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Code Issues Packages Projects Releases Wiki Activity

community: add mypy warn_unused_ignores rule (#30816)

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This commit is contained in:
Christophe Bornet 2025-04-28 17:54:12 +02:00 committed by GitHub
parent a2863f8757
commit aee7988a94
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GPG Key ID: B5690EEEBB952194
346 changed files with 795 additions and 821 deletions
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2
libs/community/langchain_community/adapters/openai.py
View File

@ -104,7 +104,7 @@ def convert_dict_to_message(_dict: Mapping[str, Any]) -> BaseMessage:
additional_kwargs["name"] = _dict["name"]
return ToolMessage(
content=_dict.get("content", ""),
tool_call_id=_dict.get("tool_call_id"), # type: ignore[arg-type]
tool_call_id=_dict.get("tool_call_id"),
additional_kwargs=additional_kwargs,
)
else:

2
libs/community/langchain_community/agent_toolkits/azure_ai_services.py
View File

@ -22,7 +22,7 @@ class AzureAiServicesToolkit(BaseToolkit):
tools: List[BaseTool] = [
AzureAiServicesDocumentIntelligenceTool(), # type: ignore[call-arg]
AzureAiServicesImageAnalysisTool(), # type: ignore[call-arg]
AzureAiServicesImageAnalysisTool(),
AzureAiServicesSpeechToTextTool(), # type: ignore[call-arg]
AzureAiServicesTextToSpeechTool(), # type: ignore[call-arg]
AzureAiServicesTextAnalyticsForHealthTool(), # type: ignore[call-arg]

2
libs/community/langchain_community/agent_toolkits/file_management/toolkit.py
View File

@ -81,7 +81,7 @@ class FileManagementToolkit(BaseToolkit):
tools: List[BaseTool] = []
for tool in allowed_tools:
tool_cls = _FILE_TOOLS_MAP[tool]
tools.append(tool_cls(root_dir=self.root_dir)) # type: ignore[call-arg]
tools.append(tool_cls(root_dir=self.root_dir))
return tools

2
libs/community/langchain_community/agent_toolkits/nla/tool.py
View File

@ -13,7 +13,7 @@ from langchain_community.tools.openapi.utils.openapi_utils import OpenAPISpec
from langchain_community.utilities.requests import Requests
class NLATool(Tool): # type: ignore[override]
class NLATool(Tool):
"""Natural Language API Tool."""
@classmethod

20
libs/community/langchain_community/agent_toolkits/openapi/planner.py
View File

@ -64,7 +64,7 @@ def _get_default_llm_chain_factory(
return partial(_get_default_llm_chain, prompt)
class RequestsGetToolWithParsing(BaseRequestsTool, BaseTool): # type: ignore[override]
class RequestsGetToolWithParsing(BaseRequestsTool, BaseTool):
"""Requests GET tool with LLM-instructed extraction of truncated responses."""
name: str = "requests_get"
@ -98,7 +98,7 @@ class RequestsGetToolWithParsing(BaseRequestsTool, BaseTool): # type: ignore[ov
raise NotImplementedError()
class RequestsPostToolWithParsing(BaseRequestsTool, BaseTool): # type: ignore[override]
class RequestsPostToolWithParsing(BaseRequestsTool, BaseTool):
"""Requests POST tool with LLM-instructed extraction of truncated responses."""
name: str = "requests_post"
@ -129,7 +129,7 @@ class RequestsPostToolWithParsing(BaseRequestsTool, BaseTool): # type: ignore[o
raise NotImplementedError()
class RequestsPatchToolWithParsing(BaseRequestsTool, BaseTool): # type: ignore[override]
class RequestsPatchToolWithParsing(BaseRequestsTool, BaseTool):
"""Requests PATCH tool with LLM-instructed extraction of truncated responses."""
name: str = "requests_patch"
@ -162,7 +162,7 @@ class RequestsPatchToolWithParsing(BaseRequestsTool, BaseTool): # type: ignore[
raise NotImplementedError()
class RequestsPutToolWithParsing(BaseRequestsTool, BaseTool): # type: ignore[override]
class RequestsPutToolWithParsing(BaseRequestsTool, BaseTool):
"""Requests PUT tool with LLM-instructed extraction of truncated responses."""
name: str = "requests_put"
@ -193,7 +193,7 @@ class RequestsPutToolWithParsing(BaseRequestsTool, BaseTool): # type: ignore[ov
raise NotImplementedError()
class RequestsDeleteToolWithParsing(BaseRequestsTool, BaseTool): # type: ignore[override]
class RequestsDeleteToolWithParsing(BaseRequestsTool, BaseTool):
"""Tool that sends a DELETE request and parses the response."""
name: str = "requests_delete"
@ -266,7 +266,7 @@ def _create_api_controller_agent(
if "GET" in allowed_operations:
get_llm_chain = LLMChain(llm=llm, prompt=PARSING_GET_PROMPT)
tools.append(
RequestsGetToolWithParsing( # type: ignore[call-arg]
RequestsGetToolWithParsing(
requests_wrapper=requests_wrapper,
llm_chain=get_llm_chain,
allow_dangerous_requests=allow_dangerous_requests,
@ -275,7 +275,7 @@ def _create_api_controller_agent(
if "POST" in allowed_operations:
post_llm_chain = LLMChain(llm=llm, prompt=PARSING_POST_PROMPT)
tools.append(
RequestsPostToolWithParsing( # type: ignore[call-arg]
RequestsPostToolWithParsing(
requests_wrapper=requests_wrapper,
llm_chain=post_llm_chain,
allow_dangerous_requests=allow_dangerous_requests,
@ -284,7 +284,7 @@ def _create_api_controller_agent(
if "PUT" in allowed_operations:
put_llm_chain = LLMChain(llm=llm, prompt=PARSING_PUT_PROMPT)
tools.append(
RequestsPutToolWithParsing( # type: ignore[call-arg]
RequestsPutToolWithParsing(
requests_wrapper=requests_wrapper,
llm_chain=put_llm_chain,
allow_dangerous_requests=allow_dangerous_requests,
@ -293,7 +293,7 @@ def _create_api_controller_agent(
if "DELETE" in allowed_operations:
delete_llm_chain = LLMChain(llm=llm, prompt=PARSING_DELETE_PROMPT)
tools.append(
RequestsDeleteToolWithParsing( # type: ignore[call-arg]
RequestsDeleteToolWithParsing(
requests_wrapper=requests_wrapper,
llm_chain=delete_llm_chain,
allow_dangerous_requests=allow_dangerous_requests,
@ -302,7 +302,7 @@ def _create_api_controller_agent(
if "PATCH" in allowed_operations:
patch_llm_chain = LLMChain(llm=llm, prompt=PARSING_PATCH_PROMPT)
tools.append(
RequestsPatchToolWithParsing( # type: ignore[call-arg]
RequestsPatchToolWithParsing(
requests_wrapper=requests_wrapper,
llm_chain=patch_llm_chain,
allow_dangerous_requests=allow_dangerous_requests,

2
libs/community/langchain_community/agent_toolkits/powerbi/toolkit.py
View File

@ -75,7 +75,7 @@ class PowerBIToolkit(BaseToolkit):
powerbi=self.powerbi,
examples=self.examples,
max_iterations=self.max_iterations,
output_token_limit=self.output_token_limit, # type: ignore[arg-type]
output_token_limit=self.output_token_limit,
tiktoken_model_name=self.tiktoken_model_name,
),
InfoPowerBITool(powerbi=self.powerbi),

4
libs/community/langchain_community/agents/openai_assistant/base.py
View File

@ -289,7 +289,7 @@ class OpenAIAssistantV2Runnable(OpenAIAssistantRunnable):
name=name,
instructions=instructions,
tools=[_get_assistants_tool(tool) for tool in tools],
tool_resources=tool_resources, # type: ignore[arg-type]
tool_resources=tool_resources,
model=model,
extra_body=extra_body,
**model_kwargs,
@ -431,7 +431,7 @@ class OpenAIAssistantV2Runnable(OpenAIAssistantRunnable):
name=name,
instructions=instructions,
tools=openai_tools,
tool_resources=tool_resources, # type: ignore[arg-type]
tool_resources=tool_resources,
model=model,
)
return cls(assistant_id=assistant.id, async_client=async_client, **kwargs)

2
libs/community/langchain_community/cache.py
View File

@ -579,7 +579,7 @@ class AsyncRedisCache(_RedisCacheBase):
try:
async with self.redis.pipeline() as pipe:
self._configure_pipeline_for_update(key, pipe, return_val, self.ttl)
await pipe.execute() # type: ignore[attr-defined]
await pipe.execute()
except Exception as e:
logger.error(f"Redis async update failed: {e}")

6
libs/community/langchain_community/chains/ernie_functions/base.py
View File

@ -378,7 +378,7 @@ def create_ernie_fn_chain(
output_key: str = "function",
output_parser: Optional[BaseLLMOutputParser] = None,
**kwargs: Any,
) -> LLMChain: # type: ignore[valid-type]
) -> LLMChain:
"""[Legacy] Create an LLM chain that uses Ernie functions.
Args:
@ -455,7 +455,7 @@ def create_ernie_fn_chain(
}
if len(ernie_functions) == 1:
llm_kwargs["function_call"] = {"name": ernie_functions[0]["name"]}
llm_chain = LLMChain( # type: ignore[misc]
llm_chain = LLMChain(
llm=llm,
prompt=prompt,
output_parser=output_parser,
@ -474,7 +474,7 @@ def create_structured_output_chain(
output_key: str = "function",
output_parser: Optional[BaseLLMOutputParser] = None,
**kwargs: Any,
) -> LLMChain: # type: ignore[valid-type]
) -> LLMChain:
"""[Legacy] Create an LLMChain that uses an Ernie function to get a structured output.
Args:

2
libs/community/langchain_community/chains/graph_qa/cypher.py
View File

@ -324,7 +324,7 @@ class GraphCypherQAChain(Chain):
cypher_generation_chain = LLMChain(
llm=cypher_llm or llm, # type: ignore[arg-type]
**use_cypher_llm_kwargs, # type: ignore[arg-type]
**use_cypher_llm_kwargs,
)
if exclude_types and include_types:

2
libs/community/langchain_community/chains/graph_qa/memgraph.py
View File

@ -235,7 +235,7 @@ class MemgraphQAChain(Chain):
llm_to_use = cypher_llm if cypher_llm is not None else llm
if prompt is not None and llm_to_use is not None:
cypher_generation_chain = prompt | llm_to_use | StrOutputParser() # type: ignore[arg-type]
cypher_generation_chain = prompt | llm_to_use | StrOutputParser()
else:
raise ValueError(
"Missing required components for the cypher generation chain: "

2
libs/community/langchain_community/chains/graph_qa/neptune_sparql.py
View File

@ -181,7 +181,7 @@ class NeptuneSparqlQAChain(Chain):
)
sparql_generation_chain = LLMChain(llm=llm, prompt=sparql_prompt)
return cls( # type: ignore[call-arg]
return cls(
qa_chain=qa_chain,
sparql_generation_chain=sparql_generation_chain,
examples=examples,

4
libs/community/langchain_community/chains/llm_requests.py
View File

@ -28,7 +28,7 @@ class LLMRequestsChain(Chain):
See https://python.langchain.com/docs/security for more information.
"""
llm_chain: LLMChain # type: ignore[valid-type]
llm_chain: LLMChain
requests_wrapper: TextRequestsWrapper = Field(
default_factory=lambda: TextRequestsWrapper(headers=DEFAULT_HEADERS),
exclude=True,
@ -88,7 +88,7 @@ class LLMRequestsChain(Chain):
# extract the text from the html
soup = BeautifulSoup(res, "html.parser")
other_keys[self.requests_key] = soup.get_text()[: self.text_length]
result = self.llm_chain.predict( # type: ignore[attr-defined]
result = self.llm_chain.predict(
callbacks=_run_manager.get_child(), **other_keys
)
return {self.output_key: result}

2
libs/community/langchain_community/chat_loaders/imessage.py
View File

@ -158,7 +158,7 @@ class IMessageChatLoader(BaseChatLoader):
continue
results.append(
HumanMessage( # type: ignore[call-arg]
HumanMessage(
role=sender,
content=content,
additional_kwargs={

2
libs/community/langchain_community/chat_loaders/slack.py
View File

@ -52,7 +52,7 @@ class SlackChatLoader(BaseChatLoader):
)
else:
results.append(
HumanMessage( # type: ignore[call-arg]
HumanMessage(
role=sender,
content=text,
additional_kwargs={

2
libs/community/langchain_community/chat_loaders/utils.py
View File

@ -78,7 +78,7 @@ def map_ai_messages_in_session(chat_sessions: ChatSession, sender: str) -> ChatS
message = AIMessage(
content=message.content,
additional_kwargs=message.additional_kwargs.copy(),
example=getattr(message, "example", None), # type: ignore[arg-type]
example=getattr(message, "example", None),
)
num_converted += 1
messages.append(message)

2
libs/community/langchain_community/chat_loaders/whatsapp.py
View File

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

2
libs/community/langchain_community/chat_message_histories/sql.py
View File

@ -85,7 +85,7 @@ def create_message_model(table_name: str, DynamicBase: Any) -> Any:
"""
# Model declared inside a function to have a dynamic table name.
class Message(DynamicBase): # type: ignore[valid-type, misc]
class Message(DynamicBase):
__tablename__ = table_name
id = Column(Integer, primary_key=True)
session_id = Column(Text)

2
libs/community/langchain_community/chat_models/anyscale.py
View File

@ -167,7 +167,7 @@ class ChatAnyscale(ChatOpenAI):
else:
values["openai_api_base"] = values["anyscale_api_base"]
values["openai_api_key"] = values["anyscale_api_key"].get_secret_value()
values["client"] = openai.ChatCompletion # type: ignore[attr-defined]
values["client"] = openai.ChatCompletion
except AttributeError as exc:
raise ValueError(
"`openai` has no `ChatCompletion` attribute, this is likely "

2
libs/community/langchain_community/chat_models/azure_openai.py
View File

@ -227,7 +227,7 @@ class AzureChatOpenAI(ChatOpenAI):
**client_params
).chat.completions
else:
values["client"] = openai.ChatCompletion # type: ignore[attr-defined]
values["client"] = openai.ChatCompletion
return values
@property

40
libs/community/langchain_community/chat_models/azureml_endpoint.py
View File

@ -304,7 +304,7 @@ class AzureMLChatOnlineEndpoint(BaseChatModel, AzureMLBaseEndpoint):
"http_client": None,
}
client = openai.OpenAI(**client_params) # type: ignore[arg-type, arg-type, arg-type, arg-type, arg-type, arg-type]
client = openai.OpenAI(**client_params)
message_dicts = [
CustomOpenAIChatContentFormatter._convert_message_to_dict(m)
for m in messages
@ -312,30 +312,30 @@ class AzureMLChatOnlineEndpoint(BaseChatModel, AzureMLBaseEndpoint):
params = {"stream": True, "stop": stop, "model": None, **kwargs}
default_chunk_class = AIMessageChunk
for chunk in client.chat.completions.create(messages=message_dicts, **params): # type: ignore[arg-type]
for chunk in client.chat.completions.create(messages=message_dicts, **params):
if not isinstance(chunk, dict):
chunk = chunk.dict() # type: ignore[attr-defined]
if len(chunk["choices"]) == 0: # type: ignore[call-overload]
chunk = chunk.dict()
if len(chunk["choices"]) == 0:
continue
choice = chunk["choices"][0] # type: ignore[call-overload]
chunk = _convert_delta_to_message_chunk( # type: ignore[assignment]
choice["delta"], # type: ignore[arg-type, index]
default_chunk_class, # type: ignore[arg-type, index]
choice = chunk["choices"][0]
chunk = _convert_delta_to_message_chunk(
choice["delta"],
default_chunk_class,
)
generation_info = {}
if finish_reason := choice.get("finish_reason"): # type: ignore[union-attr]
if finish_reason := choice.get("finish_reason"):
generation_info["finish_reason"] = finish_reason
logprobs = choice.get("logprobs") # type: ignore[union-attr]
logprobs = choice.get("logprobs")
if logprobs:
generation_info["logprobs"] = logprobs
default_chunk_class = chunk.__class__ # type: ignore[assignment]
chunk = ChatGenerationChunk( # type: ignore[assignment]
message=chunk, # type: ignore[arg-type]
generation_info=generation_info or None, # type: ignore[arg-type]
default_chunk_class = chunk.__class__
chunk = ChatGenerationChunk(
message=chunk,
generation_info=generation_info or None,
)
if run_manager:
run_manager.on_llm_new_token(chunk.text, chunk=chunk, logprobs=logprobs) # type: ignore[attr-defined, arg-type]
yield chunk # type: ignore[misc]
run_manager.on_llm_new_token(chunk.text, chunk=chunk, logprobs=logprobs)
yield chunk
async def _astream(
self,
@ -359,7 +359,7 @@ class AzureMLChatOnlineEndpoint(BaseChatModel, AzureMLBaseEndpoint):
"http_client": None,
}
async_client = openai.AsyncOpenAI(**client_params) # type: ignore[arg-type, arg-type, arg-type, arg-type, arg-type, arg-type]
async_client = openai.AsyncOpenAI(**client_params)
message_dicts = [
CustomOpenAIChatContentFormatter._convert_message_to_dict(m)
for m in messages
@ -367,9 +367,9 @@ class AzureMLChatOnlineEndpoint(BaseChatModel, AzureMLBaseEndpoint):
params = {"stream": True, "stop": stop, "model": None, **kwargs}
default_chunk_class = AIMessageChunk
async for chunk in await async_client.chat.completions.create( # type: ignore[attr-defined]
messages=message_dicts, # type: ignore[arg-type]
**params, # type: ignore[arg-type]
async for chunk in await async_client.chat.completions.create(
messages=message_dicts,
**params,
):
if not isinstance(chunk, dict):
chunk = chunk.dict()

4
libs/community/langchain_community/chat_models/baichuan.py
View File

@ -128,7 +128,7 @@ def _convert_dict_to_message(_dict: Mapping[str, Any]) -> BaseMessage:
return AIMessage(
content=content,
additional_kwargs=additional_kwargs,
tool_calls=tool_calls, # type: ignore[arg-type]
tool_calls=tool_calls,
invalid_tool_calls=invalid_tool_calls,
)
elif role == "tool":
@ -137,7 +137,7 @@ def _convert_dict_to_message(_dict: Mapping[str, Any]) -> BaseMessage:
additional_kwargs["name"] = _dict["name"]
return ToolMessage(
content=content,
tool_call_id=_dict.get("tool_call_id"), # type: ignore[arg-type]
tool_call_id=_dict.get("tool_call_id"),
additional_kwargs=additional_kwargs,
)
elif role == "system":

2
libs/community/langchain_community/chat_models/baidu_qianfan_endpoint.py
View File

@ -821,7 +821,7 @@ class QianfanChatEndpoint(BaseChatModel):
if is_pydantic_schema:
output_parser: OutputParserLike = PydanticToolsParser(
tools=[schema], # type: ignore[list-item]
first_tool_only=True, # type: ignore[list-item]
first_tool_only=True,
)
else:
key_name = convert_to_openai_tool(schema)["function"]["name"]

4
libs/community/langchain_community/chat_models/cloudflare_workersai.py
View File

@ -213,7 +213,7 @@ class ChatCloudflareWorkersAI(BaseChatModel):
if is_pydantic_schema:
output_parser: OutputParserLike = PydanticToolsParser(
tools=[schema], # type: ignore[list-item]
first_tool_only=True, # type: ignore[list-item]
first_tool_only=True,
)
else:
output_parser = JsonOutputKeyToolsParser(
@ -222,7 +222,7 @@ class ChatCloudflareWorkersAI(BaseChatModel):
elif method == "json_mode":
llm = self.bind(response_format={"type": "json_object"})
output_parser = (
PydanticOutputParser(pydantic_object=schema) # type: ignore[type-var, arg-type]
PydanticOutputParser(pydantic_object=schema) # type: ignore[arg-type]
if is_pydantic_schema
else JsonOutputParser()
)

2
libs/community/langchain_community/chat_models/everlyai.py
View File

@ -110,7 +110,7 @@ class ChatEverlyAI(ChatOpenAI):
"Please install it with `pip install openai`.",
) from e
try:
values["client"] = openai.ChatCompletion # type: ignore[attr-defined]
values["client"] = openai.ChatCompletion
except AttributeError as exc:
raise ValueError(
"`openai` has no `ChatCompletion` attribute, this is likely "

22
libs/community/langchain_community/chat_models/jinachat.py
View File

@ -70,11 +70,11 @@ def _create_retry_decorator(llm: JinaChat) -> Callable[[Any], Any]:
stop=stop_after_attempt(llm.max_retries),
wait=wait_exponential(multiplier=1, min=min_seconds, max=max_seconds),
retry=(
retry_if_exception_type(openai.error.Timeout) # type: ignore[attr-defined]
| retry_if_exception_type(openai.error.APIError) # type: ignore[attr-defined]
| retry_if_exception_type(openai.error.APIConnectionError) # type: ignore[attr-defined]
| retry_if_exception_type(openai.error.RateLimitError) # type: ignore[attr-defined]
| retry_if_exception_type(openai.error.ServiceUnavailableError) # type: ignore[attr-defined]
retry_if_exception_type(openai.error.Timeout)
| retry_if_exception_type(openai.error.APIError)
| retry_if_exception_type(openai.error.APIConnectionError)
| retry_if_exception_type(openai.error.RateLimitError)
| retry_if_exception_type(openai.error.ServiceUnavailableError)
),
before_sleep=before_sleep_log(logger, logging.WARNING),
)
@ -234,7 +234,7 @@ class JinaChat(BaseChatModel):
"Please install it with `pip install openai`."
)
try:
values["client"] = openai.ChatCompletion # type: ignore[attr-defined]
values["client"] = openai.ChatCompletion
except AttributeError:
raise ValueError(
"`openai` has no `ChatCompletion` attribute, this is likely "
@ -266,11 +266,11 @@ class JinaChat(BaseChatModel):
stop=stop_after_attempt(self.max_retries),
wait=wait_exponential(multiplier=1, min=min_seconds, max=max_seconds),
retry=(
retry_if_exception_type(openai.error.Timeout) # type: ignore[attr-defined]
| retry_if_exception_type(openai.error.APIError) # type: ignore[attr-defined]
| retry_if_exception_type(openai.error.APIConnectionError) # type: ignore[attr-defined]
| retry_if_exception_type(openai.error.RateLimitError) # type: ignore[attr-defined]
| retry_if_exception_type(openai.error.ServiceUnavailableError) # type: ignore[attr-defined]
retry_if_exception_type(openai.error.Timeout)
| retry_if_exception_type(openai.error.APIError)
| retry_if_exception_type(openai.error.APIConnectionError)
| retry_if_exception_type(openai.error.RateLimitError)
| retry_if_exception_type(openai.error.ServiceUnavailableError)
),
before_sleep=before_sleep_log(logger, logging.WARNING),
)

2
libs/community/langchain_community/chat_models/konko.py
View File

@ -42,7 +42,7 @@ DEFAULT_MODEL = "meta-llama/Llama-2-13b-chat-hf"
logger = logging.getLogger(__name__)
class ChatKonko(ChatOpenAI): # type: ignore[override]
class ChatKonko(ChatOpenAI):
"""`ChatKonko` Chat large language models API.
To use, you should have the ``konko`` python package installed, and the

2
libs/community/langchain_community/chat_models/llamacpp.py
View File

@ -664,7 +664,7 @@ def _convert_dict_to_message(_dict: Mapping[str, Any]) -> BaseMessage:
additional_kwargs=additional_kwargs,
name=name,
id=id_,
tool_calls=tool_calls, # type: ignore[arg-type]
tool_calls=tool_calls,
invalid_tool_calls=invalid_tool_calls,
)
elif role == "system":

2
libs/community/langchain_community/chat_models/minimax.py
View File

@ -777,7 +777,7 @@ class MiniMaxChat(BaseChatModel):
if is_pydantic_schema:
output_parser: OutputParserLike = PydanticToolsParser(
tools=[schema], # type: ignore[list-item]
first_tool_only=True, # type: ignore[list-item]
first_tool_only=True,
)
else:
key_name = convert_to_openai_tool(schema)["function"]["name"]

2
libs/community/langchain_community/chat_models/moonshot.py
View File

@ -12,7 +12,7 @@ from langchain_community.chat_models import ChatOpenAI
from langchain_community.llms.moonshot import MOONSHOT_SERVICE_URL_BASE, MoonshotCommon
class MoonshotChat(MoonshotCommon, ChatOpenAI): # type: ignore[misc, override, override]
class MoonshotChat(MoonshotCommon, ChatOpenAI): # type: ignore[misc]
"""Moonshot chat model integration.
Setup:

2
libs/community/langchain_community/chat_models/oci_data_science.py
View File

@ -587,7 +587,7 @@ class ChatOCIModelDeployment(BaseChatModel, BaseOCIModelDeployment):
if method == "json_mode":
llm = self.bind(response_format={"type": "json_object"})
output_parser = (
PydanticOutputParser(pydantic_object=schema) # type: ignore[type-var, arg-type]
PydanticOutputParser(pydantic_object=schema) # type: ignore[arg-type]
if is_pydantic_schema
else JsonOutputParser()
)

2
libs/community/langchain_community/chat_models/oci_generative_ai.py
View File

@ -725,7 +725,7 @@ class ChatOCIGenAI(BaseChatModel, OCIGenAIBase):
elif method == "json_mode":
llm = self.bind(response_format={"type": "json_object"})
output_parser = (
PydanticOutputParser(pydantic_object=schema) # type: ignore[type-var, arg-type]
PydanticOutputParser(pydantic_object=schema) # type: ignore[arg-type]
if is_pydantic_schema
else JsonOutputParser()
)

2
libs/community/langchain_community/chat_models/octoai.py
View File

@ -98,7 +98,7 @@ class ChatOctoAI(ChatOpenAI):
else:
values["openai_api_base"] = values["octoai_api_base"]
values["openai_api_key"] = values["octoai_api_token"].get_secret_value()
values["client"] = openai.ChatCompletion # type: ignore[attr-defined]
values["client"] = openai.ChatCompletion
except ImportError:
raise ImportError(
"Could not import openai python package. "

