infra: add -p to mkdir in lint steps (#17013)

Previously, if this did not find a mypy cache then it wouldnt run

this makes it always run

adding mypy ignore comments with existing uncaught issues to unblock other prs

---------

Co-authored-by: Erick Friis <erick@langchain.dev>
Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>

.github/workflows/_lint.yml

@@ -86,7 +86,7 @@ jobs:
         with:
           path: |
             ${{ env.WORKDIR }}/.mypy_cache
-          key: mypy-lint-${{ runner.os }}-${{ runner.arch }}-py${{ matrix.python-version }}-${{ inputs.working-directory }}-${{ hashFiles(format('{0}/poetry.lock', env.WORKDIR)) }}
+          key: mypy-lint-${{ runner.os }}-${{ runner.arch }}-py${{ matrix.python-version }}-${{ inputs.working-directory }}-${{ hashFiles(format('{0}/poetry.lock', inputs.working-directory)) }}
 
 
       - name: Analysing the code with our lint
@@ -105,7 +105,7 @@ jobs:
         # It doesn't matter how you change it, any change will cause a cache-bust.
         working-directory: ${{ inputs.working-directory }}
         run: |
-          poetry install --with test,test_integration
+          poetry install --with test
 
       - name: Get .mypy_cache_test to speed up mypy
         uses: actions/cache@v3
@@ -114,7 +114,7 @@ jobs:
         with:
           path: |
             ${{ env.WORKDIR }}/.mypy_cache_test
-          key: mypy-test-${{ runner.os }}-${{ runner.arch }}-py${{ matrix.python-version }}-${{ inputs.working-directory }}-${{ hashFiles(format('{0}/poetry.lock', env.WORKDIR)) }}
+          key: mypy-test-${{ runner.os }}-${{ runner.arch }}-py${{ matrix.python-version }}-${{ inputs.working-directory }}-${{ hashFiles(format('{0}/poetry.lock', inputs.working-directory)) }}
 
       - name: Analysing the code with our lint
         working-directory: ${{ inputs.working-directory }}

libs/community/Makefile

@@ -41,7 +41,7 @@ lint lint_diff lint_package lint_tests:
 	poetry run ruff .
 	[ "$(PYTHON_FILES)" = "" ] || poetry run ruff format $(PYTHON_FILES) --diff
 	[ "$(PYTHON_FILES)" = "" ] || poetry run ruff --select I $(PYTHON_FILES)
-	[ "$(PYTHON_FILES)" = "" ] || mkdir -p $(MYPY_CACHE) || poetry run mypy $(PYTHON_FILES) --cache-dir $(MYPY_CACHE)
+	[ "$(PYTHON_FILES)" = "" ] || mkdir -p $(MYPY_CACHE) && poetry run mypy $(PYTHON_FILES) --cache-dir $(MYPY_CACHE)
 
 format format_diff:
 	poetry run ruff format $(PYTHON_FILES)

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

@@ -84,7 +84,7 @@ class RequestsGetToolWithParsing(BaseRequestsTool, BaseTool):
             raise e
         data_params = data.get("params")
         response = self.requests_wrapper.get(data["url"], params=data_params)
-        response = response[: self.response_length]
+        response = response[: self.response_length]  # type: ignore[index]
         return self.llm_chain.predict(
             response=response, instructions=data["output_instructions"]
         ).strip()
@@ -115,7 +115,7 @@ class RequestsPostToolWithParsing(BaseRequestsTool, BaseTool):
         except json.JSONDecodeError as e:
             raise e
         response = self.requests_wrapper.post(data["url"], data["data"])
-        response = response[: self.response_length]
+        response = response[: self.response_length]  # type: ignore[index]
         return self.llm_chain.predict(
             response=response, instructions=data["output_instructions"]
         ).strip()
@@ -146,7 +146,7 @@ class RequestsPatchToolWithParsing(BaseRequestsTool, BaseTool):
         except json.JSONDecodeError as e:
             raise e
         response = self.requests_wrapper.patch(data["url"], data["data"])
-        response = response[: self.response_length]
+        response = response[: self.response_length]  # type: ignore[index]
         return self.llm_chain.predict(
             response=response, instructions=data["output_instructions"]
         ).strip()
@@ -177,7 +177,7 @@ class RequestsPutToolWithParsing(BaseRequestsTool, BaseTool):
         except json.JSONDecodeError as e:
             raise e
         response = self.requests_wrapper.put(data["url"], data["data"])
-        response = response[: self.response_length]
+        response = response[: self.response_length]  # type: ignore[index]
         return self.llm_chain.predict(
             response=response, instructions=data["output_instructions"]
         ).strip()
@@ -209,7 +209,7 @@ class RequestsDeleteToolWithParsing(BaseRequestsTool, BaseTool):
         except json.JSONDecodeError as e:
             raise e
         response = self.requests_wrapper.delete(data["url"])
-        response = response[: self.response_length]
+        response = response[: self.response_length]  # type: ignore[index]
         return self.llm_chain.predict(
             response=response, instructions=data["output_instructions"]
         ).strip()

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

@@ -177,12 +177,12 @@ def create_sql_agent(
     elif agent_type == AgentType.OPENAI_FUNCTIONS:
         if prompt is None:
             messages = [
-                SystemMessage(content=prefix),
+                SystemMessage(content=prefix),  # type: ignore[arg-type]
                 HumanMessagePromptTemplate.from_template("{input}"),
                 AIMessage(content=suffix or SQL_FUNCTIONS_SUFFIX),
                 MessagesPlaceholder(variable_name="agent_scratchpad"),
             ]
-            prompt = ChatPromptTemplate.from_messages(messages)
+            prompt = ChatPromptTemplate.from_messages(messages)  # type: ignore[arg-type]
         agent = RunnableAgent(
             runnable=create_openai_functions_agent(llm, tools, prompt),
             input_keys_arg=["input"],
@@ -191,12 +191,12 @@ def create_sql_agent(
     elif agent_type == "openai-tools":
         if prompt is None:
             messages = [
-                SystemMessage(content=prefix),
+                SystemMessage(content=prefix),  # type: ignore[arg-type]
                 HumanMessagePromptTemplate.from_template("{input}"),
                 AIMessage(content=suffix or SQL_FUNCTIONS_SUFFIX),
                 MessagesPlaceholder(variable_name="agent_scratchpad"),
             ]
-            prompt = ChatPromptTemplate.from_messages(messages)
+            prompt = ChatPromptTemplate.from_messages(messages)  # type: ignore[arg-type]
         agent = RunnableMultiActionAgent(
             runnable=create_openai_tools_agent(llm, tools, prompt),
             input_keys_arg=["input"],

libs/community/langchain_community/callbacks/mlflow_callback.py

@@ -723,7 +723,7 @@ class MlflowCallbackHandler(BaseMetadataCallbackHandler, BaseCallbackHandler):
         )
         return session_analysis_df
 
-    def _contain_llm_records(self):
+    def _contain_llm_records(self):  # type: ignore[no-untyped-def]
         return bool(self.records["on_llm_start_records"])
 
     def flush_tracker(self, langchain_asset: Any = None, finish: bool = False) -> None:

libs/community/langchain_community/chat_message_histories/elasticsearch.py

@@ -47,7 +47,7 @@ class ElasticsearchChatMessageHistory(BaseChatMessageHistory):
     ):
         self.index: str = index
         self.session_id: str = session_id
-        self.ensure_ascii: bool = esnsure_ascii
+        self.ensure_ascii: bool = esnsure_ascii  # type: ignore[assignment]
 
         # Initialize Elasticsearch client from passed client arg or connection info
         if es_connection is not None:

libs/community/langchain_community/chat_message_histories/tidb.py

@@ -40,7 +40,7 @@ class TiDBChatMessageHistory(BaseChatMessageHistory):
         self.session_id = session_id
         self.table_name = table_name
         self.earliest_time = earliest_time
-        self.cache = []
+        self.cache = []  # type: ignore[var-annotated]
 
         # Set up SQLAlchemy engine and session
         self.engine = create_engine(connection_string)
@@ -102,7 +102,7 @@ class TiDBChatMessageHistory(BaseChatMessageHistory):
             logger.error(f"Error loading messages to cache: {e}")
 
     @property
-    def messages(self) -> List[BaseMessage]:
+    def messages(self) -> List[BaseMessage]:  # type: ignore[override]
         """returns all messages"""
         if len(self.cache) == 0:
             self.reload_cache()

libs/community/langchain_community/chat_message_histories/zep.py

@@ -149,7 +149,7 @@ class ZepChatMessageHistory(BaseChatMessageHistory):
             return None
         return zep_memory
 
-    def add_user_message(
+    def add_user_message(  # type: ignore[override]
         self, message: str, metadata: Optional[Dict[str, Any]] = None
     ) -> None:
         """Convenience method for adding a human message string to the store.
@@ -160,7 +160,7 @@ class ZepChatMessageHistory(BaseChatMessageHistory):
         """
         self.add_message(HumanMessage(content=message), metadata=metadata)
 
-    def add_ai_message(
+    def add_ai_message(  # type: ignore[override]
         self, message: str, metadata: Optional[Dict[str, Any]] = None
     ) -> None:
         """Convenience method for adding an AI message string to the store.

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):
+    def __init__(self):  # type: ignore[no-untyped-def]
         raise TypeError(
             "`LlamaContentFormatter` is deprecated for chat models. Use "
             "`LlamaChatContentFormatter` instead."
@@ -72,7 +72,7 @@ class LlamaChatContentFormatter(ContentFormatterBase):
     def supported_api_types(self) -> List[AzureMLEndpointApiType]:
         return [AzureMLEndpointApiType.realtime, AzureMLEndpointApiType.serverless]
 
-    def format_request_payload(
+    def format_request_payload(  # type: ignore[override]
         self,
         messages: List[BaseMessage],
         model_kwargs: Dict,
@@ -98,9 +98,9 @@ class LlamaChatContentFormatter(ContentFormatterBase):
             raise ValueError(
                 f"`api_type` {api_type} is not supported by this formatter"
             )
-        return str.encode(request_payload)
+        return str.encode(request_payload)  # type: ignore[return-value]
 
-    def format_response_payload(
+    def format_response_payload(  # type: ignore[override]
         self, output: bytes, api_type: AzureMLEndpointApiType
     ) -> ChatGeneration:
         """Formats response"""
@@ -108,7 +108,7 @@ class LlamaChatContentFormatter(ContentFormatterBase):
             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
+                raise ValueError(self.format_error_msg.format(api_type=api_type)) from e  # type: ignore[union-attr]
             return ChatGeneration(
                 message=BaseMessage(
                     content=choice.strip(),
@@ -125,7 +125,7 @@ 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
+                raise ValueError(self.format_error_msg.format(api_type=api_type)) from e  # type: ignore[union-attr]
             return ChatGeneration(
                 message=BaseMessage(
                     content=choice["message"]["content"].strip(),

libs/community/langchain_community/chat_models/edenai.py

@@ -175,7 +175,7 @@ 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()}",
+            "Authorization": f"Bearer {self.edenai_api_key.get_secret_value()}",  # type: ignore[union-attr]
             "User-Agent": self.get_user_agent(),
         }
         formatted_data = _format_edenai_messages(messages=messages)
@@ -216,7 +216,7 @@ 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()}",
+            "Authorization": f"Bearer {self.edenai_api_key.get_secret_value()}",  # type: ignore[union-attr]
             "User-Agent": self.get_user_agent(),
         }
         formatted_data = _format_edenai_messages(messages=messages)
@@ -265,7 +265,7 @@ class ChatEdenAI(BaseChatModel):
 
         url = f"{self.edenai_api_url}/text/chat"
         headers = {
-            "Authorization": f"Bearer {self.edenai_api_key.get_secret_value()}",
+            "Authorization": f"Bearer {self.edenai_api_key.get_secret_value()}",  # type: ignore[union-attr]
             "User-Agent": self.get_user_agent(),
         }
         formatted_data = _format_edenai_messages(messages=messages)
@@ -323,7 +323,7 @@ class ChatEdenAI(BaseChatModel):
 
         url = f"{self.edenai_api_url}/text/chat"
         headers = {
-            "Authorization": f"Bearer {self.edenai_api_key.get_secret_value()}",
+            "Authorization": f"Bearer {self.edenai_api_key.get_secret_value()}",  # type: ignore[union-attr]
             "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"),
+                    content=response.get("result"),  # type: ignore[arg-type]
                     additional_kwargs={**additional_kwargs},
                 )
             )

libs/community/langchain_community/chat_models/gpt_router.py

@@ -56,7 +56,7 @@ class GPTRouterModel(BaseModel):
     provider_name: str
 
 
-def get_ordered_generation_requests(
+def get_ordered_generation_requests(  # type: ignore[no-untyped-def, no-untyped-def]
     models_priority_list: List[GPTRouterModel], **kwargs
 ):
     """
@@ -100,7 +100,7 @@ def completion_with_retry(
     models_priority_list: List[GPTRouterModel],
     run_manager: Optional[CallbackManagerForLLMRun] = None,
     **kwargs: Any,
-) -> Union[GenerationResponse, Generator[ChunkedGenerationResponse]]:
+) -> Union[GenerationResponse, Generator[ChunkedGenerationResponse]]:  # type: ignore[type-arg]
     """Use tenacity to retry the completion call."""
     retry_decorator = _create_retry_decorator(llm, run_manager=run_manager)
 
@@ -122,7 +122,7 @@ async def acompletion_with_retry(
     models_priority_list: List[GPTRouterModel],
     run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
     **kwargs: Any,
-) -> Union[GenerationResponse, AsyncGenerator[ChunkedGenerationResponse]]:
+) -> Union[GenerationResponse, AsyncGenerator[ChunkedGenerationResponse]]:  # type: ignore[type-arg]
     """Use tenacity to retry the async completion call."""
 
     retry_decorator = _create_retry_decorator(llm, run_manager=run_manager)
@@ -282,7 +282,7 @@ class GPTRouter(BaseChatModel):
         )
         return self._create_chat_result(response)
 
-    def _create_chat_generation_chunk(
+    def _create_chat_generation_chunk(  # type: ignore[no-untyped-def, no-untyped-def]
         self, data: Mapping[str, Any], default_chunk_class
     ):
         chunk = _convert_delta_to_message_chunk(
@@ -293,7 +293,7 @@ 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)
+        chunk = ChatGenerationChunk(message=chunk, generation_info=generation_info)  # type: ignore[assignment]
         return chunk, default_chunk_class
 
     def _stream(

libs/community/langchain_community/chat_models/huggingface.py

@@ -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
+            self.model_id = self.llm.repo_id  # type: ignore[assignment]
             return
 
         else:

libs/community/langchain_community/chat_models/konko.py

@@ -169,7 +169,7 @@ class ChatKonko(ChatOpenAI):
         }
 
         if openai_api_key:
-            headers["X-OpenAI-Api-Key"] = openai_api_key.get_secret_value()
+            headers["X-OpenAI-Api-Key"] = openai_api_key.get_secret_value()  # type: ignore[union-attr]
 
         models_response = requests.get(models_url, headers=headers)
 

libs/community/langchain_community/chat_models/ollama.py

@@ -74,10 +74,10 @@ 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":
+            if message.content[0].get("type") == "text":  # type: ignore[union-attr]
-                message_text = f"[INST] {message.content[0]['text']} [/INST]"
+                message_text = f"[INST] {message.content[0]['text']} [/INST]"  # type: ignore[index]
-            elif message.content[0].get("type") == "image_url":
+            elif message.content[0].get("type") == "image_url":  # type: ignore[union-attr]
-                message_text = message.content[0]["image_url"]["url"]
+                message_text = message.content[0]["image_url"]["url"]  # type: ignore[index, index]
         elif isinstance(message, AIMessage):
             message_text = f"{message.content}"
         elif isinstance(message, SystemMessage):
@@ -112,11 +112,11 @@ class ChatOllama(BaseChatModel, _OllamaCommon):
                 content = message.content
             else:
                 for content_part in message.content:
-                    if content_part.get("type") == "text":
+                    if content_part.get("type") == "text":  # type: ignore[union-attr]
-                        content += f"\n{content_part['text']}"
+                        content += f"\n{content_part['text']}"  # type: ignore[index]
-                    elif content_part.get("type") == "image_url":
+                    elif content_part.get("type") == "image_url":  # type: ignore[union-attr]
-                        if isinstance(content_part.get("image_url"), str):
+                        if isinstance(content_part.get("image_url"), str):  # type: ignore[union-attr]
-                            image_url_components = content_part["image_url"].split(",")
+                            image_url_components = content_part["image_url"].split(",")  # type: ignore[index]
                             # Support data:image/jpeg;base64,<image> format
                             # and base64 strings
                             if len(image_url_components) > 1:
@@ -142,7 +142,7 @@ class ChatOllama(BaseChatModel, _OllamaCommon):
                 }
             )
 
