diff --git a/integrations/cometapi/pyproject.toml b/integrations/cometapi/pyproject.toml
index 2b31e2288f..03289b72da 100644
--- a/integrations/cometapi/pyproject.toml
+++ b/integrations/cometapi/pyproject.toml
@@ -85,7 +85,7 @@ module = [
 ignore_missing_imports = true
 
 [tool.ruff]
-target-version = "py38"
+target-version = "py39"
 line-length = 120
 
 [tool.ruff.lint]
diff --git a/integrations/cometapi/src/haystack_integrations/components/generators/cometapi/chat/chat_generator.py b/integrations/cometapi/src/haystack_integrations/components/generators/cometapi/chat/chat_generator.py
index cc389302e8..5586eb928f 100644
--- a/integrations/cometapi/src/haystack_integrations/components/generators/cometapi/chat/chat_generator.py
+++ b/integrations/cometapi/src/haystack_integrations/components/generators/cometapi/chat/chat_generator.py
@@ -1,4 +1,4 @@
-from typing import Any, Dict, List, Optional, Union
+from typing import Any, Optional, Union
 
 from haystack.components.generators.chat import OpenAIChatGenerator
 from haystack.dataclasses import StreamingCallbackT
@@ -35,12 +35,12 @@ def __init__(
         api_key: Secret = Secret.from_env_var("COMET_API_KEY"),
         model: str = "gpt-4o-mini",
         streaming_callback: Optional[StreamingCallbackT] = None,
-        generation_kwargs: Optional[Dict[str, Any]] = None,
+        generation_kwargs: Optional[dict[str, Any]] = None,
         timeout: Optional[int] = None,
         max_retries: Optional[int] = None,
-        tools: Optional[Union[List[Union[Tool, Toolset]], Toolset]] = None,
+        tools: Optional[Union[list[Union[Tool, Toolset]], Toolset]] = None,
         tools_strict: bool = False,
-        http_client_kwargs: Optional[Dict[str, Any]] = None,
+        http_client_kwargs: Optional[dict[str, Any]] = None,
     ):
         api_base_url = "https://api.cometapi.com/v1"
 
diff --git a/integrations/elasticsearch/pyproject.toml b/integrations/elasticsearch/pyproject.toml
index b1a337e939..7716242fa1 100644
--- a/integrations/elasticsearch/pyproject.toml
+++ b/integrations/elasticsearch/pyproject.toml
@@ -83,7 +83,7 @@ disallow_incomplete_defs = true
 allow-direct-references = true
 
 [tool.ruff]
-target-version = "py38"
+target-version = "py39"
 line-length = 120
 
 [tool.ruff.lint]
diff --git a/integrations/elasticsearch/src/haystack_integrations/components/retrievers/elasticsearch/bm25_retriever.py b/integrations/elasticsearch/src/haystack_integrations/components/retrievers/elasticsearch/bm25_retriever.py
index 8576924c4a..d61409fadf 100644
--- a/integrations/elasticsearch/src/haystack_integrations/components/retrievers/elasticsearch/bm25_retriever.py
+++ b/integrations/elasticsearch/src/haystack_integrations/components/retrievers/elasticsearch/bm25_retriever.py
@@ -1,7 +1,7 @@
 # SPDX-FileCopyrightText: 2023-present deepset GmbH <info@deepset.ai>
 #
 # SPDX-License-Identifier: Apache-2.0
-from typing import Any, Dict, List, Optional, Union
+from typing import Any, Optional, Union
 
 from haystack import component, default_from_dict, default_to_dict
 from haystack.dataclasses import Document
@@ -47,7 +47,7 @@ def __init__(
         self,
         *,
         document_store: ElasticsearchDocumentStore,
-        filters: Optional[Dict[str, Any]] = None,
+        filters: Optional[dict[str, Any]] = None,
         fuzziness: str = "AUTO",
         top_k: int = 10,
         scale_score: bool = False,
@@ -79,7 +79,7 @@ def __init__(
         self._scale_score = scale_score
         self._filter_policy = FilterPolicy.from_str(filter_policy) if isinstance(filter_policy, str) else filter_policy
 
-    def to_dict(self) -> Dict[str, Any]:
+    def to_dict(self) -> dict[str, Any]:
         """
         Serializes the component to a dictionary.
 
@@ -97,7 +97,7 @@ def to_dict(self) -> Dict[str, Any]:
         )
 
     @classmethod
-    def from_dict(cls, data: Dict[str, Any]) -> "ElasticsearchBM25Retriever":
+    def from_dict(cls, data: dict[str, Any]) -> "ElasticsearchBM25Retriever":
         """
         Deserializes the component from a dictionary.
 
@@ -115,10 +115,10 @@ def from_dict(cls, data: Dict[str, Any]) -> "ElasticsearchBM25Retriever":
             data["init_parameters"]["filter_policy"] = FilterPolicy.from_str(filter_policy)
         return default_from_dict(cls, data)
 
-    @component.output_types(documents=List[Document])
+    @component.output_types(documents=list[Document])
     def run(
-        self, query: str, filters: Optional[Dict[str, Any]] = None, top_k: Optional[int] = None
-    ) -> Dict[str, List[Document]]:
+        self, query: str, filters: Optional[dict[str, Any]] = None, top_k: Optional[int] = None
+    ) -> dict[str, list[Document]]:
         """
         Retrieve documents using the BM25 keyword-based algorithm.
 
@@ -140,10 +140,10 @@ def run(
         )
         return {"documents": docs}
 
-    @component.output_types(documents=List[Document])
+    @component.output_types(documents=list[Document])
     async def run_async(
-        self, query: str, filters: Optional[Dict[str, Any]] = None, top_k: Optional[int] = None
-    ) -> Dict[str, List[Document]]:
+        self, query: str, filters: Optional[dict[str, Any]] = None, top_k: Optional[int] = None
+    ) -> dict[str, list[Document]]:
         """
         Asynchronously retrieve documents using the BM25 keyword-based algorithm.
 
diff --git a/integrations/elasticsearch/src/haystack_integrations/components/retrievers/elasticsearch/embedding_retriever.py b/integrations/elasticsearch/src/haystack_integrations/components/retrievers/elasticsearch/embedding_retriever.py
index c9cb2cbe41..913d861230 100644
--- a/integrations/elasticsearch/src/haystack_integrations/components/retrievers/elasticsearch/embedding_retriever.py
+++ b/integrations/elasticsearch/src/haystack_integrations/components/retrievers/elasticsearch/embedding_retriever.py
@@ -1,7 +1,7 @@
 # SPDX-FileCopyrightText: 2023-present deepset GmbH <info@deepset.ai>
 #
 # SPDX-License-Identifier: Apache-2.0
-from typing import Any, Dict, List, Optional, Union
+from typing import Any, Optional, Union
 
 from haystack import component, default_from_dict, default_to_dict
 from haystack.dataclasses import Document
@@ -49,7 +49,7 @@ def __init__(
         self,
         *,
         document_store: ElasticsearchDocumentStore,
-        filters: Optional[Dict[str, Any]] = None,
+        filters: Optional[dict[str, Any]] = None,
         top_k: int = 10,
         num_candidates: Optional[int] = None,
         filter_policy: Union[str, FilterPolicy] = FilterPolicy.REPLACE,
@@ -78,7 +78,7 @@ def __init__(
         self._num_candidates = num_candidates
         self._filter_policy = FilterPolicy.from_str(filter_policy) if isinstance(filter_policy, str) else filter_policy
 
-    def to_dict(self) -> Dict[str, Any]:
+    def to_dict(self) -> dict[str, Any]:
         """
         Serializes the component to a dictionary.
 
@@ -95,7 +95,7 @@ def to_dict(self) -> Dict[str, Any]:
         )
 
     @classmethod
-    def from_dict(cls, data: Dict[str, Any]) -> "ElasticsearchEmbeddingRetriever":
+    def from_dict(cls, data: dict[str, Any]) -> "ElasticsearchEmbeddingRetriever":
         """
         Deserializes the component from a dictionary.
 
@@ -113,10 +113,10 @@ def from_dict(cls, data: Dict[str, Any]) -> "ElasticsearchEmbeddingRetriever":
             data["init_parameters"]["filter_policy"] = FilterPolicy.from_str(filter_policy)
         return default_from_dict(cls, data)
 
-    @component.output_types(documents=List[Document])
+    @component.output_types(documents=list[Document])
     def run(
-        self, query_embedding: List[float], filters: Optional[Dict[str, Any]] = None, top_k: Optional[int] = None
-    ) -> Dict[str, List[Document]]:
+        self, query_embedding: list[float], filters: Optional[dict[str, Any]] = None, top_k: Optional[int] = None
+    ) -> dict[str, list[Document]]:
         """
         Retrieve documents using a vector similarity metric.
 
@@ -138,10 +138,10 @@ def run(
         )
         return {"documents": docs}
 
-    @component.output_types(documents=List[Document])
+    @component.output_types(documents=list[Document])
     async def run_async(
-        self, query_embedding: List[float], filters: Optional[Dict[str, Any]] = None, top_k: Optional[int] = None
-    ) -> Dict[str, List[Document]]:
+        self, query_embedding: list[float], filters: Optional[dict[str, Any]] = None, top_k: Optional[int] = None
+    ) -> dict[str, list[Document]]:
         """
         Asynchronously retrieve documents using a vector similarity metric.
 
diff --git a/integrations/elasticsearch/src/haystack_integrations/document_stores/elasticsearch/document_store.py b/integrations/elasticsearch/src/haystack_integrations/document_stores/elasticsearch/document_store.py
index 0e1dd76b7f..800b103704 100644
--- a/integrations/elasticsearch/src/haystack_integrations/document_stores/elasticsearch/document_store.py
+++ b/integrations/elasticsearch/src/haystack_integrations/document_stores/elasticsearch/document_store.py
@@ -8,7 +8,7 @@
 
 
 from collections.abc import Mapping
-from typing import Any, Dict, List, Literal, Optional, Tuple, Union
+from typing import Any, Literal, Optional, Union
 
 import numpy as np
 from elastic_transport import NodeConfig
@@ -25,7 +25,7 @@
 
 logger = logging.getLogger(__name__)
 
-Hosts = Union[str, List[Union[str, Mapping[str, Union[str, int]], NodeConfig]]]
+Hosts = Union[str, list[Union[str, Mapping[str, Union[str, int]], NodeConfig]]]
 
 # document scores are essentially unbounded and will be scaled to values between 0 and 1 if scale_score is set to
 # True. Scaling uses the expit function (inverse of the logit function) after applying a scaling factor
@@ -71,7 +71,7 @@ def __init__(
         self,
         *,
         hosts: Optional[Hosts] = None,
-        custom_mapping: Optional[Dict[str, Any]] = None,
+        custom_mapping: Optional[dict[str, Any]] = None,
         index: str = "default",
         api_key: Secret = Secret.from_env_var("ELASTIC_API_KEY", strict=False),
         api_key_id: Secret = Secret.from_env_var("ELASTIC_API_KEY_ID", strict=False),
@@ -120,7 +120,7 @@ def __init__(
         self._kwargs = kwargs
         self._initialized = False
 
-        if self._custom_mapping and not isinstance(self._custom_mapping, Dict):
+        if self._custom_mapping and not isinstance(self._custom_mapping, dict):
             msg = "custom_mapping must be a dictionary"
             raise ValueError(msg)
 
@@ -186,7 +186,7 @@ def _ensure_initialized(self):
 
             self._initialized = True
 
-    def _handle_auth(self) -> Optional[Union[str, Tuple[str, str]]]:
+    def _handle_auth(self) -> Optional[Union[str, tuple[str, str]]]:
         """
         Handles authentication for the Elasticsearch client.
 
