 @@ class QdrantVectorStore(VectorStore):
@classmethod
def from_existing_collection(
cls: Type[QdrantVectorStore],
cls: type[QdrantVectorStore],
collection_name: str,
embedding: Optional[Embeddings] = None,
retrieval_mode: RetrievalMode = RetrievalMode.DENSE,
@@ -427,11 +421,11 @@ class QdrantVectorStore(VectorStore):
def add_texts( # type: ignore
self,
texts: Iterable[str],
metadatas: Optional[List[dict]] = None,
metadatas: Optional[list[dict]] = None,
ids: Optional[Sequence[str | int]] = None,
batch_size: int = 64,
**kwargs: Any,
) -> List[str | int]:
) -> list[str | int]:
"""Add texts with embeddings to the vectorstore.
Returns:
@@ -459,7 +453,7 @@ class QdrantVectorStore(VectorStore):
consistency: Optional[models.ReadConsistency] = None,
hybrid_fusion: Optional[models.FusionQuery] = None,
**kwargs: Any,
) -> List[Document]:
) -> list[Document]:
"""Return docs most similar to query.
Returns:
@@ -489,7 +483,7 @@ class QdrantVectorStore(VectorStore):
consistency: Optional[models.ReadConsistency] = None,
hybrid_fusion: Optional[models.FusionQuery] = None,
**kwargs: Any,
) -> List[Tuple[Document, float]]:
) -> list[tuple[Document, float]]:
"""Return docs most similar to query.
Returns:
@@ -570,7 +564,7 @@ class QdrantVectorStore(VectorStore):
def similarity_search_with_score_by_vector(
self,
embedding: List[float],
embedding: list[float],
k: int = 4,
filter: Optional[models.Filter] = None,
search_params: Optional[models.SearchParams] = None,
@@ -578,7 +572,7 @@ class QdrantVectorStore(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.
Returns:
@@ -623,7 +617,7 @@ class QdrantVectorStore(VectorStore):
def similarity_search_by_vector(
self,
embedding: List[float],
embedding: list[float],
k: int = 4,
filter: Optional[models.Filter] = None,
search_params: Optional[models.SearchParams] = None,
@@ -631,7 +625,7 @@ class QdrantVectorStore(VectorStore):
score_threshold: Optional[float] = None,
consistency: Optional[models.ReadConsistency] = None,
**kwargs: Any,
) -> List[Document]:
) -> list[Document]:
"""Return docs most similar to embedding vector.
Returns:
@@ -660,7 +654,7 @@ class QdrantVectorStore(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 with dense vectors.
Maximal marginal relevance optimizes for similarity to query AND diversity
@@ -693,7 +687,7 @@ class QdrantVectorStore(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,
@@ -702,7 +696,7 @@ class QdrantVectorStore(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 with dense vectors.
Maximal marginal relevance optimizes for similarity to query AND diversity
@@ -726,7 +720,7 @@ class QdrantVectorStore(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,
@@ -735,7 +729,7 @@ class QdrantVectorStore(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.
@@ -782,7 +776,7 @@ class QdrantVectorStore(VectorStore):
def delete( # type: ignore
self,
ids: Optional[List[str | int]] = None,
ids: Optional[list[str | int]] = None,
**kwargs: Any,
) -> Optional[bool]:
"""Delete documents by their ids.
@@ -800,7 +794,7 @@ class QdrantVectorStore(VectorStore):
)
return result.status == models.UpdateStatus.COMPLETED
def get_by_ids(self, ids: Sequence[str | int], /) -> List[Document]:
def get_by_ids(self, ids: Sequence[str | int], /) -> list[Document]:
results = self.client.retrieve(self.collection_name, ids, with_payload=True)
return [
@@ -815,11 +809,11 @@ class QdrantVectorStore(VectorStore):
@classmethod
def construct_instance(
cls: Type[QdrantVectorStore],
cls: type[QdrantVectorStore],
embedding: Optional[Embeddings] = None,
retrieval_mode: RetrievalMode = RetrievalMode.DENSE,
sparse_embedding: Optional[SparseEmbeddings] = None,
client_options: Dict[str, Any] = {},
client_options: dict[str, Any] = {},
collection_name: Optional[str] = None,
distance: models.Distance = models.Distance.COSINE,
content_payload_key: str = CONTENT_KEY,
@@ -827,9 +821,9 @@ class QdrantVectorStore(VectorStore):
vector_name: str = VECTOR_NAME,
sparse_vector_name: str = SPARSE_VECTOR_NAME,
force_recreate: bool = False,
collection_create_options: Dict[str, Any] = {},
vector_params: Dict[str, Any] = {},
sparse_vector_params: Dict[str, Any] = {},
collection_create_options: dict[str, Any] = {},
vector_params: dict[str, Any] = {},
sparse_vector_params: dict[str, Any] = {},
validate_embeddings: bool = True,
validate_collection_config: bool = True,
) -> QdrantVectorStore:
@@ -960,7 +954,7 @@ class QdrantVectorStore(VectorStore):
def _generate_batches(
self,
texts: Iterable[str],
metadatas: Optional[List[dict]] = None,
metadatas: Optional[list[dict]] = None,
ids: Optional[Sequence[str | int]] = None,
batch_size: int = 64,
) -> Generator[tuple[list[str | int], list[models.PointStruct]], Any, None]:
@@ -994,10 +988,10 @@ class QdrantVectorStore(VectorStore):
@staticmethod
def _build_payloads(
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:
@@ -1018,7 +1012,7 @@ class QdrantVectorStore(VectorStore):
def _build_vectors(
self,
texts: Iterable[str],
) -> List[models.VectorStruct]:
) -> list[models.VectorStruct]:
if self.retrieval_mode == RetrievalMode.DENSE:
batch_embeddings = self.embeddings.embed_documents(list(texts))
return [
@@ -1068,7 +1062,7 @@ class QdrantVectorStore(VectorStore):
@classmethod
def _validate_collection_config(
cls: Type[QdrantVectorStore],
cls: type[QdrantVectorStore],
client: QdrantClient,
collection_name: str,
retrieval_mode: RetrievalMode,
@@ -1097,17 +1091,17 @@ class QdrantVectorStore(VectorStore):
@classmethod
def _validate_collection_for_dense(
cls: Type[QdrantVectorStore],
cls: type[QdrantVectorStore],
client: QdrantClient,
collection_name: str,
vector_name: str,
distance: models.Distance,
dense_embeddings: Union[Embeddings, List[float], None],
dense_embeddings: Union[Embeddings, list[float], None],
) -> None:
collection_info = client.get_collection(collection_name=collection_name)
vector_config = collection_info.config.params.vectors
if isinstance(vector_config, Dict):
if isinstance(vector_config, dict):
# vector_config is a Dict[str, VectorParams]
if vector_name not in vector_config:
raise QdrantVectorStoreError(
@@ -1164,7 +1158,7 @@ class QdrantVectorStore(VectorStore):
@classmethod
def _validate_collection_for_sparse(
cls: Type[QdrantVectorStore],
cls: type[QdrantVectorStore],
client: QdrantClient,
collection_name: str,
sparse_vector_name: str,
@@ -1185,7 +1179,7 @@ class QdrantVectorStore(VectorStore):
@classmethod
def _validate_embeddings(
cls: Type[QdrantVectorStore],
cls: type[QdrantVectorStore],
retrieval_mode: RetrievalMode,
embedding: Optional[Embeddings],
sparse_embedding: Optional[SparseEmbeddings],