chore!: mistral - drop Python 3.9 and use X|Y typing (#2716)

Author: anakin87

integrations/mistral/pyproject.toml

@@ -7,23 +7,22 @@ name = "mistral-haystack"
 dynamic = ["version"]
 description = ''
 readme = "README.md"
-requires-python = ">=3.9"
+requires-python = ">=3.10"
 license = "Apache-2.0"
 keywords = []
 authors = [{ name = "deepset GmbH", email = "info@deepset.ai" }]
 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>=2.19.0", "mistralai>=1.9.11"]
+dependencies = ["haystack-ai>=2.22.0", "mistralai>=1.9.11"]
 
 [project.urls]
 Documentation = "https://github.com/deepset-ai/haystack-core-integrations/tree/main/integrations/mistral#readme"
@@ -78,7 +77,6 @@ check_untyped_defs = true
 disallow_incomplete_defs = true
 
 [tool.ruff]
-target-version = "py39"
 line-length = 120
 
 [tool.ruff.lint]
@@ -125,10 +123,6 @@ ignore = [
   "B008",
   "S101",
 ]
-unfixable = [
-  # Don't touch unused imports
-  "F401",
-]
 
 [tool.ruff.lint.isort]
 known-first-party = ["haystack_integrations"]

integrations/mistral/src/haystack_integrations/components/converters/mistral/ocr_document_converter.py

@@ -1,7 +1,7 @@
 import json
 import re
 from pathlib import Path
-from typing import Any, Optional, Union
+from typing import Any
 
 from haystack import Document, component, default_from_dict, default_to_dict, logging
 from haystack.components.converters.utils import (
@@ -103,9 +103,9 @@ def __init__(
         api_key: Secret = Secret.from_env_var("MISTRAL_API_KEY"),
         model: str = "mistral-ocr-2505",
         include_image_base64: bool = False,
-        pages: Optional[list[int]] = None,
-        image_limit: Optional[int] = None,
-        image_min_size: Optional[int] = None,
+        pages: list[int] | None = None,
+        image_limit: int | None = None,
+        image_min_size: int | None = None,
         cleanup_uploaded_files: bool = True,
     ):
         """
@@ -175,10 +175,10 @@ def from_dict(cls, data: dict[str, Any]) -> "MistralOCRDocumentConverter":
     @component.output_types(documents=list[Document], raw_mistral_response=list[dict[str, Any]])
     def run(
         self,
-        sources: list[Union[str, Path, ByteStream, DocumentURLChunk, FileChunk, ImageURLChunk]],
-        meta: Optional[Union[dict[str, Any], list[dict[str, Any]]]] = None,
-        bbox_annotation_schema: Optional[type[BaseModel]] = None,
-        document_annotation_schema: Optional[type[BaseModel]] = None,
+        sources: list[str | Path | ByteStream | DocumentURLChunk | FileChunk | ImageURLChunk],
+        meta: dict[str, Any] | list[dict[str, Any]] | None = None,
+        bbox_annotation_schema: type[BaseModel] | None = None,
+        document_annotation_schema: type[BaseModel] | None = None,
     ) -> dict[str, Any]:
         """
         Extract text from documents using Mistral OCR.
@@ -234,7 +234,7 @@ def run(
         raw_responses = []
         uploaded_file_ids = []
 
-        for source, user_metadata in zip(sources, meta_list):
+        for source, user_metadata in zip(sources, meta_list, strict=True):
             document, raw_response, uploaded_file_id = self._process_single_source(
                 source,
                 user_metadata,
@@ -259,12 +259,12 @@ def run(
 
     def _process_single_source(
         self,
-        source: Union[str, Path, ByteStream, DocumentURLChunk, FileChunk, ImageURLChunk],
+        source: str | Path | ByteStream | DocumentURLChunk | FileChunk | ImageURLChunk,
         user_metadata: dict[str, Any],
-        bbox_annotation_format: Optional[Any],
-        document_annotation_format: Optional[Any],
-        document_annotation_schema: Optional[type[BaseModel]],
-    ) -> tuple[Optional[Document], Optional[dict[str, Any]], Optional[str]]:
+        bbox_annotation_format: Any | None,
+        document_annotation_format: Any | None,
+        document_annotation_schema: type[BaseModel] | None,
+    ) -> tuple[Document | None, dict[str, Any] | None, str | None]:
         """
         Process a single source and return the document, raw response, and file_id if uploaded.
 
@@ -334,8 +334,8 @@ def _cleanup_uploaded_files(self, file_ids: list[str]) -> None:
 
     def _convert_source_to_chunk(
         self,
-        source: Union[str, Path, ByteStream, DocumentURLChunk, FileChunk, ImageURLChunk],
-    ) -> Union[DocumentURLChunk, FileChunk, ImageURLChunk]:
+        source: str | Path | ByteStream | DocumentURLChunk | FileChunk | ImageURLChunk,
+    ) -> DocumentURLChunk | FileChunk | ImageURLChunk:
         """
         Convert various source types to Mistral-compatible chunk format.
 
