community[patch]: chat model mypy fixes (#17061)

Related to #17048

libs/community/langchain_community/chat_models/azureml_endpoint.py

@@ -20,7 +20,7 @@ from langchain_community.llms.azureml_endpoint import (
 
 
 class LlamaContentFormatter(ContentFormatterBase):
-    def __init__(self):  # type: ignore[no-untyped-def]
+    def __init__(self) -> None:
         raise TypeError(
             "`LlamaContentFormatter` is deprecated for chat models. Use "
             "`LlamaChatContentFormatter` instead."
@@ -72,12 +72,12 @@ class LlamaChatContentFormatter(ContentFormatterBase):
     def supported_api_types(self) -> List[AzureMLEndpointApiType]:
         return [AzureMLEndpointApiType.realtime, AzureMLEndpointApiType.serverless]
 
-    def format_request_payload(  # type: ignore[override]
+    def format_messages_request_payload(
         self,
         messages: List[BaseMessage],
         model_kwargs: Dict,
         api_type: AzureMLEndpointApiType,
-    ) -> str:
+    ) -> bytes:
         """Formats the request according to the chosen api"""
         chat_messages = [
             LlamaChatContentFormatter._convert_message_to_dict(message)
@@ -98,17 +98,19 @@ class LlamaChatContentFormatter(ContentFormatterBase):
             raise ValueError(
                 f"`api_type` {api_type} is not supported by this formatter"
             )
-        return str.encode(request_payload)  # type: ignore[return-value]
+        return str.encode(request_payload)
 
-    def format_response_payload(  # type: ignore[override]
-        self, output: bytes, api_type: AzureMLEndpointApiType
+    def format_response_payload(
+        self,
+        output: bytes,
+        api_type: AzureMLEndpointApiType = AzureMLEndpointApiType.realtime,
     ) -> ChatGeneration:
         """Formats response"""
         if api_type == AzureMLEndpointApiType.realtime:
             try:
                 choice = json.loads(output)["output"]
             except (KeyError, IndexError, TypeError) as e:
-                raise ValueError(self.format_error_msg.format(api_type=api_type)) from e  # type: ignore[union-attr]
+                raise ValueError(self.format_error_msg.format(api_type=api_type)) from e
             return ChatGeneration(
                 message=BaseMessage(
                     content=choice.strip(),
@@ -125,7 +127,7 @@ class LlamaChatContentFormatter(ContentFormatterBase):
                         "model. Expected `dict` but `{type(choice)}` was received."
                     )
             except (KeyError, IndexError, TypeError) as e:
-                raise ValueError(self.format_error_msg.format(api_type=api_type)) from e  # type: ignore[union-attr]
+                raise ValueError(self.format_error_msg.format(api_type=api_type)) from e
             return ChatGeneration(
                 message=BaseMessage(
                     content=choice["message"]["content"].strip(),
@@ -187,7 +189,7 @@ class AzureMLChatOnlineEndpoint(BaseChatModel, AzureMLBaseEndpoint):
         if stop:
             _model_kwargs["stop"] = stop
 
-        request_payload = self.content_formatter.format_request_payload(
+        request_payload = self.content_formatter.format_messages_request_payload(
             messages, _model_kwargs, self.endpoint_api_type
         )
         response_payload = self.http_client.call(

libs/community/langchain_community/chat_models/deepinfra.py

@@ -327,7 +327,7 @@ class ChatDeepInfra(BaseChatModel):
             if chunk:
                 yield ChatGenerationChunk(message=chunk, generation_info=None)
                 if run_manager:
-                    run_manager.on_llm_new_token(chunk.content)  # type: ignore[arg-type]
+                    run_manager.on_llm_new_token(str(chunk.content))
 
     async def _astream(
         self,
@@ -349,7 +349,7 @@ class ChatDeepInfra(BaseChatModel):
                 if chunk:
                     yield ChatGenerationChunk(message=chunk, generation_info=None)
                     if run_manager:
-                        await run_manager.on_llm_new_token(chunk.content)  # type: ignore[arg-type]
+                        await run_manager.on_llm_new_token(str(chunk.content))
 
     async def _agenerate(
         self,

libs/community/langchain_community/chat_models/edenai.py

@@ -165,6 +165,12 @@ class ChatEdenAI(BaseChatModel):
         """Return type of chat model."""
         return "edenai-chat"
 