@@ -206,7 +206,7 @@ def _handle_auth(self) -> Optional[Union[str, Tuple[str, str]]]:
 
         """
 
-        api_key: Optional[Union[str, Tuple[str, str]]]  # make the type checker happy
+        api_key: Optional[Union[str, tuple[str, str]]]  # make the type checker happy
 
         api_key_resolved = self._api_key.resolve_value()
         api_key_id_resolved = self._api_key_id.resolve_value()
@@ -247,7 +247,7 @@ def async_client(self) -> AsyncElasticsearch:
         assert self._async_client is not None
         return self._async_client
 
-    def to_dict(self) -> Dict[str, Any]:
+    def to_dict(self) -> dict[str, Any]:
         """
         Serializes the component to a dictionary.
 
@@ -269,7 +269,7 @@ def to_dict(self) -> Dict[str, Any]:
         )
 
     @classmethod
-    def from_dict(cls, data: Dict[str, Any]) -> "ElasticsearchDocumentStore":
+    def from_dict(cls, data: dict[str, Any]) -> "ElasticsearchDocumentStore":
         """
         Deserializes the component from a dictionary.
 
@@ -300,7 +300,7 @@ async def count_documents_async(self) -> int:
         result = await self._async_client.count(index=self._index)  # type: ignore
         return result["count"]
 
-    def _search_documents(self, **kwargs: Any) -> List[Document]:
+    def _search_documents(self, **kwargs: Any) -> list[Document]:
         """
         Calls the Elasticsearch client's search method and handles pagination.
         """
@@ -308,7 +308,7 @@ def _search_documents(self, **kwargs: Any) -> List[Document]:
         if top_k is None and "knn" in kwargs and "k" in kwargs["knn"]:
             top_k = kwargs["knn"]["k"]
 
-        documents: List[Document] = []
+        documents: list[Document] = []
         from_ = 0
         # Handle pagination
         while True:
@@ -327,7 +327,7 @@ def _search_documents(self, **kwargs: Any) -> List[Document]:
                 break
         return documents
 
-    async def _search_documents_async(self, **kwargs: Any) -> List[Document]:
+    async def _search_documents_async(self, **kwargs: Any) -> list[Document]:
         """
         Asynchronously calls the Elasticsearch client's search method and handles pagination.
         """
@@ -335,7 +335,7 @@ async def _search_documents_async(self, **kwargs: Any) -> List[Document]:
         if top_k is None and "knn" in kwargs and "k" in kwargs["knn"]:
             top_k = kwargs["knn"]["k"]
 
-        documents: List[Document] = []
+        documents: list[Document] = []
         from_ = 0
 
         # handle pagination
@@ -352,7 +352,7 @@ async def _search_documents_async(self, **kwargs: Any) -> List[Document]:
 
         return documents
 
-    def filter_documents(self, filters: Optional[Dict[str, Any]] = None) -> List[Document]:
+    def filter_documents(self, filters: Optional[dict[str, Any]] = None) -> list[Document]:
         """
         The main query method for the document store. It retrieves all documents that match the filters.
 
@@ -370,7 +370,7 @@ def filter_documents(self, filters: Optional[Dict[str, Any]] = None) -> List[Doc
         documents = self._search_documents(query=query)
         return documents
 
-    async def filter_documents_async(self, filters: Optional[Dict[str, Any]] = None) -> List[Document]:
+    async def filter_documents_async(self, filters: Optional[dict[str, Any]] = None) -> list[Document]:
         """
         Asynchronously retrieves all documents that match the filters.
 
@@ -389,7 +389,7 @@ async def filter_documents_async(self, filters: Optional[Dict[str, Any]] = None)
         return documents
 
     @staticmethod
-    def _deserialize_document(hit: Dict[str, Any]) -> Document:
+    def _deserialize_document(hit: dict[str, Any]) -> Document:
         """
         Creates a `Document` from the search hit provided.
         This is mostly useful in self.filter_documents().
@@ -404,7 +404,7 @@ def _deserialize_document(hit: Dict[str, Any]) -> Document:
 
         return Document.from_dict(data)
 
-    def write_documents(self, documents: List[Document], policy: DuplicatePolicy = DuplicatePolicy.NONE) -> int:
+    def write_documents(self, documents: list[Document], policy: DuplicatePolicy = DuplicatePolicy.NONE) -> int:
         """
         Writes `Document`s to Elasticsearch.
 
@@ -482,7 +482,7 @@ def write_documents(self, documents: List[Document], policy: DuplicatePolicy = D
         return documents_written
 
     async def write_documents_async(
-        self, documents: List[Document], policy: DuplicatePolicy = DuplicatePolicy.NONE
+        self, documents: list[Document], policy: DuplicatePolicy = DuplicatePolicy.NONE
     ) -> int:
         """
         Asynchronously writes `Document`s to Elasticsearch.
@@ -550,7 +550,7 @@ async def write_documents_async(
             msg = f"Failed to write documents to Elasticsearch: {e!s}"
             raise DocumentStoreError(msg) from e
 
-    def delete_documents(self, document_ids: List[str]) -> None:
+    def delete_documents(self, document_ids: list[str]) -> None:
         """
         Deletes all documents with a matching document_ids from the document store.
 
@@ -564,7 +564,7 @@ def delete_documents(self, document_ids: List[str]) -> None:
             raise_on_error=False,
         )
 
-    def _prepare_delete_all_request(self, *, is_async: bool) -> Dict[str, Any]:
+    def _prepare_delete_all_request(self, *, is_async: bool) -> dict[str, Any]:
         return {
             "index": self._index,
             "body": {"query": {"match_all": {}}},  # Delete all documents
@@ -572,7 +572,7 @@ def _prepare_delete_all_request(self, *, is_async: bool) -> Dict[str, Any]:
             "refresh": True,  # Ensure changes are visible immediately
         }
 
-    async def delete_documents_async(self, document_ids: List[str]) -> None:
+    async def delete_documents_async(self, document_ids: list[str]) -> None:
         """
         Asynchronously deletes all documents with a matching document_ids from the document store.
 
@@ -681,11 +681,11 @@ def _bm25_retrieval(
         self,
         query: str,
         *,
-        filters: Optional[Dict[str, Any]] = None,
+        filters: Optional[dict[str, Any]] = None,
         fuzziness: str = "AUTO",
         top_k: int = 10,
         scale_score: bool = False,
-    ) -> List[Document]:
+    ) -> list[Document]:
         """
         Retrieves documents using BM25 retrieval.
 
@@ -700,7 +700,7 @@ def _bm25_retrieval(
             msg = "query must be a non empty string"
             raise ValueError(msg)
 
-        body: Dict[str, Any] = {
+        body: dict[str, Any] = {
             "size": top_k,
             "query": {
                 "bool": {
@@ -735,11 +735,11 @@ async def _bm25_retrieval_async(
         self,
         query: str,
         *,
-        filters: Optional[Dict[str, Any]] = None,
+        filters: Optional[dict[str, Any]] = None,
         fuzziness: str = "AUTO",
         top_k: int = 10,
         scale_score: bool = False,
-    ) -> List[Document]:
+    ) -> list[Document]:
         """
         Asynchronously retrieves documents using BM25 retrieval.
 
@@ -789,12 +789,12 @@ async def _bm25_retrieval_async(
 
     def _embedding_retrieval(
         self,
-        query_embedding: List[float],
+        query_embedding: list[float],
         *,
-        filters: Optional[Dict[str, Any]] = None,
+        filters: Optional[dict[str, Any]] = None,
         top_k: int = 10,
         num_candidates: Optional[int] = None,
-    ) -> List[Document]:
+    ) -> list[Document]:
         """
         Retrieves documents using dense vector similarity search.
 
@@ -811,7 +811,7 @@ def _embedding_retrieval(
         if not num_candidates:
             num_candidates = top_k * 10
 
-        body: Dict[str, Any] = {
+        body: dict[str, Any] = {
             "knn": {
                 "field": "embedding",
                 "query_vector": query_embedding,
@@ -828,12 +828,12 @@ def _embedding_retrieval(
 
     async def _embedding_retrieval_async(
         self,
-        query_embedding: List[float],
+        query_embedding: list[float],
         *,
-        filters: Optional[Dict[str, Any]] = None,
+        filters: Optional[dict[str, Any]] = None,
         top_k: int = 10,
         num_candidates: Optional[int] = None,
-    ) -> List[Document]:
+    ) -> list[Document]:
         """
         Asynchronously retrieves documents using dense vector similarity search.
 
diff --git a/integrations/elasticsearch/src/haystack_integrations/document_stores/elasticsearch/filters.py b/integrations/elasticsearch/src/haystack_integrations/document_stores/elasticsearch/filters.py
index 1e2c2f29f9..32fa3b52f9 100644
--- a/integrations/elasticsearch/src/haystack_integrations/document_stores/elasticsearch/filters.py
+++ b/integrations/elasticsearch/src/haystack_integrations/document_stores/elasticsearch/filters.py
@@ -2,12 +2,12 @@
 #
 # SPDX-License-Identifier: Apache-2.0
 from datetime import datetime
-from typing import Any, Dict, List
+from typing import Any
 
 from haystack.errors import FilterError
 
 
-def _normalize_filters(filters: Dict[str, Any]) -> Dict[str, Any]:
+def _normalize_filters(filters: dict[str, Any]) -> dict[str, Any]:
     """
     Converts Haystack filters in ElasticSearch compatible filters.
     """
@@ -20,7 +20,7 @@ def _normalize_filters(filters: Dict[str, Any]) -> Dict[str, Any]:
     return _parse_logical_condition(filters)
 
 
-def _parse_logical_condition(condition: Dict[str, Any]) -> Dict[str, Any]:
+def _parse_logical_condition(condition: dict[str, Any]) -> dict[str, Any]:
     if "operator" not in condition:
         msg = f"'operator' key missing in {condition}"
         raise FilterError(msg)
@@ -43,7 +43,7 @@ def _parse_logical_condition(condition: Dict[str, Any]) -> Dict[str, Any]:
         raise FilterError(msg)
 
 
-def _equal(field: str, value: Any) -> Dict[str, Any]:
+def _equal(field: str, value: Any) -> dict[str, Any]:
     if value is None:
         return {"bool": {"must_not": {"exists": {"field": field}}}}
 
@@ -62,7 +62,7 @@ def _equal(field: str, value: Any) -> Dict[str, Any]:
     return {"term": {field: value}}
 
 
-def _not_equal(field: str, value: Any) -> Dict[str, Any]:
+def _not_equal(field: str, value: Any) -> dict[str, Any]:
     if value is None:
         return {"exists": {"field": field}}
 
@@ -75,7 +75,7 @@ def _not_equal(field: str, value: Any) -> Dict[str, Any]:
     return {"bool": {"must_not": {"term": {field: value}}}}
 
 
-def _greater_than(field: str, value: Any) -> Dict[str, Any]:
+def _greater_than(field: str, value: Any) -> dict[str, Any]:
     if value is None:
         # When the value is None and '>' is used we create a filter that would return a Document
         # if it has a field set and not set at the same time.
@@ -97,7 +97,7 @@ def _greater_than(field: str, value: Any) -> Dict[str, Any]:
     return {"range": {field: {"gt": value}}}
 
 
-def _greater_than_equal(field: str, value: Any) -> Dict[str, Any]:
+def _greater_than_equal(field: str, value: Any) -> dict[str, Any]:
     if value is None:
         # When the value is None and '>=' is used we create a filter that would return a Document
         # if it has a field set and not set at the same time.
@@ -119,7 +119,7 @@ def _greater_than_equal(field: str, value: Any) -> Dict[str, Any]:
     return {"range": {field: {"gte": value}}}
 
 
-def _less_than(field: str, value: Any) -> Dict[str, Any]:
+def _less_than(field: str, value: Any) -> dict[str, Any]:
     if value is None:
         # When the value is None and '<' is used we create a filter that would return a Document
         # if it has a field set and not set at the same time.
@@ -141,7 +141,7 @@ def _less_than(field: str, value: Any) -> Dict[str, Any]:
     return {"range": {field: {"lt": value}}}
 
