chore!: qdrant - drop Python 3.9 and use X|Y typing

integrations/qdrant/pyproject.toml

@@ -7,7 +7,7 @@ name = "qdrant-haystack"
 dynamic = ["version"]
 description = 'An integration of Qdrant ANN vector database backend with Haystack'
 readme = "README.md"
-requires-python = ">=3.9"
+requires-python = ">=3.10"
 license = "Apache-2.0"
 keywords = []
 authors = [
@@ -18,15 +18,14 @@ classifiers = [
   "License :: OSI Approved :: Apache Software License",
   "Development Status :: 4 - Beta",
   "Programming Language :: Python",
-  "Programming Language :: Python :: 3.9",
   "Programming Language :: Python :: 3.10",
   "Programming Language :: Python :: 3.11",
   "Programming Language :: Python :: 3.12",
   "Programming Language :: Python :: 3.13",
   "Programming Language :: Python :: Implementation :: CPython",
   "Programming Language :: Python :: Implementation :: PyPy",
 ]
-dependencies = ["haystack-ai>=2.11.0", "qdrant-client>=1.12.0"]
+dependencies = ["haystack-ai>=2.22.0", "qdrant-client>=1.12.0"]
 
 [project.urls]
 Source = "https://github.com/deepset-ai/haystack-core-integrations"
@@ -80,7 +79,6 @@ disallow_incomplete_defs = true
 
 
 [tool.ruff]
-target-version = "py39"
 line-length = 120
 
 [tool.ruff.lint]
@@ -132,10 +130,6 @@ ignore = [
   # Ignore assertions
   "S101",
 ]
-unfixable = [
-  # Don't touch unused imports
-  "F401",
-]
 
 [tool.ruff.lint.flake8-tidy-imports]
 ban-relative-imports = "parents"

integrations/qdrant/src/haystack_integrations/components/retrievers/qdrant/retriever.py