+    @property
+    def _api_key(self) -> str:
+        if self.edenai_api_key:
+            return self.edenai_api_key.get_secret_value()
+        return ""
+
     def _stream(
         self,
         messages: List[BaseMessage],
@@ -175,7 +181,7 @@ class ChatEdenAI(BaseChatModel):
         """Call out to EdenAI's chat endpoint."""
         url = f"{self.edenai_api_url}/text/chat/stream"
         headers = {
-            "Authorization": f"Bearer {self.edenai_api_key.get_secret_value()}",  # type: ignore[union-attr]
+            "Authorization": f"Bearer {self._api_key}",
             "User-Agent": self.get_user_agent(),
         }
         formatted_data = _format_edenai_messages(messages=messages)
@@ -216,7 +222,7 @@ class ChatEdenAI(BaseChatModel):
     ) -> AsyncIterator[ChatGenerationChunk]:
         url = f"{self.edenai_api_url}/text/chat/stream"
         headers = {
-            "Authorization": f"Bearer {self.edenai_api_key.get_secret_value()}",  # type: ignore[union-attr]
+            "Authorization": f"Bearer {self._api_key}",
             "User-Agent": self.get_user_agent(),
         }
         formatted_data = _format_edenai_messages(messages=messages)
@@ -265,7 +271,7 @@ class ChatEdenAI(BaseChatModel):
 
         url = f"{self.edenai_api_url}/text/chat"
         headers = {
-            "Authorization": f"Bearer {self.edenai_api_key.get_secret_value()}",  # type: ignore[union-attr]
+            "Authorization": f"Bearer {self._api_key}",
             "User-Agent": self.get_user_agent(),
         }
         formatted_data = _format_edenai_messages(messages=messages)
@@ -323,7 +329,7 @@ class ChatEdenAI(BaseChatModel):
 
         url = f"{self.edenai_api_url}/text/chat"
         headers = {
-            "Authorization": f"Bearer {self.edenai_api_key.get_secret_value()}",  # type: ignore[union-attr]
+            "Authorization": f"Bearer {self._api_key}",
             "User-Agent": self.get_user_agent(),
         }
         formatted_data = _format_edenai_messages(messages=messages)

libs/community/langchain_community/chat_models/ernie.py

@@ -214,7 +214,7 @@ class ErnieBotChat(BaseChatModel):
         generations = [
             ChatGeneration(
                 message=AIMessage(
-                    content=response.get("result"),  # type: ignore[arg-type]
+                    content=response.get("result", ""),
                     additional_kwargs={**additional_kwargs},
                 )
             )

libs/community/langchain_community/chat_models/gpt_router.py

@@ -14,6 +14,7 @@ from typing import (
     Mapping,
     Optional,
     Tuple,
+    Type,
     Union,
 )
 
@@ -27,7 +28,7 @@ from langchain_core.language_models.chat_models import (
     generate_from_stream,
 )
 from langchain_core.language_models.llms import create_base_retry_decorator
-from langchain_core.messages import AIMessageChunk, BaseMessage
+from langchain_core.messages import AIMessageChunk, BaseMessage, BaseMessageChunk
 from langchain_core.outputs import ChatGeneration, ChatGenerationChunk, ChatResult
 from langchain_core.pydantic_v1 import BaseModel, Field, SecretStr, root_validator
 from langchain_core.utils import convert_to_secret_str, get_from_dict_or_env
@@ -56,9 +57,9 @@ class GPTRouterModel(BaseModel):
     provider_name: str
 
