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

integrations/google_genai/pyproject.toml

@@ -7,7 +7,7 @@ name = "google-genai-haystack"
 dynamic = ["version"]
 description = 'Use models like Gemini via Google Gen AI SDK'
 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>=2.19.0", "google-genai[aiohttp]>=1.18.0", "jsonref>=1.0.0"]
+dependencies = ["haystack-ai>=2.22.0", "google-genai[aiohttp]>=1.18.0", "jsonref>=1.0.0"]
 
 [project.urls]
 Documentation = "https://github.com/deepset-ai/haystack-core-integrations/tree/main/integrations/google_genai#readme"
@@ -87,7 +86,6 @@ module = [
 ignore_missing_imports = true
 
 [tool.ruff]
-target-version = "py39"
 line-length = 120
 
 [tool.ruff.lint]
@@ -140,10 +138,6 @@ ignore = [
   # Allow function call argument defaults e.g. `Secret.from_env_var`
   "B008",
 ]
-unfixable = [
-  # Don't touch unused imports
-  "F401",
-]
 
 [tool.ruff.lint.isort]
 known-first-party = ["haystack_integrations"]

integrations/google_genai/src/haystack_integrations/components/common/google_genai/utils.py

@@ -2,7 +2,7 @@
 #
 # SPDX-License-Identifier: Apache-2.0
 
-from typing import Literal, Optional
+from typing import Literal
 
 from google.genai import Client
 from haystack import logging
@@ -14,8 +14,8 @@
 def _get_client(
     api_key: Secret,
     api: Literal["gemini", "vertex"],
-    vertex_ai_project: Optional[str],
-    vertex_ai_location: Optional[str],
+    vertex_ai_project: str | None,
+    vertex_ai_location: str | None,
 ) -> Client:
     """
     Internal utility function to get a Google GenAI client.

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

@@ -2,7 +2,7 @@
 #
 # SPDX-License-Identifier: Apache-2.0
 
-from typing import Any, Literal, Optional, Union
+from typing import Any, Literal
 
 from google.genai import types
 from haystack import Document, component, default_from_dict, default_to_dict, logging
@@ -75,16 +75,16 @@ def __init__(
         *,
         api_key: Secret = Secret.from_env_var(["GOOGLE_API_KEY", "GEMINI_API_KEY"], strict=False),
         api: Literal["gemini", "vertex"] = "gemini",
-        vertex_ai_project: Optional[str] = None,
-        vertex_ai_location: Optional[str] = None,
+        vertex_ai_project: str | None = None,
+        vertex_ai_location: str | None = None,
         model: str = "text-embedding-004",
         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",
-        config: Optional[dict[str, Any]] = None,
+        config: dict[str, Any] | None = None,
     ) -> None:
         """
         Creates an GoogleGenAIDocumentEmbedder component.
@@ -195,7 +195,7 @@ def _prepare_texts_to_embed(self, documents: list[Document]) -> list[str]:
 
     def _embed_batch(
         self, texts_to_embed: list[str], batch_size: int
-    ) -> tuple[list[Optional[list[float]]], dict[str, Any]]:
+    ) -> tuple[list[list[float] | None], dict[str, Any]]:
         """
         Embed a list of texts in batches.
         """
@@ -227,7 +227,7 @@ def _embed_batch(
 
     async def _embed_batch_async(
         self, texts_to_embed: list[str], batch_size: int
-    ) -> tuple[list[Optional[list[float]]], dict[str, Any]]:
+    ) -> tuple[list[list[float] | None], dict[str, Any]]:
         """
         Embed a list of texts in batches asynchronously.
         """
@@ -257,7 +257,7 @@ async def _embed_batch_async(
         return all_embeddings, meta
 
     @component.output_types(documents=list[Document], meta=dict[str, Any])
-    def run(self, documents: list[Document]) -> Union[dict[str, list[Document]], dict[str, Any]]:
+    def run(self, documents: list[Document]) -> dict[str, list[Document]] | dict[str, Any]:
         """
         Embeds a list of documents.
 
@@ -281,13 +281,13 @@ def run(self, documents: list[Document]) -> Union[dict[str, list[Document]], dic
         meta: dict[str, Any]
         embeddings, meta = self._embed_batch(texts_to_embed=texts_to_embed, batch_size=self._batch_size)
 
-        for doc, emb in zip(documents, embeddings):
+        for doc, emb in zip(documents, embeddings, strict=True):
             doc.embedding = emb
 
         return {"documents": documents, "meta": meta}
 
     @component.output_types(documents=list[Document], meta=dict[str, Any])
-    async def run_async(self, documents: list[Document]) -> Union[dict[str, list[Document]], dict[str, Any]]:
+    async def run_async(self, documents: list[Document]) -> dict[str, list[Document]] | dict[str, Any]:
         """
         Embeds a list of documents asynchronously.
 