14
libs/community/langchain_community/chat_models/openai.py
View File

@ -88,11 +88,11 @@ def _create_retry_decorator(
import openai
errors = [
openai.error.Timeout, # type: ignore[attr-defined]
openai.error.APIError, # type: ignore[attr-defined]
openai.error.APIConnectionError, # type: ignore[attr-defined]
openai.error.RateLimitError, # type: ignore[attr-defined]
openai.error.ServiceUnavailableError, # type: ignore[attr-defined]
openai.error.Timeout,
openai.error.APIError,
openai.error.APIConnectionError,
openai.error.RateLimitError,
openai.error.ServiceUnavailableError,
]
return create_base_retry_decorator(
error_types=errors, max_retries=llm.max_retries, run_manager=run_manager
@ -358,7 +358,7 @@ class ChatOpenAI(BaseChatModel):
**client_params
).chat.completions
elif not values.get("client"):
values["client"] = openai.ChatCompletion # type: ignore[attr-defined]
values["client"] = openai.ChatCompletion
else:
pass
return values
@ -595,7 +595,7 @@ class ChatOpenAI(BaseChatModel):
if self.openai_proxy:
import openai
openai.proxy = {"http": self.openai_proxy, "https": self.openai_proxy} # type: ignore[attr-defined]
openai.proxy = {"http": self.openai_proxy, "https": self.openai_proxy}
return {**self._default_params, **openai_creds}
def _get_invocation_params(

2
libs/community/langchain_community/chat_models/perplexity.py
View File

@ -486,7 +486,7 @@ class ChatPerplexity(BaseChatModel):
if is_pydantic_schema and hasattr(
schema, "model_json_schema"
): # accounting for pydantic v1 and v2
response_format = schema.model_json_schema() # type: ignore[union-attr]
response_format = schema.model_json_schema()
elif is_pydantic_schema:
response_format = schema.schema() # type: ignore[union-attr]
elif isinstance(schema, dict):

6
libs/community/langchain_community/chat_models/sambanova.py
View File

@ -636,7 +636,7 @@ class ChatSambaNovaCloud(BaseChatModel):
if is_pydantic_schema:
output_parser: OutputParserLike[Any] = PydanticToolsParser(
tools=[schema], # type: ignore[list-item]
first_tool_only=True, # type: ignore[list-item]
first_tool_only=True,
)
else:
output_parser = JsonOutputKeyToolsParser(
@ -648,7 +648,7 @@ class ChatSambaNovaCloud(BaseChatModel):
# llm = self.bind(response_format={"type": "json_object"})
if is_pydantic_schema:
schema = cast(Type[BaseModel], schema)
output_parser = PydanticOutputParser(pydantic_object=schema) # type: ignore[type-var, arg-type]
output_parser = PydanticOutputParser(pydantic_object=schema)
else:
output_parser = JsonOutputParser()
@ -666,7 +666,7 @@ class ChatSambaNovaCloud(BaseChatModel):
# )
if is_pydantic_schema:
schema = cast(Type[BaseModel], schema)
output_parser = PydanticOutputParser(pydantic_object=schema) # type: ignore[type-var, arg-type]
output_parser = PydanticOutputParser(pydantic_object=schema)
else:
output_parser = JsonOutputParser()
else:

2
libs/community/langchain_community/chat_models/solar.py
View File

@ -13,7 +13,7 @@ from langchain_community.llms.solar import SOLAR_SERVICE_URL_BASE, SolarCommon
@deprecated( # type: ignore[arg-type]
since="0.0.34", removal="1.0", alternative_import="langchain_upstage.ChatUpstage"
)
class SolarChat(SolarCommon, ChatOpenAI): # type: ignore[override, override]
class SolarChat(SolarCommon, ChatOpenAI):
"""Wrapper around Solar large language models.
To use, you should have the ``openai`` python package installed, and the
environment variable ``SOLAR_API_KEY`` set with your API key.

2
libs/community/langchain_community/chat_models/symblai_nebula.py
View File

@ -176,7 +176,7 @@ class ChatNebula(BaseChatModel):
json_payload = json.dumps(payload)
async with ClientSession() as session:
async with session.post( # type: ignore[call-arg]
async with session.post( # type: ignore[call-arg,unused-ignore]
url, data=json_payload, headers=headers, stream=True
) as response:
response.raise_for_status()

6
libs/community/langchain_community/chat_models/tongyi.py
View File

@ -140,7 +140,7 @@ def convert_dict_to_message(
else AIMessage(
content=content,
additional_kwargs=additional_kwargs,
tool_calls=tool_calls, # type: ignore[arg-type]
tool_calls=tool_calls,
invalid_tool_calls=invalid_tool_calls,
)
)
@ -163,7 +163,7 @@ def convert_dict_to_message(
if is_chunk
else ToolMessage(
content=_dict.get("content", ""),
tool_call_id=_dict.get("tool_call_id"), # type: ignore[arg-type]
tool_call_id=_dict.get("tool_call_id"),
additional_kwargs=additional_kwargs,
)
)
@ -894,7 +894,7 @@ class ChatTongyi(BaseChatModel):
if is_pydantic_schema:
output_parser: OutputParserLike = PydanticToolsParser(
tools=[schema], # type: ignore[list-item]
first_tool_only=True, # type: ignore[list-item]
first_tool_only=True,
)
else:
key_name = convert_to_openai_tool(schema)["function"]["name"]

2
libs/community/langchain_community/chat_models/vertexai.py
View File

@ -209,7 +209,7 @@ def _get_question(messages: List[BaseMessage]) -> HumanMessage:
removal="1.0",
alternative_import="langchain_google_vertexai.ChatVertexAI",
)
class ChatVertexAI(_VertexAICommon, BaseChatModel): # type: ignore[override]
class ChatVertexAI(_VertexAICommon, BaseChatModel):
"""`Vertex AI` Chat large language models API."""
model_name: str = "chat-bison"

4
libs/community/langchain_community/chat_models/zhipuai.py
View File

@ -162,7 +162,7 @@ def _convert_dict_to_message(dct: Dict[str, Any]) -> BaseMessage:
additional_kwargs["name"] = dct["name"]
return ToolMessage(
content=content,
tool_call_id=dct.get("tool_call_id"), # type: ignore[arg-type]
tool_call_id=dct.get("tool_call_id"),
additional_kwargs=additional_kwargs,
)
return ChatMessage(role=role, content=content) # type: ignore[arg-type]
@ -861,7 +861,7 @@ class ChatZhipuAI(BaseChatModel):
if is_pydantic_schema:
output_parser: OutputParserLike = PydanticToolsParser(
tools=[schema], # type: ignore[list-item]
first_tool_only=True, # type: ignore[list-item]
first_tool_only=True,
)
else:
output_parser = JsonOutputKeyToolsParser(

4
libs/community/langchain_community/document_loaders/async_html.py
View File

@ -226,8 +226,8 @@ class AsyncHtmlLoader(BaseLoader):
# in a separate loop, in a separate thread.
with ThreadPoolExecutor(max_workers=1) as executor:
future: Future[List[str]] = executor.submit(
asyncio.run, # type: ignore[arg-type]
self.fetch_all(self.web_paths), # type: ignore[arg-type]
asyncio.run,
self.fetch_all(self.web_paths),
)
results = future.result()
except RuntimeError:

2
libs/community/langchain_community/document_loaders/blob_loaders/cloud_blob_loader.py
View File

@ -224,7 +224,7 @@ class CloudBlobLoader(BlobLoader):
yield self.path
return
paths = self.path.glob(self.glob) # type: ignore[attr-defined]
paths = self.path.glob(self.glob)
for path in paths:
if self.exclude:
if any(path.match(glob) for glob in self.exclude):

8
libs/community/langchain_community/document_loaders/concurrent.py
View File

@ -24,9 +24,9 @@ class ConcurrentLoader(GenericLoader):
def __init__(
self,
blob_loader: BlobLoader, # type: ignore[valid-type]
blob_loader: BlobLoader,
blob_parser: BaseBlobParser,
num_workers: int = 4, # type: ignore[valid-type]
num_workers: int = 4,
) -> None:
super().__init__(blob_loader, blob_parser)
self.num_workers = num_workers
@ -40,7 +40,7 @@ class ConcurrentLoader(GenericLoader):
) as executor:
futures = {
executor.submit(self.blob_parser.lazy_parse, blob)
for blob in self.blob_loader.yield_blobs() # type: ignore[attr-defined]
for blob in self.blob_loader.yield_blobs()
}
for future in concurrent.futures.as_completed(futures):
yield from future.result()
@ -72,7 +72,7 @@ class ConcurrentLoader(GenericLoader):
num_workers: Max number of concurrent workers to use.
parser_kwargs: Keyword arguments to pass to the parser.
"""
blob_loader = FileSystemBlobLoader( # type: ignore[attr-defined, misc]
blob_loader = FileSystemBlobLoader(
path,
glob=glob,
exclude=exclude,

2
libs/community/langchain_community/document_loaders/confluence.py
View File

@ -428,7 +428,7 @@ class ConfluenceLoader(BaseLoader):
self.number_of_retries # type: ignore[arg-type]
),
wait=wait_exponential(
multiplier=1, # type: ignore[arg-type]
multiplier=1,
min=self.min_retry_seconds, # type: ignore[arg-type]
max=self.max_retry_seconds, # type: ignore[arg-type]
),

2
libs/community/langchain_community/document_loaders/csv_loader.py
View File

@ -223,4 +223,4 @@ class UnstructuredCSVLoader(UnstructuredFileLoader):
def _get_elements(self) -> List:
from unstructured.partition.csv import partition_csv
return partition_csv(filename=self.file_path, **self.unstructured_kwargs) # type: ignore[arg-type]
return partition_csv(filename=self.file_path, **self.unstructured_kwargs)

6
libs/community/langchain_community/document_loaders/doc_intelligence.py
View File

@ -101,7 +101,7 @@ class AzureAIDocumentIntelligenceLoader(BaseLoader):
self.url_path = url_path
self.bytes_source = bytes_source
self.parser = AzureAIDocumentIntelligenceParser( # type: ignore[misc]
self.parser = AzureAIDocumentIntelligenceParser(
api_endpoint=api_endpoint,
api_key=api_key,
api_version=api_version,
@ -116,10 +116,10 @@ class AzureAIDocumentIntelligenceLoader(BaseLoader):
) -> Iterator[Document]:
"""Lazy load the document as pages."""
if self.file_path is not None:
blob = Blob.from_path(self.file_path) # type: ignore[attr-defined]
blob = Blob.from_path(self.file_path)
yield from self.parser.parse(blob)
elif self.url_path is not None:
yield from self.parser.parse_url(self.url_path) # type: ignore[arg-type]
yield from self.parser.parse_url(self.url_path)
elif self.bytes_source is not None:
yield from self.parser.parse_bytes(self.bytes_source)
else:

6
libs/community/langchain_community/document_loaders/email.py
View File

@ -60,16 +60,16 @@ class UnstructuredEmailLoader(UnstructuredFileLoader):
def _get_elements(self) -> List:
from unstructured.file_utils.filetype import FileType, detect_filetype
filetype = detect_filetype(self.file_path) # type: ignore[arg-type]
filetype = detect_filetype(self.file_path)
if filetype == FileType.EML:
from unstructured.partition.email import partition_email
return partition_email(filename=self.file_path, **self.unstructured_kwargs) # type: ignore[arg-type]
return partition_email(filename=self.file_path, **self.unstructured_kwargs)
elif satisfies_min_unstructured_version("0.5.8") and filetype == FileType.MSG:
from unstructured.partition.msg import partition_msg
return partition_msg(filename=self.file_path, **self.unstructured_kwargs) # type: ignore[arg-type]
return partition_msg(filename=self.file_path, **self.unstructured_kwargs)
else:
raise ValueError(
f"Filetype {filetype} is not supported in UnstructuredEmailLoader."

2
libs/community/langchain_community/document_loaders/epub.py
View File

@ -52,4 +52,4 @@ class UnstructuredEPubLoader(UnstructuredFileLoader):
def _get_elements(self) -> List:
from unstructured.partition.epub import partition_epub
return partition_epub(filename=self.file_path, **self.unstructured_kwargs) # type: ignore[arg-type]
return partition_epub(filename=self.file_path, **self.unstructured_kwargs)

2
libs/community/langchain_community/document_loaders/excel.py
View File

@ -49,4 +49,4 @@ class UnstructuredExcelLoader(UnstructuredFileLoader):
def _get_elements(self) -> List:
from unstructured.partition.xlsx import partition_xlsx
return partition_xlsx(filename=self.file_path, **self.unstructured_kwargs) # type: ignore[arg-type]
return partition_xlsx(filename=self.file_path, **self.unstructured_kwargs)

6
libs/community/langchain_community/document_loaders/generic.py
View File

@ -96,7 +96,7 @@ class GenericLoader(BaseLoader):
def __init__(
self,
blob_loader: BlobLoader, # type: ignore[valid-type]
blob_loader: BlobLoader,
blob_parser: BaseBlobParser,
) -> None:
"""A generic document loader.
@ -112,7 +112,7 @@ class GenericLoader(BaseLoader):
self,
) -> Iterator[Document]:
"""Load documents lazily. Use this when working at a large scale."""
for blob in self.blob_loader.yield_blobs(): # type: ignore[attr-defined]
for blob in self.blob_loader.yield_blobs():
yield from self.blob_parser.lazy_parse(blob)
def load_and_split(
@ -159,7 +159,7 @@ class GenericLoader(BaseLoader):
Returns:
A generic document loader.
"""
blob_loader = FileSystemBlobLoader( # type: ignore[attr-defined, misc]
blob_loader = FileSystemBlobLoader(
path,
glob=glob,
exclude=exclude,

2
libs/community/langchain_community/document_loaders/git.py
View File

@ -74,7 +74,7 @@ class GitLoader(BaseLoader):
file_path = os.path.join(self.repo_path, item.path)
ignored_files = repo.ignored([file_path]) # type: ignore[arg-type]
ignored_files = repo.ignored([file_path])
if len(ignored_files):
continue

2
libs/community/langchain_community/document_loaders/html.py
View File

@ -48,4 +48,4 @@ class UnstructuredHTMLLoader(UnstructuredFileLoader):
def _get_elements(self) -> List:
from unstructured.partition.html import partition_html
return partition_html(filename=self.file_path, **self.unstructured_kwargs) # type: ignore[arg-type]
return partition_html(filename=self.file_path, **self.unstructured_kwargs)

2
libs/community/langchain_community/document_loaders/image.py
View File

@ -48,4 +48,4 @@ class UnstructuredImageLoader(UnstructuredFileLoader):
def _get_elements(self) -> List:
from unstructured.partition.image import partition_image
return partition_image(filename=self.file_path, **self.unstructured_kwargs) # type: ignore[arg-type]
return partition_image(filename=self.file_path, **self.unstructured_kwargs)

6
libs/community/langchain_community/document_loaders/image_captions.py
View File

@ -76,13 +76,13 @@ class ImageCaptionLoader(BaseLoader):
try:
if isinstance(image, bytes):
image = Image.open(BytesIO(image)).convert("RGB") # type: ignore[assignment]
image = Image.open(BytesIO(image)).convert("RGB")
elif isinstance(image, str) and (
image.startswith("http://") or image.startswith("https://")
):
image = Image.open(requests.get(image, stream=True).raw).convert("RGB") # type: ignore[assignment, arg-type]
image = Image.open(requests.get(image, stream=True).raw).convert("RGB")
else:
image = Image.open(image).convert("RGB") # type: ignore[assignment]
image = Image.open(image).convert("RGB")
except Exception:
if isinstance(image_source, bytes):
msg = "Could not get image data from bytes"

2
libs/community/langchain_community/document_loaders/markdown.py
View File

@ -93,4 +93,4 @@ class UnstructuredMarkdownLoader(UnstructuredFileLoader):
def _get_elements(self) -> List:
from unstructured.partition.md import partition_md
return partition_md(filename=self.file_path, **self.unstructured_kwargs) # type: ignore[arg-type]
return partition_md(filename=self.file_path, **self.unstructured_kwargs)

2
libs/community/langchain_community/document_loaders/odt.py
View File

@ -52,4 +52,4 @@ class UnstructuredODTLoader(UnstructuredFileLoader):
def _get_elements(self) -> List:
from unstructured.partition.odt import partition_odt
return partition_odt(filename=self.file_path, **self.unstructured_kwargs) # type: ignore[arg-type]
return partition_odt(filename=self.file_path, **self.unstructured_kwargs)

2
libs/community/langchain_community/document_loaders/org_mode.py
View File

@ -52,4 +52,4 @@ class UnstructuredOrgModeLoader(UnstructuredFileLoader):
def _get_elements(self) -> List:
from unstructured.partition.org import partition_org
return partition_org(filename=self.file_path, **self.unstructured_kwargs) # type: ignore[arg-type]
return partition_org(filename=self.file_path, **self.unstructured_kwargs)

2
libs/community/langchain_community/document_loaders/parsers/audio.py
View File

@ -322,7 +322,7 @@ class OpenAIWhisperParser(BaseBlobParser):
model=self.model, file=file_obj, **self._create_params
)
else:
transcript = openai.Audio.transcribe(self.model, file_obj) # type: ignore[attr-defined]
transcript = openai.Audio.transcribe(self.model, file_obj)
break
except Exception as e:
attempts += 1

10
libs/community/langchain_community/document_loaders/parsers/msword.py
View File