@@ -371,7 +371,7 @@ def _process_ocr_response(
         self,
         ocr_response: OCRResponse,
         user_metadata: dict[str, Any],
-        document_annotation_schema: Optional[type[BaseModel]],
+        document_annotation_schema: type[BaseModel] | None,
     ) -> Document:
         """
         Convert an OCR response from Mistral API into a single Haystack Document.

integrations/mistral/src/haystack_integrations/components/embedders/mistral/document_embedder.py

@@ -1,7 +1,7 @@
 # SPDX-FileCopyrightText: 2023-present deepset GmbH <info@deepset.ai>
 #
 # SPDX-License-Identifier: Apache-2.0
-from typing import Any, Optional
+from typing import Any
 
 from haystack import component, default_to_dict
 from haystack.components.embedders import OpenAIDocumentEmbedder
@@ -34,17 +34,17 @@ def __init__(
         self,
         api_key: Secret = Secret.from_env_var("MISTRAL_API_KEY"),
         model: str = "mistral-embed",
-        api_base_url: Optional[str] = "https://api.mistral.ai/v1",
+        api_base_url: str | None = "https://api.mistral.ai/v1",
         prefix: str = "",
         suffix: str = "",
         batch_size: int = 32,
         progress_bar: bool = True,
-        meta_fields_to_embed: Optional[list[str]] = None,
+        meta_fields_to_embed: list[str] | None = None,
         embedding_separator: str = "\n",
         *,
-        timeout: Optional[float] = None,
-        max_retries: Optional[int] = None,
-        http_client_kwargs: Optional[dict[str, Any]] = None,
+        timeout: float | None = None,
+        max_retries: int | None = None,
+        http_client_kwargs: dict[str, Any] | None = None,
     ):
         """
         Creates a MistralDocumentEmbedder component.

integrations/mistral/src/haystack_integrations/components/embedders/mistral/text_embedder.py

@@ -1,7 +1,7 @@
 # SPDX-FileCopyrightText: 2023-present deepset GmbH <info@deepset.ai>
 #
 # SPDX-License-Identifier: Apache-2.0
-from typing import Any, Optional
+from typing import Any
 
 from haystack import component, default_to_dict
 from haystack.components.embedders import OpenAITextEmbedder
@@ -32,13 +32,13 @@ def __init__(
         self,
         api_key: Secret = Secret.from_env_var("MISTRAL_API_KEY"),
         model: str = "mistral-embed",
-        api_base_url: Optional[str] = "https://api.mistral.ai/v1",
+        api_base_url: str | None = "https://api.mistral.ai/v1",
         prefix: str = "",
         suffix: str = "",
         *,
-        timeout: Optional[float] = None,
-        max_retries: Optional[int] = None,
-        http_client_kwargs: Optional[dict[str, Any]] = None,
+        timeout: float | None = None,
+        max_retries: int | None = None,
+        http_client_kwargs: dict[str, Any] | None = None,
     ):
         """
         Creates an MistralTextEmbedder component.

integrations/mistral/src/haystack_integrations/components/generators/mistral/chat/chat_generator.py

@@ -2,7 +2,7 @@
 #
 # SPDX-License-Identifier: Apache-2.0
 
-from typing import Any, Optional
+from typing import Any
 
 from haystack import component, default_to_dict, logging
 from haystack.components.generators.chat import OpenAIChatGenerator
@@ -63,14 +63,14 @@ def __init__(
         self,
         api_key: Secret = Secret.from_env_var("MISTRAL_API_KEY"),
         model: str = "mistral-small-latest",
-        streaming_callback: Optional[StreamingCallbackT] = None,
-        api_base_url: Optional[str] = "https://api.mistral.ai/v1",
-        generation_kwargs: Optional[dict[str, Any]] = None,
-        tools: Optional[ToolsType] = None,
+        streaming_callback: StreamingCallbackT | None = None,
+        api_base_url: str | None = "https://api.mistral.ai/v1",
+        generation_kwargs: dict[str, Any] | None = None,
+        tools: ToolsType | None = None,
         *,
-        timeout: Optional[float] = None,
-        max_retries: Optional[int] = None,
-        http_client_kwargs: Optional[dict[str, Any]] = None,
+        timeout: float | None = None,
+        max_retries: int | None = None,
+        http_client_kwargs: dict[str, Any] | None = None,
     ):
         """
         Creates an instance of MistralChatGenerator. Unless specified otherwise in the `model`, this is for Mistral's
@@ -137,10 +137,10 @@ def _prepare_api_call(
         self,
         *,
         messages: list[ChatMessage],
-        streaming_callback: Optional[StreamingCallbackT] = None,
-        generation_kwargs: Optional[dict[str, Any]] = None,
-        tools: Optional[ToolsType] = None,
-        tools_strict: Optional[bool] = None,
+        streaming_callback: StreamingCallbackT | None = None,
+        generation_kwargs: dict[str, Any] | None = None,
+        tools: ToolsType | None = None,
+        tools_strict: bool | None = None,
     ) -> dict[str, Any]:
         api_args = super(MistralChatGenerator, self)._prepare_api_call(  # noqa: UP008
             messages=messages,