-        return ollama_messages
+        return ollama_messages  # type: ignore[return-value]
 
     def _create_chat_stream(
         self,
@@ -337,7 +337,7 @@ class ChatOllama(BaseChatModel, _OllamaCommon):
                             verbose=self.verbose,
                         )
         except OllamaEndpointNotFoundError:
-            async for chunk in self._legacy_astream(messages, stop, **kwargs):
+            async for chunk in self._legacy_astream(messages, stop, **kwargs):  # type: ignore[attr-defined]
                 yield chunk
 
     @deprecated("0.0.3", alternative="_stream")

libs/community/langchain_community/chat_models/tongyi.py

@@ -197,7 +197,7 @@ class ChatTongyi(BaseChatModel):
         return {
             "model": self.model_name,
             "top_p": self.top_p,
-            "api_key": self.dashscope_api_key.get_secret_value(),
+            "api_key": self.dashscope_api_key.get_secret_value(),  # type: ignore[union-attr]
             "result_format": "message",
             **self.model_kwargs,
         }

libs/community/langchain_community/chat_models/vertexai.py

@@ -121,7 +121,7 @@ def _parse_chat_history_gemini(
             elif path.startswith("data:image/"):
                 # extract base64 component from image uri
                 try:
-                    encoded = re.search(r"data:image/\w{2,4};base64,(.*)", path).group(
+                    encoded = re.search(r"data:image/\w{2,4};base64,(.*)", path).group(  # type: ignore[union-attr]
                         1
                     )
                 except AttributeError:

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):
+class ChatYandexGPT(_BaseYandexGPT, BaseChatModel):  # type: ignore[misc]
     """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)
+    res = stub.Completion(request, metadata=self._grpc_metadata)  # type: ignore[attr-defined]
     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)
+        operation = await stub.Completion(request, metadata=self._grpc_metadata)  # type: ignore[attr-defined]
         async with grpc.aio.secure_channel(
             operation_api_url, channel_credentials
         ) as operation_channel:
@@ -210,7 +210,8 @@ async def _amake_request(self: ChatYandexGPT, messages: List[BaseMessage]) -> st
                 await asyncio.sleep(1)
                 operation_request = GetOperationRequest(operation_id=operation.id)
                 operation = await operation_stub.Get(
-                    operation_request, metadata=self._grpc_metadata
+                    operation_request,
+                    metadata=self._grpc_metadata,  # type: ignore[attr-defined]
                 )
 
         completion_response = CompletionResponse()

libs/community/langchain_community/chat_models/zhipuai.py

@@ -161,7 +161,7 @@ class ChatZhipuAI(BaseChatModel):
 
         return attributes
 
-    def __init__(self, *args, **kwargs):
+    def __init__(self, *args, **kwargs):  # type: ignore[no-untyped-def]
         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):
+    def invoke(self, prompt):  # type: ignore[no-untyped-def]
         if self.model == "chatglm_turbo":
             return self.zhipuai.model_api.invoke(
                 model=self.model,
@@ -195,7 +195,7 @@ class ChatZhipuAI(BaseChatModel):
             )
         return None
 
-    def sse_invoke(self, prompt):
+    def sse_invoke(self, prompt):  # type: ignore[no-untyped-def]
         if self.model == "chatglm_turbo":
             return self.zhipuai.model_api.sse_invoke(
                 model=self.model,
@@ -218,7 +218,7 @@ class ChatZhipuAI(BaseChatModel):
             )
         return None
 
-    async def async_invoke(self, prompt):
+    async def async_invoke(self, prompt):  # type: ignore[no-untyped-def]
         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):
+    async def async_invoke_result(self, task_id):  # type: ignore[no-untyped-def]
         loop = asyncio.get_running_loop()
         response = await loop.run_in_executor(
             None,
@@ -270,11 +270,14 @@ class ChatZhipuAI(BaseChatModel):
 
         else:
             stream_iter = self._stream(
-                prompt=prompt, stop=stop, run_manager=run_manager, **kwargs
+                prompt=prompt,  # type: ignore[arg-type]
+                stop=stop,
+                run_manager=run_manager,
+                **kwargs,
             )
             return generate_from_stream(stream_iter)
 
-    async def _agenerate(
+    async def _agenerate(  # type: ignore[override]
         self,
         messages: List[BaseMessage],
         stop: Optional[List[str]] = None,
@@ -307,7 +310,7 @@ class ChatZhipuAI(BaseChatModel):
             generations=[ChatGeneration(message=AIMessage(content=content))]
         )
 
-    def _stream(
+    def _stream(  # type: ignore[override]
         self,
         prompt: List[Dict[str, str]],
         stop: Optional[List[str]] = None,

libs/community/langchain_community/document_loaders/assemblyai.py

@@ -123,7 +123,7 @@ class AssemblyAIAudioLoaderById(BaseLoader):
 
     """
 
-    def __init__(self, transcript_id, api_key, transcript_format):
+    def __init__(self, transcript_id, api_key, transcript_format):  # type: ignore[no-untyped-def]
         """
         Initializes the AssemblyAI AssemblyAIAudioLoaderById.
 

libs/community/langchain_community/document_loaders/astradb.py

@@ -65,7 +65,7 @@ class AstraDBLoader(BaseLoader):
         return list(self.lazy_load())
 
     def lazy_load(self) -> Iterator[Document]:
-        queue = Queue(self.nb_prefetched)
+        queue = Queue(self.nb_prefetched)  # type: ignore[var-annotated]
         t = threading.Thread(target=self.fetch_results, args=(queue,))
         t.start()
         while True:
@@ -95,7 +95,7 @@ class AstraDBLoader(BaseLoader):
                 item = await run_in_executor(None, lambda it: next(it, done), iterator)
                 if item is done:
                     break
-                yield item
+                yield item  # type: ignore[misc]
             return
         async_collection = await self.astra_env.async_astra_db.collection(
             self.collection_name
@@ -116,13 +116,13 @@ class AstraDBLoader(BaseLoader):
                 },
             )
 
-    def fetch_results(self, queue: Queue):
+    def fetch_results(self, queue: Queue):  # type: ignore[no-untyped-def]
         self.fetch_page_result(queue)
         while self.find_options.get("pageState"):
             self.fetch_page_result(queue)
         queue.put(None)
 
-    def fetch_page_result(self, queue: Queue):
+    def fetch_page_result(self, queue: Queue):  # type: ignore[no-untyped-def]
         res = self.collection.find(
             filter=self.filter,
             options=self.find_options,

libs/community/langchain_community/document_loaders/base.py

@@ -64,10 +64,10 @@ class BaseLoader(ABC):
         iterator = await run_in_executor(None, self.lazy_load)
         done = object()
         while True:
-            doc = await run_in_executor(None, next, iterator, done)
+            doc = await run_in_executor(None, next, iterator, done)  # type: ignore[call-arg, arg-type]
             if doc is done:
                 break
-            yield doc
+            yield doc  # type: ignore[misc]
 
 
 class BaseBlobParser(ABC):

libs/community/langchain_community/document_loaders/cassandra.py

@@ -33,14 +33,14 @@ class CassandraLoader(BaseLoader):
         page_content_mapper: Callable[[Any], str] = str,
         metadata_mapper: Callable[[Any], dict] = lambda _: {},
         *,
-        query_parameters: Union[dict, Sequence] = None,
+        query_parameters: Union[dict, Sequence] = None,  # type: ignore[assignment]
-        query_timeout: Optional[float] = _NOT_SET,
+        query_timeout: Optional[float] = _NOT_SET,  # type: ignore[assignment]
         query_trace: bool = False,
-        query_custom_payload: dict = None,
+        query_custom_payload: dict = None,  # type: ignore[assignment]
         query_execution_profile: Any = _NOT_SET,
         query_paging_state: Any = None,
         query_host: Host = None,
-        query_execute_as: str = None,
+        query_execute_as: str = None,  # type: ignore[assignment]
     ) -> None:
         """
         Document Loader for Apache Cassandra.
@@ -85,7 +85,7 @@ class CassandraLoader(BaseLoader):
             self.query = f"SELECT * FROM {_keyspace}.{table};"
             self.metadata = {"table": table, "keyspace": _keyspace}
         else:
-            self.query = query
+            self.query = query  # type: ignore[assignment]
             self.metadata = {}
 
         self.session = session or check_resolve_session(session)

libs/community/langchain_community/document_loaders/chm.py

@@ -27,7 +27,7 @@ class UnstructuredCHMLoader(UnstructuredFileLoader):
     def _get_elements(self) -> List:
         from unstructured.partition.html import partition_html
 
-        with CHMParser(self.file_path) as f:
+        with CHMParser(self.file_path) as f:  # type: ignore[arg-type]
             return [
                 partition_html(text=item["content"], **self.unstructured_kwargs)
                 for item in f.load_all()
@@ -45,10 +45,10 @@ class CHMParser(object):
         self.file = chm.CHMFile()
         self.file.LoadCHM(path)
 
-    def __enter__(self):
+    def __enter__(self):  # type: ignore[no-untyped-def]
         return self
 
-    def __exit__(self, exc_type, exc_value, traceback):
+    def __exit__(self, exc_type, exc_value, traceback):  # type: ignore[no-untyped-def]
         if self.file:
             self.file.CloseCHM()
 

libs/community/langchain_community/document_loaders/doc_intelligence.py

@@ -89,4 +89,4 @@ class AzureAIDocumentIntelligenceLoader(BaseLoader):
             blob = Blob.from_path(self.file_path)
             yield from self.parser.parse(blob)
         else:
-            yield from self.parser.parse_url(self.url_path)
+            yield from self.parser.parse_url(self.url_path)  # type: ignore[arg-type]

libs/community/langchain_community/document_loaders/mediawikidump.py

@@ -60,7 +60,7 @@ class MWDumpLoader(BaseLoader):
         self.skip_redirects = skip_redirects
         self.stop_on_error = stop_on_error
 
-    def _load_dump_file(self):
+    def _load_dump_file(self):  # type: ignore[no-untyped-def]
         try:
             import mwxml
         except ImportError as e:
@@ -70,7 +70,7 @@ class MWDumpLoader(BaseLoader):
 
         return mwxml.Dump.from_file(open(self.file_path, encoding=self.encoding))
 
-    def _load_single_page_from_dump(self, page) -> Document:
+    def _load_single_page_from_dump(self, page) -> Document:  # type: ignore[no-untyped-def, return]
         """Parse a single page."""
         try:
             import mwparserfromhell

libs/community/langchain_community/document_loaders/parsers/vsdx.py

@@ -11,7 +11,7 @@ from langchain_community.document_loaders.blob_loaders import Blob
 
 
 class VsdxParser(BaseBlobParser, ABC):
-    def parse(self, blob: Blob) -> Iterator[Document]:
+    def parse(self, blob: Blob) -> Iterator[Document]:  # type: ignore[override]
         """Parse a vsdx file."""
         return self.lazy_parse(blob)
 
@@ -21,7 +21,7 @@ class VsdxParser(BaseBlobParser, ABC):
 
         with blob.as_bytes_io() as pdf_file_obj:
             with zipfile.ZipFile(pdf_file_obj, "r") as zfile:
-                pages = self.get_pages_content(zfile, blob.source)
+                pages = self.get_pages_content(zfile, blob.source)  # type: ignore[arg-type]
 
         yield from [
             Document(
@@ -60,13 +60,13 @@ class VsdxParser(BaseBlobParser, ABC):
 
         if "visio/pages/pages.xml" not in zfile.namelist():
             print("WARNING - No pages.xml file found in {}".format(source))
-            return
+            return  # type: ignore[return-value]
         if "visio/pages/_rels/pages.xml.rels" not in zfile.namelist():
             print("WARNING - No pages.xml.rels file found in {}".format(source))
-            return
+            return  # type: ignore[return-value]
         if "docProps/app.xml" not in zfile.namelist():
             print("WARNING - No app.xml file found in {}".format(source))
-            return
+            return  # type: ignore[return-value]
 
         pagesxml_content: dict = xmltodict.parse(zfile.read("visio/pages/pages.xml"))
         appxml_content: dict = xmltodict.parse(zfile.read("docProps/app.xml"))
@@ -79,7 +79,7 @@ class VsdxParser(BaseBlobParser, ABC):
                 rel["@Name"].strip() for rel in pagesxml_content["Pages"]["Page"]
             ]
         else:
-            disordered_names: List[str] = [
+            disordered_names: List[str] = [  # type: ignore[no-redef]
                 pagesxml_content["Pages"]["Page"]["@Name"].strip()
             ]
         if isinstance(pagesxmlrels_content["Relationships"]["Relationship"], list):
@@ -88,7 +88,7 @@ class VsdxParser(BaseBlobParser, ABC):
                 for rel in pagesxmlrels_content["Relationships"]["Relationship"]
             ]
         else:
-            disordered_paths: List[str] = [
+            disordered_paths: List[str] = [  # type: ignore[no-redef]
                 "visio/pages/"
                 + pagesxmlrels_content["Relationships"]["Relationship"]["@Target"]
             ]

libs/community/langchain_community/embeddings/baichuan.py

@@ -89,7 +89,7 @@ class BaichuanTextEmbeddings(BaseModel, Embeddings):
             print(f"Exception occurred while trying to get embeddings: {str(e)}")
             return None
 
-    def embed_documents(self, texts: List[str]) -> Optional[List[List[float]]]:
+    def embed_documents(self, texts: List[str]) -> Optional[List[List[float]]]:  # type: ignore[override]
         """Public method to get embeddings for a list of documents.
 
         Args:
@@ -100,7 +100,7 @@ class BaichuanTextEmbeddings(BaseModel, Embeddings):
         """
         return self._embed(texts)
 
-    def embed_query(self, text: str) -> Optional[List[float]]:
+    def embed_query(self, text: str) -> Optional[List[float]]:  # type: ignore[override]
         """Public method to get embedding for a single query text.
 
