enhancement: Adopt PEP 585 type hinting (part 3) (#2510)

* Update integs

* Update filters.py

Author: Amnah199

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]

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"
 

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]

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.
 

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.