@@ -1,4 +1,4 @@
-from typing import Any, Optional, Union
+from typing import Any
 
 from haystack import Document, component, default_from_dict, default_to_dict
 from haystack.dataclasses.sparse_embedding import SparseEmbedding
@@ -43,14 +43,14 @@ class QdrantEmbeddingRetriever:
     def __init__(
         self,
         document_store: QdrantDocumentStore,
-        filters: Optional[Union[dict[str, Any], models.Filter]] = None,
+        filters: dict[str, Any] | models.Filter | None = None,
         top_k: int = 10,
         scale_score: bool = False,
         return_embedding: bool = False,
-        filter_policy: Union[str, FilterPolicy] = FilterPolicy.REPLACE,
-        score_threshold: Optional[float] = None,
-        group_by: Optional[str] = None,
-        group_size: Optional[int] = None,
+        filter_policy: str | FilterPolicy = FilterPolicy.REPLACE,
+        score_threshold: float | None = None,
+        group_by: str | None = None,
+        group_size: int | None = None,
     ) -> None:
         """
         Create a QdrantEmbeddingRetriever component.
@@ -134,13 +134,13 @@ def from_dict(cls, data: dict[str, Any]) -> "QdrantEmbeddingRetriever":
     def run(
         self,
         query_embedding: list[float],
-        filters: Optional[Union[dict[str, Any], models.Filter]] = None,
-        top_k: Optional[int] = None,
-        scale_score: Optional[bool] = None,
-        return_embedding: Optional[bool] = None,
-        score_threshold: Optional[float] = None,
-        group_by: Optional[str] = None,
-        group_size: Optional[int] = None,
+        filters: dict[str, Any] | models.Filter | None = None,
+        top_k: int | None = None,
+        scale_score: bool | None = None,
+        return_embedding: bool | None = None,
+        score_threshold: float | None = None,
+        group_by: str | None = None,
+        group_size: int | None = None,
     ) -> dict[str, list[Document]]:
         """
         Run the Embedding Retriever on the given input data.
@@ -189,13 +189,13 @@ def run(
     async def run_async(
         self,
         query_embedding: list[float],
-        filters: Optional[Union[dict[str, Any], models.Filter]] = None,
-        top_k: Optional[int] = None,
-        scale_score: Optional[bool] = None,
-        return_embedding: Optional[bool] = None,
-        score_threshold: Optional[float] = None,
-        group_by: Optional[str] = None,
-        group_size: Optional[int] = None,
+        filters: dict[str, Any] | models.Filter | None = None,
+        top_k: int | None = None,
+        scale_score: bool | None = None,
+        return_embedding: bool | None = None,
+        score_threshold: float | None = None,
+        group_by: str | None = None,
+        group_size: int | None = None,
     ) -> dict[str, list[Document]]:
         """
         Asynchronously run the Embedding Retriever on the given input data.
@@ -271,14 +271,14 @@ class QdrantSparseEmbeddingRetriever:
     def __init__(
         self,
         document_store: QdrantDocumentStore,
-        filters: Optional[Union[dict[str, Any], models.Filter]] = None,
+        filters: dict[str, Any] | models.Filter | None = None,
         top_k: int = 10,
         scale_score: bool = False,
         return_embedding: bool = False,
-        filter_policy: Union[str, FilterPolicy] = FilterPolicy.REPLACE,
-        score_threshold: Optional[float] = None,
-        group_by: Optional[str] = None,
-        group_size: Optional[int] = None,
+        filter_policy: str | FilterPolicy = FilterPolicy.REPLACE,
+        score_threshold: float | None = None,
+        group_by: str | None = None,
+        group_size: int | None = None,
     ) -> None:
         """
         Create a QdrantSparseEmbeddingRetriever component.
@@ -362,13 +362,13 @@ def from_dict(cls, data: dict[str, Any]) -> "QdrantSparseEmbeddingRetriever":
     def run(
         self,
         query_sparse_embedding: SparseEmbedding,
-        filters: Optional[Union[dict[str, Any], models.Filter]] = None,
-        top_k: Optional[int] = None,
-        scale_score: Optional[bool] = None,
-        return_embedding: Optional[bool] = None,
-        score_threshold: Optional[float] = None,
-        group_by: Optional[str] = None,
-        group_size: Optional[int] = None,
+        filters: dict[str, Any] | models.Filter | None = None,
+        top_k: int | None = None,
+        scale_score: bool | None = None,
+        return_embedding: bool | None = None,
+        score_threshold: float | None = None,
+        group_by: str | None = None,
+        group_size: int | None = None,
     ) -> dict[str, list[Document]]:
         """
         Run the Sparse Embedding Retriever on the given input data.
@@ -422,13 +422,13 @@ def run(
     async def run_async(
         self,
         query_sparse_embedding: SparseEmbedding,
-        filters: Optional[Union[dict[str, Any], models.Filter]] = None,
-        top_k: Optional[int] = None,
-        scale_score: Optional[bool] = None,
-        return_embedding: Optional[bool] = None,
-        score_threshold: Optional[float] = None,
-        group_by: Optional[str] = None,
-        group_size: Optional[int] = None,
+        filters: dict[str, Any] | models.Filter | None = None,
+        top_k: int | None = None,
+        scale_score: bool | None = None,
+        return_embedding: bool | None = None,
+        score_threshold: float | None = None,
+        group_by: str | None = None,
+        group_size: int | None = None,
     ) -> dict[str, list[Document]]:
         """
         Asynchronously run the Sparse Embedding Retriever on the given input data.
@@ -515,13 +515,13 @@ class QdrantHybridRetriever:
     def __init__(
         self,
         document_store: QdrantDocumentStore,
-        filters: Optional[Union[dict[str, Any], models.Filter]] = None,
+        filters: dict[str, Any] | models.Filter | None = None,
         top_k: int = 10,
         return_embedding: bool = False,
-        filter_policy: Union[str, FilterPolicy] = FilterPolicy.REPLACE,
-        score_threshold: Optional[float] = None,
-        group_by: Optional[str] = None,
-        group_size: Optional[int] = None,
+        filter_policy: str | FilterPolicy = FilterPolicy.REPLACE,
+        score_threshold: float | None = None,
+        group_by: str | None = None,
+        group_size: int | None = None,
     ) -> None:
         """
         Create a QdrantHybridRetriever component.
@@ -600,12 +600,12 @@ def run(
         self,
         query_embedding: list[float],
         query_sparse_embedding: SparseEmbedding,
-        filters: Optional[Union[dict[str, Any], models.Filter]] = None,
-        top_k: Optional[int] = None,
-        return_embedding: Optional[bool] = None,
-        score_threshold: Optional[float] = None,
-        group_by: Optional[str] = None,
-        group_size: Optional[int] = None,
+        filters: dict[str, Any] | models.Filter | None = None,
+        top_k: int | None = None,
+        return_embedding: bool | None = None,
+        score_threshold: float | None = None,
+        group_by: str | None = None,
+        group_size: int | None = None,
     ) -> dict[str, list[Document]]:
         """
         Run the Sparse Embedding Retriever on the given input data.
@@ -660,12 +660,12 @@ async def run_async(
         self,
         query_embedding: list[float],
         query_sparse_embedding: SparseEmbedding,
-        filters: Optional[Union[dict[str, Any], models.Filter]] = None,
-        top_k: Optional[int] = None,
-        return_embedding: Optional[bool] = None,
-        score_threshold: Optional[float] = None,
-        group_by: Optional[str] = None,
-        group_size: Optional[int] = None,
+        filters: dict[str, Any] | models.Filter | None = None,
+        top_k: int | None = None,
+        return_embedding: bool | None = None,
+        score_threshold: float | None = None,
+        group_by: str | None = None,
+        group_size: int | None = None,
     ) -> dict[str, list[Document]]:
         """
         Asynchronously run the Sparse Embedding Retriever on the given input data.

integrations/qdrant/src/haystack_integrations/document_stores/qdrant/converters.py

@@ -1,5 +1,4 @@
 import uuid
-from typing import Union
 
 from haystack import logging
 from haystack.dataclasses import Document
@@ -58,7 +57,7 @@ def convert_id(_id: str) -> str:
     return uuid.uuid5(UUID_NAMESPACE, _id).hex
 
 
-QdrantPoint = Union[rest.ScoredPoint, rest.Record]
+QdrantPoint = rest.ScoredPoint | rest.Record
 
 
 def convert_qdrant_point_to_haystack_document(point: QdrantPoint, use_sparse_embeddings: bool) -> Document: