community[patch]: upgrade to recent version of mypy (#21616)

This PR upgrades community to a recent version of mypy. It inserts type:
ignore on all existing failures.

libs/community/langchain_community/adapters/openai.py

@@ -95,18 +95,18 @@ def convert_dict_to_message(_dict: Mapping[str, Any]) -> BaseMessage:
     elif role == "system":
         return SystemMessage(content=_dict.get("content", ""))
     elif role == "function":
-        return FunctionMessage(content=_dict.get("content", ""), name=_dict.get("name"))
+        return FunctionMessage(content=_dict.get("content", ""), name=_dict.get("name"))  # type: ignore[arg-type]
     elif role == "tool":
         additional_kwargs = {}
         if "name" in _dict:
             additional_kwargs["name"] = _dict["name"]
         return ToolMessage(
             content=_dict.get("content", ""),
-            tool_call_id=_dict.get("tool_call_id"),
+            tool_call_id=_dict.get("tool_call_id"),  # type: ignore[arg-type]
             additional_kwargs=additional_kwargs,
         )
     else:
-        return ChatMessage(content=_dict.get("content", ""), role=role)
+        return ChatMessage(content=_dict.get("content", ""), role=role)  # type: ignore[arg-type]
 
 
 def convert_message_to_dict(message: BaseMessage) -> dict:

libs/community/langchain_community/agent_toolkits/azure_ai_services.py

@@ -21,11 +21,11 @@ class AzureAiServicesToolkit(BaseToolkit):
         """Get the tools in the toolkit."""
 
         tools: List[BaseTool] = [
-            AzureAiServicesDocumentIntelligenceTool(),
-            AzureAiServicesImageAnalysisTool(),
-            AzureAiServicesSpeechToTextTool(),
-            AzureAiServicesTextToSpeechTool(),
-            AzureAiServicesTextAnalyticsForHealthTool(),
+            AzureAiServicesDocumentIntelligenceTool(),  # type: ignore[call-arg]
+            AzureAiServicesImageAnalysisTool(),  # type: ignore[call-arg]
+            AzureAiServicesSpeechToTextTool(),  # type: ignore[call-arg]
+            AzureAiServicesTextToSpeechTool(),  # type: ignore[call-arg]
+            AzureAiServicesTextAnalyticsForHealthTool(),  # type: ignore[call-arg]
         ]
 
         return tools

libs/community/langchain_community/agent_toolkits/azure_cognitive_services.py

@@ -21,13 +21,13 @@ class AzureCognitiveServicesToolkit(BaseToolkit):
         """Get the tools in the toolkit."""
 
         tools: List[BaseTool] = [
-            AzureCogsFormRecognizerTool(),
-            AzureCogsSpeech2TextTool(),
-            AzureCogsText2SpeechTool(),
-            AzureCogsTextAnalyticsHealthTool(),
+            AzureCogsFormRecognizerTool(),  # type: ignore[call-arg]
+            AzureCogsSpeech2TextTool(),  # type: ignore[call-arg]
+            AzureCogsText2SpeechTool(),  # type: ignore[call-arg]
+            AzureCogsTextAnalyticsHealthTool(),  # type: ignore[call-arg]
         ]
 
         # TODO: Remove check once azure-ai-vision supports MacOS.
         if sys.platform.startswith("linux") or sys.platform.startswith("win"):
-            tools.append(AzureCogsImageAnalysisTool())
+            tools.append(AzureCogsImageAnalysisTool())  # type: ignore[call-arg]
         return tools

libs/community/langchain_community/agent_toolkits/clickup/toolkit.py

@@ -102,7 +102,7 @@ class ClickupToolkit(BaseToolkit):
             )
             for action in operations
         ]
-        return cls(tools=tools)
+        return cls(tools=tools)  # type: ignore[arg-type]
 
     def get_tools(self) -> List[BaseTool]:
         """Get the tools in the toolkit."""

libs/community/langchain_community/agent_toolkits/connery/toolkit.py

@@ -45,6 +45,6 @@ class ConneryToolkit(BaseToolkit):
             ConneryToolkit: The Connery Toolkit.
         """
 
-        instance = cls(tools=connery_service.list_actions())
+        instance = cls(tools=connery_service.list_actions())  # type: ignore[arg-type]
 
         return instance

libs/community/langchain_community/agent_toolkits/file_management/toolkit.py

@@ -73,7 +73,7 @@ class FileManagementToolkit(BaseToolkit):
         tools: List[BaseTool] = []
         for tool in allowed_tools:
             tool_cls = _FILE_TOOLS_MAP[tool]
-            tools.append(tool_cls(root_dir=self.root_dir))
+            tools.append(tool_cls(root_dir=self.root_dir))  # type: ignore[call-arg]
         return tools
 
 

libs/community/langchain_community/agent_toolkits/github/toolkit.py

@@ -308,7 +308,7 @@ class GitHubToolkit(BaseToolkit):
             )
             for action in operations
         ]
-        return cls(tools=tools)
+        return cls(tools=tools)  # type: ignore[arg-type]
 
     def get_tools(self) -> List[BaseTool]:
         """Get the tools in the toolkit."""

libs/community/langchain_community/agent_toolkits/gitlab/toolkit.py

@@ -88,7 +88,7 @@ class GitLabToolkit(BaseToolkit):
             )
             for action in operations
         ]
-        return cls(tools=tools)
+        return cls(tools=tools)  # type: ignore[arg-type]
 
     def get_tools(self) -> List[BaseTool]:
         """Get the tools in the toolkit."""

libs/community/langchain_community/agent_toolkits/jira/toolkit.py

@@ -64,7 +64,7 @@ class JiraToolkit(BaseToolkit):
             )
             for action in operations
         ]
-        return cls(tools=tools)
+        return cls(tools=tools)  # type: ignore[arg-type]
 
     def get_tools(self) -> List[BaseTool]:
         """Get the tools in the toolkit."""

libs/community/langchain_community/agent_toolkits/nasa/toolkit.py

@@ -51,7 +51,7 @@ class NasaToolkit(BaseToolkit):
             )
             for action in operations
         ]
-        return cls(tools=tools)
+        return cls(tools=tools)  # type: ignore[arg-type]
 
     def get_tools(self) -> List[BaseTool]:
         """Get the tools in the toolkit."""

libs/community/langchain_community/agent_toolkits/openapi/planner.py

@@ -262,12 +262,12 @@ def _create_api_controller_agent(
     get_llm_chain = LLMChain(llm=llm, prompt=PARSING_GET_PROMPT)
     post_llm_chain = LLMChain(llm=llm, prompt=PARSING_POST_PROMPT)
     tools: List[BaseTool] = [
-        RequestsGetToolWithParsing(
+        RequestsGetToolWithParsing(  # type: ignore[call-arg]
             requests_wrapper=requests_wrapper,
             llm_chain=get_llm_chain,
             allow_dangerous_requests=allow_dangerous_requests,
         ),
-        RequestsPostToolWithParsing(
+        RequestsPostToolWithParsing(  # type: ignore[call-arg]
             requests_wrapper=requests_wrapper,
             llm_chain=post_llm_chain,
             allow_dangerous_requests=allow_dangerous_requests,

libs/community/langchain_community/agent_toolkits/powerbi/toolkit.py

@@ -66,7 +66,7 @@ class PowerBIToolkit(BaseToolkit):
                 powerbi=self.powerbi,
                 examples=self.examples,
                 max_iterations=self.max_iterations,
-                output_token_limit=self.output_token_limit,
+                output_token_limit=self.output_token_limit,  # type: ignore[arg-type]
                 tiktoken_model_name=self.tiktoken_model_name,
             ),
             InfoPowerBITool(powerbi=self.powerbi),

libs/community/langchain_community/agent_toolkits/sql/base.py

@@ -136,7 +136,7 @@ def create_sql_agent(
             "Must provide exactly one of 'toolkit' or 'db'. Received both."
         )
 
-    toolkit = toolkit or SQLDatabaseToolkit(llm=llm, db=db)
+    toolkit = toolkit or SQLDatabaseToolkit(llm=llm, db=db)  # type: ignore[arg-type]
     agent_type = agent_type or AgentType.ZERO_SHOT_REACT_DESCRIPTION
     tools = toolkit.get_tools() + list(extra_tools)
     if prompt is None:

libs/community/langchain_community/agent_toolkits/steam/toolkit.py

@@ -42,7 +42,7 @@ class SteamToolkit(BaseToolkit):
             )
             for action in operations
         ]
-        return cls(tools=tools)
+        return cls(tools=tools)  # type: ignore[arg-type]
 
     def get_tools(self) -> List[BaseTool]:
         """Get the tools in the toolkit."""

libs/community/langchain_community/agent_toolkits/zapier/toolkit.py

@@ -29,7 +29,7 @@ class ZapierToolkit(BaseToolkit):
             )
             for action in actions
         ]
-        return cls(tools=tools)
+        return cls(tools=tools)  # type: ignore[arg-type]
 
     @classmethod
     async def async_from_zapier_nla_wrapper(
@@ -46,7 +46,7 @@ class ZapierToolkit(BaseToolkit):
             )
             for action in actions
         ]
-        return cls(tools=tools)
+        return cls(tools=tools)  # type: ignore[arg-type]
 
     def get_tools(self) -> List[BaseTool]:
         """Get the tools in the toolkit."""

libs/community/langchain_community/cache.py

@@ -420,7 +420,7 @@ class _RedisCacheBase(BaseCache, ABC):
                     )
                     # In a previous life we stored the raw text directly
                     # in the table, so assume it's in that format.
-                    generations.append(Generation(text=text))
+                    generations.append(Generation(text=text))  # type: ignore[arg-type]
         return generations if generations else None
 
     @staticmethod

libs/community/langchain_community/chains/ernie_functions/base.py

@@ -376,7 +376,7 @@ def create_ernie_fn_chain(
     output_key: str = "function",
     output_parser: Optional[BaseLLMOutputParser] = None,
     **kwargs: Any,
-) -> LLMChain:
+) -> LLMChain:  # type: ignore[valid-type]
     """[Legacy] Create an LLM chain that uses Ernie functions.
 
