chore: drop UP045 (#33362)

Python 3.9 EOL
2026-06-09 10:17:00 +00:00 · 2025-10-08 21:17:53 -04:00
parent 0039b3b046
commit 31eeb50ce0
119 changed files with 1423 additions and 1504 deletions
--- a/libs/partners/qdrant/langchain_qdrant/qdrant.py
+++ b/libs/partners/qdrant/langchain_qdrant/qdrant.py
@@ -8,8 +8,6 @@ from operator import itemgetter
 from typing import (
    TYPE_CHECKING,
    Any,
-    Optional,
-    Union,
 )

 import numpy as np
@@ -218,13 +216,13 @@ class QdrantVectorStore(VectorStore):
        self,
        client: QdrantClient,
        collection_name: str,
-        embedding: Optional[Embeddings] = None,
+        embedding: Embeddings | None = None,
        retrieval_mode: RetrievalMode = RetrievalMode.DENSE,
        vector_name: str = VECTOR_NAME,
        content_payload_key: str = CONTENT_KEY,
        metadata_payload_key: str = METADATA_KEY,
        distance: models.Distance = models.Distance.COSINE,
-        sparse_embedding: Optional[SparseEmbeddings] = None,
+        sparse_embedding: SparseEmbeddings | None = None,
        sparse_vector_name: str = SPARSE_VECTOR_NAME,
        validate_embeddings: bool = True,  # noqa: FBT001, FBT002
        validate_collection_config: bool = True,  # noqa: FBT001, FBT002
@@ -278,7 +276,7 @@ class QdrantVectorStore(VectorStore):
        return self._client

    @property
-    def embeddings(self) -> Optional[Embeddings]:
+    def embeddings(self) -> Embeddings | None:
        """Get the dense embeddings instance that is being used.

        Returns:
@@ -347,31 +345,31 @@ class QdrantVectorStore(VectorStore):
    def from_texts(
        cls: type[QdrantVectorStore],
        texts: list[str],
-        embedding: Optional[Embeddings] = None,
-        metadatas: Optional[list[dict]] = None,
-        ids: Optional[Sequence[str | int]] = None,
-        collection_name: Optional[str] = None,
-        location: Optional[str] = None,
-        url: Optional[str] = None,
-        port: Optional[int] = 6333,
+        embedding: Embeddings | None = None,
+        metadatas: list[dict] | None = None,
+        ids: Sequence[str | int] | None = None,
+        collection_name: str | None = None,
+        location: str | None = None,
+        url: str | None = None,
+        port: int | None = 6333,
        grpc_port: int = 6334,
        prefer_grpc: bool = False,  # noqa: FBT001, FBT002
-        https: Optional[bool] = None,  # noqa: FBT001
-        api_key: Optional[str] = None,
-        prefix: Optional[str] = None,
-        timeout: Optional[int] = None,
-        host: Optional[str] = None,
-        path: Optional[str] = None,
+        https: bool | None = None,  # noqa: FBT001
+        api_key: str | None = None,
+        prefix: str | None = None,
+        timeout: int | None = None,
+        host: str | None = None,
+        path: str | None = None,
        distance: models.Distance = models.Distance.COSINE,
        content_payload_key: str = CONTENT_KEY,
        metadata_payload_key: str = METADATA_KEY,
        vector_name: str = VECTOR_NAME,
        retrieval_mode: RetrievalMode = RetrievalMode.DENSE,
-        sparse_embedding: Optional[SparseEmbeddings] = None,
+        sparse_embedding: SparseEmbeddings | None = None,
        sparse_vector_name: str = SPARSE_VECTOR_NAME,
-        collection_create_options: Optional[dict[str, Any]] = None,
-        vector_params: Optional[dict[str, Any]] = None,
-        sparse_vector_params: Optional[dict[str, Any]] = None,
+        collection_create_options: dict[str, Any] | None = None,
+        vector_params: dict[str, Any] | None = None,
+        sparse_vector_params: dict[str, Any] | None = None,
        batch_size: int = 64,
        force_recreate: bool = False,  # noqa: FBT001, FBT002