 
-def get_ordered_generation_requests(  # type: ignore[no-untyped-def, no-untyped-def]
-    models_priority_list: List[GPTRouterModel], **kwargs
-):
+def get_ordered_generation_requests(
+    models_priority_list: List[GPTRouterModel], **kwargs: Any
+) -> List:
     """
     Return the body for the model router input.
     """
@@ -100,7 +101,7 @@ def completion_with_retry(
     models_priority_list: List[GPTRouterModel],
     run_manager: Optional[CallbackManagerForLLMRun] = None,
     **kwargs: Any,
-) -> Union[GenerationResponse, Generator[ChunkedGenerationResponse]]:  # type: ignore[type-arg]
+) -> Union[GenerationResponse, Generator[ChunkedGenerationResponse, None, None]]:
     """Use tenacity to retry the completion call."""
     retry_decorator = _create_retry_decorator(llm, run_manager=run_manager)
 
@@ -122,7 +123,7 @@ async def acompletion_with_retry(
     models_priority_list: List[GPTRouterModel],
     run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
     **kwargs: Any,
-) -> Union[GenerationResponse, AsyncGenerator[ChunkedGenerationResponse]]:  # type: ignore[type-arg]
+) -> Union[GenerationResponse, AsyncGenerator[ChunkedGenerationResponse, None]]:
     """Use tenacity to retry the async completion call."""
 
     retry_decorator = _create_retry_decorator(llm, run_manager=run_manager)
@@ -282,9 +283,9 @@ class GPTRouter(BaseChatModel):
         )
         return self._create_chat_result(response)
 
-    def _create_chat_generation_chunk(  # type: ignore[no-untyped-def, no-untyped-def]
-        self, data: Mapping[str, Any], default_chunk_class
-    ):
+    def _create_chat_generation_chunk(
+        self, data: Mapping[str, Any], default_chunk_class: Type[BaseMessageChunk]
+    ) -> Tuple[ChatGenerationChunk, Type[BaseMessageChunk]]:
         chunk = _convert_delta_to_message_chunk(
             {"content": data.get("text", "")}, default_chunk_class
         )
@@ -293,8 +294,8 @@ class GPTRouter(BaseChatModel):
             dict(finish_reason=finish_reason) if finish_reason is not None else None
         )
         default_chunk_class = chunk.__class__
-        chunk = ChatGenerationChunk(message=chunk, generation_info=generation_info)  # type: ignore[assignment]
-        return chunk, default_chunk_class
+        gen_chunk = ChatGenerationChunk(message=chunk, generation_info=generation_info)
+        return gen_chunk, default_chunk_class
 
     def _stream(
         self,
@@ -306,7 +307,7 @@ class GPTRouter(BaseChatModel):
         message_dicts, params = self._create_message_dicts(messages, stop)
         params = {**params, **kwargs, "stream": True}
 
-        default_chunk_class = AIMessageChunk
+        default_chunk_class: Type[BaseMessageChunk] = AIMessageChunk
         generator_response = completion_with_retry(
             self,
             messages=message_dicts,
@@ -339,7 +340,7 @@ class GPTRouter(BaseChatModel):
         message_dicts, params = self._create_message_dicts(messages, stop)
         params = {**params, **kwargs, "stream": True}
 
-        default_chunk_class = AIMessageChunk
+        default_chunk_class: Type[BaseMessageChunk] = AIMessageChunk
         generator_response = acompletion_with_retry(
             self,
             messages=message_dicts,

libs/community/langchain_community/chat_models/huggingface.py

@@ -44,7 +44,7 @@ class ChatHuggingFace(BaseChatModel):
     llm: Union[HuggingFaceTextGenInference, HuggingFaceEndpoint, HuggingFaceHub]
     system_message: SystemMessage = SystemMessage(content=DEFAULT_SYSTEM_PROMPT)
     tokenizer: Any = None
-    model_id: str = None  # type: ignore
+    model_id: Optional[str] = None
 
     def __init__(self, **kwargs: Any):
         super().__init__(**kwargs)
@@ -144,7 +144,7 @@ class ChatHuggingFace(BaseChatModel):
 
         elif isinstance(self.llm, HuggingFaceHub):
             # no need to look up model_id for HuggingFaceHub LLM
-            self.model_id = self.llm.repo_id  # type: ignore[assignment]
+            self.model_id = self.llm.repo_id
             return
 
         else:

libs/community/langchain_community/chat_models/javelin_ai_gateway.py

@@ -25,7 +25,7 @@ logger = logging.getLogger(__name__)
 