 
-def _less_than_equal(field: str, value: Any) -> Dict[str, Any]:
+def _less_than_equal(field: str, value: Any) -> dict[str, Any]:
     if value is None:
         # When the value is None and '<=' is used we create a filter that would return a Document
         # if it has a field set and not set at the same time.
@@ -163,14 +163,14 @@ def _less_than_equal(field: str, value: Any) -> Dict[str, Any]:
     return {"range": {field: {"lte": value}}}
 
 
-def _in(field: str, value: Any) -> Dict[str, Any]:
+def _in(field: str, value: Any) -> dict[str, Any]:
     if not isinstance(value, list):
         msg = f"{field}'s value must be a list when using 'in' or 'not in' comparators"
         raise FilterError(msg)
     return {"terms": {field: value}}
 
 
-def _not_in(field: str, value: Any) -> Dict[str, Any]:
+def _not_in(field: str, value: Any) -> dict[str, Any]:
     if not isinstance(value, list):
         msg = f"{field}'s value must be a list when using 'in' or 'not in' comparators"
         raise FilterError(msg)
@@ -189,7 +189,7 @@ def _not_in(field: str, value: Any) -> Dict[str, Any]:
 }
 
 
-def _parse_comparison_condition(condition: Dict[str, Any]) -> Dict[str, Any]:
+def _parse_comparison_condition(condition: dict[str, Any]) -> dict[str, Any]:
     if "field" not in condition:
         # 'field' key is only found in comparison dictionaries.
         # We assume this is a logic dictionary since it's not present.
@@ -215,7 +215,7 @@ def _parse_comparison_condition(condition: Dict[str, Any]) -> Dict[str, Any]:
     return COMPARISON_OPERATORS[operator](field, value)
 
 
-def _normalize_ranges(conditions: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
+def _normalize_ranges(conditions: list[dict[str, Any]]) -> list[dict[str, Any]]:
     """
     Merges range conditions acting on a same field.
 
@@ -235,7 +235,7 @@ def _normalize_ranges(conditions: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
     range_conditions = [next(iter(c["range"].items())) for c in conditions if "range" in c]
     if range_conditions:
         conditions = [c for c in conditions if "range" not in c]
-        range_conditions_dict: Dict[str, Any] = {}
+        range_conditions_dict: dict[str, Any] = {}
         for field_name, comparison in range_conditions:
             if field_name not in range_conditions_dict:
                 range_conditions_dict[field_name] = {}
diff --git a/integrations/elasticsearch/tests/test_document_store.py b/integrations/elasticsearch/tests/test_document_store.py
index 81dd81d054..2eab29f8ce 100644
--- a/integrations/elasticsearch/tests/test_document_store.py
+++ b/integrations/elasticsearch/tests/test_document_store.py
@@ -4,7 +4,6 @@
 
 import random
 import time
-from typing import List
 from unittest.mock import Mock, patch
 
 import pytest
@@ -245,7 +244,7 @@ def document_store(self, request):
         store.client.options(ignore_status=[400, 404]).indices.delete(index=index)
         store.client.close()
 
-    def assert_documents_are_equal(self, received: List[Document], expected: List[Document]):
+    def assert_documents_are_equal(self, received: list[Document], expected: list[Document]):
         """
         The ElasticSearchDocumentStore.filter_documents() method returns a Documents with their score set.
         We don't want to compare the score, so we set it to None before comparing the documents.
diff --git a/integrations/fastembed/pyproject.toml b/integrations/fastembed/pyproject.toml
index c13bc6dac8..af322fa8b3 100644
--- a/integrations/fastembed/pyproject.toml
+++ b/integrations/fastembed/pyproject.toml
@@ -75,7 +75,7 @@ check_untyped_defs = true
 disallow_incomplete_defs = true
 
 [tool.ruff]
-target-version = "py38"
+target-version = "py39"
 line-length = 120
 
 [tool.ruff.lint]
diff --git a/integrations/fastembed/src/haystack_integrations/components/embedders/fastembed/embedding_backend/fastembed_backend.py b/integrations/fastembed/src/haystack_integrations/components/embedders/fastembed/embedding_backend/fastembed_backend.py
index 65b0ea296d..214c9715d6 100644
--- a/integrations/fastembed/src/haystack_integrations/components/embedders/fastembed/embedding_backend/fastembed_backend.py
+++ b/integrations/fastembed/src/haystack_integrations/components/embedders/fastembed/embedding_backend/fastembed_backend.py
@@ -1,4 +1,4 @@
-from typing import Any, ClassVar, Dict, List, Optional
+from typing import Any, ClassVar, Optional
 
 from haystack.dataclasses.sparse_embedding import SparseEmbedding
 from tqdm import tqdm
@@ -12,7 +12,7 @@ class _FastembedEmbeddingBackendFactory:
     Factory class to create instances of fastembed embedding backends.
     """
 
-    _instances: ClassVar[Dict[str, "_FastembedEmbeddingBackend"]] = {}
+    _instances: ClassVar[dict[str, "_FastembedEmbeddingBackend"]] = {}
 
     @staticmethod
     def get_embedding_backend(
@@ -49,7 +49,7 @@ def __init__(
             model_name=model_name, cache_dir=cache_dir, threads=threads, local_files_only=local_files_only
         )
 
-    def embed(self, data: List[str], progress_bar: bool = True, **kwargs: Any) -> List[List[float]]:
+    def embed(self, data: list[str], progress_bar: bool = True, **kwargs: Any) -> list[list[float]]:
         # the embed method returns a Iterable[np.ndarray], so we convert it to a list of lists
         embeddings = []
         embeddings_iterable = self.model.embed(data, **kwargs)
@@ -65,7 +65,7 @@ class _FastembedSparseEmbeddingBackendFactory:
     Factory class to create instances of fastembed sparse embedding backends.
     """
 
-    _instances: ClassVar[Dict[str, "_FastembedSparseEmbeddingBackend"]] = {}
+    _instances: ClassVar[dict[str, "_FastembedSparseEmbeddingBackend"]] = {}
 
     @staticmethod
     def get_embedding_backend(
@@ -73,7 +73,7 @@ def get_embedding_backend(
         cache_dir: Optional[str] = None,
         threads: Optional[int] = None,
         local_files_only: bool = False,
-        model_kwargs: Optional[Dict[str, Any]] = None,
+        model_kwargs: Optional[dict[str, Any]] = None,
     ) -> "_FastembedSparseEmbeddingBackend":
         embedding_backend_id = f"{model_name}{cache_dir}{threads}{local_files_only}{model_kwargs}"
 
@@ -102,7 +102,7 @@ def __init__(
         cache_dir: Optional[str] = None,
         threads: Optional[int] = None,
         local_files_only: bool = False,
-        model_kwargs: Optional[Dict[str, Any]] = None,
+        model_kwargs: Optional[dict[str, Any]] = None,
     ):
         model_kwargs = model_kwargs or {}
 
@@ -114,7 +114,7 @@ def __init__(
             **model_kwargs,
         )
 
-    def embed(self, data: List[str], progress_bar: bool = True, **kwargs: Any) -> List[SparseEmbedding]:
+    def embed(self, data: list[str], progress_bar: bool = True, **kwargs: Any) -> list[SparseEmbedding]:
         # The embed method returns a Iterable[SparseEmbedding], so we convert to Haystack SparseEmbedding type.
         # Each SparseEmbedding contains an `indices` key containing a list of int and
         # an `values` key containing a list of floats.
diff --git a/integrations/fastembed/src/haystack_integrations/components/embedders/fastembed/fastembed_document_embedder.py b/integrations/fastembed/src/haystack_integrations/components/embedders/fastembed/fastembed_document_embedder.py
index 1424a506fa..bcd1a6111d 100644
--- a/integrations/fastembed/src/haystack_integrations/components/embedders/fastembed/fastembed_document_embedder.py
+++ b/integrations/fastembed/src/haystack_integrations/components/embedders/fastembed/fastembed_document_embedder.py
@@ -1,4 +1,4 @@
-from typing import Any, Dict, List, Optional
+from typing import Any, Optional
 
 from haystack import Document, component, default_to_dict
 
@@ -66,7 +66,7 @@ def __init__(
         progress_bar: bool = True,
         parallel: Optional[int] = None,
         local_files_only: bool = False,
-        meta_fields_to_embed: Optional[List[str]] = None,
+        meta_fields_to_embed: Optional[list[str]] = None,
         embedding_separator: str = "\n",
     ):
         """
@@ -103,7 +103,7 @@ def __init__(
         self.meta_fields_to_embed = meta_fields_to_embed or []
         self.embedding_separator = embedding_separator
 
-    def to_dict(self) -> Dict[str, Any]:
+    def to_dict(self) -> dict[str, Any]:
         """
         Serializes the component to a dictionary.
         :returns:
@@ -136,7 +136,7 @@ def warm_up(self):
                 local_files_only=self.local_files_only,
             )
 
-    def _prepare_texts_to_embed(self, documents: List[Document]) -> List[str]:
+    def _prepare_texts_to_embed(self, documents: list[Document]) -> list[str]:
         texts_to_embed = []
         for doc in documents:
             meta_values_to_embed = [
@@ -149,8 +149,8 @@ def _prepare_texts_to_embed(self, documents: List[Document]) -> List[str]:
             texts_to_embed.append(text_to_embed)
         return texts_to_embed
 
-    @component.output_types(documents=List[Document])
-    def run(self, documents: List[Document]) -> Dict[str, List[Document]]:
+    @component.output_types(documents=list[Document])
+    def run(self, documents: list[Document]) -> dict[str, list[Document]]:
         """
         Embeds a list of Documents.
 
diff --git a/integrations/fastembed/src/haystack_integrations/components/embedders/fastembed/fastembed_sparse_document_embedder.py b/integrations/fastembed/src/haystack_integrations/components/embedders/fastembed/fastembed_sparse_document_embedder.py
index 242cef70a8..40137b4ea9 100644
--- a/integrations/fastembed/src/haystack_integrations/components/embedders/fastembed/fastembed_sparse_document_embedder.py
+++ b/integrations/fastembed/src/haystack_integrations/components/embedders/fastembed/fastembed_sparse_document_embedder.py
@@ -1,4 +1,4 @@
-from typing import Any, Dict, List, Optional
+from typing import Any, Optional
 
 from haystack import Document, component, default_to_dict
 
@@ -60,9 +60,9 @@ def __init__(
         progress_bar: bool = True,
         parallel: Optional[int] = None,
         local_files_only: bool = False,
-        meta_fields_to_embed: Optional[List[str]] = None,
+        meta_fields_to_embed: Optional[list[str]] = None,
         embedding_separator: str = "\n",
-        model_kwargs: Optional[Dict[str, Any]] = None,
+        model_kwargs: Optional[dict[str, Any]] = None,
     ):
         """
         Create an FastembedDocumentEmbedder component.
@@ -96,7 +96,7 @@ def __init__(
         self.embedding_separator = embedding_separator
         self.model_kwargs = model_kwargs
 
-    def to_dict(self) -> Dict[str, Any]:
+    def to_dict(self) -> dict[str, Any]:
         """
         Serializes the component to a dictionary.
         :returns:
@@ -129,7 +129,7 @@ def warm_up(self):
                 model_kwargs=self.model_kwargs,
             )
 
-    def _prepare_texts_to_embed(self, documents: List[Document]) -> List[str]:
+    def _prepare_texts_to_embed(self, documents: list[Document]) -> list[str]:
         texts_to_embed = []
         for doc in documents:
             meta_values_to_embed = [
@@ -140,8 +140,8 @@ def _prepare_texts_to_embed(self, documents: List[Document]) -> List[str]:
             texts_to_embed.append(text_to_embed)
         return texts_to_embed
 
-    @component.output_types(documents=List[Document])
-    def run(self, documents: List[Document]) -> Dict[str, List[Document]]:
+    @component.output_types(documents=list[Document])
+    def run(self, documents: list[Document]) -> dict[str, list[Document]]:
         """
         Embeds a list of Documents.
 
diff --git a/integrations/fastembed/src/haystack_integrations/components/embedders/fastembed/fastembed_sparse_text_embedder.py b/integrations/fastembed/src/haystack_integrations/components/embedders/fastembed/fastembed_sparse_text_embedder.py
index 71342565be..cac95f697a 100644
--- a/integrations/fastembed/src/haystack_integrations/components/embedders/fastembed/fastembed_sparse_text_embedder.py
+++ b/integrations/fastembed/src/haystack_integrations/components/embedders/fastembed/fastembed_sparse_text_embedder.py
@@ -1,4 +1,4 @@
-from typing import Any, Dict, Optional
+from typing import Any, Optional
 
 from haystack import component, default_to_dict
 from haystack.dataclasses.sparse_embedding import SparseEmbedding
@@ -35,7 +35,7 @@ def __init__(
         progress_bar: bool = True,
         parallel: Optional[int] = None,
         local_files_only: bool = False,
-        model_kwargs: Optional[Dict[str, Any]] = None,
+        model_kwargs: Optional[dict[str, Any]] = None,
     ):
         """
         Create a FastembedSparseTextEmbedder component.
@@ -62,7 +62,7 @@ def __init__(
         self.local_files_only = local_files_only
         self.model_kwargs = model_kwargs
 
-    def to_dict(self) -> Dict[str, Any]:
+    def to_dict(self) -> dict[str, Any]:
         """
         Serializes the component to a dictionary.
 