         Args:

libs/community/langchain_community/embeddings/edenai.py

@@ -56,7 +56,7 @@ class EdenAiEmbeddings(BaseModel, Embeddings):
         headers = {
             "accept": "application/json",
             "content-type": "application/json",
-            "authorization": f"Bearer {self.edenai_api_key.get_secret_value()}",
+            "authorization": f"Bearer {self.edenai_api_key.get_secret_value()}",  # type: ignore[union-attr]
             "User-Agent": self.get_user_agent(),
         }
 

libs/community/langchain_community/embeddings/embaas.py

@@ -85,7 +85,7 @@ class EmbaasEmbeddings(BaseModel, Embeddings):
     def _handle_request(self, payload: EmbaasEmbeddingsPayload) -> List[List[float]]:
         """Sends a request to the Embaas API and handles the response."""
         headers = {
-            "Authorization": f"Bearer {self.embaas_api_key.get_secret_value()}",
+            "Authorization": f"Bearer {self.embaas_api_key.get_secret_value()}",  # type: ignore[union-attr]
             "Content-Type": "application/json",
         }
 

libs/community/langchain_community/embeddings/gradient_ai.py

@@ -162,5 +162,5 @@ class TinyAsyncGradientEmbeddingClient:  #: :meta private:
     It might be entirely removed in the future.
     """
 
-    def __init__(self, *args, **kwargs) -> None:
+    def __init__(self, *args, **kwargs) -> None:  # type: ignore[no-untyped-def]
         raise ValueError("Deprecated,TinyAsyncGradientEmbeddingClient was removed.")

libs/community/langchain_community/embeddings/llm_rails.py

@@ -56,7 +56,7 @@ class LLMRailsEmbeddings(BaseModel, Embeddings):
         """
         response = requests.post(
             "https://api.llmrails.com/v1/embeddings",
-            headers={"X-API-KEY": self.api_key.get_secret_value()},
+            headers={"X-API-KEY": self.api_key.get_secret_value()},  # type: ignore[union-attr]
             json={"input": texts, "model": self.model},
             timeout=60,
         )

libs/community/langchain_community/embeddings/minimax.py

@@ -110,7 +110,7 @@ class MiniMaxEmbeddings(BaseModel, Embeddings):
 
         # HTTP headers for authorization
         headers = {
-            "Authorization": f"Bearer {self.minimax_api_key.get_secret_value()}",
+            "Authorization": f"Bearer {self.minimax_api_key.get_secret_value()}",  # type: ignore[union-attr]
             "Content-Type": "application/json",
         }
 

libs/community/langchain_community/embeddings/mlflow.py

@@ -71,7 +71,8 @@ class MlflowEmbeddings(Embeddings, BaseModel):
         embeddings: List[List[float]] = []
         for txt in _chunk(texts, 20):
             resp = self._client.predict(
-                endpoint=self.endpoint, inputs={"input": txt, **params}
+                endpoint=self.endpoint,
+                inputs={"input": txt, **params},  # type: ignore[arg-type]
             )
             embeddings.extend(r["embedding"] for r in resp["data"])
         return embeddings

libs/community/langchain_community/embeddings/oci_generative_ai.py

@@ -63,16 +63,16 @@ class OCIGenAIEmbeddings(BaseModel, Embeddings):
     If not specified , DEFAULT will be used 
     """
 
-    model_id: str = None
+    model_id: str = None  # type: ignore[assignment]
     """Id of the model to call, e.g., cohere.embed-english-light-v2.0"""
 
     model_kwargs: Optional[Dict] = None
     """Keyword arguments to pass to the model"""
 
-    service_endpoint: str = None
+    service_endpoint: str = None  # type: ignore[assignment]
     """service endpoint url"""
 
-    compartment_id: str = None
+    compartment_id: str = None  # type: ignore[assignment]
     """OCID of compartment"""
 
     truncate: Optional[str] = "END"
@@ -109,7 +109,7 @@ class OCIGenAIEmbeddings(BaseModel, Embeddings):
                 client_kwargs.pop("signer", None)
             elif values["auth_type"] == OCIAuthType(2).name:
 
-                def make_security_token_signer(oci_config):
+                def make_security_token_signer(oci_config):  # type: ignore[no-untyped-def]
                     pk = oci.signer.load_private_key_from_file(
                         oci_config.get("key_file"), None
                     )

libs/community/langchain_community/embeddings/spacy_embeddings.py

@@ -78,7 +78,7 @@ class SpacyEmbeddings(BaseModel, Embeddings):
         Returns:
             A list of embeddings, one for each document.
         """
-        return [self.nlp(text).vector.tolist() for text in texts]
+        return [self.nlp(text).vector.tolist() for text in texts]  # type: ignore[misc]
 
     def embed_query(self, text: str) -> List[float]:
         """
@@ -90,7 +90,7 @@ class SpacyEmbeddings(BaseModel, Embeddings):
         Returns:
             The embedding for the text.
         """
-        return self.nlp(text).vector.tolist()
+        return self.nlp(text).vector.tolist()  # type: ignore[misc]
 
     async def aembed_documents(self, texts: List[str]) -> List[List[float]]:
         """

libs/community/langchain_community/embeddings/yandex.py

@@ -42,10 +42,10 @@ class YandexGPTEmbeddings(BaseModel, Embeddings):
             embeddings = YandexGPTEmbeddings(iam_token="t1.9eu...", model_uri="emb://<folder-id>/text-search-query/latest")
     """
 
-    iam_token: SecretStr = ""
+    iam_token: SecretStr = ""  # type: ignore[assignment]
     """Yandex Cloud IAM token for service account
     with the `ai.languageModels.user` role"""
-    api_key: SecretStr = ""
+    api_key: SecretStr = ""  # type: ignore[assignment]
     """Yandex Cloud Api Key for service account
     with the `ai.languageModels.user` role"""
     model_uri: str = ""
@@ -146,7 +146,7 @@ def _embed_with_retry(llm: YandexGPTEmbeddings, **kwargs: Any) -> Any:
     return _completion_with_retry(**kwargs)
 
 
-def _make_request(self: YandexGPTEmbeddings, texts: List[str]):
+def _make_request(self: YandexGPTEmbeddings, texts: List[str]):  # type: ignore[no-untyped-def]
     try:
         import grpc
         from yandex.cloud.ai.foundation_models.v1.foundation_models_service_pb2 import (  # noqa: E501
@@ -167,7 +167,7 @@ def _make_request(self: YandexGPTEmbeddings, texts: List[str]):
     for text in texts:
         request = TextEmbeddingRequest(model_uri=self.model_uri, text=text)
         stub = EmbeddingsServiceStub(channel)
-        res = stub.TextEmbedding(request, metadata=self._grpc_metadata)
+        res = stub.TextEmbedding(request, metadata=self._grpc_metadata)  # type: ignore[attr-defined]
         result.append(list(res.embedding))
         time.sleep(self.sleep_interval)
 

libs/community/langchain_community/graphs/neo4j_graph.py

@@ -56,7 +56,7 @@ def value_sanitize(d: Dict[str, Any]) -> Dict[str, Any]:
                         cleaned_list.append(value_sanitize(item))
                     else:
                         cleaned_list.append(item)
-                new_dict[key] = cleaned_list
+                new_dict[key] = cleaned_list  # type: ignore[assignment]
         else:
             new_dict[key] = value
     return new_dict

libs/community/langchain_community/graphs/ontotext_graphdb_graph.py

@@ -95,12 +95,13 @@ class OntotextGraphDBGraph:
 
         if local_file:
             ontology_schema_graph = self._load_ontology_schema_from_file(
-                local_file, local_file_format
+                local_file,
+                local_file_format,  # type: ignore[arg-type]
             )
         else:
-            self._validate_user_query(query_ontology)
+            self._validate_user_query(query_ontology)  # type: ignore[arg-type]
             ontology_schema_graph = self._load_ontology_schema_with_query(
-                query_ontology
+                query_ontology  # type: ignore[arg-type]
             )
         self.schema = ontology_schema_graph.serialize(format="turtle")
 
@@ -139,7 +140,7 @@ class OntotextGraphDBGraph:
             )
 
     @staticmethod
-    def _load_ontology_schema_from_file(local_file: str, local_file_format: str = None):
+    def _load_ontology_schema_from_file(local_file: str, local_file_format: str = None):  # type: ignore[no-untyped-def, assignment]
         """
         Parse the ontology schema statements from the provided file
         """
@@ -176,7 +177,7 @@ class OntotextGraphDBGraph:
                 "Invalid query type. Only CONSTRUCT queries are supported."
             )
 
-    def _load_ontology_schema_with_query(self, query: str):
+    def _load_ontology_schema_with_query(self, query: str):  # type: ignore[no-untyped-def]
         """
         Execute the query for collecting the ontology schema statements
         """

libs/community/langchain_community/graphs/tigergraph_graph.py

@@ -31,7 +31,7 @@ class TigerGraph(GraphStore):
     def schema(self) -> Dict[str, Any]:
         return self._schema
 
-    def get_schema(self) -> str:
+    def get_schema(self) -> str:  # type: ignore[override]
         if self._schema:
             return str(self._schema)
         else:
@@ -71,10 +71,10 @@ class TigerGraph(GraphStore):
         """
         return self._conn.getSchema(force=True)
 
-    def refresh_schema(self):
+    def refresh_schema(self):  # type: ignore[no-untyped-def]
         self.generate_schema()
 
-    def query(self, query: str) -> Dict[str, Any]:
+    def query(self, query: str) -> Dict[str, Any]:  # type: ignore[override]
         """Query the TigerGraph database."""
         answer = self._conn.ai.query(query)
         return answer

libs/community/langchain_community/llms/azureml_endpoint.py

@@ -165,7 +165,7 @@ class GPT2ContentFormatter(ContentFormatterBase):
     def supported_api_types(self) -> List[AzureMLEndpointApiType]:
         return [AzureMLEndpointApiType.realtime]
 
-    def format_request_payload(
+    def format_request_payload(  # type: ignore[override]
         self, prompt: str, model_kwargs: Dict, api_type: AzureMLEndpointApiType
     ) -> bytes:
         prompt = ContentFormatterBase.escape_special_characters(prompt)
@@ -174,13 +174,13 @@ class GPT2ContentFormatter(ContentFormatterBase):
         )
         return str.encode(request_payload)
 
-    def format_response_payload(
+    def format_response_payload(  # type: ignore[override]
         self, output: bytes, api_type: AzureMLEndpointApiType
     ) -> Generation:
         try:
             choice = json.loads(output)[0]["0"]
         except (KeyError, IndexError, TypeError) as e:
-            raise ValueError(self.format_error_msg.format(api_type=api_type)) from e
+            raise ValueError(self.format_error_msg.format(api_type=api_type)) from e  # type: ignore[union-attr]
         return Generation(text=choice)
 
 
@@ -207,7 +207,7 @@ class HFContentFormatter(ContentFormatterBase):
     def supported_api_types(self) -> List[AzureMLEndpointApiType]:
         return [AzureMLEndpointApiType.realtime]
 
-    def format_request_payload(
+    def format_request_payload(  # type: ignore[override]
         self, prompt: str, model_kwargs: Dict, api_type: AzureMLEndpointApiType
     ) -> bytes:
         ContentFormatterBase.escape_special_characters(prompt)
@@ -216,13 +216,13 @@ class HFContentFormatter(ContentFormatterBase):
         )
         return str.encode(request_payload)
 
-    def format_response_payload(
+    def format_response_payload(  # type: ignore[override]
         self, output: bytes, api_type: AzureMLEndpointApiType
     ) -> Generation:
         try:
             choice = json.loads(output)[0]["0"]["generated_text"]
         except (KeyError, IndexError, TypeError) as e:
-            raise ValueError(self.format_error_msg.format(api_type=api_type)) from e
+            raise ValueError(self.format_error_msg.format(api_type=api_type)) from e  # type: ignore[union-attr]
         return Generation(text=choice)
 
 
@@ -233,7 +233,7 @@ class DollyContentFormatter(ContentFormatterBase):
     def supported_api_types(self) -> List[AzureMLEndpointApiType]:
         return [AzureMLEndpointApiType.realtime]
 
-    def format_request_payload(
+    def format_request_payload(  # type: ignore[override]
         self, prompt: str, model_kwargs: Dict, api_type: AzureMLEndpointApiType
     ) -> bytes:
         prompt = ContentFormatterBase.escape_special_characters(prompt)
@@ -245,13 +245,13 @@ class DollyContentFormatter(ContentFormatterBase):
         )
         return str.encode(request_payload)
 
-    def format_response_payload(
+    def format_response_payload(  # type: ignore[override]
         self, output: bytes, api_type: AzureMLEndpointApiType
     ) -> Generation:
         try:
             choice = json.loads(output)[0]
         except (KeyError, IndexError, TypeError) as e:
-            raise ValueError(self.format_error_msg.format(api_type=api_type)) from e
+            raise ValueError(self.format_error_msg.format(api_type=api_type)) from e  # type: ignore[union-attr]
         return Generation(text=choice)
 
 
@@ -262,7 +262,7 @@ class LlamaContentFormatter(ContentFormatterBase):
     def supported_api_types(self) -> List[AzureMLEndpointApiType]:
         return [AzureMLEndpointApiType.realtime, AzureMLEndpointApiType.serverless]
 
-    def format_request_payload(
+    def format_request_payload(  # type: ignore[override]
         self, prompt: str, model_kwargs: Dict, api_type: AzureMLEndpointApiType
     ) -> bytes:
         """Formats the request according to the chosen api"""
@@ -284,7 +284,7 @@ class LlamaContentFormatter(ContentFormatterBase):
             )
         return str.encode(request_payload)
 
-    def format_response_payload(
+    def format_response_payload(  # type: ignore[override]
         self, output: bytes, api_type: AzureMLEndpointApiType
     ) -> Generation:
         """Formats response"""
@@ -292,7 +292,7 @@ class LlamaContentFormatter(ContentFormatterBase):
             try:
                 choice = json.loads(output)[0]["0"]
             except (KeyError, IndexError, TypeError) as e:
-                raise ValueError(self.format_error_msg.format(api_type=api_type)) from e
+                raise ValueError(self.format_error_msg.format(api_type=api_type)) from e  # type: ignore[union-attr]
             return Generation(text=choice)
         if api_type == AzureMLEndpointApiType.serverless:
             try:
@@ -304,7 +304,7 @@ class LlamaContentFormatter(ContentFormatterBase):
                         "received."
                     )
             except (KeyError, IndexError, TypeError) as e:
-                raise ValueError(self.format_error_msg.format(api_type=api_type)) from e
+                raise ValueError(self.format_error_msg.format(api_type=api_type)) from e  # type: ignore[union-attr]
             return Generation(
                 text=choice["text"].strip(),
                 generation_info=dict(
@@ -397,7 +397,7 @@ class AzureMLBaseEndpoint(BaseModel):
     ) -> AzureMLEndpointApiType:
         """Validate that endpoint api type is compatible with the URL format."""
         endpoint_url = values.get("endpoint_url")
-        if field_value == AzureMLEndpointApiType.realtime and not endpoint_url.endswith(
+        if field_value == AzureMLEndpointApiType.realtime and not endpoint_url.endswith(  # type: ignore[union-attr]
             "/score"
         ):
             raise ValueError(
@@ -407,8 +407,8 @@ class AzureMLBaseEndpoint(BaseModel):
                 "`/v1/chat/completions`, use `endpoint_api_type='serverless'` instead."
             )
         if field_value == AzureMLEndpointApiType.serverless and not (
-            endpoint_url.endswith("/v1/completions")
+            endpoint_url.endswith("/v1/completions")  # type: ignore[union-attr]
-            or endpoint_url.endswith("/v1/chat/completions")
+            or endpoint_url.endswith("/v1/chat/completions")  # type: ignore[union-attr]
         ):
             raise ValueError(
                 "Endpoints of type `serverless` should follow the format "
@@ -426,7 +426,9 @@ class AzureMLBaseEndpoint(BaseModel):
         deployment_name = values.get("deployment_name")
 
         http_client = AzureMLEndpointClient(
-            endpoint_url, endpoint_key.get_secret_value(), deployment_name
+            endpoint_url,  # type: ignore
+            endpoint_key.get_secret_value(),  # type: ignore
+            deployment_name,  # type: ignore
         )
         return http_client
 

libs/community/langchain_community/llms/baichuan.py

@@ -56,11 +56,11 @@ class BaichuanLLM(LLM):
     def _post(self, request: Any) -> Any:
         headers = {
             "Content-Type": "application/json",
-            "Authorization": f"Bearer {self.baichuan_api_key.get_secret_value()}",
+            "Authorization": f"Bearer {self.baichuan_api_key.get_secret_value()}",  # type: ignore[union-attr]
         }
         try:
             response = requests.post(
-                self.baichuan_api_host,
+                self.baichuan_api_host,  # type: ignore[arg-type]
                 headers=headers,
                 json=request,
                 timeout=self.timeout,

libs/community/langchain_community/llms/bedrock.py

@@ -395,8 +395,8 @@ class BedrockBase(BaseModel, ABC):
         """
         return {
             "amazon-bedrock-guardrailDetails": {
-                "guardrailId": self.guardrails.get("id"),
+                "guardrailId": self.guardrails.get("id"),  # type: ignore[union-attr]
-                "guardrailVersion": self.guardrails.get("version"),
+                "guardrailVersion": self.guardrails.get("version"),  # type: ignore[union-attr]
             }
         }
 
@@ -427,7 +427,7 @@ class BedrockBase(BaseModel, ABC):
 
         if self._guardrails_enabled:
             request_options["guardrail"] = "ENABLED"
-            if self.guardrails.get("trace"):
+            if self.guardrails.get("trace"):  # type: ignore[union-attr]
                 request_options["trace"] = "ENABLED"
 
         try:
@@ -446,7 +446,7 @@ class BedrockBase(BaseModel, ABC):
         # Verify and raise a callback error if any intervention occurs or a signal is
         # sent from a Bedrock service,
         # such as when guardrails are triggered.
-        services_trace = self._get_bedrock_services_signal(body)
+        services_trace = self._get_bedrock_services_signal(body)  # type: ignore[arg-type]
 
         if services_trace.get("signal") and run_manager is not None:
             run_manager.on_llm_error(
@@ -468,7 +468,7 @@ class BedrockBase(BaseModel, ABC):
 
         if (
             self._guardrails_enabled
-            and self.guardrails.get("trace")
+            and self.guardrails.get("trace")  # type: ignore[union-attr]
             and self._is_guardrails_intervention(body)
         ):
             return {
@@ -526,7 +526,7 @@ class BedrockBase(BaseModel, ABC):
 
         if self._guardrails_enabled:
             request_options["guardrail"] = "ENABLED"
-            if self.guardrails.get("trace"):
+            if self.guardrails.get("trace"):  # type: ignore[union-attr]
                 request_options["trace"] = "ENABLED"
 
         try:
@@ -540,7 +540,7 @@ class BedrockBase(BaseModel, ABC):
         ):
             yield chunk
             # verify and raise callback error if any middleware intervened
-            self._get_bedrock_services_signal(chunk.generation_info)
+            self._get_bedrock_services_signal(chunk.generation_info)  # type: ignore[arg-type]
 
             if run_manager is not None:
                 run_manager.on_llm_new_token(chunk.text, chunk=chunk)
@@ -588,7 +588,7 @@ class BedrockBase(BaseModel, ABC):
             ):
                 await run_manager.on_llm_new_token(chunk.text, chunk=chunk)
             elif run_manager is not None:
-                run_manager.on_llm_new_token(chunk.text, chunk=chunk)
+                run_manager.on_llm_new_token(chunk.text, chunk=chunk)  # type: ignore[unused-coroutine]
 
 
 class Bedrock(LLM, BedrockBase):

libs/community/langchain_community/llms/oci_generative_ai.py

@@ -42,10 +42,10 @@ class OCIGenAIBase(BaseModel, ABC):
     If not specified , DEFAULT will be used 
     """
 