     Args:
@@ -453,7 +453,7 @@ def create_ernie_fn_chain(
     }
     if len(ernie_functions) == 1:
         llm_kwargs["function_call"] = {"name": ernie_functions[0]["name"]}
-    llm_chain = LLMChain(
+    llm_chain = LLMChain(  # type: ignore[misc]
         llm=llm,
         prompt=prompt,
         output_parser=output_parser,
@@ -472,7 +472,7 @@ def create_structured_output_chain(
     output_key: str = "function",
     output_parser: Optional[BaseLLMOutputParser] = None,
     **kwargs: Any,
-) -> LLMChain:
+) -> LLMChain:  # type: ignore[valid-type]
     """[Legacy] Create an LLMChain that uses an Ernie function to get a structured output.
 
     Args:

libs/community/langchain_community/chat_loaders/imessage.py

@@ -148,7 +148,7 @@ class IMessageChatLoader(BaseChatLoader):
                 continue
 
             results.append(
-                HumanMessage(
+                HumanMessage(  # type: ignore[call-arg]
                     role=sender,
                     content=content,
                     additional_kwargs={

libs/community/langchain_community/chat_loaders/slack.py

@@ -51,7 +51,7 @@ class SlackChatLoader(BaseChatLoader):
                 )
             else:
                 results.append(
-                    HumanMessage(
+                    HumanMessage(  # type: ignore[call-arg]
                         role=sender,
                         content=text,
                         additional_kwargs={

libs/community/langchain_community/chat_loaders/utils.py

@@ -77,7 +77,7 @@ def map_ai_messages_in_session(chat_sessions: ChatSession, sender: str) -> ChatS
             message = AIMessage(
                 content=message.content,
                 additional_kwargs=message.additional_kwargs.copy(),
-                example=getattr(message, "example", None),
+                example=getattr(message, "example", None),  # type: ignore[arg-type]
             )
             num_converted += 1
         messages.append(message)

libs/community/langchain_community/chat_loaders/whatsapp.py

@@ -73,7 +73,7 @@ class WhatsAppChatLoader(BaseChatLoader):
                 timestamp, sender, text = result.groups()
                 if not self._ignore_lines.match(text.strip()):
                     results.append(
-                        HumanMessage(
+                        HumanMessage(  # type: ignore[call-arg]
                             role=sender,
                             content=text,
                             additional_kwargs={

libs/community/langchain_community/chat_models/azureml_endpoint.py

@@ -419,4 +419,4 @@ def _convert_delta_to_message_chunk(
     elif role or default_class == ChatMessageChunk:
         return ChatMessageChunk(content=content, role=role)
     else:
-        return default_class(content=content)
+        return default_class(content=content)  # type: ignore[call-arg]

libs/community/langchain_community/chat_models/baichuan.py

@@ -66,9 +66,9 @@ def _convert_delta_to_message_chunk(
     elif role == "assistant" or default_class == AIMessageChunk:
         return AIMessageChunk(content=content)
     elif role or default_class == ChatMessageChunk:
-        return ChatMessageChunk(content=content, role=role)
+        return ChatMessageChunk(content=content, role=role)  # type: ignore[arg-type]
     else:
-        return default_class(content=content)
+        return default_class(content=content)  # type: ignore[call-arg]
 
 
 class ChatBaichuan(BaseChatModel):

libs/community/langchain_community/chat_models/baidu_qianfan_endpoint.py

@@ -383,7 +383,7 @@ class QianfanChatEndpoint(BaseChatModel):
                 additional_kwargs = msg.additional_kwargs.get("function_call", {})
                 chunk = ChatGenerationChunk(
                     text=res["result"],
-                    message=AIMessageChunk(
+                    message=AIMessageChunk(  # type: ignore[call-arg]
                         content=msg.content,
                         role="assistant",
                         additional_kwargs=additional_kwargs,
@@ -410,7 +410,7 @@ class QianfanChatEndpoint(BaseChatModel):
                 additional_kwargs = msg.additional_kwargs.get("function_call", {})
                 chunk = ChatGenerationChunk(
                     text=res["result"],
-                    message=AIMessageChunk(
+                    message=AIMessageChunk(  # type: ignore[call-arg]
                         content=msg.content,
                         role="assistant",
                         additional_kwargs=additional_kwargs,
@@ -552,7 +552,8 @@ class QianfanChatEndpoint(BaseChatModel):
         llm = self.bind_tools([schema])
         if is_pydantic_schema:
             output_parser: OutputParserLike = PydanticToolsParser(
-                tools=[schema], first_tool_only=True
+                tools=[schema],  # type: ignore[list-item]
+                first_tool_only=True,  # type: ignore[list-item]
             )
         else:
             key_name = convert_to_openai_tool(schema)["function"]["name"]

libs/community/langchain_community/chat_models/coze.py

@@ -69,7 +69,7 @@ def _convert_delta_to_message_chunk(_dict: Mapping[str, Any]) -> BaseMessageChun
     elif role == "assistant":
         return AIMessageChunk(content=content)
     else:
-        return ChatMessageChunk(content=content, role=role)
+        return ChatMessageChunk(content=content, role=role)  # type: ignore[arg-type]
 
 
 class ChatCoze(BaseChatModel):

libs/community/langchain_community/chat_models/deepinfra.py

@@ -118,9 +118,9 @@ def _convert_delta_to_message_chunk(
     elif role == "function" or default_class == FunctionMessageChunk:
         return FunctionMessageChunk(content=content, name=_dict["name"])
     elif role or default_class == ChatMessageChunk:
-        return ChatMessageChunk(content=content, role=role)
+        return ChatMessageChunk(content=content, role=role)  # type: ignore[arg-type]
     else:
-        return default_class(content=content)
+        return default_class(content=content)  # type: ignore[call-arg]
 
 
 def _convert_message_to_dict(message: BaseMessage) -> dict:

libs/community/langchain_community/chat_models/fireworks.py

@@ -58,7 +58,7 @@ def _convert_delta_to_message_chunk(
     elif role or default_class == ChatMessageChunk:
         return ChatMessageChunk(content=content, role=role)
     else:
-        return default_class(content=content)
+        return default_class(content=content)  # type: ignore[call-arg]
 
 
 def convert_dict_to_message(_dict: Any) -> BaseMessage:

libs/community/langchain_community/chat_models/gigachat.py

@@ -108,9 +108,9 @@ def _convert_delta_to_message_chunk(
     elif role == "function" or default_class == FunctionMessageChunk:
         return FunctionMessageChunk(content=content, name=_dict["name"])
     elif role or default_class == ChatMessageChunk:
-        return ChatMessageChunk(content=content, role=role)
+        return ChatMessageChunk(content=content, role=role)  # type: ignore[arg-type]
     else:
-        return default_class(content=content)
+        return default_class(content=content)  # type: ignore[call-arg]
 
 
 class GigaChat(_BaseGigaChat, BaseChatModel):

libs/community/langchain_community/chat_models/hunyuan.py

@@ -72,9 +72,9 @@ def _convert_delta_to_message_chunk(
     elif role == "assistant" or default_class == AIMessageChunk:
         return AIMessageChunk(content=content)
     elif role or default_class == ChatMessageChunk:
-        return ChatMessageChunk(content=content, role=role)
+        return ChatMessageChunk(content=content, role=role)  # type: ignore[arg-type]
     else:
-        return default_class(content=content)
+        return default_class(content=content)  # type: ignore[call-arg]
 
 
 # signature generation

libs/community/langchain_community/chat_models/jinachat.py

@@ -103,9 +103,9 @@ def _convert_delta_to_message_chunk(
     elif role == "system" or default_class == SystemMessageChunk:
         return SystemMessageChunk(content=content)
     elif role or default_class == ChatMessageChunk:
-        return ChatMessageChunk(content=content, role=role)
+        return ChatMessageChunk(content=content, role=role)  # type: ignore[arg-type]
     else:
-        return default_class(content=content)
+        return default_class(content=content)  # type: ignore[call-arg]
 
 
 def _convert_dict_to_message(_dict: Mapping[str, Any]) -> BaseMessage:

libs/community/langchain_community/chat_models/litellm.py

@@ -131,9 +131,9 @@ def _convert_delta_to_message_chunk(
     elif role == "function" or default_class == FunctionMessageChunk:
         return FunctionMessageChunk(content=content, name=_dict["name"])
     elif role or default_class == ChatMessageChunk:
-        return ChatMessageChunk(content=content, role=role)
+        return ChatMessageChunk(content=content, role=role)  # type: ignore[arg-type]
     else:
-        return default_class(content=content)
+        return default_class(content=content)  # type: ignore[call-arg]
 
 
 def _convert_message_to_dict(message: BaseMessage) -> dict:

libs/community/langchain_community/chat_models/llama_edge.py

@@ -64,9 +64,9 @@ def _convert_delta_to_message_chunk(
     elif role == "assistant" or default_class == AIMessageChunk:
         return AIMessageChunk(content=content)
     elif role or default_class == ChatMessageChunk:
-        return ChatMessageChunk(content=content, role=role)
+        return ChatMessageChunk(content=content, role=role)  # type: ignore[arg-type]
     else:
-        return default_class(content=content)
+        return default_class(content=content)  # type: ignore[call-arg]
 
 
 class LlamaEdgeChatService(BaseChatModel):

libs/community/langchain_community/chat_models/minimax.py

@@ -82,7 +82,7 @@ class MiniMaxChat(MinimaxCommon, BaseChatModel):
 
         # This is required since the stop are not enforced by the model parameters
         text = text if stop is None else enforce_stop_tokens(text, stop)
-        return ChatResult(generations=[ChatGeneration(message=AIMessage(text))])
+        return ChatResult(generations=[ChatGeneration(message=AIMessage(text))])  # type: ignore[misc]
 
     async def _agenerate(
         self,

libs/community/langchain_community/chat_models/openai.py

@@ -139,9 +139,9 @@ def _convert_delta_to_message_chunk(
     elif role == "tool" or default_class == ToolMessageChunk:
         return ToolMessageChunk(content=content, tool_call_id=_dict["tool_call_id"])
     elif role or default_class == ChatMessageChunk:
-        return ChatMessageChunk(content=content, role=role)
+        return ChatMessageChunk(content=content, role=role)  # type: ignore[arg-type]
     else:
-        return default_class(content=content)
+        return default_class(content=content)  # type: ignore[call-arg]
 
 
 @deprecated(

libs/community/langchain_community/chat_models/perplexity.py

@@ -198,9 +198,9 @@ class ChatPerplexity(BaseChatModel):
         elif role == "tool" or default_class == ToolMessageChunk:
             return ToolMessageChunk(content=content, tool_call_id=_dict["tool_call_id"])
         elif role or default_class == ChatMessageChunk:
-            return ChatMessageChunk(content=content, role=role)
+            return ChatMessageChunk(content=content, role=role)  # type: ignore[arg-type]
         else:
-            return default_class(content=content)
+            return default_class(content=content)  # type: ignore[call-arg]
 
     def _stream(
         self,

libs/community/langchain_community/chat_models/premai.py

@@ -136,7 +136,7 @@ def _convert_delta_response_to_message_chunk(
     elif role or default_class == ChatMessageChunk:
         return ChatMessageChunk(content=content, role=role), finish_reasons
     else:
-        return default_class(content=content), finish_reasons
+        return default_class(content=content), finish_reasons  # type: ignore[call-arg]
 
 
 def _messages_to_prompt_dict(

libs/community/langchain_community/chat_models/solar.py

@@ -10,7 +10,7 @@ from langchain_community.chat_models import ChatOpenAI
 from langchain_community.llms.solar import SOLAR_SERVICE_URL_BASE, SolarCommon
 
 
-@deprecated(
+@deprecated(  # type: ignore[arg-type]
     since="0.0.34", removal="0.3.0", alternative_import="langchain_upstage.ChatUpstage"
 )
 class SolarChat(SolarCommon, ChatOpenAI):

libs/community/langchain_community/chat_models/sparkllm.py

@@ -85,7 +85,7 @@ def _convert_delta_to_message_chunk(
     elif msg_role or default_class == ChatMessageChunk:
         return ChatMessageChunk(content=msg_content, role=msg_role)
     else:
-        return default_class(content=msg_content)
+        return default_class(content=msg_content)  # type: ignore[call-arg]
 
 
 class ChatSparkLLM(BaseChatModel):
@@ -382,10 +382,10 @@ class _SparkLLMClient:
             on_close=self.on_close,
             on_open=self.on_open,
         )
-        ws.messages = messages
-        ws.user_id = user_id
-        ws.model_kwargs = self.model_kwargs if model_kwargs is None else model_kwargs
-        ws.streaming = streaming
+        ws.messages = messages  # type: ignore[attr-defined]
+        ws.user_id = user_id  # type: ignore[attr-defined]
+        ws.model_kwargs = self.model_kwargs if model_kwargs is None else model_kwargs  # type: ignore[attr-defined]
+        ws.streaming = streaming  # type: ignore[attr-defined]
         ws.run_forever()
 
     def arun(

libs/community/langchain_community/chat_models/tongyi.py

@@ -94,7 +94,7 @@ def convert_dict_to_message(
             else AIMessage(
                 content=content,
                 additional_kwargs=additional_kwargs,
-                tool_calls=tool_calls,
+                tool_calls=tool_calls,  # type: ignore[arg-type]
                 invalid_tool_calls=invalid_tool_calls,
             )
         )

libs/community/langchain_community/chat_models/yuan2.py

@@ -437,9 +437,9 @@ def _convert_delta_to_message_chunk(
     elif role == "system" or default_class == SystemMessageChunk:
         return SystemMessageChunk(content=content)
     elif role or default_class == ChatMessageChunk:
-        return ChatMessageChunk(content=content, role=role)
+        return ChatMessageChunk(content=content, role=role)  # type: ignore[arg-type]
     else:
-        return default_class(content=content)
+        return default_class(content=content)  # type: ignore[call-arg]
 
 
 def _convert_dict_to_message(_dict: Mapping[str, Any]) -> BaseMessage:
@@ -451,7 +451,7 @@ def _convert_dict_to_message(_dict: Mapping[str, Any]) -> BaseMessage:
     elif role == "system":
         return SystemMessage(content=_dict.get("content", ""))
     else:
-        return ChatMessage(content=_dict.get("content", ""), role=role)
+        return ChatMessage(content=_dict.get("content", ""), role=role)  # type: ignore[arg-type]
 
 
 def _convert_message_to_dict(message: BaseMessage) -> dict:

libs/community/langchain_community/chat_models/zhipuai.py

@@ -101,7 +101,7 @@ def _convert_dict_to_message(dct: Dict[str, Any]) -> BaseMessage:
         if tool_calls is not None:
             additional_kwargs["tool_calls"] = tool_calls
         return AIMessage(content=content, additional_kwargs=additional_kwargs)
-    return ChatMessage(role=role, content=content)
+    return ChatMessage(role=role, content=content)  # type: ignore[arg-type]
 
 
 def _convert_message_to_dict(message: BaseMessage) -> Dict[str, Any]:
@@ -144,8 +144,8 @@ def _convert_delta_to_message_chunk(
     if role == "assistant" or default_class == AIMessageChunk:
         return AIMessageChunk(content=content, additional_kwargs=additional_kwargs)
     if role or default_class == ChatMessageChunk:
-        return ChatMessageChunk(content=content, role=role)
-    return default_class(content=content)
+        return ChatMessageChunk(content=content, role=role)  # type: ignore[arg-type]
+    return default_class(content=content)  # type: ignore[call-arg]
 
 
 def _truncate_params(payload: Dict[str, Any]) -> None:

libs/community/langchain_community/document_loaders/base_o365.py

@@ -70,7 +70,7 @@ def fetch_mime_types(file_types: Sequence[_FileType]) -> Dict[str, str]:
 class O365BaseLoader(BaseLoader, BaseModel):
     """Base class for all loaders that uses O365 Package"""
 
-    settings: _O365Settings = Field(default_factory=_O365Settings)
+    settings: _O365Settings = Field(default_factory=_O365Settings)  # type: ignore[arg-type]
     """Settings for the Office365 API client."""
     auth_with_token: bool = False
     """Whether to authenticate with a token or not. Defaults to False."""

libs/community/langchain_community/document_loaders/kinetica_loader.py

@@ -86,7 +86,7 @@ class KineticaLoader(BaseLoader):
         query_result = self._execute_query()
         if isinstance(query_result, Exception):
             print(f"An error occurred during the query: {query_result}")  # noqa: T201
-            return []
+            return []  # type: ignore[return-value]
         page_content_columns, metadata_columns = self._get_columns(query_result)
         if "*" in page_content_columns:
             page_content_columns = list(query_result[0].keys())

libs/community/langchain_community/document_loaders/mhtml.py

@@ -58,8 +58,8 @@ class MHTMLLoader(BaseLoader):
                 parts = [message]
 
             for part in parts:
-                if part.get_content_type() == "text/html":
-                    html = part.get_payload(decode=True).decode()
+                if part.get_content_type() == "text/html":  # type: ignore[union-attr]
+                    html = part.get_payload(decode=True).decode()  # type: ignore[union-attr]
 
                     soup = BeautifulSoup(html, **self.bs_kwargs)
                     text = soup.get_text(self.get_text_separator)

libs/community/langchain_community/document_loaders/onenote.py

@@ -31,7 +31,7 @@ class _OneNoteGraphSettings(BaseSettings):
 class OneNoteLoader(BaseLoader, BaseModel):
     """Load pages from OneNote notebooks."""
 
-    settings: _OneNoteGraphSettings = Field(default_factory=_OneNoteGraphSettings)
+    settings: _OneNoteGraphSettings = Field(default_factory=_OneNoteGraphSettings)  # type: ignore[arg-type]
     """Settings for the Microsoft Graph API client."""
     auth_with_token: bool = False
     """Whether to authenticate with a token or not. Defaults to False."""

libs/community/langchain_community/document_loaders/pdf.py

@@ -691,7 +691,7 @@ class AmazonTextractPDFLoader(BasePDFLoader):
         # raises ValueError when multi-page and not on S3"""
 
         if self.web_path and self._is_s3_url(self.web_path):
-            blob = Blob(path=self.web_path)  # type: ignore[misc]
+            blob = Blob(path=self.web_path)  # type: ignore[call-arg] # type: ignore[misc]
         else:
             blob = Blob.from_path(self.file_path)  # type: ignore[attr-defined]
             if AmazonTextractPDFLoader._get_number_of_pages(blob) > 1:

libs/community/langchain_community/document_loaders/pubmed.py

@@ -28,8 +28,8 @@ class PubMedLoader(BaseLoader):
         """
         self.query = query
         self.load_max_docs = load_max_docs
-        self._client = PubMedAPIWrapper(
-            top_k_results=load_max_docs,
+        self._client = PubMedAPIWrapper(  # type: ignore[call-arg]
+            top_k_results=load_max_docs,  # type: ignore[arg-type]
         )
 
     def lazy_load(self) -> Iterator[Document]:

libs/community/langchain_community/document_loaders/snowflake_loader.py

@@ -111,7 +111,7 @@ class SnowflakeLoader(BaseLoader):
         query_result = self._execute_query()
         if isinstance(query_result, Exception):
             print(f"An error occurred during the query: {query_result}")  # noqa: T201
-            return []
+            return []  # type: ignore[return-value]
         page_content_columns, metadata_columns = self._get_columns(query_result)
         if "*" in page_content_columns:
             page_content_columns = list(query_result[0].keys())

libs/community/langchain_community/document_loaders/tensorflow_datasets.py

@@ -66,10 +66,10 @@ class TensorflowDatasetLoader(BaseLoader):
         ] = sample_to_document_function
         """Custom function that transform a dataset sample into a Document."""
 
-        self._tfds_client = TensorflowDatasets(
+        self._tfds_client = TensorflowDatasets(  # type: ignore[call-arg]
             dataset_name=self.dataset_name,
             split_name=self.split_name,
-            load_max_docs=self.load_max_docs,
+            load_max_docs=self.load_max_docs,  # type: ignore[arg-type]
             sample_to_document_function=self.sample_to_document_function,
         )
 

libs/community/langchain_community/document_loaders/weather.py

@@ -32,7 +32,7 @@ class WeatherDataLoader(BaseLoader):
     def from_params(
         cls, places: Sequence[str], *, openweathermap_api_key: Optional[str] = None
     ) -> WeatherDataLoader:
-        client = OpenWeatherMapAPIWrapper(openweathermap_api_key=openweathermap_api_key)
+        client = OpenWeatherMapAPIWrapper(openweathermap_api_key=openweathermap_api_key)  # type: ignore[call-arg]
         return cls(client, places)
 
     def lazy_load(

libs/community/langchain_community/document_loaders/wikipedia.py

@@ -50,10 +50,10 @@ class WikipediaLoader(BaseLoader):
             A list of Document objects representing the loaded
                 Wikipedia pages.
         """
-        client = WikipediaAPIWrapper(
+        client = WikipediaAPIWrapper(  # type: ignore[call-arg]
             lang=self.lang,
-            top_k_results=self.load_max_docs,
-            load_all_available_meta=self.load_all_available_meta,
-            doc_content_chars_max=self.doc_content_chars_max,
+            top_k_results=self.load_max_docs,  # type: ignore[arg-type]
+            load_all_available_meta=self.load_all_available_meta,  # type: ignore[arg-type]
+            doc_content_chars_max=self.doc_content_chars_max,  # type: ignore[arg-type]
         )
         yield from client.load(self.query)

libs/community/langchain_community/indexes/_sql_record_manager.py

@@ -312,7 +312,7 @@ class SQLRecordManager(RecordManager):
 
                 # Note: uses SQLite insert to make on_conflict_do_update work.
                 # This code needs to be generalized a bit to work with more dialects.
-                insert_stmt = pg_insert(UpsertionRecord).values(records_to_upsert)
+                insert_stmt = pg_insert(UpsertionRecord).values(records_to_upsert)  # type: ignore[assignment]
                 stmt = insert_stmt.on_conflict_do_update(  # type: ignore[attr-defined]
                     "uix_key_namespace",  # Name of constraint
                     set_=dict(
@@ -387,7 +387,7 @@ class SQLRecordManager(RecordManager):
 
                 # Note: uses SQLite insert to make on_conflict_do_update work.
                 # This code needs to be generalized a bit to work with more dialects.
-                insert_stmt = pg_insert(UpsertionRecord).values(records_to_upsert)
+                insert_stmt = pg_insert(UpsertionRecord).values(records_to_upsert)  # type: ignore[assignment]
                 stmt = insert_stmt.on_conflict_do_update(  # type: ignore[attr-defined]
                     "uix_key_namespace",  # Name of constraint
                     set_=dict(
@@ -470,7 +470,7 @@ class SQLRecordManager(RecordManager):
             if limit:
                 query = query.limit(limit)  # type: ignore[attr-defined]
             records = query.all()  # type: ignore[attr-defined]
-        return [r.key for r in records]
+        return [r.key for r in records]  # type: ignore[misc]
 
     async def alist_keys(
         self,

libs/community/langchain_community/llms/aleph_alpha.py

@@ -282,6 +282,6 @@ class AlephAlpha(LLM):
 
 
 if __name__ == "__main__":
-    aa = AlephAlpha()
+    aa = AlephAlpha()  # type: ignore[call-arg]
 
     print(aa.invoke("How are you?"))  # noqa: T201

libs/community/langchain_community/llms/databricks.py

@@ -490,7 +490,7 @@ class Databricks(LLM):
                 task=self.task,
             )
         elif self.cluster_id and self.cluster_driver_port:
-            self._client = _DatabricksClusterDriverProxyClient(
+            self._client = _DatabricksClusterDriverProxyClient(  # type: ignore[call-arg]
                 host=self.host,
                 api_token=self.api_token,
                 cluster_id=self.cluster_id,

libs/community/langchain_community/llms/minimax.py

@@ -87,7 +87,7 @@ class MinimaxCommon(BaseModel):
             "MINIMAX_API_HOST",
             default="https://api.minimax.chat",
         )
-        values["_client"] = _MinimaxEndpointClient(
+        values["_client"] = _MinimaxEndpointClient(  # type: ignore[call-arg]
             host=values["minimax_api_host"],
             api_key=values["minimax_api_key"],
             group_id=values["minimax_group_id"],

libs/community/langchain_community/llms/ollama.py

@@ -423,7 +423,7 @@ class Ollama(BaseLLM, _OllamaCommon):
                 **kwargs,
             )
             generations.append([final_chunk])
-        return LLMResult(generations=generations)
+        return LLMResult(generations=generations)  # type: ignore[arg-type]
 
     async def _agenerate(  # type: ignore[override]
         self,
@@ -459,7 +459,7 @@ class Ollama(BaseLLM, _OllamaCommon):
                 **kwargs,
             )
             generations.append([final_chunk])
-        return LLMResult(generations=generations)
+        return LLMResult(generations=generations)  # type: ignore[arg-type]
 
     def _stream(
         self,

libs/community/langchain_community/llms/openllm.py

@@ -155,7 +155,7 @@ class OpenLLM(LLM):
             client = client_cls(server_url, timeout)
 
             super().__init__(
-                **{
+                **{  # type: ignore[arg-type]
                     "server_url": server_url,
                     "timeout": timeout,
                     "server_type": server_type,
@@ -180,7 +180,7 @@ class OpenLLM(LLM):
                 **llm_kwargs,
             )
             super().__init__(
-                **{
+                **{  # type: ignore[arg-type]
                     "model_name": model_name,
                     "model_id": model_id,
                     "embedded": embedded,

libs/community/langchain_community/llms/sparkllm.py

@@ -274,10 +274,10 @@ class _SparkLLMClient:
             on_close=self.on_close,
             on_open=self.on_open,
         )
-        ws.messages = messages
-        ws.user_id = user_id
-        ws.model_kwargs = self.model_kwargs if model_kwargs is None else model_kwargs
-        ws.streaming = streaming
+        ws.messages = messages  # type: ignore[attr-defined]
+        ws.user_id = user_id  # type: ignore[attr-defined]
+        ws.model_kwargs = self.model_kwargs if model_kwargs is None else model_kwargs  # type: ignore[attr-defined]
+        ws.streaming = streaming  # type: ignore[attr-defined]
         ws.run_forever()
 
     def arun(

libs/community/langchain_community/llms/textgen.py

@@ -330,13 +330,13 @@ class TextGen(LLM):
             result = websocket_client.recv()
             result = json.loads(result)
 
-            if result["event"] == "text_stream":
+            if result["event"] == "text_stream":  # type: ignore[call-overload, index]
                 chunk = GenerationChunk(
-                    text=result["text"],
+                    text=result["text"],  # type: ignore[call-overload, index]
                     generation_info=None,
                 )
                 yield chunk
-            elif result["event"] == "stream_end":
+            elif result["event"] == "stream_end":  # type: ignore[call-overload, index]
                 websocket_client.close()
                 return
 
@@ -403,13 +403,13 @@ class TextGen(LLM):
             result = websocket_client.recv()
             result = json.loads(result)
 
-            if result["event"] == "text_stream":
+            if result["event"] == "text_stream":  # type: ignore[call-overload, index]
                 chunk = GenerationChunk(
-                    text=result["text"],
+                    text=result["text"],  # type: ignore[call-overload, index]
                     generation_info=None,
                 )
                 yield chunk
-            elif result["event"] == "stream_end":
+            elif result["event"] == "stream_end":  # type: ignore[call-overload, index]
                 websocket_client.close()
                 return
 

libs/community/langchain_community/llms/titan_takeoff.py

@@ -137,7 +137,7 @@ class TitanTakeoff(LLM):
             ImportError: If you haven't installed takeoff-client, you will
             get an ImportError. To remedy run `pip install 'takeoff-client==0.4.0'`
         """
-        super().__init__(
+        super().__init__(  # type: ignore[call-arg]
             base_url=base_url, port=port, mgmt_port=mgmt_port, streaming=streaming
         )
         try:

libs/community/langchain_community/llms/vertexai.py

@@ -363,7 +363,7 @@ class VertexAI(_VertexAICommon, BaseLLM):
             generations.append(
                 [self._response_to_generation(r) for r in res.candidates]
             )
-        return LLMResult(generations=generations)
+        return LLMResult(generations=generations)  # type: ignore[arg-type]
 
     def _stream(
         self,

libs/community/langchain_community/llms/xinference.py

@@ -100,7 +100,7 @@ class Xinference(LLM):
         model_kwargs = model_kwargs or {}
 
         super().__init__(
-            **{
+            **{  # type: ignore[arg-type]
                 "server_url": server_url,
                 "model_uid": model_uid,
                 "model_kwargs": model_kwargs,

libs/community/langchain_community/retrievers/breebs.py

@@ -21,7 +21,7 @@ class BreebsRetriever(BaseRetriever):
     url = "https://breebs.promptbreeders.com/knowledge"
 
     def __init__(self, breeb_key: str):
-        super().__init__(breeb_key=breeb_key)
+        super().__init__(breeb_key=breeb_key)  # type: ignore[call-arg]
         self.breeb_key = breeb_key
 
     def _get_relevant_documents(

libs/community/langchain_community/retrievers/dria_index.py

@@ -23,7 +23,7 @@ class DriaRetriever(BaseRetriever):
             contract_id: The contract ID of the knowledge base to interact with.
         """
         api_wrapper = DriaAPIWrapper(api_key=api_key, contract_id=contract_id)
-        super().__init__(api_wrapper=api_wrapper, **kwargs)
+        super().__init__(api_wrapper=api_wrapper, **kwargs)  # type: ignore[call-arg]
 
     def create_knowledge_base(
         self,

libs/community/langchain_community/retrievers/thirdai_neuraldb.py

@@ -73,7 +73,7 @@ class NeuralDBRetriever(BaseRetriever):
         NeuralDBRetriever._verify_thirdai_library(thirdai_key)
         from thirdai import neural_db as ndb
 
-        return cls(thirdai_key=thirdai_key, db=ndb.NeuralDB(**model_kwargs))
+        return cls(thirdai_key=thirdai_key, db=ndb.NeuralDB(**model_kwargs))  # type: ignore[arg-type]
 
     @classmethod
     def from_checkpoint(
@@ -108,7 +108,7 @@ class NeuralDBRetriever(BaseRetriever):
         NeuralDBRetriever._verify_thirdai_library(thirdai_key)
         from thirdai import neural_db as ndb
 
-        return cls(thirdai_key=thirdai_key, db=ndb.NeuralDB.from_checkpoint(checkpoint))
+        return cls(thirdai_key=thirdai_key, db=ndb.NeuralDB.from_checkpoint(checkpoint))  # type: ignore[arg-type]
 
     @root_validator()
     def validate_environments(cls, values: Dict) -> Dict:

libs/community/langchain_community/tools/arxiv/tool.py

@@ -27,7 +27,7 @@ class ArxivQueryRun(BaseTool):
         "from scientific articles on arxiv.org. "
         "Input should be a search query."
     )
-    api_wrapper: ArxivAPIWrapper = Field(default_factory=ArxivAPIWrapper)
+    api_wrapper: ArxivAPIWrapper = Field(default_factory=ArxivAPIWrapper)  # type: ignore[arg-type]
     args_schema: Type[BaseModel] = ArxivInput
 
     def _run(

libs/community/langchain_community/tools/audio/huggingface_text_to_speech_inference.py

@@ -72,7 +72,7 @@ class HuggingFaceTextToSpeechModelInference(BaseTool):
                 f"Invalid value for 'file_naming_func': {file_naming_func}"
             )
 
-        super().__init__(
+        super().__init__(  # type: ignore[call-arg]
             model=model,
             file_extension=file_extension,
             api_url=f"{self._HUGGINGFACE_API_URL_ROOT}/{model}",

libs/community/langchain_community/tools/github/tool.py

@@ -19,7 +19,7 @@ from langchain_community.utilities.github import GitHubAPIWrapper
 class GitHubAction(BaseTool):
     """Tool for interacting with the GitHub API."""
 
-    api_wrapper: GitHubAPIWrapper = Field(default_factory=GitHubAPIWrapper)
+    api_wrapper: GitHubAPIWrapper = Field(default_factory=GitHubAPIWrapper)  # type: ignore[arg-type]
     mode: str
     name: str = ""
     description: str = ""

libs/community/langchain_community/tools/gitlab/tool.py

@@ -19,7 +19,7 @@ from langchain_community.utilities.gitlab import GitLabAPIWrapper
 class GitLabAction(BaseTool):
     """Tool for interacting with the GitLab API."""
 
-    api_wrapper: GitLabAPIWrapper = Field(default_factory=GitLabAPIWrapper)
+    api_wrapper: GitLabAPIWrapper = Field(default_factory=GitLabAPIWrapper)  # type: ignore[arg-type]
     mode: str
     name: str = ""
     description: str = ""

libs/community/langchain_community/tools/gmail/base.py

@@ -34,4 +34,4 @@ class GmailBaseTool(BaseTool):
         Returns:
             A tool.
         """
-        return cls(service=api_resource)
+        return cls(service=api_resource)  # type: ignore[call-arg]

libs/community/langchain_community/tools/gmail/get_message.py

@@ -53,10 +53,10 @@ class GmailGetMessage(GmailBaseTool):
                 ctype = part.get_content_type()
                 cdispo = str(part.get("Content-Disposition"))
                 if ctype == "text/plain" and "attachment" not in cdispo:
-                    message_body = part.get_payload(decode=True).decode("utf-8")
+                    message_body = part.get_payload(decode=True).decode("utf-8")  # type: ignore[union-attr]
                     break
         else:
-            message_body = email_msg.get_payload(decode=True).decode("utf-8")
+            message_body = email_msg.get_payload(decode=True).decode("utf-8")  # type: ignore[union-attr]
 
         body = clean_email_body(message_body)
 

libs/community/langchain_community/tools/gmail/search.py

@@ -99,14 +99,14 @@ class GmailSearch(GmailBaseTool):
                     cdispo = str(part.get("Content-Disposition"))
                     if ctype == "text/plain" and "attachment" not in cdispo:
                         try:
-                            message_body = part.get_payload(decode=True).decode("utf-8")
+                            message_body = part.get_payload(decode=True).decode("utf-8")  # type: ignore[union-attr]
                         except UnicodeDecodeError:
-                            message_body = part.get_payload(decode=True).decode(
+                            message_body = part.get_payload(decode=True).decode(  # type: ignore[union-attr]
                                 "latin-1"
                             )
                         break
             else:
-                message_body = email_msg.get_payload(decode=True).decode("utf-8")
+                message_body = email_msg.get_payload(decode=True).decode("utf-8")  # type: ignore[union-attr]
 
             body = clean_email_body(message_body)
 

libs/community/langchain_community/tools/google_places/tool.py

@@ -31,7 +31,7 @@ class GooglePlacesTool(BaseTool):
         "discover addressed from ambiguous text. "
         "Input should be a search query."
     )
-    api_wrapper: GooglePlacesAPIWrapper = Field(default_factory=GooglePlacesAPIWrapper)
+    api_wrapper: GooglePlacesAPIWrapper = Field(default_factory=GooglePlacesAPIWrapper)  # type: ignore[arg-type]
     args_schema: Type[BaseModel] = GooglePlacesSchema
 
     def _run(

libs/community/langchain_community/tools/jira/tool.py

@@ -30,7 +30,7 @@ from langchain_community.utilities.jira import JiraAPIWrapper
 class JiraAction(BaseTool):
     """Tool that queries the Atlassian Jira API."""
 
-    api_wrapper: JiraAPIWrapper = Field(default_factory=JiraAPIWrapper)
+    api_wrapper: JiraAPIWrapper = Field(default_factory=JiraAPIWrapper)  # type: ignore[arg-type]
     mode: str
     name: str = ""
     description: str = ""

libs/community/langchain_community/tools/nuclia/tool.py

@@ -75,7 +75,7 @@ class NucliaUnderstandingAPI(BaseTool):
         else:
             self._config["NUA_KEY"] = key
         self._config["enable_ml"] = enable_ml
-        super().__init__()
+        super().__init__()  # type: ignore[call-arg]
 
     def _run(
         self,

libs/community/langchain_community/tools/openapi/utils/api_models.py

@@ -530,7 +530,7 @@ class APIOperation(BaseModel):
             description=description or "",
             base_url=spec.base_url,
             path=path,
-            method=method,
+            method=method,  # type: ignore[arg-type]
             properties=properties,
             request_body=api_request_body,
         )

libs/community/langchain_community/tools/openweathermap/tool.py

@@ -13,7 +13,7 @@ class OpenWeatherMapQueryRun(BaseTool):
     """Tool that queries the OpenWeatherMap API."""
 