 # Ignoring type because below is valid pydantic code
 # Unexpected keyword argument "extra" for "__init_subclass__" of "object"  [call-arg]
-class ChatParams(BaseModel, extra=Extra.allow):  # type: ignore[call-arg]
+class ChatParams(BaseModel, extra=Extra.allow):
     """Parameters for the `Javelin AI Gateway` LLM."""
 
     temperature: float = 0.0

libs/community/langchain_community/chat_models/konko.py

@@ -13,6 +13,7 @@ from typing import (
     Set,
     Tuple,
     Union,
+    cast,
 )
 
 import requests
@@ -169,7 +170,9 @@ class ChatKonko(ChatOpenAI):
         }
 
         if openai_api_key:
-            headers["X-OpenAI-Api-Key"] = openai_api_key.get_secret_value()  # type: ignore[union-attr]
+            headers["X-OpenAI-Api-Key"] = cast(
+                SecretStr, openai_api_key
+            ).get_secret_value()
 
         models_response = requests.get(models_url, headers=headers)
 

libs/community/langchain_community/chat_models/mlflow_ai_gateway.py

@@ -25,7 +25,7 @@ logger = logging.getLogger(__name__)
 
 # Ignoring type because below is valid pydantic code
 # Unexpected keyword argument "extra" for "__init_subclass__" of "object"  [call-arg]
-class ChatParams(BaseModel, extra=Extra.allow):  # type: ignore[call-arg]
+class ChatParams(BaseModel, extra=Extra.allow):
     """Parameters for the `MLflow AI Gateway` LLM."""
 
     temperature: float = 0.0

libs/community/langchain_community/chat_models/ollama.py

@@ -1,5 +1,5 @@
 import json
-from typing import Any, AsyncIterator, Dict, Iterator, List, Optional, Union
+from typing import Any, AsyncIterator, Dict, Iterator, List, Optional, Union, cast
 
 from langchain_core._api import deprecated
 from langchain_core.callbacks import (
@@ -74,10 +74,15 @@ class ChatOllama(BaseChatModel, _OllamaCommon):
         if isinstance(message, ChatMessage):
             message_text = f"\n\n{message.role.capitalize()}: {message.content}"
         elif isinstance(message, HumanMessage):
-            if message.content[0].get("type") == "text":  # type: ignore[union-attr]
-                message_text = f"[INST] {message.content[0]['text']} [/INST]"  # type: ignore[index]
-            elif message.content[0].get("type") == "image_url":  # type: ignore[union-attr]
-                message_text = message.content[0]["image_url"]["url"]  # type: ignore[index, index]
+            if isinstance(message.content, List):
+                first_content = cast(List[Dict], message.content)[0]
+                content_type = first_content.get("type")
+                if content_type == "text":
+                    message_text = f"[INST] {first_content['text']} [/INST]"
+                elif content_type == "image_url":
+                    message_text = first_content["image_url"]["url"]
+            else:
+                message_text = f"[INST] {message.content} [/INST]"
         elif isinstance(message, AIMessage):
             message_text = f"{message.content}"
         elif isinstance(message, SystemMessage):
@@ -94,7 +99,7 @@ class ChatOllama(BaseChatModel, _OllamaCommon):
     def _convert_messages_to_ollama_messages(
         self, messages: List[BaseMessage]
     ) -> List[Dict[str, Union[str, List[str]]]]:
-        ollama_messages = []
+        ollama_messages: List = []
         for message in messages:
             role = ""
             if isinstance(message, HumanMessage):
@@ -111,12 +116,12 @@ class ChatOllama(BaseChatModel, _OllamaCommon):
             if isinstance(message.content, str):
                 content = message.content
             else:
-                for content_part in message.content:
-                    if content_part.get("type") == "text":  # type: ignore[union-attr]
-                        content += f"\n{content_part['text']}"  # type: ignore[index]
-                    elif content_part.get("type") == "image_url":  # type: ignore[union-attr]
-                        if isinstance(content_part.get("image_url"), str):  # type: ignore[union-attr]
-                            image_url_components = content_part["image_url"].split(",")  # type: ignore[index]
+                for content_part in cast(List[Dict], message.content):
+                    if content_part.get("type") == "text":
+                        content += f"\n{content_part['text']}"
+                    elif content_part.get("type") == "image_url":
+                        if isinstance(content_part.get("image_url"), str):
+                            image_url_components = content_part["image_url"].split(",")
                             # Support data:image/jpeg;base64,<image> format
                             # and base64 strings
                             if len(image_url_components) > 1:
@@ -142,7 +147,7 @@ class ChatOllama(BaseChatModel, _OllamaCommon):
                 }
             )
 