        validate_embeddings: bool = True,  # noqa: FBT001, FBT002
@@ -442,25 +440,25 @@ class QdrantVectorStore(VectorStore):
    def from_existing_collection(
        cls: type[QdrantVectorStore],
        collection_name: str,
-        embedding: Optional[Embeddings] = None,
+        embedding: Embeddings | None = None,
        retrieval_mode: RetrievalMode = RetrievalMode.DENSE,
-        location: Optional[str] = None,
-        url: Optional[str] = None,
-        port: Optional[int] = 6333,
+        location: str | None = None,
+        url: str | None = None,
+        port: int | None = 6333,
        grpc_port: int = 6334,
        prefer_grpc: bool = False,  # noqa: FBT001, FBT002
-        https: Optional[bool] = None,  # noqa: FBT001
-        api_key: Optional[str] = None,
-        prefix: Optional[str] = None,
-        timeout: Optional[int] = None,
-        host: Optional[str] = None,
-        path: Optional[str] = None,
+        https: bool | None = None,  # noqa: FBT001
+        api_key: str | None = None,
+        prefix: str | None = None,
+        timeout: int | None = None,
+        host: str | None = None,
+        path: str | None = None,
        distance: models.Distance = models.Distance.COSINE,
        content_payload_key: str = CONTENT_KEY,
        metadata_payload_key: str = METADATA_KEY,
        vector_name: str = VECTOR_NAME,
        sparse_vector_name: str = SPARSE_VECTOR_NAME,
-        sparse_embedding: Optional[SparseEmbeddings] = None,
+        sparse_embedding: SparseEmbeddings | None = None,
        validate_embeddings: bool = True,  # noqa: FBT001, FBT002
        validate_collection_config: bool = True,  # noqa: FBT001, FBT002
        **kwargs: Any,
@@ -503,8 +501,8 @@ class QdrantVectorStore(VectorStore):
    def add_texts(  # type: ignore[override]
        self,
        texts: Iterable[str],
-        metadatas: Optional[list[dict]] = None,
-        ids: Optional[Sequence[str | int]] = None,
+        metadatas: list[dict] | None = None,
+        ids: Sequence[str | int] | None = None,
        batch_size: int = 64,
        **kwargs: Any,
    ) -> list[str | int]:
@@ -529,12 +527,12 @@ class QdrantVectorStore(VectorStore):
        self,
        query: str,
        k: int = 4,
-        filter: Optional[models.Filter] = None,  # noqa: A002
-        search_params: Optional[models.SearchParams] = None,
+        filter: models.Filter | None = None,  # noqa: A002
+        search_params: models.SearchParams | None = None,
        offset: int = 0,
-        score_threshold: Optional[float] = None,
-        consistency: Optional[models.ReadConsistency] = None,
-        hybrid_fusion: Optional[models.FusionQuery] = None,
+        score_threshold: float | None = None,
+        consistency: models.ReadConsistency | None = None,
+        hybrid_fusion: models.FusionQuery | None = None,
        **kwargs: Any,
    ) -> list[Document]:
        """Return docs most similar to query.
@@ -560,12 +558,12 @@ class QdrantVectorStore(VectorStore):
        self,
        query: str,
        k: int = 4,
-        filter: Optional[models.Filter] = None,  # noqa: A002
-        search_params: Optional[models.SearchParams] = None,
+        filter: models.Filter | None = None,  # noqa: A002
+        search_params: models.SearchParams | None = None,
        offset: int = 0,
-        score_threshold: Optional[float] = None,
-        consistency: Optional[models.ReadConsistency] = None,
-        hybrid_fusion: Optional[models.FusionQuery] = None,
+        score_threshold: float | None = None,
+        consistency: models.ReadConsistency | None = None,
+        hybrid_fusion: models.FusionQuery | None = None,
        **kwargs: Any,
    ) -> list[tuple[Document, float]]:
        """Return docs most similar to query.