-    model_id: str = None
+    model_id: str = None  # type: ignore[assignment]
     """Id of the model to call, e.g., cohere.command"""
 
-    provider: str = None
+    provider: str = None  # type: ignore[assignment]
     """Provider name of the model. Default to None, 
     will try to be derived from the model_id
     otherwise, requires user input
@@ -54,10 +54,10 @@ class OCIGenAIBase(BaseModel, ABC):
     model_kwargs: Optional[Dict] = None
     """Keyword arguments to pass to the model"""
 
-    service_endpoint: str = None
+    service_endpoint: str = None  # type: ignore[assignment]
     """service endpoint url"""
 
-    compartment_id: str = None
+    compartment_id: str = None  # type: ignore[assignment]
     """OCID of compartment"""
 
     is_stream: bool = False
@@ -94,7 +94,7 @@ class OCIGenAIBase(BaseModel, ABC):
                 client_kwargs.pop("signer", None)
             elif values["auth_type"] == OCIAuthType(2).name:
 
-                def make_security_token_signer(oci_config):
+                def make_security_token_signer(oci_config):  # type: ignore[no-untyped-def]
                     pk = oci.signer.load_private_key_from_file(
                         oci_config.get("key_file"), None
                     )

libs/community/langchain_community/llms/ollama.py

@@ -297,7 +297,7 @@ class _OllamaCommon(BaseLanguageModel):
                             "Ollama call failed with status code 404."
                         )
                     else:
-                        optional_detail = await response.json().get("error")
+                        optional_detail = await response.json().get("error")  # type: ignore[attr-defined]
                         raise ValueError(
                             f"Ollama call failed with status code {response.status}."
                             f" Details: {optional_detail}"
@@ -380,7 +380,7 @@ class Ollama(BaseLLM, _OllamaCommon):
         """Return type of llm."""
         return "ollama-llm"
 
-    def _generate(
+    def _generate(  # type: ignore[override]
         self,
         prompts: List[str],
         stop: Optional[List[str]] = None,
@@ -416,7 +416,7 @@ class Ollama(BaseLLM, _OllamaCommon):
             generations.append([final_chunk])
         return LLMResult(generations=generations)
 
-    async def _agenerate(
+    async def _agenerate(  # type: ignore[override]
         self,
         prompts: List[str],
         stop: Optional[List[str]] = None,
@@ -445,7 +445,7 @@ class Ollama(BaseLLM, _OllamaCommon):
                 prompt,
                 stop=stop,
                 images=images,
-                run_manager=run_manager,
+                run_manager=run_manager,  # type: ignore[arg-type]
                 verbose=self.verbose,
                 **kwargs,
             )

libs/community/langchain_community/llms/pipelineai.py

@@ -102,7 +102,7 @@ class PipelineAI(LLM, BaseModel):
                 "Could not import pipeline-ai python package. "
                 "Please install it with `pip install pipeline-ai`."
             )
-        client = PipelineCloud(token=self.pipeline_api_key.get_secret_value())
+        client = PipelineCloud(token=self.pipeline_api_key.get_secret_value())  # type: ignore[union-attr]
         params = self.pipeline_kwargs or {}
         params = {**params, **kwargs}
 

libs/community/langchain_community/llms/stochasticai.py

@@ -107,7 +107,7 @@ class StochasticAI(LLM):
             url=self.api_url,
             json={"prompt": prompt, "params": params},
             headers={
-                "apiKey": f"{self.stochasticai_api_key.get_secret_value()}",
+                "apiKey": f"{self.stochasticai_api_key.get_secret_value()}",  # type: ignore[union-attr]
                 "Accept": "application/json",
                 "Content-Type": "application/json",
             },
@@ -119,7 +119,7 @@ class StochasticAI(LLM):
             response_get = requests.get(
                 url=response_post_json["data"]["responseUrl"],
                 headers={
-                    "apiKey": f"{self.stochasticai_api_key.get_secret_value()}",
+                    "apiKey": f"{self.stochasticai_api_key.get_secret_value()}",  # type: ignore[union-attr]
                     "Accept": "application/json",
                     "Content-Type": "application/json",
                 },

libs/community/langchain_community/llms/vertexai.py

@@ -49,7 +49,7 @@ def is_gemini_model(model_name: str) -> bool:
     return model_name is not None and "gemini" in model_name
 
 
-def completion_with_retry(
+def completion_with_retry(  # type: ignore[no-redef]
     llm: VertexAI,
     prompt: List[Union[str, "Image"]],
     stream: bool = False,
@@ -330,7 +330,7 @@ class VertexAI(_VertexAICommon, BaseLLM):
                     generation += chunk
                 generations.append([generation])
             else:
-                res = completion_with_retry(
+                res = completion_with_retry(  # type: ignore[misc]
                     self,
                     [prompt],
                     stream=should_stream,
@@ -373,7 +373,7 @@ class VertexAI(_VertexAICommon, BaseLLM):
         **kwargs: Any,
     ) -> Iterator[GenerationChunk]:
         params = self._prepare_params(stop=stop, stream=True, **kwargs)
-        for stream_resp in completion_with_retry(
+        for stream_resp in completion_with_retry(  # type: ignore[misc]
             self,
             [prompt],
             stream=True,

libs/community/langchain_community/llms/watsonxllm.py

@@ -250,9 +250,9 @@ class WatsonxLLM(BaseLLM):
         }
 
     def _get_chat_params(self, stop: Optional[List[str]] = None) -> Dict[str, Any]:
-        params: Dict[str, Any] = {**self.params} if self.params else None
+        params: Dict[str, Any] = {**self.params} if self.params else {}
         if stop is not None:
-            params = (params or {}) | {"stop_sequences": stop}
+            params["stop_sequences"] = stop
         return params
 
     def _create_llm_result(self, response: List[dict]) -> LLMResult:

libs/community/langchain_community/llms/yandex.py

@@ -25,10 +25,10 @@ logger = logging.getLogger(__name__)
 
 
 class _BaseYandexGPT(Serializable):
-    iam_token: SecretStr = ""
+    iam_token: SecretStr = ""  # type: ignore[assignment]
     """Yandex Cloud IAM token for service or user account
     with the `ai.languageModels.user` role"""
-    api_key: SecretStr = ""
+    api_key: SecretStr = ""  # type: ignore[assignment]
     """Yandex Cloud Api Key for service account
     with the `ai.languageModels.user` role"""
     folder_id: str = ""
@@ -211,7 +211,7 @@ def _make_request(
         messages=[Message(role="user", text=prompt)],
     )
     stub = TextGenerationServiceStub(channel)
-    res = stub.Completion(request, metadata=self._grpc_metadata)
+    res = stub.Completion(request, metadata=self._grpc_metadata)  # type: ignore[attr-defined]
     return list(res)[0].alternatives[0].message.text
 
 
@@ -253,7 +253,7 @@ async def _amake_request(self: YandexGPT, prompt: str) -> str:
             messages=[Message(role="user", text=prompt)],
         )
         stub = TextGenerationAsyncServiceStub(channel)
-        operation = await stub.Completion(request, metadata=self._grpc_metadata)
+        operation = await stub.Completion(request, metadata=self._grpc_metadata)  # type: ignore[attr-defined]
         async with grpc.aio.secure_channel(
             operation_api_url, channel_credentials
         ) as operation_channel:
@@ -262,7 +262,8 @@ async def _amake_request(self: YandexGPT, prompt: str) -> str:
                 await asyncio.sleep(1)
                 operation_request = GetOperationRequest(operation_id=operation.id)
                 operation = await operation_stub.Get(
-                    operation_request, metadata=self._grpc_metadata
+                    operation_request,
+                    metadata=self._grpc_metadata,  # type: ignore[attr-defined]
                 )
 
         completion_response = CompletionResponse()

libs/community/langchain_community/tools/amadeus/closest_airport.py

@@ -58,4 +58,4 @@ class AmadeusClosestAirport(AmadeusBaseTool):
             ' Location Identifier" '
         )
 
-        return self.llm.invoke(content)
+        return self.llm.invoke(content)  # type: ignore[union-attr]

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

@@ -93,10 +93,10 @@ class ShellTool(BaseTool):
                     return self.process.run(commands)
                 else:
                     logger.info("Invalid input. User aborted command execution.")
-                    return None
+                    return None  # type: ignore[return-value]
             else:
                 return self.process.run(commands)
 
         except Exception as e:
             logger.error(f"Error during command execution: {e}")
-            return None
+            return None  # type: ignore[return-value]

libs/community/langchain_community/utilities/brave_search.py

@@ -48,7 +48,7 @@ class BraveSearchWrapper(BaseModel):
         results = self._search_request(query)
         return [
             Document(
-                page_content=item.get("description"),
+                page_content=item.get("description"),  # type: ignore[arg-type]
                 metadata={"title": item.get("title"), "link": item.get("url")},
             )
             for item in results

libs/community/langchain_community/utilities/requests.py

@@ -141,9 +141,9 @@ class GenericRequestsWrapper(BaseModel):
         self, response: aiohttp.ClientResponse
     ) -> Union[str, Dict[str, Any]]:
         if self.response_content_type == "text":
-            return response.text()
+            return response.text()  # type: ignore[return-value]
         elif self.response_content_type == "json":
-            return response.json()
+            return response.json()  # type: ignore[return-value]
         else:
             raise ValueError(f"Invalid return type: {self.response_content_type}")
 
@@ -176,33 +176,33 @@ class GenericRequestsWrapper(BaseModel):
     async def aget(self, url: str, **kwargs: Any) -> Union[str, Dict[str, Any]]:
         """GET the URL and return the text asynchronously."""
         async with self.requests.aget(url, **kwargs) as response:
-            return await self._aget_resp_content(response)
+            return await self._aget_resp_content(response)  # type: ignore[misc]
 
     async def apost(
         self, url: str, data: Dict[str, Any], **kwargs: Any
     ) -> Union[str, Dict[str, Any]]:
         """POST to the URL and return the text asynchronously."""
         async with self.requests.apost(url, data, **kwargs) as response:
-            return await self._aget_resp_content(response)
+            return await self._aget_resp_content(response)  # type: ignore[misc]
 
     async def apatch(
         self, url: str, data: Dict[str, Any], **kwargs: Any
     ) -> Union[str, Dict[str, Any]]:
         """PATCH the URL and return the text asynchronously."""
         async with self.requests.apatch(url, data, **kwargs) as response:
-            return await self._aget_resp_content(response)
+            return await self._aget_resp_content(response)  # type: ignore[misc]
 
     async def aput(
         self, url: str, data: Dict[str, Any], **kwargs: Any
     ) -> Union[str, Dict[str, Any]]:
         """PUT the URL and return the text asynchronously."""
         async with self.requests.aput(url, data, **kwargs) as response:
-            return await self._aget_resp_content(response)
+            return await self._aget_resp_content(response)  # type: ignore[misc]
 
     async def adelete(self, url: str, **kwargs: Any) -> Union[str, Dict[str, Any]]:
         """DELETE the URL and return the text asynchronously."""
         async with self.requests.adelete(url, **kwargs) as response:
-            return await self._aget_resp_content(response)
+            return await self._aget_resp_content(response)  # type: ignore[misc]
 
 
 class JsonRequestsWrapper(GenericRequestsWrapper):

libs/community/langchain_community/utilities/sql_database.py

@@ -381,7 +381,7 @@ class SQLDatabase:
 
         If the statement returns no rows, an empty list is returned.
         """
-        with self._engine.begin() as connection:  # type: Connection
+        with self._engine.begin() as connection:  # type: Connection  # type: ignore[name-defined]
             if self._schema is not None:
                 if self.dialect == "snowflake":
                     connection.exec_driver_sql(
@@ -444,7 +444,7 @@ class SQLDatabase:
         ]
 
         if not include_columns:
-            res = [tuple(row.values()) for row in res]
+            res = [tuple(row.values()) for row in res]  # type: ignore[misc]
 
         if not res:
             return ""

libs/community/langchain_community/vectorstores/alibabacloud_opensearch.py

@@ -356,7 +356,7 @@ class AlibabaCloudOpenSearch(VectorStore):
                 "fields" not in item
                 or self.config.field_name_mapping["document"] not in item["fields"]
             ):
-                query_result_list.append(Document())
+                query_result_list.append(Document())  # type: ignore[call-arg]
             else:
                 fields = item["fields"]
                 query_result_list.append(

libs/community/langchain_community/vectorstores/astradb.py

@@ -140,7 +140,7 @@ class AstraDB(VectorStore):
                     if isinstance(v, list):
                         metadata_filter[k] = [AstraDB._filter_to_metadata(f) for f in v]
                     else:
-                        metadata_filter[k] = AstraDB._filter_to_metadata(v)
+                        metadata_filter[k] = AstraDB._filter_to_metadata(v)  # type: ignore[assignment]
                 else:
                     metadata_filter[f"metadata.{k}"] = v
 
@@ -253,13 +253,13 @@ class AstraDB(VectorStore):
             else:
                 self.clear()
 
-    def _ensure_astra_db_client(self):
+    def _ensure_astra_db_client(self):  # type: ignore[no-untyped-def]
         if not self.astra_db:
             raise ValueError("Missing AstraDB client")
 
     async def _setup_db(self, pre_delete_collection: bool) -> None:
         if pre_delete_collection:
-            await self.async_astra_db.delete_collection(
+            await self.async_astra_db.delete_collection(  # type: ignore[union-attr]
                 collection_name=self.collection_name,
             )
         await self._aprovision_collection()
@@ -282,7 +282,7 @@ class AstraDB(VectorStore):
         Internal-usage method, no object members are set,
         other than working on the underlying actual storage.
         """
-        self.astra_db.create_collection(
+        self.astra_db.create_collection(  # type: ignore[union-attr]
             dimension=self._get_embedding_dimension(),
             collection_name=self.collection_name,
             metric=self.metric,
@@ -295,7 +295,7 @@ class AstraDB(VectorStore):
         Internal-usage method, no object members are set,
         other than working on the underlying actual storage.
         """
-        await self.async_astra_db.create_collection(
+        await self.async_astra_db.create_collection(  # type: ignore[union-attr]
             dimension=self._get_embedding_dimension(),
             collection_name=self.collection_name,
             metric=self.metric,
@@ -328,7 +328,7 @@ class AstraDB(VectorStore):
         await self._ensure_db_setup()
         if not self.async_astra_db:
             await run_in_executor(None, self.clear)
-        await self.async_collection.delete_many({})
+        await self.async_collection.delete_many({})  # type: ignore[union-attr]
 
     def delete_by_document_id(self, document_id: str) -> bool:
         """
@@ -336,7 +336,7 @@ class AstraDB(VectorStore):
         Return True if a document has indeed been deleted, False if ID not found.
         """
         self._ensure_astra_db_client()
-        deletion_response = self.collection.delete_one(document_id)
+        deletion_response = self.collection.delete_one(document_id)  # type: ignore[union-attr]
         return ((deletion_response or {}).get("status") or {}).get(
             "deletedCount", 0
         ) == 1
@@ -434,7 +434,7 @@ class AstraDB(VectorStore):
         Use with caution.
         """
         self._ensure_astra_db_client()
-        self.astra_db.delete_collection(
+        self.astra_db.delete_collection(  # type: ignore[union-attr]
             collection_name=self.collection_name,
         )
 
@@ -448,7 +448,7 @@ class AstraDB(VectorStore):
         await self._ensure_db_setup()
         if not self.async_astra_db:
             await run_in_executor(None, self.delete_collection)
-        await self.async_astra_db.delete_collection(
+        await self.async_astra_db.delete_collection(  # type: ignore[union-attr]
             collection_name=self.collection_name,
         )
 