@@ -94,7 +94,7 @@ def warm_up(self):
             )
 
     @component.output_types(sparse_embedding=SparseEmbedding)
-    def run(self, text: str) -> Dict[str, SparseEmbedding]:
+    def run(self, text: str) -> dict[str, SparseEmbedding]:
         """
         Embeds text using the Fastembed model.
 
diff --git a/integrations/fastembed/src/haystack_integrations/components/embedders/fastembed/fastembed_text_embedder.py b/integrations/fastembed/src/haystack_integrations/components/embedders/fastembed/fastembed_text_embedder.py
index a9fbbcb5fe..0c6bb646f3 100644
--- a/integrations/fastembed/src/haystack_integrations/components/embedders/fastembed/fastembed_text_embedder.py
+++ b/integrations/fastembed/src/haystack_integrations/components/embedders/fastembed/fastembed_text_embedder.py
@@ -1,4 +1,4 @@
-from typing import Any, Dict, List, Optional
+from typing import Any, Optional
 
 from haystack import component, default_to_dict
 
@@ -64,7 +64,7 @@ def __init__(
         self.parallel = parallel
         self.local_files_only = local_files_only
 
-    def to_dict(self) -> Dict[str, Any]:
+    def to_dict(self) -> dict[str, Any]:
         """
         Serializes the component to a dictionary.
 
@@ -95,8 +95,8 @@ def warm_up(self):
                 local_files_only=self.local_files_only,
             )
 
-    @component.output_types(embedding=List[float])
-    def run(self, text: str) -> Dict[str, List[float]]:
+    @component.output_types(embedding=list[float])
+    def run(self, text: str) -> dict[str, list[float]]:
         """
         Embeds text using the Fastembed model.
 
diff --git a/integrations/fastembed/src/haystack_integrations/components/rankers/fastembed/ranker.py b/integrations/fastembed/src/haystack_integrations/components/rankers/fastembed/ranker.py
index 3006135a00..9b55fbb4e8 100644
--- a/integrations/fastembed/src/haystack_integrations/components/rankers/fastembed/ranker.py
+++ b/integrations/fastembed/src/haystack_integrations/components/rankers/fastembed/ranker.py
@@ -1,4 +1,4 @@
-from typing import Any, Dict, List, Optional
+from typing import Any, Optional
 
 from haystack import Document, component, default_from_dict, default_to_dict, logging
 
@@ -40,7 +40,7 @@ def __init__(
         batch_size: int = 64,
         parallel: Optional[int] = None,
         local_files_only: bool = False,
-        meta_fields_to_embed: Optional[List[str]] = None,
+        meta_fields_to_embed: Optional[list[str]] = None,
         meta_data_separator: str = "\n",
     ):
         """
@@ -78,7 +78,7 @@ def __init__(
         self.meta_data_separator = meta_data_separator
         self._model: Optional[TextCrossEncoder] = None
 
-    def to_dict(self) -> Dict[str, Any]:
+    def to_dict(self) -> dict[str, Any]:
         """
         Serializes the component to a dictionary.
 
@@ -99,7 +99,7 @@ def to_dict(self) -> Dict[str, Any]:
         )
 
     @classmethod
-    def from_dict(cls, data: Dict[str, Any]) -> "FastembedRanker":
+    def from_dict(cls, data: dict[str, Any]) -> "FastembedRanker":
         """
         Deserializes the component from a dictionary.
 
@@ -122,7 +122,7 @@ def warm_up(self):
                 local_files_only=self.local_files_only,
             )
 
-    def _prepare_fastembed_input_docs(self, documents: List[Document]) -> List[str]:
+    def _prepare_fastembed_input_docs(self, documents: list[Document]) -> list[str]:
         """
         Prepare the input by concatenating the document text with the metadata fields specified.
         :param documents: The list of Document objects.
@@ -139,8 +139,8 @@ def _prepare_fastembed_input_docs(self, documents: List[Document]) -> List[str]:
 
         return concatenated_input_list
 
-    @component.output_types(documents=List[Document])
-    def run(self, query: str, documents: List[Document], top_k: Optional[int] = None) -> Dict[str, List[Document]]:
+    @component.output_types(documents=list[Document])
+    def run(self, query: str, documents: list[Document], top_k: Optional[int] = None) -> dict[str, list[Document]]:
         """
         Returns a list of documents ranked by their similarity to the given query, using FastEmbed.
 
diff --git a/integrations/github/pyproject.toml b/integrations/github/pyproject.toml
index c9834638f2..fd7d9eb9aa 100644
--- a/integrations/github/pyproject.toml
+++ b/integrations/github/pyproject.toml
@@ -77,7 +77,7 @@ check_untyped_defs = true
 disallow_incomplete_defs = true
 
 [tool.ruff]
-target-version = "py38"
+target-version = "py39"
 line-length = 120
 
 [tool.ruff.lint]
diff --git a/integrations/github/src/haystack_integrations/components/connectors/github/file_editor.py b/integrations/github/src/haystack_integrations/components/connectors/github/file_editor.py
index fcbe080787..edbfdad8dd 100644
--- a/integrations/github/src/haystack_integrations/components/connectors/github/file_editor.py
+++ b/integrations/github/src/haystack_integrations/components/connectors/github/file_editor.py
@@ -3,7 +3,7 @@
 # SPDX-License-Identifier: Apache-2.0
 from base64 import b64decode, b64encode
 from enum import Enum
-from typing import Any, Dict, Optional, Union
+from typing import Any, Optional, Union
 
 import requests
 from haystack import component, default_from_dict, default_to_dict, logging
@@ -145,7 +145,7 @@ def _update_file(self, owner: str, repo: str, path: str, content: str, message:
     def _check_last_commit(self, owner: str, repo: str, branch: str) -> bool:
         """Check if last commit was made by the current token user."""
         url = f"https://api.github.com/repos/{owner}/{repo}/commits"
-        params: Dict[str, Union[str, int]] = {"per_page": 1, "sha": branch}
+        params: dict[str, Union[str, int]] = {"per_page": 1, "sha": branch}
         response = requests.get(url, headers=self._get_request_headers(), params=params, timeout=10)
         response.raise_for_status()
         last_commit = response.json()[0]
@@ -158,7 +158,7 @@ def _check_last_commit(self, owner: str, repo: str, branch: str) -> bool:
 
         return commit_author == current_user
 
-    def _edit_file(self, owner: str, repo: str, payload: Dict[str, str], branch: str) -> str:
+    def _edit_file(self, owner: str, repo: str, payload: dict[str, str], branch: str) -> str:
         """Handle file editing."""
         try:
             content, sha = self._get_file_content(owner, repo, payload["path"], branch)
@@ -180,7 +180,7 @@ def _edit_file(self, owner: str, repo: str, payload: Dict[str, str], branch: str
                 raise
             return f"Error: {e!s}"
 
-    def _undo_changes(self, owner: str, repo: str, payload: Dict[str, Any], branch: str) -> str:
+    def _undo_changes(self, owner: str, repo: str, payload: dict[str, Any], branch: str) -> str:
         """Handle undoing changes."""
         try:
             if not self._check_last_commit(owner, repo, branch):
@@ -191,7 +191,7 @@ def _undo_changes(self, owner: str, repo: str, payload: Dict[str, Any], branch:
             commits_url = f"https://api.github.com/repos/{owner}/{repo}/commits"
 
             # Get the previous commit SHA
-            params: Dict[str, Union[str, int]] = {"per_page": 2, "sha": branch}
+            params: dict[str, Union[str, int]] = {"per_page": 2, "sha": branch}
             commits = requests.get(commits_url, headers=self._get_request_headers(), params=params, timeout=10).json()
             previous_sha = commits[1]["sha"]
 
@@ -207,7 +207,7 @@ def _undo_changes(self, owner: str, repo: str, payload: Dict[str, Any], branch:
                 raise
             return f"Error: {e!s}"
 
-    def _create_file(self, owner: str, repo: str, payload: Dict[str, str], branch: str) -> str:
+    def _create_file(self, owner: str, repo: str, payload: dict[str, str], branch: str) -> str:
         """Handle file creation."""
         try:
             url = f"https://api.github.com/repos/{owner}/{repo}/contents/{payload['path']}"
@@ -224,7 +224,7 @@ def _create_file(self, owner: str, repo: str, payload: Dict[str, str], branch: s
                 raise
             return f"Error: {e!s}"
 
-    def _delete_file(self, owner: str, repo: str, payload: Dict[str, str], branch: str) -> str:
+    def _delete_file(self, owner: str, repo: str, payload: dict[str, str], branch: str) -> str:
         """Handle file deletion."""
         try:
             _, sha = self._get_file_content(owner, repo, payload["path"], branch)
@@ -245,10 +245,10 @@ def _delete_file(self, owner: str, repo: str, payload: Dict[str, str], branch: s
     def run(
         self,
         command: Union[Command, str],
-        payload: Dict[str, Any],
+        payload: dict[str, Any],
         repo: Optional[str] = None,
         branch: Optional[str] = None,
-    ) -> Dict[str, str]:
+    ) -> dict[str, str]:
         """
         Process GitHub file operations.
 
@@ -287,7 +287,7 @@ def run(
         result = command_handlers[command](owner, repo_name, payload, working_branch)
         return {"result": result}
 
-    def to_dict(self) -> Dict[str, Any]:
+    def to_dict(self) -> dict[str, Any]:
         """Serialize the component to a dictionary."""
         return default_to_dict(
             self,
@@ -298,7 +298,7 @@ def to_dict(self) -> Dict[str, Any]:
         )
 
     @classmethod
-    def from_dict(cls, data: Dict[str, Any]) -> "GitHubFileEditor":
+    def from_dict(cls, data: dict[str, Any]) -> "GitHubFileEditor":
         """Deserialize the component from a dictionary."""
         init_params = data["init_parameters"]
         deserialize_secrets_inplace(init_params, keys=["github_token"])
diff --git a/integrations/github/src/haystack_integrations/components/connectors/github/issue_commenter.py b/integrations/github/src/haystack_integrations/components/connectors/github/issue_commenter.py
index 78a770c9d2..9f3eaca6ab 100644
--- a/integrations/github/src/haystack_integrations/components/connectors/github/issue_commenter.py
+++ b/integrations/github/src/haystack_integrations/components/connectors/github/issue_commenter.py
@@ -2,7 +2,7 @@
 #
 # SPDX-License-Identifier: Apache-2.0
 import re
-from typing import Any, Dict
+from typing import Any
 
 import requests
 from haystack import component, default_from_dict, default_to_dict, logging
@@ -115,7 +115,7 @@ def _post_comment(self, owner: str, repo: str, issue_number: int, comment: str)
 
         return False
 
-    def to_dict(self) -> Dict[str, Any]:
+    def to_dict(self) -> dict[str, Any]:
         """
         Serialize the component to a dictionary.
 