@@ -310,7 +310,7 @@ async def run_async(self, documents: list[Document]) -> Union[dict[str, list[Doc
 
         embeddings, meta = await self._embed_batch_async(texts_to_embed=texts_to_embed, batch_size=self._batch_size)
 
-        for doc, emb in zip(documents, embeddings):
+        for doc, emb in zip(documents, embeddings, strict=True):
             doc.embedding = emb
 
         return {"documents": documents, "meta": meta}

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

@@ -2,7 +2,7 @@
 #
 # SPDX-License-Identifier: Apache-2.0
 
-from typing import Any, Literal, Optional, Union
+from typing import Any, Literal
 
 from google.genai import types
 from haystack import component, default_from_dict, default_to_dict, logging
@@ -76,12 +76,12 @@ def __init__(
         *,
         api_key: Secret = Secret.from_env_var(["GOOGLE_API_KEY", "GEMINI_API_KEY"], strict=False),
         api: Literal["gemini", "vertex"] = "gemini",
-        vertex_ai_project: Optional[str] = None,
-        vertex_ai_location: Optional[str] = None,
+        vertex_ai_project: str | None = None,
+        vertex_ai_location: str | None = None,
         model: str = "text-embedding-004",
         prefix: str = "",
         suffix: str = "",
-        config: Optional[dict[str, Any]] = None,
+        config: dict[str, Any] | None = None,
     ) -> None:
         """
         Creates an GoogleGenAITextEmbedder component.
@@ -177,7 +177,7 @@ def _prepare_output(self, result: types.EmbedContentResponse) -> dict[str, Any]:
         return {"embedding": embedding, "meta": {"model": self._model_name}}
 
     @component.output_types(embedding=list[float], meta=dict[str, Any])
-    def run(self, text: str) -> Union[dict[str, list[float]], dict[str, Any]]:
+    def run(self, text: str) -> dict[str, list[float]] | dict[str, Any]:
         """
         Embeds a single string.
 
@@ -194,7 +194,7 @@ def run(self, text: str) -> Union[dict[str, list[float]], dict[str, Any]]:
         return self._prepare_output(result=response)
 
     @component.output_types(embedding=list[float], meta=dict[str, Any])
-    async def run_async(self, text: str) -> Union[dict[str, list[float]], dict[str, Any]]:
+    async def run_async(self, text: str) -> dict[str, list[float]] | dict[str, Any]:
         """
         Asynchronously embed a single string.
 

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

@@ -6,7 +6,7 @@
 import json
 from collections.abc import AsyncIterator, Iterator
 from datetime import datetime, timezone
-from typing import Any, Literal, Optional
+from typing import Any, Literal
 
 from google.genai import types
 from haystack import logging
@@ -456,13 +456,13 @@ def __init__(
         *,
         api_key: Secret = Secret.from_env_var(["GOOGLE_API_KEY", "GEMINI_API_KEY"], strict=False),
         api: Literal["gemini", "vertex"] = "gemini",
-        vertex_ai_project: Optional[str] = None,
-        vertex_ai_location: Optional[str] = None,
+        vertex_ai_project: str | None = None,
+        vertex_ai_location: str | None = None,
         model: str = "gemini-2.5-flash",
-        generation_kwargs: Optional[dict[str, Any]] = None,
-        safety_settings: Optional[list[dict[str, Any]]] = None,
-        streaming_callback: Optional[StreamingCallbackT] = None,
-        tools: Optional[ToolsType] = None,
+        generation_kwargs: dict[str, Any] | None = None,
+        safety_settings: list[dict[str, Any]] | None = None,
+        streaming_callback: StreamingCallbackT | None = None,
+        tools: ToolsType | None = None,
     ):
         """
         Initialize a GoogleGenAIChatGenerator instance.
@@ -801,10 +801,10 @@ def _process_thinking_config(self, generation_kwargs: dict[str, Any]) -> dict[st
     def run(
         self,
         messages: list[ChatMessage],
-        generation_kwargs: Optional[dict[str, Any]] = None,
-        safety_settings: Optional[list[dict[str, Any]]] = None,
-        streaming_callback: Optional[StreamingCallbackT] = None,
-        tools: Optional[ToolsType] = None,
+        generation_kwargs: dict[str, Any] | None = None,
+        safety_settings: list[dict[str, Any]] | None = None,
+        streaming_callback: StreamingCallbackT | None = None,
+        tools: ToolsType | None = None,
     ) -> dict[str, Any]:
         """
         Run the Google Gen AI chat generator on the given input data.
@@ -909,10 +909,10 @@ def run(
     async def run_async(
         self,
         messages: list[ChatMessage],
-        generation_kwargs: Optional[dict[str, Any]] = None,
-        safety_settings: Optional[list[dict[str, Any]]] = None,
-        streaming_callback: Optional[StreamingCallbackT] = None,
-        tools: Optional[ToolsType] = None,
+        generation_kwargs: dict[str, Any] | None = None,
+        safety_settings: list[dict[str, Any]] | None = None,
+        streaming_callback: StreamingCallbackT | None = None,
+        tools: ToolsType | None = None,
     ) -> dict[str, Any]:
         """
         Async version of the run method. Run the Google Gen AI chat generator on the given input data.

integrations/google_genai/src/haystack_integrations/components/generators/google_genai/chat/utils.py

@@ -2,12 +2,12 @@
 #
 # SPDX-License-Identifier: Apache-2.0
 
-from typing import Any, Union
+from typing import Any
 
 
 def remove_key_from_schema(
-    schema: Union[dict[str, Any], list[Any], Any], target_key: str
-) -> Union[dict[str, Any], list[Any], Any]:
+    schema: dict[str, Any] | list[Any] | Any, target_key: str
+) -> dict[str, Any] | list[Any] | Any:
     """
     Recursively traverse a schema and remove all occurrences of the target key.