@@ -654,11 +652,11 @@ class QdrantVectorStore(VectorStore):
        self,
        embedding: list[float],
        k: int = 4,
-        filter: Optional[models.Filter] = None,  # noqa: A002
-        search_params: Optional[models.SearchParams] = None,
+        filter: models.Filter | None = None,  # noqa: A002
+        search_params: models.SearchParams | None = None,
        offset: int = 0,
-        score_threshold: Optional[float] = None,
-        consistency: Optional[models.ReadConsistency] = None,
+        score_threshold: float | None = None,
+        consistency: models.ReadConsistency | None = None,
        **kwargs: Any,
    ) -> list[tuple[Document, float]]:
        """Return docs most similar to embedding vector.
@@ -708,11 +706,11 @@ class QdrantVectorStore(VectorStore):
        self,
        embedding: list[float],
        k: int = 4,
-        filter: Optional[models.Filter] = None,  # noqa: A002
-        search_params: Optional[models.SearchParams] = None,
+        filter: models.Filter | None = None,  # noqa: A002
+        search_params: models.SearchParams | None = None,
        offset: int = 0,
-        score_threshold: Optional[float] = None,
-        consistency: Optional[models.ReadConsistency] = None,
+        score_threshold: float | None = None,
+        consistency: models.ReadConsistency | None = None,
        **kwargs: Any,
    ) -> list[Document]:
        """Return docs most similar to embedding vector.
@@ -739,10 +737,10 @@ class QdrantVectorStore(VectorStore):
        k: int = 4,
        fetch_k: int = 20,
        lambda_mult: float = 0.5,
-        filter: Optional[models.Filter] = None,  # noqa: A002
-        search_params: Optional[models.SearchParams] = None,
-        score_threshold: Optional[float] = None,
-        consistency: Optional[models.ReadConsistency] = None,
+        filter: models.Filter | None = None,  # noqa: A002
+        search_params: models.SearchParams | None = None,
+        score_threshold: float | None = None,
+        consistency: models.ReadConsistency | None = None,
        **kwargs: Any,
    ) -> list[Document]:
        """Return docs selected using the maximal marginal relevance with dense vectors.
@@ -782,10 +780,10 @@ class QdrantVectorStore(VectorStore):
        k: int = 4,
        fetch_k: int = 20,
        lambda_mult: float = 0.5,
-        filter: Optional[models.Filter] = None,  # noqa: A002
-        search_params: Optional[models.SearchParams] = None,
-        score_threshold: Optional[float] = None,
-        consistency: Optional[models.ReadConsistency] = None,
+        filter: models.Filter | None = None,  # noqa: A002
+        search_params: models.SearchParams | None = None,
+        score_threshold: float | None = None,
+        consistency: models.ReadConsistency | None = None,
        **kwargs: Any,
    ) -> list[Document]:
        """Return docs selected using the maximal marginal relevance with dense vectors.
@@ -816,10 +814,10 @@ class QdrantVectorStore(VectorStore):
        k: int = 4,
        fetch_k: int = 20,
        lambda_mult: float = 0.5,
-        filter: Optional[models.Filter] = None,  # noqa: A002
-        search_params: Optional[models.SearchParams] = None,
-        score_threshold: Optional[float] = None,
-        consistency: Optional[models.ReadConsistency] = None,
+        filter: models.Filter | None = None,  # noqa: A002
+        search_params: models.SearchParams | None = None,
+        score_threshold: float | None = None,
+        consistency: models.ReadConsistency | None = None,
        **kwargs: Any,
    ) -> list[tuple[Document, float]]:
        """Return docs selected using the maximal marginal relevance.
@@ -870,9 +868,9 @@ class QdrantVectorStore(VectorStore):

    def delete(  # type: ignore[override]
        self,
-        ids: Optional[list[str | int]] = None,
+        ids: list[str | int] | None = None,
        **kwargs: Any,
-    ) -> Optional[bool]:
+    ) -> bool | None:
        """Delete documents by their ids.