-        return ollama_messages  # type: ignore[return-value]
+        return ollama_messages
 
     def _create_chat_stream(
         self,
@@ -324,21 +329,15 @@ class ChatOllama(BaseChatModel, _OllamaCommon):
         run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
         **kwargs: Any,
     ) -> AsyncIterator[ChatGenerationChunk]:
-        try:
-            async for stream_resp in self._acreate_chat_stream(
-                messages, stop, **kwargs
-            ):
-                if stream_resp:
-                    chunk = _chat_stream_response_to_chat_generation_chunk(stream_resp)
-                    yield chunk
-                    if run_manager:
-                        await run_manager.on_llm_new_token(
-                            chunk.text,
-                            verbose=self.verbose,
-                        )
-        except OllamaEndpointNotFoundError:
-            async for chunk in self._legacy_astream(messages, stop, **kwargs):  # type: ignore[attr-defined]
+        async for stream_resp in self._acreate_chat_stream(messages, stop, **kwargs):
+            if stream_resp:
+                chunk = _chat_stream_response_to_chat_generation_chunk(stream_resp)
                 yield chunk
+                if run_manager:
+                    await run_manager.on_llm_new_token(
+                        chunk.text,
+                        verbose=self.verbose,
+                    )
 
     @deprecated("0.0.3", alternative="_stream")
     def _legacy_stream(

libs/community/langchain_community/chat_models/openai.py

@@ -554,7 +554,7 @@ class ChatOpenAI(BaseChatModel):
         if self.openai_proxy:
             import openai
 
-            openai.proxy = {"http": self.openai_proxy, "https": self.openai_proxy}  # type: ignore[assignment]  # noqa: E501
+            openai.proxy = {"http": self.openai_proxy, "https": self.openai_proxy}
         return {**self._default_params, **openai_creds}
 
     def _get_invocation_params(

libs/community/langchain_community/chat_models/tongyi.py

@@ -13,6 +13,7 @@ from typing import (
     Mapping,
     Optional,
     Union,
+    cast,
 )
 
 from langchain_core.callbacks import (
@@ -197,7 +198,7 @@ class ChatTongyi(BaseChatModel):
         return {
             "model": self.model_name,
             "top_p": self.top_p,
-            "api_key": self.dashscope_api_key.get_secret_value(),  # type: ignore[union-attr]
+            "api_key": cast(SecretStr, self.dashscope_api_key).get_secret_value(),
             "result_format": "message",
             **self.model_kwargs,
         }

libs/community/langchain_community/chat_models/vertexai.py

@@ -120,11 +120,10 @@ def _parse_chat_history_gemini(
                 image = load_image_from_gcs(path=path, project=project)
             elif path.startswith("data:image/"):
                 # extract base64 component from image uri
-                try:
-                    encoded = re.search(r"data:image/\w{2,4};base64,(.*)", path).group(  # type: ignore[union-attr]
-                        1
-                    )
-                except AttributeError:
+                encoded: Any = re.search(r"data:image/\w{2,4};base64,(.*)", path)
+                if encoded:
+                    encoded = encoded.group(1)
+                else:
                     raise ValueError(
                         "Invalid image uri. It should be in the format "
                         "data:image/<image_type>;base64,<base64_encoded_image>."