@@ -129,7 +129,7 @@ def to_dict(self) -> Dict[str, Any]:
         )
 
     @classmethod
-    def from_dict(cls, data: Dict[str, Any]) -> "GitHubIssueCommenter":
+    def from_dict(cls, data: dict[str, Any]) -> "GitHubIssueCommenter":
         """
         Deserialize the component from a dictionary.
 
diff --git a/integrations/github/src/haystack_integrations/components/connectors/github/issue_viewer.py b/integrations/github/src/haystack_integrations/components/connectors/github/issue_viewer.py
index a148bd4969..2182400594 100644
--- a/integrations/github/src/haystack_integrations/components/connectors/github/issue_viewer.py
+++ b/integrations/github/src/haystack_integrations/components/connectors/github/issue_viewer.py
@@ -2,7 +2,7 @@
 #
 # SPDX-License-Identifier: Apache-2.0
 import re
-from typing import Any, Dict, List, Optional
+from typing import Any, Optional
 
 import requests
 from haystack import Document, component, default_from_dict, default_to_dict, logging
@@ -154,7 +154,7 @@ def _create_comment_document(self, comment_data: dict, issue_number: int) -> Doc
             },
         )
 
-    def to_dict(self) -> Dict[str, Any]:
+    def to_dict(self) -> dict[str, Any]:
         """
         Serialize the component to a dictionary.
 
@@ -168,7 +168,7 @@ def to_dict(self) -> Dict[str, Any]:
         )
 
     @classmethod
-    def from_dict(cls, data: Dict[str, Any]) -> "GitHubIssueViewer":
+    def from_dict(cls, data: dict[str, Any]) -> "GitHubIssueViewer":
         """
         Deserialize the component from a dictionary.
 
@@ -179,7 +179,7 @@ def from_dict(cls, data: Dict[str, Any]) -> "GitHubIssueViewer":
         deserialize_secrets_inplace(init_params, keys=["github_token"])
         return default_from_dict(cls, data)
 
-    @component.output_types(documents=List[Document])
+    @component.output_types(documents=list[Document])
     def run(self, url: str) -> dict:
         """
         Process a GitHub issue URL and return documents.
diff --git a/integrations/github/src/haystack_integrations/components/connectors/github/pr_creator.py b/integrations/github/src/haystack_integrations/components/connectors/github/pr_creator.py
index 6b36087634..72193a63dd 100644
--- a/integrations/github/src/haystack_integrations/components/connectors/github/pr_creator.py
+++ b/integrations/github/src/haystack_integrations/components/connectors/github/pr_creator.py
@@ -2,7 +2,7 @@
 #
 # SPDX-License-Identifier: Apache-2.0
 import re
-from typing import Any, Dict, Optional
+from typing import Any, Optional
 
 import requests
 from haystack import component, default_from_dict, default_to_dict, logging
@@ -215,7 +215,7 @@ def _create_pull_request(
     @component.output_types(result=str)
     def run(
         self, issue_url: str, title: str, branch: str, base: str, body: str = "", draft: bool = False
-    ) -> Dict[str, str]:
+    ) -> dict[str, str]:
         """
         Create a new pull request from your fork to the original repository, linked to the specified issue.
 
@@ -263,7 +263,7 @@ def run(
                 raise
             return {"result": f"Error: {e!s}"}
 
-    def to_dict(self) -> Dict[str, Any]:
+    def to_dict(self) -> dict[str, Any]:
         """Serialize the component to a dictionary."""
         return default_to_dict(
             self,
@@ -272,7 +272,7 @@ def to_dict(self) -> Dict[str, Any]:
         )
 
     @classmethod
-    def from_dict(cls, data: Dict[str, Any]) -> "GitHubPRCreator":
+    def from_dict(cls, data: dict[str, Any]) -> "GitHubPRCreator":
         """Deserialize the component from a dictionary."""
         init_params = data["init_parameters"]
         deserialize_secrets_inplace(init_params, keys=["github_token"])
diff --git a/integrations/github/src/haystack_integrations/components/connectors/github/repo_forker.py b/integrations/github/src/haystack_integrations/components/connectors/github/repo_forker.py
index e0b97281f6..021c60dfeb 100644
--- a/integrations/github/src/haystack_integrations/components/connectors/github/repo_forker.py
+++ b/integrations/github/src/haystack_integrations/components/connectors/github/repo_forker.py
@@ -3,7 +3,7 @@
 # SPDX-License-Identifier: Apache-2.0
 import re
 import time
-from typing import Any, Dict, Optional
+from typing import Any, Optional
 
 import requests
 from haystack import component, default_from_dict, default_to_dict, logging
@@ -221,7 +221,7 @@ def _create_fork(self, owner: str, repo: str) -> str:
         fork_data = response.json()
         return f"{fork_data['owner']['login']}/{fork_data['name']}"
 
-    def to_dict(self) -> Dict[str, Any]:
+    def to_dict(self) -> dict[str, Any]:
         """
         Serialize the component to a dictionary.
 
@@ -239,7 +239,7 @@ def to_dict(self) -> Dict[str, Any]:
         )
 
     @classmethod
-    def from_dict(cls, data: Dict[str, Any]) -> "GitHubRepoForker":
+    def from_dict(cls, data: dict[str, Any]) -> "GitHubRepoForker":
         """
         Deserialize the component from a dictionary.
 
diff --git a/integrations/github/src/haystack_integrations/components/connectors/github/repo_viewer.py b/integrations/github/src/haystack_integrations/components/connectors/github/repo_viewer.py
index 70856a1817..692c823a5a 100644
--- a/integrations/github/src/haystack_integrations/components/connectors/github/repo_viewer.py
+++ b/integrations/github/src/haystack_integrations/components/connectors/github/repo_viewer.py
@@ -3,7 +3,7 @@
 # SPDX-License-Identifier: Apache-2.0
 import base64
 from dataclasses import dataclass
-from typing import Any, Dict, List, Optional
+from typing import Any, Optional
 
 import requests
 from haystack import Document, component, default_from_dict, default_to_dict, logging
@@ -114,7 +114,7 @@ def _get_request_headers(self) -> dict:
                 headers["Authorization"] = f"Bearer {token_value}"
         return headers
 
-    def to_dict(self) -> Dict[str, Any]:
+    def to_dict(self) -> dict[str, Any]:
         """
         Serialize the component to a dictionary.
 
@@ -130,7 +130,7 @@ def to_dict(self) -> Dict[str, Any]:
         )
 
     @classmethod
-    def from_dict(cls, data: Dict[str, Any]) -> "GitHubRepoViewer":
+    def from_dict(cls, data: dict[str, Any]) -> "GitHubRepoViewer":
         """
         Deserialize the component from a dictionary.
 
@@ -181,7 +181,7 @@ def _create_file_document(self, item: GitHubItem) -> Document:
             },
         )
 
-    def _create_directory_documents(self, items: List[GitHubItem]) -> List[Document]:
+    def _create_directory_documents(self, items: list[GitHubItem]) -> list[Document]:
         """Create a list of Documents from directory contents"""
         return [
             Document(
@@ -206,8 +206,8 @@ def _create_error_document(self, error: Exception, path: str) -> Document:
             },
         )
 
-    @component.output_types(documents=List[Document])
-    def run(self, path: str, repo: Optional[str] = None, branch: Optional[str] = None) -> Dict[str, List[Document]]:
+    @component.output_types(documents=list[Document])
+    def run(self, path: str, repo: Optional[str] = None, branch: Optional[str] = None) -> dict[str, list[Document]]:
         """
         Process a GitHub repository path and return documents.
 
diff --git a/integrations/github/src/haystack_integrations/tools/github/file_editor_tool.py b/integrations/github/src/haystack_integrations/tools/github/file_editor_tool.py
index eea05433bd..59c18530d6 100644
--- a/integrations/github/src/haystack_integrations/tools/github/file_editor_tool.py
+++ b/integrations/github/src/haystack_integrations/tools/github/file_editor_tool.py
@@ -1,7 +1,7 @@
 # SPDX-FileCopyrightText: 2023-present deepset GmbH <info@deepset.ai>
 #
 # SPDX-License-Identifier: Apache-2.0
-from typing import Any, Callable, Dict, Optional, Union
+from typing import Any, Callable, Optional, Union
 
 from haystack.core.serialization import generate_qualified_class_name
 from haystack.tools import ComponentTool
@@ -22,14 +22,14 @@ def __init__(
         *,
         name: Optional[str] = "file_editor",
         description: Optional[str] = FILE_EDITOR_PROMPT,
-        parameters: Optional[Dict[str, Any]] = FILE_EDITOR_SCHEMA,
+        parameters: Optional[dict[str, Any]] = FILE_EDITOR_SCHEMA,
         github_token: Secret = Secret.from_env_var("GITHUB_TOKEN"),
         repo: Optional[str] = None,
         branch: str = "main",
         raise_on_failure: bool = True,
-        outputs_to_string: Optional[Dict[str, Union[str, Callable[[Any], str]]]] = None,
-        inputs_from_state: Optional[Dict[str, str]] = None,
-        outputs_to_state: Optional[Dict[str, Dict[str, Union[str, Callable]]]] = None,
+        outputs_to_string: Optional[dict[str, Union[str, Callable[[Any], str]]]] = None,
+        inputs_from_state: Optional[dict[str, str]] = None,
+        outputs_to_state: Optional[dict[str, dict[str, Union[str, Callable]]]] = None,
     ):
         """
         Initialize the GitHub file editor tool.
@@ -86,7 +86,7 @@ def __init__(
             outputs_to_state=outputs_to_state,
         )
 
-    def to_dict(self) -> Dict[str, Any]:
+    def to_dict(self) -> dict[str, Any]:
         """
         Serializes the tool to a dictionary.
 
@@ -110,7 +110,7 @@ def to_dict(self) -> Dict[str, Any]:
         return {"type": generate_qualified_class_name(type(self)), "data": serialized}
 
     @classmethod
-    def from_dict(cls, data: Dict[str, Any]) -> "GitHubFileEditorTool":
+    def from_dict(cls, data: dict[str, Any]) -> "GitHubFileEditorTool":
         """
         Deserializes the tool from a dictionary.
 
diff --git a/integrations/github/src/haystack_integrations/tools/github/issue_commenter_tool.py b/integrations/github/src/haystack_integrations/tools/github/issue_commenter_tool.py
index 4408b10877..00afc4233c 100644
--- a/integrations/github/src/haystack_integrations/tools/github/issue_commenter_tool.py
+++ b/integrations/github/src/haystack_integrations/tools/github/issue_commenter_tool.py
@@ -1,7 +1,7 @@
 # SPDX-FileCopyrightText: 2023-present deepset GmbH <info@deepset.ai>
 #
 # SPDX-License-Identifier: Apache-2.0
-from typing import Any, Callable, Dict, Optional, Union
+from typing import Any, Callable, Optional, Union
 
 from haystack.core.serialization import generate_qualified_class_name
 from haystack.tools import ComponentTool
@@ -22,13 +22,13 @@ def __init__(
         *,
         name: Optional[str] = "issue_commenter",
         description: Optional[str] = ISSUE_COMMENTER_PROMPT,
-        parameters: Optional[Dict[str, Any]] = ISSUE_COMMENTER_SCHEMA,
+        parameters: Optional[dict[str, Any]] = ISSUE_COMMENTER_SCHEMA,
         github_token: Secret = Secret.from_env_var("GITHUB_TOKEN"),
         raise_on_failure: bool = True,
         retry_attempts: int = 2,
-        outputs_to_string: Optional[Dict[str, Union[str, Callable[[Any], str]]]] = None,
-        inputs_from_state: Optional[Dict[str, str]] = None,
-        outputs_to_state: Optional[Dict[str, Dict[str, Union[str, Callable]]]] = None,
+        outputs_to_string: Optional[dict[str, Union[str, Callable[[Any], str]]]] = None,
+        inputs_from_state: Optional[dict[str, str]] = None,
+        outputs_to_state: Optional[dict[str, dict[str, Union[str, Callable]]]] = None,
     ):
         """
         Initialize the GitHub issue commenter tool.
@@ -82,7 +82,7 @@ def __init__(
             outputs_to_state=outputs_to_state,
         )
 
-    def to_dict(self) -> Dict[str, Any]:
+    def to_dict(self) -> dict[str, Any]:
         """
         Serializes the tool to a dictionary.
 