@ -9,7 +9,7 @@ from langchain_community.document_loaders.blob_loaders import Blob
class MsWordParser(BaseBlobParser):
"""Parse the Microsoft Word documents from a blob."""
def lazy_parse(self, blob: Blob) -> Iterator[Document]: # type: ignore[valid-type]
def lazy_parse(self, blob: Blob) -> Iterator[Document]:
"""Parse a Microsoft Word document into the Document iterator.
Args:
@ -33,13 +33,13 @@ class MsWordParser(BaseBlobParser):
partition_docx
),
}
if blob.mimetype not in ( # type: ignore[attr-defined]
if blob.mimetype not in (
"application/msword",
"application/vnd.openxmlformats-officedocument.wordprocessingml.document",
):
raise ValueError("This blob type is not supported for this parser.")
with blob.as_bytes_io() as word_document: # type: ignore[attr-defined]
elements = mime_type_parser[blob.mimetype](file=word_document) # type: ignore[attr-defined] # type: ignore[operator] # type: ignore[operator] # type: ignore[operator] # type: ignore[operator] # type: ignore[operator] # type: ignore[operator]
with blob.as_bytes_io() as word_document:
elements = mime_type_parser[blob.mimetype](file=word_document)
text = "\n\n".join([str(el) for el in elements])
metadata = {"source": blob.source} # type: ignore[attr-defined]
metadata = {"source": blob.source}
yield Document(page_content=text, metadata=metadata)

50
libs/community/langchain_community/document_loaders/parsers/pdf.py
View File

@ -340,7 +340,7 @@ class PyPDFParser(BaseBlobParser):
self.extraction_mode = extraction_mode
self.extraction_kwargs = extraction_kwargs or {}
def lazy_parse(self, blob: Blob) -> Iterator[Document]: # type: ignore[valid-type]
def lazy_parse(self, blob: Blob) -> Iterator[Document]:
"""
Lazily parse the blob.
Insert image, if possible, between two paragraphs.
@ -380,7 +380,7 @@ class PyPDFParser(BaseBlobParser):
**self.extraction_kwargs,
)
with blob.as_bytes_io() as pdf_file_obj: # type: ignore[attr-defined]
with blob.as_bytes_io() as pdf_file_obj:
pdf_reader = pypdf.PdfReader(pdf_file_obj, password=self.password)
doc_metadata = _purge_metadata(
@ -434,7 +434,7 @@ class PyPDFParser(BaseBlobParser):
if "/XObject" not in cast(dict, page["/Resources"]).keys():
return ""
xObject = page["/Resources"]["/XObject"].get_object() # type: ignore[index]
xObject = page["/Resources"]["/XObject"].get_object()
images = []
for obj in xObject:
np_image: Any = None
@ -677,7 +677,7 @@ class PDFMinerParser(BaseBlobParser):
return metadata
def lazy_parse(self, blob: Blob) -> Iterator[Document]: # type: ignore[valid-type]
def lazy_parse(self, blob: Blob) -> Iterator[Document]:
"""
Lazily parse the blob.
Insert image, if possible, between two paragraphs.
@ -919,7 +919,7 @@ class PyMuPDFParser(BaseBlobParser):
self.extract_tables = extract_tables
self.extract_tables_settings = extract_tables_settings
def lazy_parse(self, blob: Blob) -> Iterator[Document]: # type: ignore[valid-type]
def lazy_parse(self, blob: Blob) -> Iterator[Document]:
return self._lazy_parse(
blob,
)
@ -930,7 +930,7 @@ class PyMuPDFParser(BaseBlobParser):
# text-kwargs is present for backwards compatibility.
# Users should not use it directly.
text_kwargs: Optional[dict[str, Any]] = None,
) -> Iterator[Document]: # type: ignore[valid-type]
) -> Iterator[Document]:
"""Lazily parse the blob.
Insert image, if possible, between two paragraphs.
In this way, a paragraph can be continued on the next page.
@ -990,8 +990,8 @@ class PyMuPDFParser(BaseBlobParser):
)
with PyMuPDFParser._lock:
with blob.as_bytes_io() as file_path: # type: ignore[attr-defined]
if blob.data is None: # type: ignore[attr-defined]
with blob.as_bytes_io() as file_path:
if blob.data is None:
doc = pymupdf.open(file_path)
else:
doc = pymupdf.open(stream=file_path, filetype="pdf")
@ -1066,8 +1066,8 @@ class PyMuPDFParser(BaseBlobParser):
"producer": "PyMuPDF",
"creator": "PyMuPDF",
"creationdate": "",
"source": blob.source, # type: ignore[attr-defined]
"file_path": blob.source, # type: ignore[attr-defined]
"source": blob.source,
"file_path": blob.source,
"total_pages": len(doc),
},
**{
@ -1273,7 +1273,7 @@ class PyPDFium2Parser(BaseBlobParser):
self.mode = mode
self.pages_delimiter = pages_delimiter
def lazy_parse(self, blob: Blob) -> Iterator[Document]: # type: ignore[valid-type]
def lazy_parse(self, blob: Blob) -> Iterator[Document]:
"""
Lazily parse the blob.
Insert image, if possible, between two paragraphs.
@ -1299,7 +1299,7 @@ class PyPDFium2Parser(BaseBlobParser):
# pypdfium2 is really finicky with respect to closing things,
# if done incorrectly creates seg faults.
with PyPDFium2Parser._lock:
with blob.as_bytes_io() as file_path: # type: ignore[attr-defined]
with blob.as_bytes_io() as file_path:
pdf_reader = None
try:
pdf_reader = pypdfium2.PdfDocument(
@ -1410,11 +1410,11 @@ class PDFPlumberParser(BaseBlobParser):
self.dedupe = dedupe
self.extract_images = extract_images
def lazy_parse(self, blob: Blob) -> Iterator[Document]: # type: ignore[valid-type]
def lazy_parse(self, blob: Blob) -> Iterator[Document]:
"""Lazily parse the blob."""
import pdfplumber
with blob.as_bytes_io() as file_path: # type: ignore[attr-defined]
with blob.as_bytes_io() as file_path:
doc = pdfplumber.open(file_path) # open document
yield from [
@ -1424,8 +1424,8 @@ class PDFPlumberParser(BaseBlobParser):
+ self._extract_images_from_page(page),
metadata=dict(
{
"source": blob.source, # type: ignore[attr-defined]
"file_path": blob.source, # type: ignore[attr-defined]
"source": blob.source,
"file_path": blob.source,
"page": page.page_number - 1,
"total_pages": len(doc.pages),
},
@ -1593,14 +1593,14 @@ class AmazonTextractPDFParser(BaseBlobParser):
else:
self.boto3_textract_client = client
def lazy_parse(self, blob: Blob) -> Iterator[Document]: # type: ignore[valid-type]
def lazy_parse(self, blob: Blob) -> Iterator[Document]:
"""Iterates over the Blob pages and returns an Iterator with a Document
for each page, like the other parsers If multi-page document, blob.path
has to be set to the S3 URI and for single page docs
the blob.data is taken
"""
url_parse_result = urlparse(str(blob.path)) if blob.path else None # type: ignore[attr-defined]
url_parse_result = urlparse(str(blob.path)) if blob.path else None
# Either call with S3 path (multi-page) or with bytes (single-page)
if (
url_parse_result
@ -1608,13 +1608,13 @@ class AmazonTextractPDFParser(BaseBlobParser):
and url_parse_result.netloc
):
textract_response_json = self.tc.call_textract(
input_document=str(blob.path), # type: ignore[attr-defined]
input_document=str(blob.path),
features=self.textract_features,
boto3_textract_client=self.boto3_textract_client,
)
else:
textract_response_json = self.tc.call_textract(
input_document=blob.as_bytes(), # type: ignore[attr-defined]
input_document=blob.as_bytes(),
features=self.textract_features,
call_mode=self.tc.Textract_Call_Mode.FORCE_SYNC,
boto3_textract_client=self.boto3_textract_client,
@ -1625,7 +1625,7 @@ class AmazonTextractPDFParser(BaseBlobParser):
for idx, page in enumerate(document.pages):
yield Document(
page_content=page.get_text(config=self.linearization_config),
metadata={"source": blob.source, "page": idx + 1}, # type: ignore[attr-defined]
metadata={"source": blob.source, "page": idx + 1},
)
@ -1645,23 +1645,23 @@ class DocumentIntelligenceParser(BaseBlobParser):
self.client = client
self.model = model
def _generate_docs(self, blob: Blob, result: Any) -> Iterator[Document]: # type: ignore[valid-type]
def _generate_docs(self, blob: Blob, result: Any) -> Iterator[Document]:
for p in result.pages:
content = " ".join([line.content for line in p.lines])
d = Document(
page_content=content,
metadata={
"source": blob.source, # type: ignore[attr-defined]
"source": blob.source,
"page": p.page_number,
},
)
yield d
def lazy_parse(self, blob: Blob) -> Iterator[Document]: # type: ignore[valid-type]
def lazy_parse(self, blob: Blob) -> Iterator[Document]:
"""Lazily parse the blob."""
with blob.as_bytes_io() as file_obj: # type: ignore[attr-defined]
with blob.as_bytes_io() as file_obj:
poller = self.client.begin_analyze_document(self.model, file_obj)
result = poller.result()