@@ -571,7 +571,7 @@ class AstraDB(VectorStore):
         )
 
         def _handle_batch(document_batch: List[DocDict]) -> List[str]:
-            im_result = self.collection.insert_many(
+            im_result = self.collection.insert_many(  # type: ignore[union-attr]
                 documents=document_batch,
                 options={"ordered": False},
                 partial_failures_allowed=True,
@@ -581,7 +581,7 @@ class AstraDB(VectorStore):
             )
 
             def _handle_missing_document(missing_document: DocDict) -> str:
-                replacement_result = self.collection.find_one_and_replace(
+                replacement_result = self.collection.find_one_and_replace(  # type: ignore[union-attr]
                     filter={"_id": missing_document["_id"]},
                     replacement=missing_document,
                 )
@@ -672,7 +672,7 @@ class AstraDB(VectorStore):
         )
 
         async def _handle_batch(document_batch: List[DocDict]) -> List[str]:
-            im_result = await self.async_collection.insert_many(
+            im_result = await self.async_collection.insert_many(  # type: ignore[union-attr]
                 documents=document_batch,
                 options={"ordered": False},
                 partial_failures_allowed=True,
@@ -682,7 +682,7 @@ class AstraDB(VectorStore):
             )
 
             async def _handle_missing_document(missing_document: DocDict) -> str:
-                replacement_result = await self.async_collection.find_one_and_replace(
+                replacement_result = await self.async_collection.find_one_and_replace(  # type: ignore[union-attr]
                     filter={"_id": missing_document["_id"]},
                     replacement=missing_document,
                 )
@@ -729,7 +729,7 @@ class AstraDB(VectorStore):
         metadata_parameter = self._filter_to_metadata(filter)
         #
         hits = list(
-            self.collection.paginated_find(
+            self.collection.paginated_find(  # type: ignore[union-attr]
                 filter=metadata_parameter,
                 sort={"$vector": embedding},
                 options={"limit": k, "includeSimilarity": True},
@@ -771,7 +771,7 @@ class AstraDB(VectorStore):
         if not self.async_collection:
             return await run_in_executor(
                 None,
-                self.asimilarity_search_with_score_id_by_vector,
+                self.asimilarity_search_with_score_id_by_vector,  # type: ignore[arg-type]
                 embedding,
                 k,
                 filter,
@@ -962,7 +962,7 @@ class AstraDB(VectorStore):
         )
 
     @staticmethod
-    def _get_mmr_hits(embedding, k, lambda_mult, prefetch_hits):
+    def _get_mmr_hits(embedding, k, lambda_mult, prefetch_hits):  # type: ignore[no-untyped-def]
         mmr_chosen_indices = maximal_marginal_relevance(
             np.array(embedding, dtype=np.float32),
             [prefetch_hit["$vector"] for prefetch_hit in prefetch_hits],
@@ -1008,7 +1008,7 @@ class AstraDB(VectorStore):
         metadata_parameter = self._filter_to_metadata(filter)
 
         prefetch_hits = list(
-            self.collection.paginated_find(
+            self.collection.paginated_find(  # type: ignore[union-attr]
                 filter=metadata_parameter,
                 sort={"$vector": embedding},
                 options={"limit": fetch_k, "includeSimilarity": True},
@@ -1228,7 +1228,7 @@ class AstraDB(VectorStore):
             batch_concurrency=kwargs.get("batch_concurrency"),
             overwrite_concurrency=kwargs.get("overwrite_concurrency"),
         )
-        return astra_db_store
+        return astra_db_store  # type: ignore[return-value]
 
     @classmethod
     async def afrom_texts(
@@ -1263,7 +1263,7 @@ class AstraDB(VectorStore):
             batch_concurrency=kwargs.get("batch_concurrency"),
             overwrite_concurrency=kwargs.get("overwrite_concurrency"),
         )
-        return astra_db_store
+        return astra_db_store  # type: ignore[return-value]
 
     @classmethod
     def from_documents(

libs/community/langchain_community/vectorstores/azuresearch.py

@@ -339,7 +339,7 @@ class AzureSearch(VectorStore):
         # batching support if embedding function is an Embeddings object
         if isinstance(self.embedding_function, Embeddings):
             try:
-                embeddings = self.embedding_function.embed_documents(texts)
+                embeddings = self.embedding_function.embed_documents(texts)  # type: ignore[arg-type]
             except NotImplementedError:
                 embeddings = [self.embedding_function.embed_query(x) for x in texts]
         else:

libs/community/langchain_community/vectorstores/bigquery_vector_search.py

@@ -222,7 +222,7 @@ class BigQueryVectorSearch(VectorStore):
                 self._logger.debug("Vector index already exists.")
                 self._have_index = True
 
-    def _create_index_in_background(self):
+    def _create_index_in_background(self):  # type: ignore[no-untyped-def]
         if self._have_index or self._creating_index:
             # Already have an index or in the process of creating one.
             return
@@ -231,7 +231,7 @@ class BigQueryVectorSearch(VectorStore):
         thread = Thread(target=self._create_index, daemon=True)
         thread.start()
 
-    def _create_index(self):
+    def _create_index(self):  # type: ignore[no-untyped-def]
         from google.api_core.exceptions import ClientError
 
         table = self.bq_client.get_table(self.vectors_table)
@@ -289,7 +289,7 @@ class BigQueryVectorSearch(VectorStore):
     def full_table_id(self) -> str:
         return self._full_table_id
 
-    def add_texts(
+    def add_texts(  # type: ignore[override]
         self,
         texts: List[str],
         metadatas: Optional[List[dict]] = None,

libs/community/langchain_community/vectorstores/deeplake.py

@@ -905,7 +905,7 @@ class DeepLake(VectorStore):
         return self.vectorstore.dataset
 
     @classmethod
-    def _validate_kwargs(cls, kwargs, method_name):
+    def _validate_kwargs(cls, kwargs, method_name):  # type: ignore[no-untyped-def]
         if kwargs:
             valid_items = cls._get_valid_args(method_name)
             unsupported_items = cls._get_unsupported_items(kwargs, valid_items)
@@ -917,14 +917,14 @@ class DeepLake(VectorStore):
                 )
 
     @classmethod
-    def _get_valid_args(cls, method_name):
+    def _get_valid_args(cls, method_name):  # type: ignore[no-untyped-def]
         if method_name == "search":
             return cls._valid_search_kwargs
         else:
             return []
 
     @staticmethod
-    def _get_unsupported_items(kwargs, valid_items):
+    def _get_unsupported_items(kwargs, valid_items):  # type: ignore[no-untyped-def]
         kwargs = {k: v for k, v in kwargs.items() if k not in valid_items}
         unsupported_items = None
         if kwargs:

libs/community/langchain_community/vectorstores/faiss.py

@@ -305,7 +305,7 @@ class FAISS(VectorStore):
         if filter is not None:
             if isinstance(filter, dict):
 
-                def filter_func(metadata):
+                def filter_func(metadata):  # type: ignore[no-untyped-def]
                     if all(
                         metadata.get(key) in value
                         if isinstance(value, list)
@@ -607,7 +607,7 @@ class FAISS(VectorStore):
             filtered_indices = []
             if isinstance(filter, dict):
 
-                def filter_func(metadata):
+                def filter_func(metadata):  # type: ignore[no-untyped-def]
                     if all(
                         metadata.get(key) in value
                         if isinstance(value, list)

libs/community/langchain_community/vectorstores/hanavector.py

@@ -117,7 +117,7 @@ class HanaDB(VectorStore):
             self.vector_column_length,
         )
 
-    def _table_exists(self, table_name) -> bool:
+    def _table_exists(self, table_name) -> bool:  # type: ignore[no-untyped-def]
         sql_str = (
             "SELECT COUNT(*) FROM SYS.TABLES WHERE SCHEMA_NAME = CURRENT_SCHEMA"
             " AND TABLE_NAME = ?"
@@ -133,7 +133,7 @@ class HanaDB(VectorStore):
             cur.close()
         return False
 
-    def _check_column(self, table_name, column_name, column_type, column_length=None):
+    def _check_column(self, table_name, column_name, column_type, column_length=None):  # type: ignore[no-untyped-def]
         sql_str = (
             "SELECT DATA_TYPE_NAME, LENGTH FROM SYS.TABLE_COLUMNS WHERE "
             "SCHEMA_NAME = CURRENT_SCHEMA "
@@ -166,17 +166,17 @@ class HanaDB(VectorStore):
     def embeddings(self) -> Embeddings:
         return self.embedding
 
-    def _sanitize_name(input_str: str) -> str:
+    def _sanitize_name(input_str: str) -> str:  # type: ignore[misc]
         # Remove characters that are not alphanumeric or underscores
         return re.sub(r"[^a-zA-Z0-9_]", "", input_str)
 
-    def _sanitize_int(input_int: any) -> int:
+    def _sanitize_int(input_int: any) -> int:  # type: ignore[valid-type]
         value = int(str(input_int))
         if value < -1:
             raise ValueError(f"Value ({value}) must not be smaller than -1")
         return int(str(input_int))
 
-    def _sanitize_list_float(embedding: List[float]) -> List[float]:
+    def _sanitize_list_float(embedding: List[float]) -> List[float]:  # type: ignore[misc]
         for value in embedding:
             if not isinstance(value, float):
                 raise ValueError(f"Value ({value}) does not have type float")
@@ -185,14 +185,14 @@ class HanaDB(VectorStore):
     # Compile pattern only once, for better performance
     _compiled_pattern = re.compile("^[_a-zA-Z][_a-zA-Z0-9]*$")
 
-    def _sanitize_metadata_keys(metadata: dict) -> dict:
+    def _sanitize_metadata_keys(metadata: dict) -> dict:  # type: ignore[misc]
         for key in metadata.keys():
             if not HanaDB._compiled_pattern.match(key):
                 raise ValueError(f"Invalid metadata key {key}")
 
         return metadata
 
-    def add_texts(
+    def add_texts(  # type: ignore[override]
         self,
         texts: Iterable[str],
         metadatas: Optional[List[dict]] = None,
@@ -243,7 +243,7 @@ class HanaDB(VectorStore):
         return []
 
     @classmethod
-    def from_texts(
+    def from_texts(  # type: ignore[no-untyped-def, override]
         cls: Type[HanaDB],
         texts: List[str],
         embedding: Embeddings,
@@ -277,7 +277,7 @@ class HanaDB(VectorStore):
         instance.add_texts(texts, metadatas)
         return instance
 
-    def similarity_search(
+    def similarity_search(  # type: ignore[override]
         self, query: str, k: int = 4, filter: Optional[dict] = None
     ) -> List[Document]:
         """Return docs most similar to query.
@@ -382,7 +382,7 @@ class HanaDB(VectorStore):
         )
         return [(result_item[0], result_item[1]) for result_item in whole_result]
 
-    def similarity_search_by_vector(
+    def similarity_search_by_vector(  # type: ignore[override]
         self, embedding: List[float], k: int = 4, filter: Optional[dict] = None
     ) -> List[Document]:
         """Return docs most similar to embedding vector.
@@ -401,7 +401,7 @@ class HanaDB(VectorStore):
         )
         return [doc for doc, _ in docs_and_scores]
 
-    def _create_where_by_filter(self, filter):
+    def _create_where_by_filter(self, filter):  # type: ignore[no-untyped-def]
         query_tuple = []
         where_str = ""
         if filter:
@@ -427,7 +427,7 @@ class HanaDB(VectorStore):
 
         return where_str, query_tuple
 
-    def delete(
+    def delete(  # type: ignore[override]
         self, ids: Optional[List[str]] = None, filter: Optional[dict] = None
     ) -> Optional[bool]:
         """Delete entries by filter with metadata values
@@ -459,7 +459,7 @@ class HanaDB(VectorStore):
 
         return True
 
-    async def adelete(
+    async def adelete(  # type: ignore[override]
         self, ids: Optional[List[str]] = None, filter: Optional[dict] = None
     ) -> Optional[bool]:
         """Delete by vector ID or other criteria.
@@ -473,7 +473,7 @@ class HanaDB(VectorStore):
         """
         return await run_in_executor(None, self.delete, ids=ids, filter=filter)
 
-    def max_marginal_relevance_search(
+    def max_marginal_relevance_search(  # type: ignore[override]
         self,
         query: str,
         k: int = 4,
@@ -511,11 +511,11 @@ class HanaDB(VectorStore):
             filter=filter,
         )
 
-    def _parse_float_array_from_string(array_as_string: str) -> List[float]:
+    def _parse_float_array_from_string(array_as_string: str) -> List[float]:  # type: ignore[misc]
         array_wo_brackets = array_as_string[1:-1]
         return [float(x) for x in array_wo_brackets.split(",")]
 
-    def max_marginal_relevance_search_by_vector(
+    def max_marginal_relevance_search_by_vector(  # type: ignore[override]
         self,
         embedding: List[float],
         k: int = 4,
@@ -533,7 +533,7 @@ class HanaDB(VectorStore):
 
         return [whole_result[i][0] for i in mmr_doc_indexes]
 
-    async def amax_marginal_relevance_search_by_vector(
+    async def amax_marginal_relevance_search_by_vector(  # type: ignore[override]
         self,
         embedding: List[float],
         k: int = 4,

libs/community/langchain_community/vectorstores/jaguar.py

@@ -135,7 +135,7 @@ class Jaguar(VectorStore):
     def embeddings(self) -> Optional[Embeddings]:
         return self._embedding
 
-    def add_texts(
+    def add_texts(  # type: ignore[override]
         self,
         texts: List[str],
         metadatas: Optional[List[dict]] = None,
@@ -351,7 +351,7 @@ class Jaguar(VectorStore):
         return False
 
     @classmethod
-    def from_texts(
+    def from_texts(  # type: ignore[override]
         cls,
         texts: List[str],
         embedding: Embeddings,
@@ -383,7 +383,7 @@ class Jaguar(VectorStore):
         q = "truncate store " + podstore
         self.run(q)
 
-    def delete(self, zids: List[str], **kwargs: Any) -> None:
+    def delete(self, zids: List[str], **kwargs: Any) -> None:  # type: ignore[override]
         """
         Delete records in jaguardb by a list of zero-ids
         Args:

libs/community/langchain_community/vectorstores/milvus.py

@@ -554,10 +554,10 @@ class Milvus(VectorStore):
         }
 
         if not self.auto_id:
-            insert_dict[self._primary_field] = ids
+            insert_dict[self._primary_field] = ids  # type: ignore[assignment]
 
         if self._metadata_field is not None:
-            for d in metadatas:
+            for d in metadatas:  # type: ignore[union-attr]
                 insert_dict.setdefault(self._metadata_field, []).append(d)
         else:
             # Collect the metadata into the insert dict.
@@ -901,7 +901,7 @@ class Milvus(VectorStore):
                 ret.append(documents[x])
         return ret
 
-    def delete(
+    def delete(  # type: ignore[no-untyped-def]
         self, ids: Optional[List[str]] = None, expr: Optional[str] = None, **kwargs: str
     ):
         """Delete by vector ID or boolean expression.
@@ -923,7 +923,7 @@ class Milvus(VectorStore):
             assert isinstance(
                 expr, str
             ), "Either ids list or expr string must be provided."
-        return self.col.delete(expr=expr, **kwargs)
+        return self.col.delete(expr=expr, **kwargs)  # type: ignore[union-attr]
 
     @classmethod
     def from_texts(

libs/community/langchain_community/vectorstores/pgembedding.py

@@ -398,7 +398,7 @@ class PGEmbedding(VectorStore):
         docs = [
             (
                 Document(
-                    page_content=result.EmbeddingStore.document,
+                    page_content=result.EmbeddingStore.document,  # type: ignore[arg-type]
                     metadata=result.EmbeddingStore.cmetadata,
                 ),
                 result.distance if self.embedding_function is not None else 0.0,

libs/community/langchain_community/vectorstores/pgvecto_rs.py

@@ -133,7 +133,7 @@ class PGVecto_rs(VectorStore):
             Record.from_text(text, embedding, meta)
             for text, embedding, meta in zip(texts, embeddings, metadatas or [])
         ]
-        self._store.insert(records)
+        self._store.insert(records)  # type: ignore[union-attr]
         return [str(record.id) for record in records]
 
     def add_documents(self, documents: List[Document], **kwargs: Any) -> List[str]:
@@ -177,7 +177,7 @@ class PGVecto_rs(VectorStore):
             real_filter = meta_contains(filter)
         else:
             real_filter = filter
-        results = self._store.search(
+        results = self._store.search(  # type: ignore[union-attr]
             query_vector,
             distance_func_map[distance_func],
             k,

libs/community/langchain_community/vectorstores/pgvector.py

@@ -238,7 +238,7 @@ class PGVector(VectorStore):
 
     def create_vector_extension(self) -> None:
         try:
-            with Session(self._bind) as session:
+            with Session(self._bind) as session:  # type: ignore[arg-type]
                 # The advisor lock fixes issue arising from concurrent
                 # creation of the vector extension.
                 # https://github.com/langchain-ai/langchain/issues/12933
@@ -256,24 +256,24 @@ class PGVector(VectorStore):
             raise Exception(f"Failed to create vector extension: {e}") from e
 
     def create_tables_if_not_exists(self) -> None:
-        with Session(self._bind) as session, session.begin():
+        with Session(self._bind) as session, session.begin():  # type: ignore[arg-type]
             Base.metadata.create_all(session.get_bind())
 
     def drop_tables(self) -> None:
-        with Session(self._bind) as session, session.begin():
+        with Session(self._bind) as session, session.begin():  # type: ignore[arg-type]
             Base.metadata.drop_all(session.get_bind())
 
     def create_collection(self) -> None:
         if self.pre_delete_collection:
             self.delete_collection()
-        with Session(self._bind) as session:
+        with Session(self._bind) as session:  # type: ignore[arg-type]
             self.CollectionStore.get_or_create(
                 session, self.collection_name, cmetadata=self.collection_metadata
             )
 
     def delete_collection(self) -> None:
         self.logger.debug("Trying to delete collection")
-        with Session(self._bind) as session:
+        with Session(self._bind) as session:  # type: ignore[arg-type]
             collection = self.get_collection(session)
             if not collection:
                 self.logger.warning("Collection not found")
@@ -284,7 +284,7 @@ class PGVector(VectorStore):
     @contextlib.contextmanager
     def _make_session(self) -> Generator[Session, None, None]:
         """Create a context manager for the session, bind to _conn string."""
-        yield Session(self._bind)
+        yield Session(self._bind)  # type: ignore[arg-type]
 
     def delete(
         self,
@@ -298,7 +298,7 @@ class PGVector(VectorStore):
             ids: List of ids to delete.
             collection_only: Only delete ids in the collection.
         """
-        with Session(self._bind) as session:
+        with Session(self._bind) as session:  # type: ignore[arg-type]
             if ids is not None:
                 self.logger.debug(
                     "Trying to delete vectors by ids (represented by the model "
@@ -383,7 +383,7 @@ class PGVector(VectorStore):
         if not metadatas:
             metadatas = [{} for _ in texts]
 
-        with Session(self._bind) as session:
+        with Session(self._bind) as session:  # type: ignore[arg-type]
             collection = self.get_collection(session)
             if not collection:
                 raise ValueError("Collection not found")
@@ -508,7 +508,7 @@ class PGVector(VectorStore):
         ]
         return docs
 
-    def _create_filter_clause(self, key, value):
+    def _create_filter_clause(self, key, value):  # type: ignore[no-untyped-def]
         IN, NIN, BETWEEN, GT, LT, NE = "in", "nin", "between", "gt", "lt", "ne"
         EQ, LIKE, CONTAINS, OR, AND = "eq", "like", "contains", "or", "and"
 
@@ -575,7 +575,7 @@ class PGVector(VectorStore):
         filter: Optional[Dict[str, str]] = None,
     ) -> List[Any]:
         """Query the collection."""
-        with Session(self._bind) as session:
+        with Session(self._bind) as session:  # type: ignore[arg-type]
             collection = self.get_collection(session)
             if not collection:
                 raise ValueError("Collection not found")

libs/community/langchain_community/vectorstores/surrealdb.py

@@ -115,7 +115,7 @@ class SurrealDBStore(VectorStore):
         for idx, text in enumerate(texts):
             data = {"text": text, "embedding": embeddings[idx]}
             if metadatas is not None and idx < len(metadatas):
-                data["metadata"] = metadatas[idx]
+                data["metadata"] = metadatas[idx]  # type: ignore[assignment]
             record = await self.sdb.create(
                 self.collection,
                 data,

libs/community/langchain_community/vectorstores/tencentvectordb.py

@@ -316,7 +316,7 @@ class TencentVectorDB(VectorStore):
             meta = result.get(self.field_metadata)
             if meta is not None:
                 meta = json.loads(meta)
-            doc = Document(page_content=result.get(self.field_text), metadata=meta)
+            doc = Document(page_content=result.get(self.field_text), metadata=meta)  # type: ignore[arg-type]
             pair = (doc, result.get("score", 0.0))
             ret.append(pair)
         return ret
@@ -374,7 +374,7 @@ class TencentVectorDB(VectorStore):
             meta = result.get(self.field_metadata)
             if meta is not None:
                 meta = json.loads(meta)
-            doc = Document(page_content=result.get(self.field_text), metadata=meta)
+            doc = Document(page_content=result.get(self.field_text), metadata=meta)  # type: ignore[arg-type]
             documents.append(doc)
             ordered_result_embeddings.append(result.get(self.field_vector))
         # Get the new order of results.

libs/community/langchain_community/vectorstores/thirdai_neuraldb.py

@@ -24,7 +24,7 @@ class NeuralDBVectorStore(VectorStore):
         underscore_attrs_are_private = True
 
     @staticmethod
-    def _verify_thirdai_library(thirdai_key: Optional[str] = None):
+    def _verify_thirdai_library(thirdai_key: Optional[str] = None):  # type: ignore[no-untyped-def]
         try:
             from thirdai import licensing
 
@@ -38,7 +38,7 @@ class NeuralDBVectorStore(VectorStore):
             )
 
     @classmethod
-    def from_scratch(
+    def from_scratch(  # type: ignore[no-untyped-def, no-untyped-def]
         cls,
         thirdai_key: Optional[str] = None,
         **model_kwargs,
@@ -69,10 +69,10 @@ class NeuralDBVectorStore(VectorStore):
         NeuralDBVectorStore._verify_thirdai_library(thirdai_key)
         from thirdai import neural_db as ndb
 
-        return cls(db=ndb.NeuralDB(**model_kwargs))
+        return cls(db=ndb.NeuralDB(**model_kwargs))  # type: ignore[call-arg]
 
     @classmethod
-    def from_bazaar(
+    def from_bazaar(  # type: ignore[no-untyped-def]
         cls,
         base: str,
         bazaar_cache: Optional[str] = None,
@@ -111,10 +111,10 @@ class NeuralDBVectorStore(VectorStore):
             os.mkdir(cache)
         model_bazaar = ndb.Bazaar(cache)
         model_bazaar.fetch()
-        return cls(db=model_bazaar.get_model(base))
+        return cls(db=model_bazaar.get_model(base))  # type: ignore[call-arg]
 
     @classmethod
-    def from_checkpoint(
+    def from_checkpoint(  # type: ignore[no-untyped-def]
         cls,
         checkpoint: Union[str, Path],
         thirdai_key: Optional[str] = None,
@@ -146,7 +146,7 @@ class NeuralDBVectorStore(VectorStore):
         NeuralDBVectorStore._verify_thirdai_library(thirdai_key)
         from thirdai import neural_db as ndb
 
-        return cls(db=ndb.NeuralDB.from_checkpoint(checkpoint))
+        return cls(db=ndb.NeuralDB.from_checkpoint(checkpoint))  # type: ignore[call-arg]
 
     @classmethod
     def from_texts(
@@ -187,11 +187,11 @@ class NeuralDBVectorStore(VectorStore):
         df = pd.DataFrame({"texts": texts})
         if metadatas:
             df = pd.concat([df, pd.DataFrame.from_records(metadatas)], axis=1)
-        temp = tempfile.NamedTemporaryFile("w", delete=False, delete_on_close=False)
+        temp = tempfile.NamedTemporaryFile("w", delete=False, delete_on_close=False)  # type: ignore[call-overload]
         df.to_csv(temp)
         source_id = self.insert([ndb.CSV(temp.name)], **kwargs)[0]
         offset = self.db._savable_state.documents.get_source_by_id(source_id)[1]
-        return [str(offset + i) for i in range(len(texts))]
+        return [str(offset + i) for i in range(len(texts))]  # type: ignore[arg-type]
 
     @root_validator()
     def validate_environments(cls, values: Dict) -> Dict:
@@ -205,7 +205,7 @@ class NeuralDBVectorStore(VectorStore):
         )
         return values
 
-    def insert(
+    def insert(  # type: ignore[no-untyped-def, no-untyped-def]
         self,
         sources: List[Any],
         train: bool = True,
@@ -229,7 +229,7 @@ class NeuralDBVectorStore(VectorStore):
             **kwargs,
         )
 
-    def _preprocess_sources(self, sources):
+    def _preprocess_sources(self, sources):  # type: ignore[no-untyped-def]
         """Checks if the provided sources are string paths. If they are, convert
         to NeuralDB document objects.
 
@@ -261,7 +261,7 @@ class NeuralDBVectorStore(VectorStore):
                     )
         return preprocessed_sources
 
-    def upvote(self, query: str, document_id: Union[int, str]):
+    def upvote(self, query: str, document_id: Union[int, str]):  # type: ignore[no-untyped-def]
         """The vectorstore upweights the score of a document for a specific query.
         This is useful for fine-tuning the vectorstore to user behavior.
 
@@ -271,7 +271,7 @@ class NeuralDBVectorStore(VectorStore):
         """
         self.db.text_to_result(query, int(document_id))
 
-    def upvote_batch(self, query_id_pairs: List[Tuple[str, int]]):
+    def upvote_batch(self, query_id_pairs: List[Tuple[str, int]]):  # type: ignore[no-untyped-def]
         """Given a batch of (query, document id) pairs, the vectorstore upweights
         the scores of the document for the corresponding queries.
         This is useful for fine-tuning the vectorstore to user behavior.
@@ -284,7 +284,7 @@ class NeuralDBVectorStore(VectorStore):
             [(query, int(doc_id)) for query, doc_id in query_id_pairs]
         )
 
-    def associate(self, source: str, target: str):
+    def associate(self, source: str, target: str):  # type: ignore[no-untyped-def]
         """The vectorstore associates a source phrase with a target phrase.
         When the vectorstore sees the source phrase, it will also consider results
         that are relevant to the target phrase.
@@ -295,7 +295,7 @@ class NeuralDBVectorStore(VectorStore):
         """
         self.db.associate(source, target)
 
-    def associate_batch(self, text_pairs: List[Tuple[str, str]]):
+    def associate_batch(self, text_pairs: List[Tuple[str, str]]):  # type: ignore[no-untyped-def]
         """Given a batch of (source, target) pairs, the vectorstore associates
         each source phrase with the corresponding target phrase.
 
@@ -334,7 +334,7 @@ class NeuralDBVectorStore(VectorStore):
         except Exception as e:
             raise ValueError(f"Error while retrieving documents: {e}") from e
 
-    def save(self, path: str):
+    def save(self, path: str):  # type: ignore[no-untyped-def]
         """Saves a NeuralDB instance to disk. Can be loaded into memory by
         calling NeuralDB.from_checkpoint(path)
 

libs/community/langchain_community/vectorstores/vectara.py

@@ -384,7 +384,7 @@ class Vectara(VectorStore):
                 f"(code {response.status_code}, reason {response.reason}, details "
                 f"{response.text})",
             )
-            return [], ""
+            return [], ""  # type: ignore[return-value]
 
         result = response.json()
 
@@ -454,7 +454,7 @@ class Vectara(VectorStore):
         docs = self.vectara_query(query, config)
         return docs
 
-    def similarity_search(
+    def similarity_search(  # type: ignore[override]
         self,
         query: str,
         **kwargs: Any,
@@ -474,7 +474,7 @@ class Vectara(VectorStore):
         )
         return [doc for doc, _ in docs_and_scores]
 
-    def max_marginal_relevance_search(
+    def max_marginal_relevance_search(  # type: ignore[override]
         self,
         query: str,
         fetch_k: int = 50,

libs/community/langchain_community/vectorstores/vikngdb.py

@@ -15,7 +15,7 @@ logger = logging.getLogger(__name__)
 
 
 class VikingDBConfig(object):
-    def __init__(self, host="host", region="region", ak="ak", sk="sk", scheme="http"):
+    def __init__(self, host="host", region="region", ak="ak", sk="sk", scheme="http"):  # type: ignore[no-untyped-def]
         self.host = host
         self.region = region
         self.ak = ak
@@ -47,11 +47,11 @@ class VikingDB(VectorStore):
         self.index_params = index_params
         self.drop_old = drop_old
         self.service = VikingDBService(
-            connection_args.host,
+            connection_args.host,  # type: ignore[union-attr]
-            connection_args.region,
+            connection_args.region,  # type: ignore[union-attr]
-            connection_args.ak,
+            connection_args.ak,  # type: ignore[union-attr]
-            connection_args.sk,
+            connection_args.sk,  # type: ignore[union-attr]
-            connection_args.scheme,
+            connection_args.scheme,  # type: ignore[union-attr]
         )
 
         try:
@@ -143,7 +143,7 @@ class VikingDB(VectorStore):
             scalar_index=scalar_index,
         )
 
-    def add_texts(
+    def add_texts(  # type: ignore[override]
         self,
         texts: List[str],
         metadatas: Optional[List[dict]] = None,
@@ -183,7 +183,7 @@ class VikingDB(VectorStore):
             if metadatas is not None and index < len(metadatas):
                 names = list(metadatas[index].keys())
                 for name in names:
-                    field[name] = metadatas[index].get(name)
+                    field[name] = metadatas[index].get(name)  # type: ignore[assignment]
             data.append(Data(field))
 
         total_count = len(data)
@@ -191,10 +191,10 @@ class VikingDB(VectorStore):
             end = min(i + batch_size, total_count)
             insert_data = data[i:end]
             # print(insert_data)
-            self.collection.upsert_data(insert_data)
+            self.collection.upsert_data(insert_data)  # type: ignore[union-attr]
         return pks
 
-    def similarity_search(
+    def similarity_search(  # type: ignore[override]
         self,
         query: str,
         params: Optional[dict] = None,
@@ -216,7 +216,7 @@ class VikingDB(VectorStore):
         )
         return res
 
-    def similarity_search_by_vector(
+    def similarity_search_by_vector(  # type: ignore[override]
         self,
         embedding: List[float],
         params: Optional[dict] = None,
@@ -251,7 +251,7 @@ class VikingDB(VectorStore):
             if params.get("partition") is not None:
                 partition = params["partition"]
 
-        res = self.index.search_by_vector(
+        res = self.index.search_by_vector(  # type: ignore[union-attr]
             embedding,
             filter=filter,
             limit=limit,
@@ -269,7 +269,7 @@ class VikingDB(VectorStore):
             ret.append(pair)
         return ret
 
-    def max_marginal_relevance_search(
+    def max_marginal_relevance_search(  # type: ignore[override]
         self,
         query: str,
         k: int = 4,
@@ -286,7 +286,7 @@ class VikingDB(VectorStore):
             **kwargs,
         )
 
-    def max_marginal_relevance_search_by_vector(
+    def max_marginal_relevance_search_by_vector(  # type: ignore[override]
         self,
         embedding: List[float],
         k: int = 4,
@@ -311,7 +311,7 @@ class VikingDB(VectorStore):
             if params.get("partition") is not None:
                 partition = params["partition"]
 
-        res = self.index.search_by_vector(
+        res = self.index.search_by_vector(  # type: ignore[union-attr]
             embedding,
             filter=filter,
             limit=limit,
@@ -347,10 +347,10 @@ class VikingDB(VectorStore):
     ) -> None:
         if self.collection is None:
             logger.debug("No existing collection to search.")
-        self.collection.delete_data(ids)
+        self.collection.delete_data(ids)  # type: ignore[union-attr]
 
     @classmethod
-    def from_texts(
+    def from_texts(  # type: ignore[no-untyped-def, override]
         cls,
         texts: List[str],
         embedding: Embeddings,

libs/community/poetry.lock

@@ -1,4 +1,4 @@
-# This file is automatically @generated by Poetry 1.6.1 and should not be changed by hand.
+# This file is automatically @generated by Poetry 1.7.1 and should not be changed by hand.
 