     api_wrapper: OpenWeatherMapAPIWrapper = Field(
-        default_factory=OpenWeatherMapAPIWrapper
+        default_factory=OpenWeatherMapAPIWrapper  # type: ignore[arg-type]
     )
 
     name: str = "open_weather_map"

libs/community/langchain_community/tools/playwright/base.py

@@ -54,4 +54,4 @@ class BaseBrowserTool(BaseTool):
     ) -> BaseBrowserTool:
         """Instantiate the tool."""
         lazy_import_playwright_browsers()
-        return cls(sync_browser=sync_browser, async_browser=async_browser)
+        return cls(sync_browser=sync_browser, async_browser=async_browser)  # type: ignore[call-arg]

libs/community/langchain_community/tools/pubmed/tool.py

@@ -18,7 +18,7 @@ class PubmedQueryRun(BaseTool):
         "from biomedical literature, MEDLINE, life science journals, and online books. "
         "Input should be a search query."
     )
-    api_wrapper: PubMedAPIWrapper = Field(default_factory=PubMedAPIWrapper)
+    api_wrapper: PubMedAPIWrapper = Field(default_factory=PubMedAPIWrapper)  # type: ignore[arg-type]
 
     def _run(
         self,

libs/community/langchain_community/tools/reddit_search/tool.py

@@ -42,7 +42,7 @@ class RedditSearchRun(BaseTool):
         "A tool that searches for posts on Reddit."
         "Useful when you need to know post information on a subreddit."
     )
-    api_wrapper: RedditSearchAPIWrapper = Field(default_factory=RedditSearchAPIWrapper)
+    api_wrapper: RedditSearchAPIWrapper = Field(default_factory=RedditSearchAPIWrapper)  # type: ignore[arg-type]
     args_schema: Type[BaseModel] = RedditSearchSchema
 
     def _run(

libs/community/langchain_community/tools/scenexplain/tool.py

@@ -23,7 +23,7 @@ class SceneXplainTool(BaseTool):
         "for an image. The input can be an image file of any format, and "
         "the output will be a text description that covers every detail of the image."
     )
-    api_wrapper: SceneXplainAPIWrapper = Field(default_factory=SceneXplainAPIWrapper)
+    api_wrapper: SceneXplainAPIWrapper = Field(default_factory=SceneXplainAPIWrapper)  # type: ignore[arg-type]
 
     def _run(
         self, query: str, run_manager: Optional[CallbackManagerForToolRun] = None

libs/community/langchain_community/tools/semanticscholar/tool.py

@@ -26,7 +26,7 @@ class SemanticScholarQueryRun(BaseTool):
         "Input should be a search query."
     )
     api_wrapper: SemanticScholarAPIWrapper = Field(
-        default_factory=SemanticScholarAPIWrapper
+        default_factory=SemanticScholarAPIWrapper  # type: ignore[arg-type]
     )
     args_schema: Type[BaseModel] = SemantscholarInput
 

libs/community/langchain_community/tools/spark_sql/tool.py

@@ -97,7 +97,7 @@ class QueryCheckerTool(BaseSparkSQLTool, BaseTool):
             from langchain.chains.llm import LLMChain
 
             values["llm_chain"] = LLMChain(
-                llm=values.get("llm"),
+                llm=values.get("llm"),  # type: ignore[arg-type]
                 prompt=PromptTemplate(
                     template=QUERY_CHECKER, input_variables=["query"]
                 ),

libs/community/langchain_community/tools/sql_database/tool.py

@@ -122,7 +122,7 @@ class QuerySQLCheckerTool(BaseSQLDatabaseTool, BaseTool):
             from langchain.chains.llm import LLMChain
 
             values["llm_chain"] = LLMChain(
-                llm=values.get("llm"),
+                llm=values.get("llm"),  # type: ignore[arg-type]
                 prompt=PromptTemplate(
                     template=QUERY_CHECKER, input_variables=["dialect", "query"]
                 ),

libs/community/langchain_community/tools/tavily_search/tool.py

@@ -27,7 +27,7 @@ class TavilySearchResults(BaseTool):
         "Useful for when you need to answer questions about current events. "
         "Input should be a search query."
     )
-    api_wrapper: TavilySearchAPIWrapper = Field(default_factory=TavilySearchAPIWrapper)
+    api_wrapper: TavilySearchAPIWrapper = Field(default_factory=TavilySearchAPIWrapper)  # type: ignore[arg-type]
     max_results: int = 5
     args_schema: Type[BaseModel] = TavilyInput
 
@@ -70,7 +70,7 @@ class TavilyAnswer(BaseTool):
         "Input should be a search query. "
         "This returns only the answer - not the original source data."
     )
-    api_wrapper: TavilySearchAPIWrapper = Field(default_factory=TavilySearchAPIWrapper)
+    api_wrapper: TavilySearchAPIWrapper = Field(default_factory=TavilySearchAPIWrapper)  # type: ignore[arg-type]
     args_schema: Type[BaseModel] = TavilyInput
 
     def _run(

libs/community/langchain_community/tools/zapier/tool.py

@@ -95,7 +95,7 @@ class ZapierNLARunAction(BaseTool):
 
     """
 
-    api_wrapper: ZapierNLAWrapper = Field(default_factory=ZapierNLAWrapper)
+    api_wrapper: ZapierNLAWrapper = Field(default_factory=ZapierNLAWrapper)  # type: ignore[arg-type]
     action_id: str
     params: Optional[dict] = None
     base_prompt: str = BASE_ZAPIER_TOOL_PROMPT
@@ -174,7 +174,7 @@ class ZapierNLAListActions(BaseTool):
     description: str = BASE_ZAPIER_TOOL_PROMPT + (
         "This tool returns a list of the user's exposed actions."
     )
-    api_wrapper: ZapierNLAWrapper = Field(default_factory=ZapierNLAWrapper)
+    api_wrapper: ZapierNLAWrapper = Field(default_factory=ZapierNLAWrapper)  # type: ignore[arg-type]
 
     def _run(
         self,

libs/community/langchain_community/utilities/redis.py

@@ -214,4 +214,4 @@ def _check_for_cluster(redis_client: RedisType) -> bool:
 def _redis_cluster_client(redis_url: str, **kwargs: Any) -> RedisType:
     from redis.cluster import RedisCluster
 
-    return RedisCluster.from_url(redis_url, **kwargs)
+    return RedisCluster.from_url(redis_url, **kwargs)  # type: ignore[return-value]

libs/community/poetry.lock

@@ -1,4 +1,4 @@
-# This file is automatically @generated by Poetry 1.8.2 and should not be changed by hand.
+# This file is automatically @generated by Poetry 1.6.1 and should not be changed by hand.
 
 [[package]]
 name = "aenum"
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+[package.dependencies]
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-typing-extensions = ">=3.10"
+typing-extensions = ">=4.1.0"
 
 [package.extras]
 dmypy = ["psutil (>=4.0)"]
 install-types = ["pip"]
-python2 = ["typed-ast (>=1.4.0,<2)"]
+mypyc = ["setuptools (>=50)"]
 reports = ["lxml"]
 
 [[package]]
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@@ -7159,7 +7152,6 @@ files = [
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     {file = "PyYAML-6.0.1-cp312-cp312-macosx_10_9_x86_64.whl", hash = "sha256:855fb52b0dc35af121542a76b9a84f8d1cd886ea97c84703eaa6d88e37a2ad28"},
     {file = "PyYAML-6.0.1-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:40df9b996c2b73138957fe23a16a4f0ba614f4c0efce1e9406a184b6d07fa3a9"},
-    {file = "PyYAML-6.0.1-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:a08c6f0fe150303c1c6b71ebcd7213c2858041a7e01975da3a99aed1e7a378ef"},
     {file = "PyYAML-6.0.1-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:6c22bec3fbe2524cde73d7ada88f6566758a8f7227bfbf93a408a9d86bcc12a0"},
     {file = "PyYAML-6.0.1-cp312-cp312-musllinux_1_1_x86_64.whl", hash = "sha256:8d4e9c88387b0f5c7d5f281e55304de64cf7f9c0021a3525bd3b1c542da3b0e4"},
     {file = "PyYAML-6.0.1-cp312-cp312-win32.whl", hash = "sha256:d483d2cdf104e7c9fa60c544d92981f12ad66a457afae824d146093b8c294c54"},
@@ -10092,4 +10084,4 @@ extended-testing = ["aiosqlite", "aleph-alpha-client", "anthropic", "arxiv", "as
 [metadata]
 lock-version = "2.0"
 python-versions = ">=3.8.1,<4.0"
-content-hash = "657797396fff40d21216962c43171ba5605725e1047339443d7f346e2f5f0b80"
+content-hash = "66b00eea10e05312fcafa5f68e4b863942c344051bdd93b575b0b26fce9fce21"

libs/community/pyproject.toml

@@ -174,7 +174,7 @@ optional = true
 ruff = "^0.1.5"
 
 [tool.poetry.group.typing.dependencies]
-mypy = "^0.991"
+mypy = "^1"
 types-pyyaml = "^6.0.12.2"
 types-requests = "^2.28.11.5"
 types-toml = "^0.10.8.1"

libs/community/tests/integration_tests/chat_models/test_anthropic.py

@@ -15,7 +15,7 @@ from tests.unit_tests.callbacks.fake_callback_handler import FakeCallbackHandler
 @pytest.mark.scheduled
 def test_anthropic_call() -> None:
     """Test valid call to anthropic."""
-    chat = ChatAnthropic(model="test")
+    chat = ChatAnthropic(model="test")  # type: ignore[call-arg]
     message = HumanMessage(content="Hello")
     response = chat.invoke([message])
     assert isinstance(response, AIMessage)
@@ -25,7 +25,7 @@ def test_anthropic_call() -> None:
 @pytest.mark.scheduled
 def test_anthropic_generate() -> None:
     """Test generate method of anthropic."""
-    chat = ChatAnthropic(model="test")
+    chat = ChatAnthropic(model="test")  # type: ignore[call-arg]
     chat_messages: List[List[BaseMessage]] = [
         [HumanMessage(content="How many toes do dogs have?")]
     ]
@@ -42,7 +42,7 @@ def test_anthropic_generate() -> None:
 @pytest.mark.scheduled
 def test_anthropic_streaming() -> None:
     """Test streaming tokens from anthropic."""
-    chat = ChatAnthropic(model="test", streaming=True)
+    chat = ChatAnthropic(model="test", streaming=True)  # type: ignore[call-arg]
     message = HumanMessage(content="Hello")
     response = chat.invoke([message])
     assert isinstance(response, AIMessage)
@@ -54,7 +54,7 @@ def test_anthropic_streaming_callback() -> None:
     """Test that streaming correctly invokes on_llm_new_token callback."""
     callback_handler = FakeCallbackHandler()
     callback_manager = CallbackManager([callback_handler])
-    chat = ChatAnthropic(
+    chat = ChatAnthropic(  # type: ignore[call-arg]
         model="test",
         streaming=True,
         callback_manager=callback_manager,
@@ -70,7 +70,7 @@ async def test_anthropic_async_streaming_callback() -> None:
     """Test that streaming correctly invokes on_llm_new_token callback."""
     callback_handler = FakeCallbackHandler()
     callback_manager = CallbackManager([callback_handler])
-    chat = ChatAnthropic(
+    chat = ChatAnthropic(  # type: ignore[call-arg]
         model="test",
         streaming=True,
         callback_manager=callback_manager,