@@ -105,7 +105,7 @@ def to_dict(self) -> Dict[str, Any]:
         return {"type": generate_qualified_class_name(type(self)), "data": serialized}
 
     @classmethod
-    def from_dict(cls, data: Dict[str, Any]) -> "GitHubIssueCommenterTool":
+    def from_dict(cls, data: dict[str, Any]) -> "GitHubIssueCommenterTool":
         """
         Deserializes the tool from a dictionary.
 
diff --git a/integrations/github/src/haystack_integrations/tools/github/issue_viewer_tool.py b/integrations/github/src/haystack_integrations/tools/github/issue_viewer_tool.py
index faa22e9d2d..171fdf99ce 100644
--- a/integrations/github/src/haystack_integrations/tools/github/issue_viewer_tool.py
+++ b/integrations/github/src/haystack_integrations/tools/github/issue_viewer_tool.py
@@ -1,7 +1,7 @@
 # SPDX-FileCopyrightText: 2023-present deepset GmbH <info@deepset.ai>
 #
 # SPDX-License-Identifier: Apache-2.0
-from typing import Any, Callable, Dict, Optional, Union
+from typing import Any, Callable, Optional, Union
 
 from haystack.core.serialization import generate_qualified_class_name
 from haystack.tools import ComponentTool
@@ -22,13 +22,13 @@ def __init__(
         *,
         name: Optional[str] = "issue_viewer",
         description: Optional[str] = ISSUE_VIEWER_PROMPT,
-        parameters: Optional[Dict[str, Any]] = ISSUE_VIEWER_SCHEMA,
+        parameters: Optional[dict[str, Any]] = ISSUE_VIEWER_SCHEMA,
         github_token: Optional[Secret] = None,
         raise_on_failure: bool = True,
         retry_attempts: int = 2,
-        outputs_to_string: Optional[Dict[str, Union[str, Callable[[Any], str]]]] = None,
-        inputs_from_state: Optional[Dict[str, str]] = None,
-        outputs_to_state: Optional[Dict[str, Dict[str, Union[str, Callable]]]] = None,
+        outputs_to_string: Optional[dict[str, Union[str, Callable[[Any], str]]]] = None,
+        inputs_from_state: Optional[dict[str, str]] = None,
+        outputs_to_state: Optional[dict[str, dict[str, Union[str, Callable]]]] = None,
     ):
         """
         Initialize the GitHub issue viewer tool.
@@ -82,7 +82,7 @@ def __init__(
             outputs_to_state=outputs_to_state,
         )
 
-    def to_dict(self) -> Dict[str, Any]:
+    def to_dict(self) -> dict[str, Any]:
         """
         Serializes the tool to a dictionary.
 
@@ -105,7 +105,7 @@ def to_dict(self) -> Dict[str, Any]:
         return {"type": generate_qualified_class_name(type(self)), "data": serialized}
 
     @classmethod
-    def from_dict(cls, data: Dict[str, Any]) -> "GitHubIssueViewerTool":
+    def from_dict(cls, data: dict[str, Any]) -> "GitHubIssueViewerTool":
         """
         Deserializes the tool from a dictionary.
 
diff --git a/integrations/github/src/haystack_integrations/tools/github/pr_creator_tool.py b/integrations/github/src/haystack_integrations/tools/github/pr_creator_tool.py
index 955f1ebbac..984a34880d 100644
--- a/integrations/github/src/haystack_integrations/tools/github/pr_creator_tool.py
+++ b/integrations/github/src/haystack_integrations/tools/github/pr_creator_tool.py
@@ -1,7 +1,7 @@
 # SPDX-FileCopyrightText: 2023-present deepset GmbH <info@deepset.ai>
 #
 # SPDX-License-Identifier: Apache-2.0
-from typing import Any, Callable, Dict, Optional, Union
+from typing import Any, Callable, Optional, Union
 
 from haystack.core.serialization import generate_qualified_class_name
 from haystack.tools import ComponentTool
@@ -22,12 +22,12 @@ def __init__(
         *,
         name: Optional[str] = "pr_creator",
         description: Optional[str] = PR_CREATOR_PROMPT,
-        parameters: Optional[Dict[str, Any]] = PR_CREATOR_SCHEMA,
+        parameters: Optional[dict[str, Any]] = PR_CREATOR_SCHEMA,
         github_token: Secret = Secret.from_env_var("GITHUB_TOKEN"),
         raise_on_failure: bool = True,
-        outputs_to_string: Optional[Dict[str, Union[str, Callable[[Any], str]]]] = None,
-        inputs_from_state: Optional[Dict[str, str]] = None,
-        outputs_to_state: Optional[Dict[str, Dict[str, Union[str, Callable]]]] = None,
+        outputs_to_string: Optional[dict[str, Union[str, Callable[[Any], str]]]] = None,
+        inputs_from_state: Optional[dict[str, str]] = None,
+        outputs_to_state: Optional[dict[str, dict[str, Union[str, Callable]]]] = None,
     ):
         """
         Initialize the GitHub PR creator tool.
@@ -78,7 +78,7 @@ def __init__(
             outputs_to_state=outputs_to_state,
         )
 
-    def to_dict(self) -> Dict[str, Any]:
+    def to_dict(self) -> dict[str, Any]:
         """
         Serializes the tool to a dictionary.
 
@@ -99,7 +99,7 @@ def to_dict(self) -> Dict[str, Any]:
         return {"type": generate_qualified_class_name(type(self)), "data": serialized}
 
     @classmethod
-    def from_dict(cls, data: Dict[str, Any]) -> "GitHubPRCreatorTool":
+    def from_dict(cls, data: dict[str, Any]) -> "GitHubPRCreatorTool":
         """
         Deserializes the tool from a dictionary.
 
diff --git a/integrations/github/src/haystack_integrations/tools/github/repo_forker_tool.py b/integrations/github/src/haystack_integrations/tools/github/repo_forker_tool.py
index e38f4ef1df..a662eb33c3 100644
--- a/integrations/github/src/haystack_integrations/tools/github/repo_forker_tool.py
+++ b/integrations/github/src/haystack_integrations/tools/github/repo_forker_tool.py
@@ -1,7 +1,7 @@
 # SPDX-FileCopyrightText: 2023-present deepset GmbH <info@deepset.ai>
 #
 # SPDX-License-Identifier: Apache-2.0
-from typing import Any, Callable, Dict, Optional, Union
+from typing import Any, Callable, Optional, Union
 
 from haystack.core.serialization import generate_qualified_class_name
 from haystack.tools import ComponentTool
@@ -22,12 +22,12 @@ def __init__(
         *,
         name: Optional[str] = "repo_forker",
         description: Optional[str] = REPO_FORKER_PROMPT,
-        parameters: Optional[Dict[str, Any]] = REPO_FORKER_SCHEMA,
+        parameters: Optional[dict[str, Any]] = REPO_FORKER_SCHEMA,
         github_token: Secret = Secret.from_env_var("GITHUB_TOKEN"),
         raise_on_failure: bool = True,
-        outputs_to_string: Optional[Dict[str, Union[str, Callable[[Any], str]]]] = None,
-        inputs_from_state: Optional[Dict[str, str]] = None,
-        outputs_to_state: Optional[Dict[str, Dict[str, Union[str, Callable]]]] = None,
+        outputs_to_string: Optional[dict[str, Union[str, Callable[[Any], str]]]] = None,
+        inputs_from_state: Optional[dict[str, str]] = None,
+        outputs_to_state: Optional[dict[str, dict[str, Union[str, Callable]]]] = None,
     ):
         """
         Initialize the GitHub Repo Forker tool.
@@ -79,7 +79,7 @@ def __init__(
             outputs_to_state=self.outputs_to_state,
         )
 
-    def to_dict(self) -> Dict[str, Any]:
+    def to_dict(self) -> dict[str, Any]:
         """
         Serializes the tool to a dictionary.
 
@@ -101,7 +101,7 @@ def to_dict(self) -> Dict[str, Any]:
         return {"type": generate_qualified_class_name(type(self)), "data": serialized}
 
     @classmethod
-    def from_dict(cls, data: Dict[str, Any]) -> "GitHubRepoForkerTool":
+    def from_dict(cls, data: dict[str, Any]) -> "GitHubRepoForkerTool":
         """
         Deserializes the tool from a dictionary.
 
diff --git a/integrations/github/src/haystack_integrations/tools/github/repo_viewer_tool.py b/integrations/github/src/haystack_integrations/tools/github/repo_viewer_tool.py
index bb24a1f1b4..be6eb153dc 100644
--- a/integrations/github/src/haystack_integrations/tools/github/repo_viewer_tool.py
+++ b/integrations/github/src/haystack_integrations/tools/github/repo_viewer_tool.py
@@ -1,7 +1,7 @@
 # SPDX-FileCopyrightText: 2023-present deepset GmbH <info@deepset.ai>
 #
 # SPDX-License-Identifier: Apache-2.0
-from typing import Any, Callable, Dict, Optional, Union
+from typing import Any, Callable, Optional, Union
 
 from haystack.core.serialization import generate_qualified_class_name
 from haystack.tools import ComponentTool
@@ -22,15 +22,15 @@ def __init__(
         *,
         name: Optional[str] = "repo_viewer",
         description: Optional[str] = REPO_VIEWER_PROMPT,
-        parameters: Optional[Dict[str, Any]] = REPO_VIEWER_SCHEMA,
+        parameters: Optional[dict[str, Any]] = REPO_VIEWER_SCHEMA,
         github_token: Optional[Secret] = None,
         repo: Optional[str] = None,
         branch: str = "main",
         raise_on_failure: bool = True,
         max_file_size: int = 1_000_000,  # 1MB default limit
-        outputs_to_string: Optional[Dict[str, Union[str, Callable[[Any], str]]]] = None,
-        inputs_from_state: Optional[Dict[str, str]] = None,
-        outputs_to_state: Optional[Dict[str, Dict[str, Union[str, Callable]]]] = None,
+        outputs_to_string: Optional[dict[str, Union[str, Callable[[Any], str]]]] = None,
+        inputs_from_state: Optional[dict[str, str]] = None,
+        outputs_to_state: Optional[dict[str, dict[str, Union[str, Callable]]]] = None,
     ):
         """
         Initialize the GitHub repository viewer tool.
@@ -95,7 +95,7 @@ def __init__(
             outputs_to_state=self.outputs_to_state,
         )
 
-    def to_dict(self) -> Dict[str, Any]:
+    def to_dict(self) -> dict[str, Any]:
         """
         Serializes the tool to a dictionary.
 