        Args:
@@ -905,20 +903,20 @@ class QdrantVectorStore(VectorStore):
    @classmethod
    def construct_instance(
        cls: type[QdrantVectorStore],
-        embedding: Optional[Embeddings] = None,
+        embedding: Embeddings | None = None,
        retrieval_mode: RetrievalMode = RetrievalMode.DENSE,
-        sparse_embedding: Optional[SparseEmbeddings] = None,
-        client_options: Optional[dict[str, Any]] = None,
-        collection_name: Optional[str] = None,
+        sparse_embedding: SparseEmbeddings | None = None,
+        client_options: dict[str, Any] | None = None,
+        collection_name: str | None = None,
        distance: models.Distance = models.Distance.COSINE,
        content_payload_key: str = CONTENT_KEY,
        metadata_payload_key: str = METADATA_KEY,
        vector_name: str = VECTOR_NAME,
        sparse_vector_name: str = SPARSE_VECTOR_NAME,
        force_recreate: bool = False,  # noqa: FBT001, FBT002
-        collection_create_options: Optional[dict[str, Any]] = None,
-        vector_params: Optional[dict[str, Any]] = None,
-        sparse_vector_params: Optional[dict[str, Any]] = None,
+        collection_create_options: dict[str, Any] | None = None,
+        vector_params: dict[str, Any] | None = None,
+        sparse_vector_params: dict[str, Any] | None = None,
        validate_embeddings: bool = True,  # noqa: FBT001, FBT002
        validate_collection_config: bool = True,  # noqa: FBT001, FBT002
    ) -> QdrantVectorStore:
@@ -1053,8 +1051,8 @@ class QdrantVectorStore(VectorStore):
    def _generate_batches(
        self,
        texts: Iterable[str],
-        metadatas: Optional[list[dict]] = None,
-        ids: Optional[Sequence[str | int]] = None,
+        metadatas: list[dict] | None = None,
+        ids: Sequence[str | int] | None = None,
        batch_size: int = 64,
    ) -> Generator[tuple[list[str | int], list[models.PointStruct]], Any, None]:
        texts_iterator = iter(texts)
@@ -1088,7 +1086,7 @@ class QdrantVectorStore(VectorStore):
    @staticmethod
    def _build_payloads(
        texts: Iterable[str],
-        metadatas: Optional[list[dict]],
+        metadatas: list[dict] | None,
        content_payload_key: str,
        metadata_payload_key: str,
    ) -> list[dict]:
@@ -1170,7 +1168,7 @@ class QdrantVectorStore(VectorStore):
        vector_name: str,
        sparse_vector_name: str,
        distance: models.Distance,
-        embedding: Optional[Embeddings],
+        embedding: Embeddings | None,
    ) -> None:
        if retrieval_mode == RetrievalMode.DENSE:
            cls._validate_collection_for_dense(
@@ -1197,7 +1195,7 @@ class QdrantVectorStore(VectorStore):
        collection_name: str,
        vector_name: str,
        distance: models.Distance,
-        dense_embeddings: Union[Embeddings, list[float], None],
+        dense_embeddings: Embeddings | list[float] | None,
    ) -> None:
        collection_info = client.get_collection(collection_name=collection_name)
        vector_config = collection_info.config.params.vectors
@@ -1289,8 +1287,8 @@ class QdrantVectorStore(VectorStore):
    def _validate_embeddings(
        cls: type[QdrantVectorStore],
        retrieval_mode: RetrievalMode,
-        embedding: Optional[Embeddings],
-        sparse_embedding: Optional[SparseEmbeddings],
+        embedding: Embeddings | None,
+        sparse_embedding: SparseEmbeddings | None,
    ) -> None:
        if retrieval_mode == RetrievalMode.DENSE and embedding is None:
            msg = "'embedding' cannot be None when retrieval mode is 'dense'"