 [[package]]
 name = "aenum"
@@ -3944,7 +3944,7 @@ files = [
 
 [[package]]
 name = "langchain-core"
-version = "0.1.17"
+version = "0.1.18"
 description = "Building applications with LLMs through composability"
 optional = false
 python-versions = ">=3.8.1,<4.0"
@@ -9252,4 +9252,4 @@ extended-testing = ["aiosqlite", "aleph-alpha-client", "anthropic", "arxiv", "as
 [metadata]
 lock-version = "2.0"
 python-versions = ">=3.8.1,<4.0"
-content-hash = "6e1aabbf689bf7294ffc3f9215559157b95868275421d776862ddb1499969c79"
+content-hash = "1ab63edcddcef2deb01e6fff5c376f7b0773435bb9d5b55bc1d50d19a8f1dee2"

libs/community/pyproject.toml

@@ -101,7 +101,7 @@ optional = true
 # dependencies used for running tests (e.g., pytest, freezegun, response).
 # Any dependencies that do not meet that criteria will be removed.
 pytest = "^7.3.0"
-pytest-cov = "^4.0.0"
+pytest-cov = "^4.1.0"
 pytest-dotenv = "^0.5.2"
 duckdb-engine = "^0.9.2"
 pytest-watcher = "^0.2.6"

libs/community/tests/integration_tests/chat_message_histories/test_tidb.py

@@ -57,7 +57,7 @@ def test_add_messages() -> None:
     assert len(message_store_another.messages) == 0
 
 
-def test_tidb_recent_chat_message():
+def test_tidb_recent_chat_message():  # type: ignore[no-untyped-def]
     """Test the TiDBChatMessageHistory with earliest_time parameter."""
     import time
     from datetime import datetime

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

@@ -40,7 +40,7 @@ def test_konko_key_masked_when_passed_via_constructor(
     captured = capsys.readouterr()
     assert captured.out == "**********"
 
-    print(chat.konko_secret_key, end="")
+    print(chat.konko_secret_key, end="")  # type: ignore[attr-defined]
     captured = capsys.readouterr()
     assert captured.out == "**********"
 
@@ -49,7 +49,7 @@ def test_uses_actual_secret_value_from_secret_str() -> None:
     """Test that actual secret is retrieved using `.get_secret_value()`."""
     chat = ChatKonko(openai_api_key="test-openai-key", konko_api_key="test-konko-key")
     assert cast(SecretStr, chat.konko_api_key).get_secret_value() == "test-openai-key"
-    assert cast(SecretStr, chat.konko_secret_key).get_secret_value() == "test-konko-key"
+    assert cast(SecretStr, chat.konko_secret_key).get_secret_value() == "test-konko-key"  # type: ignore[attr-defined]
 
 
 def test_konko_chat_test() -> None:

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

@@ -47,6 +47,6 @@ def test_chat_wasm_service_streaming() -> None:
     output = ""
     for chunk in chat.stream(messages):
         print(chunk.content, end="", flush=True)
-        output += chunk.content
+        output += chunk.content  # type: ignore[operator]
 
     assert "Paris" in output

libs/community/tests/integration_tests/document_loaders/test_astradb.py

@@ -167,5 +167,5 @@ class TestAstraDB:
             find_options={"limit": 30},
             extraction_function=lambda x: x["foo"],
         )
-        doc = await anext(loader.alazy_load())
+        doc = await anext(loader.alazy_load())  # type: ignore[name-defined]
         assert doc.page_content == "bar"

libs/community/tests/integration_tests/document_loaders/test_cassandra.py

@@ -14,7 +14,7 @@ CASSANDRA_TABLE = "docloader_test_table"
 
 
 @pytest.fixture(autouse=True, scope="session")
-def keyspace() -> str:
+def keyspace() -> str:  # type: ignore[misc]
     import cassio
     from cassandra.cluster import Cluster
     from cassio.config import check_resolve_session, resolve_keyspace

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

@@ -7,8 +7,8 @@ def test_baichuan_embedding_documents() -> None:
     documents = ["今天天气不错", "今天阳光灿烂"]
     embedding = BaichuanTextEmbeddings()
     output = embedding.embed_documents(documents)
-    assert len(output) == 2
+    assert len(output) == 2  # type: ignore[arg-type]
-    assert len(output[0]) == 1024
+    assert len(output[0]) == 1024  # type: ignore[index]
 
 
 def test_baichuan_embedding_query() -> None:
@@ -16,4 +16,4 @@ def test_baichuan_embedding_query() -> None:
     document = "所有的小学生都会学过只因兔同笼问题。"
     embedding = BaichuanTextEmbeddings()
     output = embedding.embed_query(document)
-    assert len(output) == 1024
+    assert len(output) == 1024  # type: ignore[arg-type]

libs/community/tests/integration_tests/graphs/test_neo4j.py

@@ -85,7 +85,7 @@ def test_neo4j_timeout() -> None:
         graph.query("UNWIND range(0,100000,1) AS i MERGE (:Foo {id:i})")
     except Exception as e:
         assert (
-            e.code
+            e.code  # type: ignore[attr-defined]
             == "Neo.ClientError.Transaction.TransactionTimedOutClientConfiguration"
         )
 

libs/community/tests/integration_tests/llms/test_azureml_endpoint.py

@@ -62,7 +62,7 @@ def test_custom_formatter() -> None:
         content_type = "application/json"
         accepts = "application/json"
 
-        def format_request_payload(self, prompt: str, model_kwargs: Dict) -> bytes:
+        def format_request_payload(self, prompt: str, model_kwargs: Dict) -> bytes:  # type: ignore[override]
             input_str = json.dumps(
                 {
                     "inputs": [prompt],
@@ -72,7 +72,7 @@ def test_custom_formatter() -> None:
             )
             return input_str.encode("utf-8")
 
-        def format_response_payload(self, output: bytes) -> str:
+        def format_response_payload(self, output: bytes) -> str:  # type: ignore[override]
             response_json = json.loads(output)
             return response_json[0]["summary_text"]
 
@@ -104,7 +104,7 @@ def test_invalid_request_format() -> None:
         content_type = "application/json"
         accepts = "application/json"
 
-        def format_request_payload(self, prompt: str, model_kwargs: Dict) -> bytes:
+        def format_request_payload(self, prompt: str, model_kwargs: Dict) -> bytes:  # type: ignore[override]
             input_str = json.dumps(
                 {
                     "incorrect_input": {"input_string": [prompt]},
@@ -113,7 +113,7 @@ def test_invalid_request_format() -> None:
             )
             return str.encode(input_str)
 
-        def format_response_payload(self, output: bytes) -> str:
+        def format_response_payload(self, output: bytes) -> str:  # type: ignore[override]
             response_json = json.loads(output)
             return response_json[0]["0"]
 

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

@@ -37,12 +37,12 @@ class BedrockAsyncCallbackHandler(AsyncCallbackHandler):
         if reason == "GUARDRAIL_INTERVENED":
             self.guardrails_intervened = True
 
-    def get_response(self):
+    def get_response(self):  # type: ignore[no-untyped-def]
         return self.guardrails_intervened
 
 
 @pytest.fixture(autouse=True)
-def bedrock_runtime_client():
+def bedrock_runtime_client():  # type: ignore[no-untyped-def]
     import boto3
 
     try:
@@ -56,7 +56,7 @@ def bedrock_runtime_client():
 
 
 @pytest.fixture(autouse=True)
-def bedrock_client():
+def bedrock_client():  # type: ignore[no-untyped-def]
     import boto3
 
     try:
@@ -70,7 +70,7 @@ def bedrock_client():
 
 
 @pytest.fixture
-def bedrock_models(bedrock_client):
+def bedrock_models(bedrock_client):  # type: ignore[no-untyped-def]
     """List bedrock models."""
     response = bedrock_client.list_foundation_models().get("modelSummaries")
     models = {}
@@ -79,7 +79,7 @@ def bedrock_models(bedrock_client):
     return models
 
 
-def test_claude_instant_v1(bedrock_runtime_client, bedrock_models):
+def test_claude_instant_v1(bedrock_runtime_client, bedrock_models):  # type: ignore[no-untyped-def]
     try:
         llm = Bedrock(
             model_id="anthropic.claude-instant-v1",
@@ -92,7 +92,7 @@ def test_claude_instant_v1(bedrock_runtime_client, bedrock_models):
         pytest.fail(f"can not instantiate claude-instant-v1: {e}", pytrace=False)
 
 
-def test_amazon_bedrock_guardrails_no_intervention_for_valid_query(
+def test_amazon_bedrock_guardrails_no_intervention_for_valid_query(  # type: ignore[no-untyped-def]
     bedrock_runtime_client, bedrock_models
 ):
     try:
@@ -112,7 +112,7 @@ def test_amazon_bedrock_guardrails_no_intervention_for_valid_query(
         pytest.fail(f"can not instantiate claude-instant-v1: {e}", pytrace=False)
 
 
-def test_amazon_bedrock_guardrails_intervention_for_invalid_query(
+def test_amazon_bedrock_guardrails_intervention_for_invalid_query(  # type: ignore[no-untyped-def]
     bedrock_runtime_client, bedrock_models
 ):
     try:

libs/community/tests/integration_tests/storage/test_astradb.py

@@ -16,7 +16,7 @@ def _has_env_vars() -> bool:
 
 
 @pytest.fixture
-def astra_db():
+def astra_db():  # type: ignore[no-untyped-def]
     from astrapy.db import AstraDB
 
     return AstraDB(
@@ -26,14 +26,14 @@ def astra_db():
     )
 
 
-def init_store(astra_db, collection_name: str):
+def init_store(astra_db, collection_name: str):  # type: ignore[no-untyped-def, no-untyped-def]
     astra_db.create_collection(collection_name)
     store = AstraDBStore(collection_name=collection_name, astra_db_client=astra_db)
     store.mset([("key1", [0.1, 0.2]), ("key2", "value2")])
     return store
 
 
-def init_bytestore(astra_db, collection_name: str):
+def init_bytestore(astra_db, collection_name: str):  # type: ignore[no-untyped-def, no-untyped-def]
     astra_db.create_collection(collection_name)
     store = AstraDBByteStore(collection_name=collection_name, astra_db_client=astra_db)
     store.mset([("key1", b"value1"), ("key2", b"value2")])
@@ -43,7 +43,7 @@ def init_bytestore(astra_db, collection_name: str):
 @pytest.mark.requires("astrapy")
 @pytest.mark.skipif(not _has_env_vars(), reason="Missing Astra DB env. vars")
 class TestAstraDBStore:
-    def test_mget(self, astra_db) -> None:
+    def test_mget(self, astra_db) -> None:  # type: ignore[no-untyped-def]
         """Test AstraDBStore mget method."""
         collection_name = "lc_test_store_mget"
         try:
@@ -52,7 +52,7 @@ class TestAstraDBStore:
         finally:
             astra_db.delete_collection(collection_name)
 
-    def test_mset(self, astra_db) -> None:
+    def test_mset(self, astra_db) -> None:  # type: ignore[no-untyped-def]
         """Test that multiple keys can be set with AstraDBStore."""
         collection_name = "lc_test_store_mset"
         try:
@@ -64,7 +64,7 @@ class TestAstraDBStore:
         finally:
             astra_db.delete_collection(collection_name)
 
-    def test_mdelete(self, astra_db) -> None:
+    def test_mdelete(self, astra_db) -> None:  # type: ignore[no-untyped-def]
         """Test that deletion works as expected."""
         collection_name = "lc_test_store_mdelete"
         try:
@@ -75,7 +75,7 @@ class TestAstraDBStore:
         finally:
             astra_db.delete_collection(collection_name)
 
-    def test_yield_keys(self, astra_db) -> None:
+    def test_yield_keys(self, astra_db) -> None:  # type: ignore[no-untyped-def]
         collection_name = "lc_test_store_yield_keys"
         try:
             store = init_store(astra_db, collection_name)
@@ -85,7 +85,7 @@ class TestAstraDBStore:
         finally:
             astra_db.delete_collection(collection_name)
 
-    def test_bytestore_mget(self, astra_db) -> None:
+    def test_bytestore_mget(self, astra_db) -> None:  # type: ignore[no-untyped-def]
         """Test AstraDBByteStore mget method."""
         collection_name = "lc_test_bytestore_mget"
         try:
@@ -94,7 +94,7 @@ class TestAstraDBStore:
         finally:
             astra_db.delete_collection(collection_name)
 
-    def test_bytestore_mset(self, astra_db) -> None:
+    def test_bytestore_mset(self, astra_db) -> None:  # type: ignore[no-untyped-def]
         """Test that multiple keys can be set with AstraDBByteStore."""
         collection_name = "lc_test_bytestore_mset"
         try:

libs/community/tests/integration_tests/utilities/test_google_trends.py

@@ -6,7 +6,7 @@ from langchain_community.utilities.google_trends import GoogleTrendsAPIWrapper
 
 
 @patch("serpapi.SerpApiClient.get_json")
-def test_unexpected_response(mocked_serpapiclient):
+def test_unexpected_response(mocked_serpapiclient):  # type: ignore[no-untyped-def]
     os.environ["SERPAPI_API_KEY"] = "123abcd"
     resp = {
         "search_metadata": {

libs/community/tests/integration_tests/vectorstores/qdrant/common.py

@@ -15,7 +15,7 @@ def qdrant_is_not_running() -> bool:
         return True
 
 
-def assert_documents_equals(actual: List[Document], expected: List[Document]):
+def assert_documents_equals(actual: List[Document], expected: List[Document]):  # type: ignore[no-untyped-def]
     assert len(actual) == len(expected)
 
     for actual_doc, expected_doc in zip(actual, expected):

libs/community/tests/integration_tests/vectorstores/test_bigquery_vector_search.py

@@ -32,7 +32,7 @@ def store(request: pytest.FixtureRequest) -> BigQueryVectorSearch:
         TestBigQueryVectorStore.dataset_name, exists_ok=True
     )
     TestBigQueryVectorStore.store = BigQueryVectorSearch(
-        project_id=os.environ.get("PROJECT", None),
+        project_id=os.environ.get("PROJECT", None),  # type: ignore[arg-type]
         embedding=FakeEmbeddings(),
         dataset_name=TestBigQueryVectorStore.dataset_name,
         table_name=TEST_TABLE_NAME,

libs/community/tests/integration_tests/vectorstores/test_deeplake.py

@@ -52,7 +52,7 @@ def test_deeplake_with_metadatas() -> None:
     assert output == [Document(page_content="foo", metadata={"page": "0"})]
 
 
-def test_deeplake_with_persistence(deeplake_datastore) -> None:
+def test_deeplake_with_persistence(deeplake_datastore) -> None:  # type: ignore[no-untyped-def]
     """Test end to end construction and search, with persistence."""
     output = deeplake_datastore.similarity_search("foo", k=1)
     assert output == [Document(page_content="foo", metadata={"page": "0"})]
@@ -72,7 +72,7 @@ def test_deeplake_with_persistence(deeplake_datastore) -> None:
     # Or on program exit
 
 
-def test_deeplake_overwrite_flag(deeplake_datastore) -> None:
+def test_deeplake_overwrite_flag(deeplake_datastore) -> None:  # type: ignore[no-untyped-def]
     """Test overwrite behavior"""
     dataset_path = deeplake_datastore.vectorstore.dataset_handler.path
 
@@ -108,7 +108,7 @@ def test_deeplake_overwrite_flag(deeplake_datastore) -> None:
         output = docsearch.similarity_search("foo", k=1)
 
 
-def test_similarity_search(deeplake_datastore) -> None:
+def test_similarity_search(deeplake_datastore) -> None:  # type: ignore[no-untyped-def]
     """Test similarity search."""
     distance_metric = "cos"
     output = deeplake_datastore.similarity_search(

libs/community/tests/integration_tests/vectorstores/test_hanavector.py

@@ -38,7 +38,7 @@ embedding = NormalizedFakeEmbeddings()
 
 
 class ConfigData:
-    def __init__(self):
+    def __init__(self):  # type: ignore[no-untyped-def]
         self.conn = None
         self.schema_name = ""
 
@@ -46,7 +46,7 @@ class ConfigData:
 test_setup = ConfigData()
 
 
-def generateSchemaName(cursor):
+def generateSchemaName(cursor):  # type: ignore[no-untyped-def]
     cursor.execute(
         "SELECT REPLACE(CURRENT_UTCDATE, '-', '') || '_' || BINTOHEX(SYSUUID) FROM "
         "DUMMY;"
@@ -59,7 +59,7 @@ def generateSchemaName(cursor):
     return f"VEC_{uid}"
 
 
-def setup_module(module):
+def setup_module(module):  # type: ignore[no-untyped-def]
     test_setup.conn = dbapi.connect(
         address=os.environ.get("HANA_DB_ADDRESS"),
         port=os.environ.get("HANA_DB_PORT"),
@@ -81,7 +81,7 @@ def setup_module(module):
         cur.close()
 