@@ -120,7 +120,7 @@ def to_dict(self) -> Dict[str, Any]:
         return {"type": generate_qualified_class_name(type(self)), "data": serialized}
 
     @classmethod
-    def from_dict(cls, data: Dict[str, Any]) -> "GitHubRepoViewerTool":
+    def from_dict(cls, data: dict[str, Any]) -> "GitHubRepoViewerTool":
         """
         Deserializes the tool from a dictionary.
 
diff --git a/integrations/github/src/haystack_integrations/tools/github/utils.py b/integrations/github/src/haystack_integrations/tools/github/utils.py
index e4a57aab6b..4321a41347 100644
--- a/integrations/github/src/haystack_integrations/tools/github/utils.py
+++ b/integrations/github/src/haystack_integrations/tools/github/utils.py
@@ -2,13 +2,13 @@
 #
 # SPDX-License-Identifier: Apache-2.0
 
-from typing import Any, Callable, Dict, List, Optional, Union
+from typing import Any, Callable, Optional, Union
 
 from haystack import Document
 from haystack.utils.callable_serialization import deserialize_callable, serialize_callable
 
 
-def message_handler(documents: List[Document], max_length: int = 150_000) -> str:
+def message_handler(documents: list[Document], max_length: int = 150_000) -> str:
     """
     Handles the tool output before conversion to ChatMessage.
 
@@ -32,9 +32,9 @@ def message_handler(documents: List[Document], max_length: int = 150_000) -> str
 
 
 def serialize_handlers(
-    serialized: Dict[str, Any],
-    outputs_to_state: Optional[Dict[str, Dict[str, Union[str, Callable]]]],
-    outputs_to_string: Optional[Dict[str, Union[str, Callable[[Any], str]]]],
+    serialized: dict[str, Any],
+    outputs_to_state: Optional[dict[str, dict[str, Union[str, Callable]]]],
+    outputs_to_string: Optional[dict[str, Union[str, Callable[[Any], str]]]],
 ) -> None:
     """
     Serializes callable handlers in outputs_to_state and outputs_to_string.
@@ -64,7 +64,7 @@ def serialize_handlers(
         serialized["outputs_to_string"] = serialized_string
 
 
-def deserialize_handlers(data: Dict[str, Any]) -> None:
+def deserialize_handlers(data: dict[str, Any]) -> None:
     """
     Deserializes callable handlers in outputs_to_state and outputs_to_string.
 
diff --git a/integrations/google_genai/pyproject.toml b/integrations/google_genai/pyproject.toml
index 63e9d49cab..7b632efb28 100644
--- a/integrations/google_genai/pyproject.toml
+++ b/integrations/google_genai/pyproject.toml
@@ -87,7 +87,7 @@ module = [
 ignore_missing_imports = true
 
 [tool.ruff]
-target-version = "py38"
+target-version = "py39"
 line-length = 120
 
 [tool.ruff.lint]
diff --git a/integrations/google_genai/src/haystack_integrations/components/embedders/google_genai/document_embedder.py b/integrations/google_genai/src/haystack_integrations/components/embedders/google_genai/document_embedder.py
index 0208280275..c7ab540fa1 100644
--- a/integrations/google_genai/src/haystack_integrations/components/embedders/google_genai/document_embedder.py
+++ b/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, Dict, List, Literal, Optional, Tuple, Union
+from typing import Any, Literal, Optional, Union
 
 from google.genai import types
 from haystack import Document, component, default_from_dict, default_to_dict, logging
@@ -82,9 +82,9 @@ def __init__(
         suffix: str = "",
         batch_size: int = 32,
         progress_bar: bool = True,
-        meta_fields_to_embed: Optional[List[str]] = None,
+        meta_fields_to_embed: Optional[list[str]] = None,
         embedding_separator: str = "\n",
-        config: Optional[Dict[str, Any]] = None,
+        config: Optional[dict[str, Any]] = None,
     ) -> None:
         """
         Creates an GoogleGenAIDocumentEmbedder component.
@@ -138,7 +138,7 @@ def __init__(
             vertex_ai_location=vertex_ai_location,
         )
 
-    def to_dict(self) -> Dict[str, Any]:
+    def to_dict(self) -> dict[str, Any]:
         """
         Serializes the component to a dictionary.
 
@@ -162,7 +162,7 @@ def to_dict(self) -> Dict[str, Any]:
         )
 
     @classmethod
-    def from_dict(cls, data: Dict[str, Any]) -> "GoogleGenAIDocumentEmbedder":
+    def from_dict(cls, data: dict[str, Any]) -> "GoogleGenAIDocumentEmbedder":
         """
         Deserializes the component from a dictionary.
 
@@ -174,11 +174,11 @@ def from_dict(cls, data: Dict[str, Any]) -> "GoogleGenAIDocumentEmbedder":
         deserialize_secrets_inplace(data["init_parameters"], keys=["api_key"])
         return default_from_dict(cls, data)
 
-    def _prepare_texts_to_embed(self, documents: List[Document]) -> List[str]:
+    def _prepare_texts_to_embed(self, documents: list[Document]) -> list[str]:
         """
         Prepare the texts to embed by concatenating the Document text with the metadata fields to embed.
         """
-        texts_to_embed: List[str] = []
+        texts_to_embed: list[str] = []
         for doc in documents:
             meta_values_to_embed = [
                 str(doc.meta[key])
@@ -194,19 +194,19 @@ def _prepare_texts_to_embed(self, documents: List[Document]) -> List[str]:
         return texts_to_embed
 
     def _embed_batch(
-        self, texts_to_embed: List[str], batch_size: int
-    ) -> Tuple[List[Optional[List[float]]], Dict[str, Any]]:
+        self, texts_to_embed: list[str], batch_size: int
+    ) -> tuple[list[Optional[list[float]]], dict[str, Any]]:
         """
         Embed a list of texts in batches.
         """
         resolved_config = types.EmbedContentConfig(**self._config) if self._config else None
 
         all_embeddings = []
-        meta: Dict[str, Any] = {}
+        meta: dict[str, Any] = {}
         for batch in tqdm(
             batched(texts_to_embed, batch_size), disable=not self._progress_bar, desc="Calculating embeddings"
         ):
-            args: Dict[str, Any] = {"model": self._model, "contents": [b[1] for b in batch]}
+            args: dict[str, Any] = {"model": self._model, "contents": [b[1] for b in batch]}
             if resolved_config:
                 args["config"] = resolved_config
 
@@ -226,18 +226,18 @@ def _embed_batch(
         return all_embeddings, meta
 
     async def _embed_batch_async(
-        self, texts_to_embed: List[str], batch_size: int
-    ) -> Tuple[List[Optional[List[float]]], Dict[str, Any]]:
+        self, texts_to_embed: list[str], batch_size: int
+    ) -> tuple[list[Optional[list[float]]], dict[str, Any]]:
         """
         Embed a list of texts in batches asynchronously.
         """
 
         all_embeddings = []
-        meta: Dict[str, Any] = {}
+        meta: dict[str, Any] = {}
         for batch in tqdm(
             batched(texts_to_embed, batch_size), disable=not self._progress_bar, desc="Calculating embeddings"
         ):
-            args: Dict[str, Any] = {"model": self._model, "contents": [b[1] for b in batch]}
+            args: dict[str, Any] = {"model": self._model, "contents": [b[1] for b in batch]}
             if self._config:
                 args["config"] = types.EmbedContentConfig(**self._config) if self._config else None
 
@@ -256,8 +256,8 @@ 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]]:
+    @component.output_types(documents=list[Document], meta=dict[str, Any])
+    def run(self, documents: list[Document]) -> Union[dict[str, list[Document]], dict[str, Any]]:
         """
         Embeds a list of documents.
 
@@ -278,7 +278,7 @@ def run(self, documents: List[Document]) -> Union[Dict[str, List[Document]], Dic
 
         texts_to_embed = self._prepare_texts_to_embed(documents=documents)
 
-        meta: Dict[str, Any]
+        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):
@@ -286,8 +286,8 @@ def run(self, documents: List[Document]) -> Union[Dict[str, List[Document]], Dic
 
         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]]:
+    @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]]:
         """
         Embeds a list of documents asynchronously.
 
diff --git a/integrations/google_genai/src/haystack_integrations/components/embedders/google_genai/text_embedder.py b/integrations/google_genai/src/haystack_integrations/components/embedders/google_genai/text_embedder.py
index 18e0ad1cb3..1582d6f161 100644
--- a/integrations/google_genai/src/haystack_integrations/components/embedders/google_genai/text_embedder.py
+++ b/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, Dict, List, Literal, Optional, Union
+from typing import Any, Literal, Optional, Union
 
 from google.genai import types
 from haystack import component, default_from_dict, default_to_dict, logging
@@ -81,7 +81,7 @@ def __init__(
         model: str = "text-embedding-004",
         prefix: str = "",
         suffix: str = "",
-        config: Optional[Dict[str, Any]] = None,
+        config: Optional[dict[str, Any]] = None,
     ) -> None:
         """
         Creates an GoogleGenAITextEmbedder component.
@@ -123,7 +123,7 @@ def __init__(
             vertex_ai_location=vertex_ai_location,
         )
 
-    def to_dict(self) -> Dict[str, Any]:
+    def to_dict(self) -> dict[str, Any]:
         """
         Serializes the component to a dictionary.
 
@@ -143,7 +143,7 @@ def to_dict(self) -> Dict[str, Any]:
         )
 
     @classmethod
-    def from_dict(cls, data: Dict[str, Any]) -> "GoogleGenAITextEmbedder":
+    def from_dict(cls, data: dict[str, Any]) -> "GoogleGenAITextEmbedder":
         """
         Deserializes the component from a dictionary.
 
@@ -155,7 +155,7 @@ def from_dict(cls, data: Dict[str, Any]) -> "GoogleGenAITextEmbedder":
         deserialize_secrets_inplace(data["init_parameters"], keys=["api_key"])
         return default_from_dict(cls, data)
 
-    def _prepare_input(self, text: str) -> Dict[str, Any]:
+    def _prepare_input(self, text: str) -> dict[str, Any]:
         if not isinstance(text, str):
             error_message_text = (
                 "GoogleGenAITextEmbedder expects a string as an input. "
@@ -166,18 +166,18 @@ def _prepare_input(self, text: str) -> Dict[str, Any]:
 
         text_to_embed = self._prefix + text + self._suffix
 
-        kwargs: Dict[str, Any] = {"model": self._model_name, "contents": text_to_embed}
+        kwargs: dict[str, Any] = {"model": self._model_name, "contents": text_to_embed}
         if self._config:
             kwargs["config"] = types.EmbedContentConfig(**self._config)
 
         return kwargs
 
-    def _prepare_output(self, result: types.EmbedContentResponse) -> Dict[str, Any]:
+    def _prepare_output(self, result: types.EmbedContentResponse) -> dict[str, Any]:
         embedding = result.embeddings[0].values if result.embeddings else []
         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]]:
+    @component.output_types(embedding=list[float], meta=dict[str, Any])
+    def run(self, text: str) -> Union[dict[str, list[float]], dict[str, Any]]:
         """
         Embeds a single string.
 