libs/community/langchain_community/chat_models/yandex.py

@@ -52,7 +52,7 @@ def _parse_chat_history(history: List[BaseMessage]) -> List[Dict[str, str]]:
     return chat_history
 
 
-class ChatYandexGPT(_BaseYandexGPT, BaseChatModel):  # type: ignore[misc]
+class ChatYandexGPT(_BaseYandexGPT, BaseChatModel):
     """Wrapper around YandexGPT large language models.
 
     There are two authentication options for the service account
@@ -156,7 +156,7 @@ def _make_request(
         messages=[Message(**message) for message in message_history],
     )
     stub = TextGenerationServiceStub(channel)
-    res = stub.Completion(request, metadata=self._grpc_metadata)  # type: ignore[attr-defined]
+    res = stub.Completion(request, metadata=self._grpc_metadata)
     return list(res)[0].alternatives[0].message.text
 
 
@@ -201,7 +201,7 @@ async def _amake_request(self: ChatYandexGPT, messages: List[BaseMessage]) -> st
             messages=[Message(**message) for message in message_history],
         )
         stub = TextGenerationAsyncServiceStub(channel)
-        operation = await stub.Completion(request, metadata=self._grpc_metadata)  # type: ignore[attr-defined]
+        operation = await stub.Completion(request, metadata=self._grpc_metadata)
         async with grpc.aio.secure_channel(
             operation_api_url, channel_credentials
         ) as operation_channel:
@@ -211,7 +211,7 @@ async def _amake_request(self: ChatYandexGPT, messages: List[BaseMessage]) -> st
                 operation_request = GetOperationRequest(operation_id=operation.id)
                 operation = await operation_stub.Get(
                     operation_request,
-                    metadata=self._grpc_metadata,  # type: ignore[attr-defined]
+                    metadata=self._grpc_metadata,
                 )
 
         completion_response = CompletionResponse()

libs/community/langchain_community/chat_models/zhipuai.py

@@ -5,7 +5,7 @@ import asyncio
 import json
 import logging
 from functools import partial
-from typing import Any, Dict, Iterator, List, Optional
+from typing import Any, Dict, Iterator, List, Optional, cast
 
 from langchain_core.callbacks import CallbackManagerForLLMRun
 from langchain_core.language_models.chat_models import (
@@ -161,7 +161,7 @@ class ChatZhipuAI(BaseChatModel):
 
         return attributes
 
-    def __init__(self, *args, **kwargs):  # type: ignore[no-untyped-def]
+    def __init__(self, *args: Any, **kwargs: Any) -> None:
         super().__init__(*args, **kwargs)
         try:
             import zhipuai
@@ -174,7 +174,7 @@ class ChatZhipuAI(BaseChatModel):
                 "Please install it via 'pip install zhipuai'"
             )
 
-    def invoke(self, prompt):  # type: ignore[no-untyped-def]
+    def invoke(self, prompt: Any) -> Any:  # type: ignore[override]
         if self.model == "chatglm_turbo":
             return self.zhipuai.model_api.invoke(
                 model=self.model,
@@ -185,17 +185,17 @@ class ChatZhipuAI(BaseChatModel):
                 return_type=self.return_type,
             )
         elif self.model == "characterglm":
-            meta = self.meta.dict()
+            _meta = cast(meta, self.meta).dict()
             return self.zhipuai.model_api.invoke(
                 model=self.model,
-                meta=meta,
+                meta=_meta,
                 prompt=prompt,
                 request_id=self.request_id,
                 return_type=self.return_type,
             )
         return None
 