4
libs/community/langchain_community/document_loaders/parsers/txt.py
View File

@ -11,6 +11,6 @@ from langchain_community.document_loaders.blob_loaders import Blob
class TextParser(BaseBlobParser):
"""Parser for text blobs."""
def lazy_parse(self, blob: Blob) -> Iterator[Document]: # type: ignore[valid-type]
def lazy_parse(self, blob: Blob) -> Iterator[Document]:
"""Lazily parse the blob."""
yield Document(page_content=blob.as_string(), metadata={"source": blob.source}) # type: ignore[attr-defined]
yield Document(page_content=blob.as_string(), metadata={"source": blob.source})

48
libs/community/langchain_community/document_loaders/pdf.py
View File

@ -91,7 +91,7 @@ class UnstructuredPDFLoader(UnstructuredFileLoader):
def _get_elements(self) -> list:
from unstructured.partition.pdf import partition_pdf
return partition_pdf(filename=self.file_path, **self.unstructured_kwargs) # type: ignore[arg-type]
return partition_pdf(filename=self.file_path, **self.unstructured_kwargs)
class BasePDFLoader(BaseLoader, ABC):
@ -299,11 +299,9 @@ class PyPDFLoader(BasePDFLoader):
In this way, a paragraph can be continued on the next page.
"""
if self.web_path:
blob = Blob.from_data( # type: ignore[attr-defined]
open(self.file_path, "rb").read(), path=self.web_path
)
blob = Blob.from_data(open(self.file_path, "rb").read(), path=self.web_path)
else:
blob = Blob.from_path(self.file_path) # type: ignore[attr-defined]
blob = Blob.from_path(self.file_path)
yield from self.parser.lazy_parse(blob)
@ -415,11 +413,9 @@ class PyPDFium2Loader(BasePDFLoader):
In this way, a paragraph can be continued on the next page.
"""
if self.web_path:
blob = Blob.from_data( # type: ignore[attr-defined]
open(self.file_path, "rb").read(), path=self.web_path
)
blob = Blob.from_data(open(self.file_path, "rb").read(), path=self.web_path)
else:
blob = Blob.from_path(self.file_path) # type: ignore[attr-defined]
blob = Blob.from_path(self.file_path)
yield from self.parser.parse(blob)
@ -674,11 +670,9 @@ class PDFMinerLoader(BasePDFLoader):
In this way, a paragraph can be continued on the next page.
"""
if self.web_path:
blob = Blob.from_data( # type: ignore[attr-defined]
open(self.file_path, "rb").read(), path=self.web_path
)
blob = Blob.from_data(open(self.file_path, "rb").read(), path=self.web_path)
else:
blob = Blob.from_path(self.file_path) # type: ignore[attr-defined]
blob = Blob.from_path(self.file_path)
yield from self.parser.lazy_parse(blob)
@ -850,9 +844,9 @@ class PyMuPDFLoader(BasePDFLoader):
)
parser = self.parser
if self.web_path:
blob = Blob.from_data(open(self.file_path, "rb").read(), path=self.web_path) # type: ignore[attr-defined]
blob = Blob.from_data(open(self.file_path, "rb").read(), path=self.web_path)
else:
blob = Blob.from_path(self.file_path) # type: ignore[attr-defined]
blob = Blob.from_path(self.file_path)
yield from parser._lazy_parse(blob, text_kwargs=kwargs)
def load(self, **kwargs: Any) -> list[Document]:
@ -1046,9 +1040,9 @@ class PDFPlumberLoader(BasePDFLoader):
extract_images=self.extract_images,
)
if self.web_path:
blob = Blob.from_data(open(self.file_path, "rb").read(), path=self.web_path) # type: ignore[attr-defined]
blob = Blob.from_data(open(self.file_path, "rb").read(), path=self.web_path)
else:
blob = Blob.from_path(self.file_path) # type: ignore[attr-defined]
blob = Blob.from_path(self.file_path)
return parser.parse(blob)
@ -1163,7 +1157,7 @@ class AmazonTextractPDFLoader(BasePDFLoader):
# raises ValueError when multipage and not on S3"""
if self.web_path and self._is_s3_url(self.web_path):
blob = Blob(path=self.web_path) # type: ignore[call-arg] # type: ignore[misc]
blob = Blob(path=self.web_path)
else:
blob = Blob.from_path(self.file_path)
if AmazonTextractPDFLoader._get_number_of_pages(blob) > 1:
@ -1176,7 +1170,7 @@ class AmazonTextractPDFLoader(BasePDFLoader):
yield from self.parser.parse(blob)
@staticmethod
def _get_number_of_pages(blob: Blob) -> int: # type: ignore[valid-type]
def _get_number_of_pages(blob: Blob) -> int:
try:
import pypdf
from PIL import Image, ImageSequence
@ -1186,22 +1180,20 @@ class AmazonTextractPDFLoader(BasePDFLoader):
"Could not import pypdf or Pilloe python package. "
"Please install it with `pip install pypdf Pillow`."
)
if blob.mimetype == "application/pdf": # type: ignore[attr-defined]
with blob.as_bytes_io() as input_pdf_file: # type: ignore[attr-defined]
if blob.mimetype == "application/pdf":
with blob.as_bytes_io() as input_pdf_file:
pdf_reader = pypdf.PdfReader(input_pdf_file)
return len(pdf_reader.pages)
elif blob.mimetype == "image/tiff": # type: ignore[attr-defined]
elif blob.mimetype == "image/tiff":
num_pages = 0
img = Image.open(blob.as_bytes()) # type: ignore[attr-defined]
img = Image.open(blob.as_bytes())
for _, _ in enumerate(ImageSequence.Iterator(img)):
num_pages += 1
return num_pages
elif blob.mimetype in ["image/png", "image/jpeg"]: # type: ignore[attr-defined]
elif blob.mimetype in ["image/png", "image/jpeg"]:
return 1
else:
raise ValueError( # type: ignore[attr-defined]
f"unsupported mime type: {blob.mimetype}"
)
raise ValueError(f"unsupported mime type: {blob.mimetype}")
class DedocPDFLoader(DedocBaseLoader):
@ -1348,7 +1340,7 @@ class DocumentIntelligenceLoader(BasePDFLoader):
self,
) -> Iterator[Document]:
"""Lazy load given path as pages."""
blob = Blob.from_path(self.file_path) # type: ignore[attr-defined]
blob = Blob.from_path(self.file_path)
yield from self.parser.parse(blob)

6
libs/community/langchain_community/document_loaders/powerpoint.py
View File

@ -59,7 +59,7 @@ class UnstructuredPowerPointLoader(UnstructuredFileLoader):
try:
import magic # noqa: F401
is_ppt = detect_filetype(self.file_path) == FileType.PPT # type: ignore[arg-type]
is_ppt = detect_filetype(self.file_path) == FileType.PPT
except ImportError:
_, extension = os.path.splitext(str(self.file_path))
is_ppt = extension == ".ppt"
@ -70,8 +70,8 @@ class UnstructuredPowerPointLoader(UnstructuredFileLoader):
if is_ppt:
from unstructured.partition.ppt import partition_ppt
return partition_ppt(filename=self.file_path, **self.unstructured_kwargs) # type: ignore[arg-type]
return partition_ppt(filename=self.file_path, **self.unstructured_kwargs)
else:
from unstructured.partition.pptx import partition_pptx
return partition_pptx(filename=self.file_path, **self.unstructured_kwargs) # type: ignore[arg-type]
return partition_pptx(filename=self.file_path, **self.unstructured_kwargs)

2
libs/community/langchain_community/document_loaders/rst.py
View File

@ -56,4 +56,4 @@ class UnstructuredRSTLoader(UnstructuredFileLoader):
def _get_elements(self) -> List:
from unstructured.partition.rst import partition_rst
return partition_rst(filename=self.file_path, **self.unstructured_kwargs) # type: ignore[arg-type]
return partition_rst(filename=self.file_path, **self.unstructured_kwargs)

2
libs/community/langchain_community/document_loaders/rtf.py
View File

@ -56,4 +56,4 @@ class UnstructuredRTFLoader(UnstructuredFileLoader):
def _get_elements(self) -> List:
from unstructured.partition.rtf import partition_rtf
return partition_rtf(filename=self.file_path, **self.unstructured_kwargs) # type: ignore[arg-type]
return partition_rtf(filename=self.file_path, **self.unstructured_kwargs)

2
libs/community/langchain_community/document_loaders/tsv.py
View File

@ -39,4 +39,4 @@ class UnstructuredTSVLoader(UnstructuredFileLoader):
def _get_elements(self) -> List:
from unstructured.partition.tsv import partition_tsv
return partition_tsv(filename=self.file_path, **self.unstructured_kwargs) # type: ignore[arg-type]
return partition_tsv(filename=self.file_path, **self.unstructured_kwargs)