libs/community/tests/integration_tests/chat_models/test_azure_openai.py

@@ -20,7 +20,7 @@ DEPLOYMENT_NAME = os.environ.get(
 
 
 def _get_llm(**kwargs: Any) -> AzureChatOpenAI:
-    return AzureChatOpenAI(
+    return AzureChatOpenAI(  # type: ignore[call-arg]
         deployment_name=DEPLOYMENT_NAME,
         openai_api_version=OPENAI_API_VERSION,
         azure_endpoint=OPENAI_API_BASE,

libs/community/tests/integration_tests/chat_models/test_baichuan.py

@@ -23,7 +23,7 @@ def test_chat_baichuan_default_non_streaming() -> None:
 
 
 def test_chat_baichuan_turbo() -> None:
-    chat = ChatBaichuan(model="Baichuan2-Turbo", streaming=True)
+    chat = ChatBaichuan(model="Baichuan2-Turbo", streaming=True)  # type: ignore[call-arg]
     message = HumanMessage(content="Hello")
     response = chat.invoke([message])
     assert isinstance(response, AIMessage)
@@ -31,7 +31,7 @@ def test_chat_baichuan_turbo() -> None:
 
 
 def test_chat_baichuan_turbo_non_streaming() -> None:
-    chat = ChatBaichuan(model="Baichuan2-Turbo")
+    chat = ChatBaichuan(model="Baichuan2-Turbo")  # type: ignore[call-arg]
     message = HumanMessage(content="Hello")
     response = chat.invoke([message])
     assert isinstance(response, AIMessage)

libs/community/tests/integration_tests/chat_models/test_bedrock.py

@@ -17,7 +17,7 @@ from tests.unit_tests.callbacks.fake_callback_handler import FakeCallbackHandler
 
 @pytest.fixture
 def chat() -> BedrockChat:
-    return BedrockChat(model_id="anthropic.claude-v2", model_kwargs={"temperature": 0})
+    return BedrockChat(model_id="anthropic.claude-v2", model_kwargs={"temperature": 0})  # type: ignore[call-arg]
 
 
 @pytest.mark.scheduled
@@ -63,7 +63,7 @@ def test_chat_bedrock_streaming() -> None:
     """Test that streaming correctly invokes on_llm_new_token callback."""
     callback_handler = FakeCallbackHandler()
     callback_manager = CallbackManager([callback_handler])
-    chat = BedrockChat(
+    chat = BedrockChat(  # type: ignore[call-arg]
         model_id="anthropic.claude-v2",
         streaming=True,
         callback_manager=callback_manager,
@@ -92,7 +92,7 @@ def test_chat_bedrock_streaming_generation_info() -> None:
 
     callback = _FakeCallback()
     callback_manager = CallbackManager([callback])
-    chat = BedrockChat(
+    chat = BedrockChat(  # type: ignore[call-arg]
         model_id="anthropic.claude-v2",
         callback_manager=callback_manager,
     )

libs/community/tests/integration_tests/chat_models/test_coze.py

@@ -9,7 +9,7 @@ from langchain_community.chat_models.coze import ChatCoze
 def test_chat_coze_default() -> None:
     chat = ChatCoze(
         coze_api_base="https://api.coze.com",
-        coze_api_key="pat_...",
+        coze_api_key="pat_...",  # type: ignore[arg-type]
         bot_id="7....",
         user="123",
         conversation_id="",
@@ -24,7 +24,7 @@ def test_chat_coze_default() -> None:
 def test_chat_coze_default_non_streaming() -> None:
     chat = ChatCoze(
         coze_api_base="https://api.coze.com",
-        coze_api_key="pat_...",
+        coze_api_key="pat_...",  # type: ignore[arg-type]
         bot_id="7....",
         user="123",
         conversation_id="",

libs/community/tests/integration_tests/chat_models/test_dappier.py

@@ -12,7 +12,7 @@ from langchain_community.chat_models.dappier import (
 @pytest.mark.scheduled
 def test_dappier_chat() -> None:
     """Test ChatDappierAI wrapper."""
-    chat = ChatDappierAI(
+    chat = ChatDappierAI(  # type: ignore[call-arg]
         dappier_endpoint="https://api.dappier.com/app/datamodelconversation",
         dappier_model="dm_01hpsxyfm2fwdt2zet9cg6fdxt",
     )
@@ -25,7 +25,7 @@ def test_dappier_chat() -> None:
 @pytest.mark.scheduled
 def test_dappier_generate() -> None:
     """Test generate method of Dappier AI."""
-    chat = ChatDappierAI(
+    chat = ChatDappierAI(  # type: ignore[call-arg]
         dappier_endpoint="https://api.dappier.com/app/datamodelconversation",
         dappier_model="dm_01hpsxyfm2fwdt2zet9cg6fdxt",
     )
@@ -45,7 +45,7 @@ def test_dappier_generate() -> None:
 @pytest.mark.scheduled
 async def test_dappier_agenerate() -> None:
     """Test async generation."""
-    chat = ChatDappierAI(
+    chat = ChatDappierAI(  # type: ignore[call-arg]
         dappier_endpoint="https://api.dappier.com/app/datamodelconversation",
         dappier_model="dm_01hpsxyfm2fwdt2zet9cg6fdxt",
     )

libs/community/tests/integration_tests/chat_models/test_edenai.py

@@ -13,7 +13,7 @@ from langchain_community.chat_models.edenai import (
 @pytest.mark.scheduled
 def test_chat_edenai() -> None:
     """Test ChatEdenAI wrapper."""
-    chat = ChatEdenAI(
+    chat = ChatEdenAI(  # type: ignore[call-arg]
         provider="openai", model="gpt-3.5-turbo", temperature=0, max_tokens=1000
     )
     message = HumanMessage(content="Who are you ?")
@@ -25,7 +25,7 @@ def test_chat_edenai() -> None:
 @pytest.mark.scheduled
 def test_edenai_generate() -> None:
     """Test generate method of edenai."""
-    chat = ChatEdenAI(provider="google")
+    chat = ChatEdenAI(provider="google")  # type: ignore[call-arg]
     chat_messages: List[List[BaseMessage]] = [
         [HumanMessage(content="What is the meaning of life?")]
     ]
@@ -42,7 +42,7 @@ def test_edenai_generate() -> None:
 @pytest.mark.scheduled
 async def test_edenai_async_generate() -> None:
     """Test async generation."""
-    chat = ChatEdenAI(provider="google", max_tokens=50)
+    chat = ChatEdenAI(provider="google", max_tokens=50)  # type: ignore[call-arg]
     message = HumanMessage(content="Hello")
     result: LLMResult = await chat.agenerate([[message], [message]])
     assert isinstance(result, LLMResult)
@@ -55,7 +55,7 @@ async def test_edenai_async_generate() -> None:
 @pytest.mark.scheduled
 def test_edenai_streaming() -> None:
     """Test streaming EdenAI chat."""
-    llm = ChatEdenAI(provider="openai", max_tokens=50)
+    llm = ChatEdenAI(provider="openai", max_tokens=50)  # type: ignore[call-arg]
 
     for chunk in llm.stream("Generate a high fantasy story."):
         assert isinstance(chunk.content, str)
@@ -64,7 +64,7 @@ def test_edenai_streaming() -> None:
 @pytest.mark.scheduled
 async def test_edenai_astream() -> None:
     """Test streaming from EdenAI."""
-    llm = ChatEdenAI(provider="openai", max_tokens=50)
+    llm = ChatEdenAI(provider="openai", max_tokens=50)  # type: ignore[call-arg]
 
     async for token in llm.astream("Generate a high fantasy story."):
         assert isinstance(token.content, str)

libs/community/tests/integration_tests/chat_models/test_google_palm.py

@@ -12,7 +12,7 @@ from langchain_community.chat_models import ChatGooglePalm
 
 def test_chat_google_palm() -> None:
     """Test Google PaLM Chat API wrapper."""
-    chat = ChatGooglePalm()
+    chat = ChatGooglePalm()  # type: ignore[call-arg]
     message = HumanMessage(content="Hello")
     response = chat.invoke([message])
     assert isinstance(response, BaseMessage)
@@ -21,7 +21,7 @@ def test_chat_google_palm() -> None:
 
 def test_chat_google_palm_system_message() -> None:
     """Test Google PaLM Chat API wrapper with system message."""
-    chat = ChatGooglePalm()
+    chat = ChatGooglePalm()  # type: ignore[call-arg]
     system_message = SystemMessage(content="You are to chat with the user.")
     human_message = HumanMessage(content="Hello")
     response = chat.invoke([system_message, human_message])
@@ -31,7 +31,7 @@ def test_chat_google_palm_system_message() -> None:
 