-    def sse_invoke(self, prompt):  # type: ignore[no-untyped-def]
+    def sse_invoke(self, prompt: Any) -> Any:
         if self.model == "chatglm_turbo":
             return self.zhipuai.model_api.sse_invoke(
                 model=self.model,
@@ -207,18 +207,18 @@ class ChatZhipuAI(BaseChatModel):
                 incremental=self.incremental,
             )
         elif self.model == "characterglm":
-            meta = self.meta.dict()
+            _meta = cast(meta, self.meta).dict()
             return self.zhipuai.model_api.sse_invoke(
                 model=self.model,
                 prompt=prompt,
-                meta=meta,
+                meta=_meta,
                 request_id=self.request_id,
                 return_type=self.return_type,
                 incremental=self.incremental,
             )
         return None
 
-    async def async_invoke(self, prompt):  # type: ignore[no-untyped-def]
+    async def async_invoke(self, prompt: Any) -> Any:
         loop = asyncio.get_running_loop()
         partial_func = partial(
             self.zhipuai.model_api.async_invoke, model=self.model, prompt=prompt
@@ -229,7 +229,7 @@ class ChatZhipuAI(BaseChatModel):
         )
         return response
 
-    async def async_invoke_result(self, task_id):  # type: ignore[no-untyped-def]
+    async def async_invoke_result(self, task_id: Any) -> Any:
         loop = asyncio.get_running_loop()
         response = await loop.run_in_executor(
             None,
@@ -247,7 +247,7 @@ class ChatZhipuAI(BaseChatModel):
         **kwargs: Any,
     ) -> ChatResult:
         """Generate a chat response."""
-        prompt = []
+        prompt: List = []
         for message in messages:
             if isinstance(message, AIMessage):
                 role = "assistant"
@@ -270,7 +270,7 @@ class ChatZhipuAI(BaseChatModel):
 
         else:
             stream_iter = self._stream(
-                prompt=prompt,  # type: ignore[arg-type]
+                prompt=prompt,
                 stop=stop,
                 run_manager=run_manager,
                 **kwargs,

libs/community/langchain_community/llms/azureml_endpoint.py

@@ -101,7 +101,7 @@ class ContentFormatterBase:
     accepts: Optional[str] = "application/json"
     """The MIME type of the response data returned from the endpoint"""
 
-    format_error_msg: Optional[str] = (
+    format_error_msg: str = (
         "Error while formatting response payload for chat model of type "
         " `{api_type}`. Are you using the right formatter for the deployed "
         " model and endpoint type?"
@@ -134,17 +134,17 @@ class ContentFormatterBase:
 
         return [AzureMLEndpointApiType.realtime]
 
-    @abstractmethod
     def format_request_payload(
         self,
         prompt: str,
         model_kwargs: Dict,
         api_type: AzureMLEndpointApiType = AzureMLEndpointApiType.realtime,
-    ) -> bytes:
+    ) -> Any:
         """Formats the request body according to the input schema of
         the model. Returns bytes or seekable file like object in the
         format specified in the content_type request header.
         """
+        raise NotImplementedError()
 
     @abstractmethod
     def format_response_payload(

libs/community/langchain_community/llms/yandex.py

@@ -1,7 +1,7 @@
 from __future__ import annotations
 
 import logging
-from typing import Any, Callable, Dict, List, Mapping, Optional
+from typing import Any, Callable, Dict, List, Optional, Sequence
 
 from langchain_core.callbacks import (
     AsyncCallbackManagerForLLMRun,
@@ -54,13 +54,14 @@ class _BaseYandexGPT(Serializable):
     """Maximum number of retries to make when generating."""
     sleep_interval: float = 1.0
     """Delay between API requests"""
+    _grpc_metadata: Sequence
 
     @property
     def _llm_type(self) -> str:
         return "yandex_gpt"
 
     @property
-    def _identifying_params(self) -> Mapping[str, Any]:
+    def _identifying_params(self) -> Dict[str, Any]:
         """Get the identifying parameters."""
         return {
             "model_uri": self.model_uri,