 
-def teardown_module(module):
+def teardown_module(module):  # type: ignore[no-untyped-def]
     try:
         cur = test_setup.conn.cursor()
         sql_str = f"DROP SCHEMA {test_setup.schema_name} CASCADE"
@@ -100,13 +100,13 @@ def texts() -> List[str]:
 @pytest.fixture
 def metadatas() -> List[str]:
     return [
-        {"start": 0, "end": 100, "quality": "good", "ready": True},
+        {"start": 0, "end": 100, "quality": "good", "ready": True},  # type: ignore[list-item]
-        {"start": 100, "end": 200, "quality": "bad", "ready": False},
+        {"start": 100, "end": 200, "quality": "bad", "ready": False},  # type: ignore[list-item]
-        {"start": 200, "end": 300, "quality": "ugly", "ready": True},
+        {"start": 200, "end": 300, "quality": "ugly", "ready": True},  # type: ignore[list-item]
     ]
 
 
-def drop_table(connection, table_name):
+def drop_table(connection, table_name):  # type: ignore[no-untyped-def]
     try:
         cur = connection.cursor()
         sql_str = f"DROP TABLE {table_name}"
@@ -825,7 +825,7 @@ def test_hanavector_filter_prepared_statement_params(
     rows = cur.fetchall()
     assert len(rows) == 1
 
-    query_value = "good"
+    query_value = "good"  # type: ignore[assignment]
     sql_str = f"SELECT * FROM {table_name} WHERE JSON_VALUE(VEC_META, '$.quality') = ?"
     cur.execute(sql_str, (query_value))
     rows = cur.fetchall()
@@ -839,14 +839,14 @@ def test_hanavector_filter_prepared_statement_params(
     assert len(rows) == 1
 
     # query_value = True
-    query_value = "true"
+    query_value = "true"  # type: ignore[assignment]
     sql_str = f"SELECT * FROM {table_name} WHERE JSON_VALUE(VEC_META, '$.ready') = ?"
     cur.execute(sql_str, (query_value))
     rows = cur.fetchall()
     assert len(rows) == 2
 
     # query_value = False
-    query_value = "false"
+    query_value = "false"  # type: ignore[assignment]
     sql_str = f"SELECT * FROM {table_name} WHERE JSON_VALUE(VEC_META, '$.ready') = ?"
     cur.execute(sql_str, (query_value))
     rows = cur.fetchall()

libs/community/tests/integration_tests/vectorstores/test_lantern.py

@@ -31,7 +31,7 @@ def fix_distance_precision(
 class FakeEmbeddingsWithAdaDimension(FakeEmbeddings):
     """Fake embeddings functionality for testing."""
 
-    def __init__(self):
+    def __init__(self):  # type: ignore[no-untyped-def]
         super(FakeEmbeddingsWithAdaDimension, self).__init__(size=ADA_TOKEN_COUNT)
 
     def embed_documents(self, texts: List[str]) -> List[List[float]]:

libs/community/tests/integration_tests/vectorstores/test_thirdai_neuraldb.py

@@ -7,7 +7,7 @@ from langchain_community.vectorstores import NeuralDBVectorStore
 
 
 @pytest.fixture(scope="session")
-def test_csv():
+def test_csv():  # type: ignore[no-untyped-def]
     csv = "thirdai-test.csv"
     with open(csv, "w") as o:
         o.write("column_1,column_2\n")
@@ -16,13 +16,13 @@ def test_csv():
     os.remove(csv)
 
 
-def assert_result_correctness(documents):
+def assert_result_correctness(documents):  # type: ignore[no-untyped-def]
     assert len(documents) == 1
     assert documents[0].page_content == "column_1: column one\n\ncolumn_2: column two"
 
 
 @pytest.mark.requires("thirdai[neural_db]")
-def test_neuraldb_retriever_from_scratch(test_csv):
+def test_neuraldb_retriever_from_scratch(test_csv):  # type: ignore[no-untyped-def]
     retriever = NeuralDBVectorStore.from_scratch()
     retriever.insert([test_csv])
     documents = retriever.similarity_search("column")
@@ -30,7 +30,7 @@ def test_neuraldb_retriever_from_scratch(test_csv):
 
 
 @pytest.mark.requires("thirdai[neural_db]")
-def test_neuraldb_retriever_from_checkpoint(test_csv):
+def test_neuraldb_retriever_from_checkpoint(test_csv):  # type: ignore[no-untyped-def]
     checkpoint = "thirdai-test-save.ndb"
     if os.path.exists(checkpoint):
         shutil.rmtree(checkpoint)
@@ -47,7 +47,7 @@ def test_neuraldb_retriever_from_checkpoint(test_csv):
 
 
 @pytest.mark.requires("thirdai[neural_db]")
-def test_neuraldb_retriever_from_bazaar(test_csv):
+def test_neuraldb_retriever_from_bazaar(test_csv):  # type: ignore[no-untyped-def]
     retriever = NeuralDBVectorStore.from_bazaar("General QnA")
     retriever.insert([test_csv])
     documents = retriever.similarity_search("column")
@@ -55,7 +55,7 @@ def test_neuraldb_retriever_from_bazaar(test_csv):
 
 
 @pytest.mark.requires("thirdai[neural_db]")
-def test_neuraldb_retriever_other_methods(test_csv):
+def test_neuraldb_retriever_other_methods(test_csv):  # type: ignore[no-untyped-def]
     retriever = NeuralDBVectorStore.from_scratch()
     retriever.insert([test_csv])
     # Make sure they don't throw an error.

libs/community/tests/integration_tests/vectorstores/test_vectara.py

@@ -25,7 +25,7 @@ def get_abbr(s: str) -> str:
 
 
 @pytest.fixture(scope="function")
-def vectara1():
+def vectara1():  # type: ignore[no-untyped-def]
     # Set up code
     # create a new Vectara instance
     vectara1: Vectara = Vectara()
@@ -54,7 +54,7 @@ def vectara1():
         vectara1._delete_doc(doc_id)
 
 
-def test_vectara_add_documents(vectara1) -> None:
+def test_vectara_add_documents(vectara1) -> None:  # type: ignore[no-untyped-def]
     """Test add_documents."""
 
     # test without filter
@@ -164,7 +164,7 @@ models can greatly improve the training of DNNs and other deep discriminative mo
 
 
 @pytest.fixture(scope="function")
-def vectara3():
+def vectara3():  # type: ignore[no-untyped-def]
     # Set up code
     vectara3: Vectara = Vectara()
 
@@ -210,7 +210,7 @@ def vectara3():
         vectara3._delete_doc(doc_id)
 
 
-def test_vectara_mmr(vectara3) -> None:
+def test_vectara_mmr(vectara3) -> None:  # type: ignore[no-untyped-def]
     # test max marginal relevance
     output1 = vectara3.max_marginal_relevance_search(
         "generative AI",
@@ -241,7 +241,7 @@ def test_vectara_mmr(vectara3) -> None:
     )
 
 
-def test_vectara_with_summary(vectara3) -> None:
+def test_vectara_with_summary(vectara3) -> None:  # type: ignore[no-untyped-def]
     """Test vectara summary."""
     # test summarization
     num_results = 10

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

@@ -35,6 +35,6 @@ def test_edenai_messages_formatting(messages: List[BaseMessage], expected: str)
     ("role", "role_response"),
     [("ai", "assistant"), ("human", "user"), ("chat", "user")],
 )
-def test_edenai_message_role(role: str, role_response) -> None:
+def test_edenai_message_role(role: str, role_response) -> None:  # type: ignore[no-untyped-def]
     role = _message_role(role)
     assert role == role_response

libs/community/tests/unit_tests/embeddings/test_gradient_ai.py

@@ -29,7 +29,7 @@ class GradientEmbeddingsModel(MagicMock):
         embeddings = []
         for i, inp in enumerate(inputs):
             # verify correct ordering
-            inp = inp["input"]
+            inp = inp["input"]  # type: ignore[assignment]
             if "pizza" in inp:
                 v = [1.0, 0.0, 0.0]
             elif "document" in inp:
@@ -45,14 +45,14 @@ class GradientEmbeddingsModel(MagicMock):
         output.embeddings = embeddings
         return output
 
-    async def aembed(self, *args) -> Any:
+    async def aembed(self, *args) -> Any:  # type: ignore[no-untyped-def]
         return self.embed(*args)
 
 
 class MockGradient(MagicMock):
     """Mock Gradient package."""
 
-    def __init__(self, access_token: str, workspace_id, host):
+    def __init__(self, access_token: str, workspace_id, host):  # type: ignore[no-untyped-def]
         assert access_token == _GRADIENT_SECRET
         assert workspace_id == _GRADIENT_WORKSPACE_ID
         assert host == _GRADIENT_BASE_URL

libs/community/tests/unit_tests/embeddings/test_oci_gen_ai_embedding.py

@@ -8,7 +8,7 @@ from langchain_community.embeddings import OCIGenAIEmbeddings
 
 
 class MockResponseDict(dict):
-    def __getattr__(self, val):
+    def __getattr__(self, val):  # type: ignore[no-untyped-def]
         return self[val]
 
 
@@ -25,7 +25,7 @@ def test_embedding_call(monkeypatch: MonkeyPatch, test_model_id: str) -> None:
         client=oci_gen_ai_client,
     )
 
-    def mocked_response(invocation_obj):
+    def mocked_response(invocation_obj):  # type: ignore[no-untyped-def]
         docs = invocation_obj.inputs
 
         embeddings = []

libs/community/tests/unit_tests/graphs/test_neo4j_graph.py

@@ -1,14 +1,14 @@
 from langchain_community.graphs.neo4j_graph import value_sanitize
 
 
-def test_value_sanitize_with_small_list():
+def test_value_sanitize_with_small_list():  # type: ignore[no-untyped-def]
     small_list = list(range(15))  # list size > LIST_LIMIT
     input_dict = {"key1": "value1", "small_list": small_list}
     expected_output = {"key1": "value1", "small_list": small_list}
     assert value_sanitize(input_dict) == expected_output
 
 
-def test_value_sanitize_with_oversized_list():
+def test_value_sanitize_with_oversized_list():  # type: ignore[no-untyped-def]
     oversized_list = list(range(150))  # list size > LIST_LIMIT
     input_dict = {"key1": "value1", "oversized_list": oversized_list}
     expected_output = {
@@ -18,14 +18,14 @@ def test_value_sanitize_with_oversized_list():
     assert value_sanitize(input_dict) == expected_output
 
 
-def test_value_sanitize_with_nested_oversized_list():
+def test_value_sanitize_with_nested_oversized_list():  # type: ignore[no-untyped-def]
     oversized_list = list(range(150))  # list size > LIST_LIMIT
     input_dict = {"key1": "value1", "oversized_list": {"key": oversized_list}}
     expected_output = {"key1": "value1", "oversized_list": {}}
     assert value_sanitize(input_dict) == expected_output
 
 
-def test_value_sanitize_with_dict_in_list():
+def test_value_sanitize_with_dict_in_list():  # type: ignore[no-untyped-def]
     oversized_list = list(range(150))  # list size > LIST_LIMIT
     input_dict = {"key1": "value1", "oversized_list": [1, 2, {"key": oversized_list}]}
     expected_output = {"key1": "value1", "oversized_list": [1, 2, {}]}

libs/community/tests/unit_tests/graphs/test_ontotext_graphdb_graph.py

@@ -15,7 +15,7 @@ class TestOntotextGraphDBGraph(unittest.TestCase):
 
         with self.assertRaises(TypeError) as e:
             OntotextGraphDBGraph._validate_user_query(
-                [
+                [  # type: ignore[arg-type]
                     "PREFIX starwars: <https://swapi.co/ontology/> "
                     "PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#> "
                     "DESCRIBE starwars: ?term "

libs/community/tests/unit_tests/llms/test_oci_generative_ai.py

@@ -8,7 +8,7 @@ from langchain_community.llms import OCIGenAI
 
 
 class MockResponseDict(dict):
-    def __getattr__(self, val):
+    def __getattr__(self, val):  # type: ignore[no-untyped-def]
         return self[val]
 
 
@@ -23,7 +23,7 @@ def test_llm_call(monkeypatch: MonkeyPatch, test_model_id: str) -> None:
 
     provider = llm._get_provider()
 
-    def mocked_response(*args):
+    def mocked_response(*args):  # type: ignore[no-untyped-def]
         response_text = "This is the completion."
 
         if provider == "cohere":

libs/community/tests/unit_tests/llms/test_ollama.py

@@ -4,11 +4,11 @@ from pytest import MonkeyPatch
 from langchain_community.llms.ollama import Ollama
 
 
-def mock_response_stream():
+def mock_response_stream():  # type: ignore[no-untyped-def]
     mock_response = [b'{ "response": "Response chunk 1" }']
 
     class MockRaw:
-        def read(self, chunk_size):
+        def read(self, chunk_size):  # type: ignore[no-untyped-def]
             try:
                 return mock_response.pop()
             except IndexError:
@@ -31,7 +31,7 @@ def test_pass_headers_if_provided(monkeypatch: MonkeyPatch) -> None:
         timeout=300,
     )
 
-    def mock_post(url, headers, json, stream, timeout):
+    def mock_post(url, headers, json, stream, timeout):  # type: ignore[no-untyped-def]
         assert url == "https://ollama-hostname:8000/api/generate/"
         assert headers == {
             "Content-Type": "application/json",
@@ -52,7 +52,7 @@ def test_pass_headers_if_provided(monkeypatch: MonkeyPatch) -> None:
 def test_handle_if_headers_not_provided(monkeypatch: MonkeyPatch) -> None:
     llm = Ollama(base_url="https://ollama-hostname:8000", model="foo", timeout=300)
 
-    def mock_post(url, headers, json, stream, timeout):
+    def mock_post(url, headers, json, stream, timeout):  # type: ignore[no-untyped-def]
         assert url == "https://ollama-hostname:8000/api/generate/"
         assert headers == {
             "Content-Type": "application/json",
@@ -72,7 +72,7 @@ def test_handle_kwargs_top_level_parameters(monkeypatch: MonkeyPatch) -> None:
     """Test that top level params are sent to the endpoint as top level params"""
     llm = Ollama(base_url="https://ollama-hostname:8000", model="foo", timeout=300)
 
-    def mock_post(url, headers, json, stream, timeout):
+    def mock_post(url, headers, json, stream, timeout):  # type: ignore[no-untyped-def]
         assert url == "https://ollama-hostname:8000/api/generate/"
         assert headers == {
             "Content-Type": "application/json",
@@ -118,7 +118,7 @@ def test_handle_kwargs_with_unknown_param(monkeypatch: MonkeyPatch) -> None:
     """
     llm = Ollama(base_url="https://ollama-hostname:8000", model="foo", timeout=300)
 
-    def mock_post(url, headers, json, stream, timeout):
+    def mock_post(url, headers, json, stream, timeout):  # type: ignore[no-untyped-def]
         assert url == "https://ollama-hostname:8000/api/generate/"
         assert headers == {
             "Content-Type": "application/json",
@@ -165,7 +165,7 @@ def test_handle_kwargs_with_options(monkeypatch: MonkeyPatch) -> None:
     """
     llm = Ollama(base_url="https://ollama-hostname:8000", model="foo", timeout=300)
 
-    def mock_post(url, headers, json, stream, timeout):
+    def mock_post(url, headers, json, stream, timeout):  # type: ignore[no-untyped-def]
         assert url == "https://ollama-hostname:8000/api/generate/"
         assert headers == {
             "Content-Type": "application/json",

libs/core/Makefile

@@ -42,7 +42,7 @@ lint lint_diff lint_package lint_tests:
 	[ "$(PYTHON_FILES)" = "" ] || poetry run ruff format $(PYTHON_FILES) --diff
 	[ "$(PYTHON_FILES)" = "" ] || poetry run ruff --select I $(PYTHON_FILES)
 	[ "$(PYTHON_FILES)" = "" ] || poetry run mypy $(PYTHON_FILES)
-	[ "$(PYTHON_FILES)" = "" ] || mkdir $(MYPY_CACHE) || poetry run mypy $(PYTHON_FILES) --cache-dir $(MYPY_CACHE)
+	[ "$(PYTHON_FILES)" = "" ] || mkdir -p $(MYPY_CACHE) && poetry run mypy $(PYTHON_FILES) --cache-dir $(MYPY_CACHE)
 
 format format_diff:
 	poetry run ruff format $(PYTHON_FILES)