 def test_chat_google_palm_generate() -> None:
     """Test Google PaLM Chat API wrapper with generate."""
-    chat = ChatGooglePalm(n=2, temperature=1.0)
+    chat = ChatGooglePalm(n=2, temperature=1.0)  # type: ignore[call-arg]
     message = HumanMessage(content="Hello")
     response = chat.generate([[message], [message]])
     assert isinstance(response, LLMResult)
@@ -48,7 +48,7 @@ def test_chat_google_palm_multiple_completions() -> None:
     """Test Google PaLM Chat API wrapper with multiple completions."""
     # The API de-dupes duplicate responses, so set temperature higher. This
     # could be a flakey test though...
-    chat = ChatGooglePalm(n=5, temperature=1.0)
+    chat = ChatGooglePalm(n=5, temperature=1.0)  # type: ignore[call-arg]
     message = HumanMessage(content="Hello")
     response = chat._generate([message])
     assert isinstance(response, ChatResult)
@@ -60,7 +60,7 @@ def test_chat_google_palm_multiple_completions() -> None:
 
 async def test_async_chat_google_palm() -> None:
     """Test async generation."""
-    chat = ChatGooglePalm(n=2, temperature=1.0)
+    chat = ChatGooglePalm(n=2, temperature=1.0)  # type: ignore[call-arg]
     message = HumanMessage(content="Hello")
     response = await chat.agenerate([[message], [message]])
     assert isinstance(response, LLMResult)

libs/community/tests/integration_tests/chat_models/test_gpt_router.py

@@ -16,9 +16,9 @@ from tests.unit_tests.callbacks.fake_callback_handler import FakeCallbackHandler
 
 
 def test_api_key_is_string() -> None:
-    gpt_router = GPTRouter(
+    gpt_router = GPTRouter(  # type: ignore[call-arg]
         gpt_router_api_base="https://example.com",
-        gpt_router_api_key="secret-api-key",
+        gpt_router_api_key="secret-api-key",  # type: ignore[arg-type]
     )
     assert isinstance(gpt_router.gpt_router_api_key, SecretStr)
 
@@ -26,9 +26,9 @@ def test_api_key_is_string() -> None:
 def test_api_key_masked_when_passed_via_constructor(
     capsys: CaptureFixture,
 ) -> None:
-    gpt_router = GPTRouter(
+    gpt_router = GPTRouter(  # type: ignore[call-arg]
         gpt_router_api_base="https://example.com",
-        gpt_router_api_key="secret-api-key",
+        gpt_router_api_key="secret-api-key",  # type: ignore[arg-type]
     )
     print(gpt_router.gpt_router_api_key, end="")  # noqa: T201
     captured = capsys.readouterr()

libs/community/tests/integration_tests/chat_models/test_jinachat.py

@@ -14,7 +14,7 @@ from tests.unit_tests.callbacks.fake_callback_handler import FakeCallbackHandler
 
 
 def test_jinachat_api_key_is_secret_string() -> None:
-    llm = JinaChat(jinachat_api_key="secret-api-key")
+    llm = JinaChat(jinachat_api_key="secret-api-key")  # type: ignore[arg-type, call-arg]
     assert isinstance(llm.jinachat_api_key, SecretStr)
 
 
@@ -23,7 +23,7 @@ def test_jinachat_api_key_masked_when_passed_from_env(
 ) -> None:
     """Test initialization with an API key provided via an env variable"""
     monkeypatch.setenv("JINACHAT_API_KEY", "secret-api-key")
-    llm = JinaChat()
+    llm = JinaChat()  # type: ignore[call-arg]
     print(llm.jinachat_api_key, end="")  # noqa: T201
     captured = capsys.readouterr()
 
@@ -34,7 +34,7 @@ def test_jinachat_api_key_masked_when_passed_via_constructor(
     capsys: CaptureFixture,
 ) -> None:
     """Test initialization with an API key provided via the initializer"""
-    llm = JinaChat(jinachat_api_key="secret-api-key")
+    llm = JinaChat(jinachat_api_key="secret-api-key")  # type: ignore[arg-type, call-arg]
     print(llm.jinachat_api_key, end="")  # noqa: T201
     captured = capsys.readouterr()
 
@@ -43,13 +43,13 @@ def test_jinachat_api_key_masked_when_passed_via_constructor(
 
 def test_uses_actual_secret_value_from_secretstr() -> None:
     """Test that actual secret is retrieved using `.get_secret_value()`."""
-    llm = JinaChat(jinachat_api_key="secret-api-key")
+    llm = JinaChat(jinachat_api_key="secret-api-key")  # type: ignore[arg-type, call-arg]
     assert cast(SecretStr, llm.jinachat_api_key).get_secret_value() == "secret-api-key"
 
 
 def test_jinachat() -> None:
     """Test JinaChat wrapper."""
-    chat = JinaChat(max_tokens=10)
+    chat = JinaChat(max_tokens=10)  # type: ignore[call-arg]
     message = HumanMessage(content="Hello")
     response = chat.invoke([message])
     assert isinstance(response, BaseMessage)
@@ -58,7 +58,7 @@ def test_jinachat() -> None:
 
 def test_jinachat_system_message() -> None:
     """Test JinaChat wrapper with system message."""
-    chat = JinaChat(max_tokens=10)
+    chat = JinaChat(max_tokens=10)  # type: ignore[call-arg]
     system_message = SystemMessage(content="You are to chat with the user.")
     human_message = HumanMessage(content="Hello")
     response = chat.invoke([system_message, human_message])
@@ -68,7 +68,7 @@ def test_jinachat_system_message() -> None:
 
 def test_jinachat_generate() -> None:
     """Test JinaChat wrapper with generate."""
-    chat = JinaChat(max_tokens=10)
+    chat = JinaChat(max_tokens=10)  # type: ignore[call-arg]
     message = HumanMessage(content="Hello")
     response = chat.generate([[message], [message]])
     assert isinstance(response, LLMResult)
@@ -85,7 +85,7 @@ def test_jinachat_streaming() -> None:
     """Test that streaming correctly invokes on_llm_new_token callback."""
     callback_handler = FakeCallbackHandler()
     callback_manager = CallbackManager([callback_handler])
-    chat = JinaChat(
+    chat = JinaChat(  # type: ignore[call-arg]
         max_tokens=10,
         streaming=True,
         temperature=0,
@@ -100,7 +100,7 @@ def test_jinachat_streaming() -> None:
 
 async def test_async_jinachat() -> None:
     """Test async generation."""
-    chat = JinaChat(max_tokens=102)
+    chat = JinaChat(max_tokens=102)  # type: ignore[call-arg]
     message = HumanMessage(content="Hello")
     response = await chat.agenerate([[message], [message]])
     assert isinstance(response, LLMResult)
@@ -117,7 +117,7 @@ async def test_async_jinachat_streaming() -> None:
     """Test that streaming correctly invokes on_llm_new_token callback."""
     callback_handler = FakeCallbackHandler()
     callback_manager = CallbackManager([callback_handler])
-    chat = JinaChat(
+    chat = JinaChat(  # type: ignore[call-arg]
         max_tokens=10,
         streaming=True,
         temperature=0,
@@ -140,18 +140,18 @@ async def test_async_jinachat_streaming() -> None:
 def test_jinachat_extra_kwargs() -> None:
     """Test extra kwargs to chat openai."""
     # Check that foo is saved in extra_kwargs.
-    llm = JinaChat(foo=3, max_tokens=10)
+    llm = JinaChat(foo=3, max_tokens=10)  # type: ignore[call-arg]
     assert llm.max_tokens == 10
     assert llm.model_kwargs == {"foo": 3}
 
     # Test that if extra_kwargs are provided, they are added to it.
-    llm = JinaChat(foo=3, model_kwargs={"bar": 2})
+    llm = JinaChat(foo=3, model_kwargs={"bar": 2})  # type: ignore[call-arg]
     assert llm.model_kwargs == {"foo": 3, "bar": 2}
 
     # Test that if provided twice it errors
     with pytest.raises(ValueError):
-        JinaChat(foo=3, model_kwargs={"foo": 2})
+        JinaChat(foo=3, model_kwargs={"foo": 2})  # type: ignore[call-arg]
 
     # Test that if explicit param is specified in kwargs it errors
     with pytest.raises(ValueError):
-        JinaChat(model_kwargs={"temperature": 0.2})
+        JinaChat(model_kwargs={"temperature": 0.2})  # type: ignore[call-arg]

libs/community/tests/integration_tests/chat_models/test_kinetica.py

@@ -74,7 +74,7 @@ class TestChatKinetica:
         """Create an LLM instance."""
         import gpudb
 
-        kinetica_llm = ChatKinetica()
+        kinetica_llm = ChatKinetica()  # type: ignore[call-arg]
         LOG.info(kinetica_llm._identifying_params)
 
         assert isinstance(kinetica_llm.kdbc, gpudb.GPUdb)
@@ -83,7 +83,7 @@ class TestChatKinetica:
     @pytest.mark.vcr()
     def test_load_context(self) -> None:
         """Load the LLM context from the DB."""
-        kinetica_llm = ChatKinetica()
+        kinetica_llm = ChatKinetica()  # type: ignore[call-arg]
         ctx_messages = kinetica_llm.load_messages_from_context(self.context_name)
 
         system_message = ctx_messages[0]
@@ -96,7 +96,7 @@ class TestChatKinetica:
     @pytest.mark.vcr()
     def test_generate(self) -> None:
         """Generate SQL from a chain."""
-        kinetica_llm = ChatKinetica()
+        kinetica_llm = ChatKinetica()  # type: ignore[call-arg]
 
         # create chain
         ctx_messages = kinetica_llm.load_messages_from_context(self.context_name)
@@ -113,7 +113,7 @@ class TestChatKinetica:
     @pytest.mark.vcr()
     def test_full_chain(self) -> None:
         """Generate SQL from a chain and execute the query."""
-        kinetica_llm = ChatKinetica()
+        kinetica_llm = ChatKinetica()  # type: ignore[call-arg]
 
         # create chain
         ctx_messages = kinetica_llm.load_messages_from_context(self.context_name)