@@ -193,8 +193,8 @@ def run(self, text: str) -> Union[Dict[str, List[float]], Dict[str, Any]]:
         response = self._client.models.embed_content(**create_kwargs)
         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]]:
+    @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]]:
         """
         Asynchronously embed a single string.
 
diff --git a/integrations/google_genai/src/haystack_integrations/components/generators/google_genai/chat/chat_generator.py b/integrations/google_genai/src/haystack_integrations/components/generators/google_genai/chat/chat_generator.py
index 346e1a2d4b..5a1b836782 100644
--- a/integrations/google_genai/src/haystack_integrations/components/generators/google_genai/chat/chat_generator.py
+++ b/integrations/google_genai/src/haystack_integrations/components/generators/google_genai/chat/chat_generator.py
@@ -4,8 +4,9 @@
 
 import base64
 import json
+from collections.abc import AsyncIterator, Iterator
 from datetime import datetime, timezone
-from typing import Any, AsyncIterator, Dict, Iterator, List, Literal, Optional
+from typing import Any, Literal, Optional
 
 from google.genai import types
 from haystack import logging
@@ -40,7 +41,7 @@
 from haystack_integrations.components.generators.google_genai.chat.utils import remove_key_from_schema
 
 # Mapping from Google GenAI finish reasons to Haystack FinishReason values
-FINISH_REASON_MAPPING: Dict[str, FinishReason] = {
+FINISH_REASON_MAPPING: dict[str, FinishReason] = {
     "STOP": "stop",
     "MAX_TOKENS": "length",
     "SAFETY": "content_filter",
@@ -91,7 +92,7 @@ def _convert_message_to_google_genai_format(message: ChatMessage) -> types.Conte
 
         # Reconstruct parts with their original thought signatures
         for sig_info in thought_signatures:
-            part_dict: Dict[str, Any] = {}
+            part_dict: dict[str, Any] = {}
 
             # Check what type of content this part had
             if sig_info.get("has_text"):
@@ -207,7 +208,7 @@ def _convert_message_to_google_genai_format(message: ChatMessage) -> types.Conte
     return types.Content(role=role, parts=parts)
 
 
-def _sanitize_tool_schema(tool_schema: Dict[str, Any]) -> Dict[str, Any]:
+def _sanitize_tool_schema(tool_schema: dict[str, Any]) -> dict[str, Any]:
     """
     Sanitizes a tool schema to remove any keys that are not supported by Google Gen AI.
 
@@ -231,7 +232,7 @@ def _sanitize_tool_schema(tool_schema: Dict[str, Any]) -> Dict[str, Any]:
     return final_schema
 
 
-def _convert_tools_to_google_genai_format(tools: ToolsType) -> List[types.Tool]:
+def _convert_tools_to_google_genai_format(tools: ToolsType) -> list[types.Tool]:
     """
     Converts a list of Haystack Tools, Toolsets, or a mix to Google Gen AI Tool format.
 
@@ -241,7 +242,7 @@ def _convert_tools_to_google_genai_format(tools: ToolsType) -> List[types.Tool]:
     # Flatten Tools and Toolsets into a single list of Tools
     flattened_tools = flatten_tools_or_toolsets(tools)
 
-    function_declarations: List[types.FunctionDeclaration] = []
+    function_declarations: list[types.FunctionDeclaration] = []
     for tool in flattened_tools:
         parameters = _sanitize_tool_schema(tool.parameters)
         function_declarations.append(
@@ -321,7 +322,7 @@ def _convert_google_genai_response_to_chatmessage(response: types.GenerateConten
         usage["thoughts_token_count"] = usage_metadata.thoughts_token_count
 
         # Create meta with reasoning content and thought signatures if available
-    meta: Dict[str, Any] = {
+    meta: dict[str, Any] = {
         "model": model,
         "finish_reason": FINISH_REASON_MAPPING.get(finish_reason or ""),
         "usage": usage,
@@ -458,8 +459,8 @@ def __init__(
         vertex_ai_project: Optional[str] = None,
         vertex_ai_location: Optional[str] = None,
         model: str = "gemini-2.0-flash",
-        generation_kwargs: Optional[Dict[str, Any]] = None,
-        safety_settings: Optional[List[Dict[str, Any]]] = None,
+        generation_kwargs: Optional[dict[str, Any]] = None,
+        safety_settings: Optional[list[dict[str, Any]]] = None,
         streaming_callback: Optional[StreamingCallbackT] = None,
         tools: Optional[ToolsType] = None,
     ):
@@ -506,7 +507,7 @@ def __init__(
         self._streaming_callback = streaming_callback
         self._tools = tools
 
-    def to_dict(self) -> Dict[str, Any]:
+    def to_dict(self) -> dict[str, Any]:
         """
         Serializes the component to a dictionary.
 
@@ -528,7 +529,7 @@ def to_dict(self) -> Dict[str, Any]:
         )
 
     @classmethod
-    def from_dict(cls, data: Dict[str, Any]) -> "GoogleGenAIChatGenerator":
+    def from_dict(cls, data: dict[str, Any]) -> "GoogleGenAIChatGenerator":
         """
         Deserializes the component from a dictionary.
 
@@ -556,10 +557,10 @@ def _convert_google_chunk_to_streaming_chunk(
         :returns: A StreamingChunk object.
         """
         content = ""
-        tool_calls: List[ToolCallDelta] = []
+        tool_calls: list[ToolCallDelta] = []
         finish_reason = None
-        reasoning_deltas: List[Dict[str, str]] = []
-        thought_signature_deltas: List[Dict[str, Any]] = []  # Track thought signatures in streaming
+        reasoning_deltas: list[dict[str, str]] = []
+        thought_signature_deltas: list[dict[str, Any]] = []  # Track thought signatures in streaming
 
         if chunk.candidates:
             candidate = chunk.candidates[0]
@@ -620,7 +621,7 @@ def _convert_google_chunk_to_streaming_chunk(
         start = index == 0 or len(tool_calls) > 0
 
         # Create meta with reasoning deltas and thought signatures if available
-        meta: Dict[str, Any] = {
+        meta: dict[str, Any] = {
             "received_at": datetime.now(timezone.utc).isoformat(),
             "model": self._model,
             "usage": usage,
@@ -644,7 +645,7 @@ def _convert_google_chunk_to_streaming_chunk(
             meta=meta,
         )
 
-    def _aggregate_streaming_chunks_with_reasoning(self, chunks: List[StreamingChunk]) -> ChatMessage:
+    def _aggregate_streaming_chunks_with_reasoning(self, chunks: list[StreamingChunk]) -> ChatMessage:
         """
         Aggregate streaming chunks into a final ChatMessage with reasoning content and thought signatures.
 
@@ -660,7 +661,7 @@ def _aggregate_streaming_chunks_with_reasoning(self, chunks: List[StreamingChunk
 
         # Now enhance with Google-specific features: reasoning content, thinking token usage, and thought signatures
         reasoning_text_parts: list[str] = []
-        thought_signatures: List[Dict[str, Any]] = []
+        thought_signatures: list[dict[str, Any]] = []
         thoughts_token_count = None
 
         for chunk in chunks:
@@ -708,7 +709,7 @@ def _aggregate_streaming_chunks_with_reasoning(self, chunks: List[StreamingChunk
 
     def _handle_streaming_response(
         self, response_stream: Iterator[types.GenerateContentResponse], streaming_callback: StreamingCallbackT
-    ) -> Dict[str, List[ChatMessage]]:
+    ) -> dict[str, list[ChatMessage]]:
         """
         Handle streaming response from Google Gen AI generate_content_stream.
         :param response_stream: The streaming response from generate_content_stream.
@@ -740,7 +741,7 @@ def _handle_streaming_response(
 
     async def _handle_streaming_response_async(
         self, response_stream: AsyncIterator[types.GenerateContentResponse], streaming_callback: AsyncStreamingCallbackT
-    ) -> Dict[str, List[ChatMessage]]:
+    ) -> dict[str, list[ChatMessage]]:
         """
         Handle async streaming response from Google Gen AI generate_content_stream.
         :param response_stream: The async streaming response from generate_content_stream.
@@ -773,7 +774,7 @@ async def _handle_streaming_response_async(
             msg = f"Error in async streaming response: {e}"
             raise RuntimeError(msg) from e
 
-    def _process_thinking_config(self, generation_kwargs: Dict[str, Any]) -> Dict[str, Any]:
+    def _process_thinking_config(self, generation_kwargs: dict[str, Any]) -> dict[str, Any]:
         """
         Process thinking configuration from generation_kwargs.
 
@@ -796,15 +797,15 @@ def _process_thinking_config(self, generation_kwargs: Dict[str, Any]) -> Dict[st
 
         return generation_kwargs
 
-    @component.output_types(replies=List[ChatMessage])
+    @component.output_types(replies=list[ChatMessage])
     def run(
         self,
-        messages: List[ChatMessage],
-        generation_kwargs: Optional[Dict[str, Any]] = None,
-        safety_settings: Optional[List[Dict[str, Any]]] = None,
+        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,
-    ) -> Dict[str, Any]:
+    ) -> dict[str, Any]:
         """
         Run the Google Gen AI chat generator on the given input data.
 
@@ -851,7 +852,7 @@ def run(
             chat_messages = messages[1:]
 
         # Convert messages to Google Gen AI Content format
-        contents: List[types.ContentUnionDict] = []
+        contents: list[types.ContentUnionDict] = []
         for msg in chat_messages:
             contents.append(_convert_message_to_google_genai_format(msg))
 
@@ -904,15 +905,15 @@ def run(
             error_msg = f"Error in Google Gen AI chat generation: {e}"
             raise RuntimeError(error_msg) from e
 
-    @component.output_types(replies=List[ChatMessage])
+    @component.output_types(replies=list[ChatMessage])
     async def run_async(
         self,
-        messages: List[ChatMessage],
-        generation_kwargs: Optional[Dict[str, Any]] = None,
-        safety_settings: Optional[List[Dict[str, Any]]] = None,
+        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,
-    ) -> Dict[str, Any]:
+    ) -> dict[str, Any]:
         """
         Async version of the run method. Run the Google Gen AI chat generator on the given input data.
 
@@ -960,7 +961,7 @@ async def run_async(
             chat_messages = messages[1:]
 
         # Convert messages to Google Gen AI Content format
-        contents: List[types.ContentUnion] = []
+        contents: list[types.ContentUnion] = []
         for msg in chat_messages:
             contents.append(_convert_message_to_google_genai_format(msg))
 
diff --git a/integrations/google_genai/src/haystack_integrations/components/generators/google_genai/chat/utils.py b/integrations/google_genai/src/haystack_integrations/components/generators/google_genai/chat/utils.py
index 9cecb6a85e..038f531d1d 100644
--- a/integrations/google_genai/src/haystack_integrations/components/generators/google_genai/chat/utils.py
+++ b/integrations/google_genai/src/haystack_integrations/components/generators/google_genai/chat/utils.py
@@ -1,9 +1,13 @@
-from typing import Any, Dict, List, Union
+# SPDX-FileCopyrightText: 2023-present deepset GmbH <info@deepset.ai>
+#
+# SPDX-License-Identifier: Apache-2.0
+
+from typing import Any, Union
 
 
 def remove_key_from_schema(
-    schema: Union[Dict[str, Any], List[Any], Any], target_key: str
-) -> Union[Dict[str, Any], List[Any], Any]:
+    schema: Union[dict[str, Any], list[Any], Any], target_key: str
+) -> Union[dict[str, Any], list[Any], Any]:
     """
     Recursively traverse a schema and remove all occurrences of the target key.
 
diff --git a/integrations/google_genai/tests/test_document_embedder.py b/integrations/google_genai/tests/test_document_embedder.py
index 6abbf89011..2579801bc6 100644
--- a/integrations/google_genai/tests/test_document_embedder.py
+++ b/integrations/google_genai/tests/test_document_embedder.py
@@ -4,7 +4,6 @@
 
 import os
 import random
-from typing import List
 
 import pytest
 from haystack import Document
@@ -13,7 +12,7 @@
 from haystack_integrations.components.embedders.google_genai import GoogleGenAIDocumentEmbedder
 
 
-def mock_google_response(contents: List[str], model: str = "text-embedding-004", **kwargs) -> dict:
+def mock_google_response(contents: list[str], model: str = "text-embedding-004", **kwargs) -> dict:
     secure_random = random.SystemRandom()
     dict_response = {
         "embedding": [[secure_random.random() for _ in range(768)] for _ in contents],