4
libs/community/langchain_community/document_loaders/vsdx.py
View File

@ -37,7 +37,7 @@ class VsdxLoader(BaseLoader, ABC):
elif not os.path.isfile(self.file_path):
raise ValueError("File path %s is not a valid file or url" % self.file_path)
self.parser = VsdxParser() # type: ignore[misc]
self.parser = VsdxParser()
def __del__(self) -> None:
if hasattr(self, "temp_file"):
@ -50,5 +50,5 @@ class VsdxLoader(BaseLoader, ABC):
return bool(parsed.netloc) and bool(parsed.scheme)
def load(self) -> List[Document]:
blob = Blob.from_path(self.file_path) # type: ignore[attr-defined]
blob = Blob.from_path(self.file_path)
return list(self.parser.parse(blob))

2
libs/community/langchain_community/document_loaders/weather.py
View File

@ -33,7 +33,7 @@ class WeatherDataLoader(BaseLoader):
def from_params(
cls, places: Sequence[str], *, openweathermap_api_key: Optional[str] = None
) -> WeatherDataLoader:
client = OpenWeatherMapAPIWrapper(openweathermap_api_key=openweathermap_api_key) # type: ignore[call-arg]
client = OpenWeatherMapAPIWrapper(openweathermap_api_key=openweathermap_api_key)
return cls(client, places)
def lazy_load(

6
libs/community/langchain_community/document_loaders/word_document.py
View File

@ -121,7 +121,7 @@ class UnstructuredWordDocumentLoader(UnstructuredFileLoader):
try:
import magic # noqa: F401
is_doc = detect_filetype(self.file_path) == FileType.DOC # type: ignore[arg-type]
is_doc = detect_filetype(self.file_path) == FileType.DOC
except ImportError:
_, extension = os.path.splitext(str(self.file_path))
is_doc = extension == ".doc"
@ -132,8 +132,8 @@ class UnstructuredWordDocumentLoader(UnstructuredFileLoader):
if is_doc:
from unstructured.partition.doc import partition_doc
return partition_doc(filename=self.file_path, **self.unstructured_kwargs) # type: ignore[arg-type]
return partition_doc(filename=self.file_path, **self.unstructured_kwargs)
else:
from unstructured.partition.docx import partition_docx
return partition_docx(filename=self.file_path, **self.unstructured_kwargs) # type: ignore[arg-type]
return partition_docx(filename=self.file_path, **self.unstructured_kwargs)

2
libs/community/langchain_community/document_loaders/xml.py
View File

@ -46,4 +46,4 @@ class UnstructuredXMLLoader(UnstructuredFileLoader):
def _get_elements(self) -> List:
from unstructured.partition.xml import partition_xml
return partition_xml(filename=self.file_path, **self.unstructured_kwargs) # type: ignore[arg-type]
return partition_xml(filename=self.file_path, **self.unstructured_kwargs)

4
libs/community/langchain_community/document_transformers/google_translate.py
View File

@ -33,7 +33,7 @@ class GoogleTranslateTransformer(BaseDocumentTransformer):
"""
try:
from google.api_core.client_options import ClientOptions
from google.cloud import translate # type: ignore[attr-defined]
from google.cloud import translate
except ImportError as exc:
raise ImportError(
"Install Google Cloud Translate to use this parser."
@ -76,7 +76,7 @@ class GoogleTranslateTransformer(BaseDocumentTransformer):
Options: `text/plain`, `text/html`
"""
try:
from google.cloud import translate # type: ignore[attr-defined]
from google.cloud import translate
except ImportError as exc:
raise ImportError(
"Install Google Cloud Translate to use this parser."

2
libs/community/langchain_community/document_transformers/openai_functions.py
View File

@ -58,7 +58,7 @@ class OpenAIMetadataTagger(BaseDocumentTransformer, BaseModel):
new_documents = []
for document in documents:
extracted_metadata: Dict = self.tagging_chain.run(document.page_content) # type: ignore[assignment]
extracted_metadata: Dict = self.tagging_chain.run(document.page_content)
new_document = Document(
page_content=document.page_content,
metadata={**extracted_metadata, **document.metadata},

2
libs/community/langchain_community/embeddings/anyscale.py
View File

@ -68,7 +68,7 @@ class AnyscaleEmbeddings(OpenAIEmbeddings):
else:
values["openai_api_base"] = values["anyscale_api_base"]
values["openai_api_key"] = values["anyscale_api_key"].get_secret_value()
values["client"] = openai.Embedding # type: ignore[attr-defined]
values["client"] = openai.Embedding
return values
@property

8
libs/community/langchain_community/embeddings/azure_openai.py
View File

@ -20,7 +20,7 @@ from langchain_community.utils.openai import is_openai_v1
removal="1.0",
alternative_import="langchain_openai.AzureOpenAIEmbeddings",
)
class AzureOpenAIEmbeddings(OpenAIEmbeddings): # type: ignore[override]
class AzureOpenAIEmbeddings(OpenAIEmbeddings):
"""`Azure OpenAI` Embeddings API."""
azure_endpoint: Union[str, None] = None
@ -170,16 +170,16 @@ class AzureOpenAIEmbeddings(OpenAIEmbeddings): # type: ignore[override]
"default_query": self.default_query,
"http_client": self.http_client,
}
self.client = openai.AzureOpenAI(**client_params).embeddings # type: ignore[arg-type, arg-type, arg-type, arg-type, arg-type, arg-type, arg-type, arg-type, arg-type]
self.client = openai.AzureOpenAI(**client_params).embeddings
if self.azure_ad_async_token_provider:
client_params["azure_ad_token_provider"] = (
self.azure_ad_async_token_provider
)
self.async_client = openai.AsyncAzureOpenAI(**client_params).embeddings # type: ignore[arg-type, arg-type, arg-type, arg-type, arg-type, arg-type, arg-type, arg-type, arg-type]
self.async_client = openai.AsyncAzureOpenAI(**client_params).embeddings
else:
self.client = openai.Embedding # type: ignore[attr-defined]
self.client = openai.Embedding
return self
@property

28
libs/community/langchain_community/embeddings/localai.py
View File

@ -46,11 +46,11 @@ def _create_retry_decorator(embeddings: LocalAIEmbeddings) -> Callable[[Any], An
stop=stop_after_attempt(embeddings.max_retries),
wait=wait_exponential(multiplier=1, min=min_seconds, max=max_seconds),
retry=(
retry_if_exception_type(openai.error.Timeout) # type: ignore[attr-defined]
| retry_if_exception_type(openai.error.APIError) # type: ignore[attr-defined]
| retry_if_exception_type(openai.error.APIConnectionError) # type: ignore[attr-defined]
| retry_if_exception_type(openai.error.RateLimitError) # type: ignore[attr-defined]
| retry_if_exception_type(openai.error.ServiceUnavailableError) # type: ignore[attr-defined]
retry_if_exception_type(openai.error.Timeout)
| retry_if_exception_type(openai.error.APIError)
| retry_if_exception_type(openai.error.APIConnectionError)
| retry_if_exception_type(openai.error.RateLimitError)
| retry_if_exception_type(openai.error.ServiceUnavailableError)
),
before_sleep=before_sleep_log(logger, logging.WARNING),
)
@ -68,11 +68,11 @@ def _async_retry_decorator(embeddings: LocalAIEmbeddings) -> Any:
stop=stop_after_attempt(embeddings.max_retries),
wait=wait_exponential(multiplier=1, min=min_seconds, max=max_seconds),
retry=(
retry_if_exception_type(openai.error.Timeout) # type: ignore[attr-defined]
| retry_if_exception_type(openai.error.APIError) # type: ignore[attr-defined]
| retry_if_exception_type(openai.error.APIConnectionError) # type: ignore[attr-defined]
| retry_if_exception_type(openai.error.RateLimitError) # type: ignore[attr-defined]
| retry_if_exception_type(openai.error.ServiceUnavailableError) # type: ignore[attr-defined]
retry_if_exception_type(openai.error.Timeout)
| retry_if_exception_type(openai.error.APIError)
| retry_if_exception_type(openai.error.APIConnectionError)
| retry_if_exception_type(openai.error.RateLimitError)
| retry_if_exception_type(openai.error.ServiceUnavailableError)
),
before_sleep=before_sleep_log(logger, logging.WARNING),
)
@ -93,7 +93,7 @@ def _check_response(response: dict) -> dict:
if any(len(d["embedding"]) == 1 for d in response["data"]):
import openai
raise openai.error.APIError("LocalAI API returned an empty embedding") # type: ignore[attr-defined]
raise openai.error.APIError("LocalAI API returned an empty embedding")
return response
@ -230,7 +230,7 @@ class LocalAIEmbeddings(BaseModel, Embeddings):
try:
import openai
values["client"] = openai.Embedding # type: ignore[attr-defined]
values["client"] = openai.Embedding
except ImportError:
raise ImportError(
"Could not import openai python package. "
@ -253,10 +253,10 @@ class LocalAIEmbeddings(BaseModel, Embeddings):
if self.openai_proxy:
import openai
openai.proxy = { # type: ignore[attr-defined]
openai.proxy = {
"http": self.openai_proxy,
"https": self.openai_proxy,
} # type: ignore[assignment]
}
return openai_args
def _embedding_func(self, text: str, *, engine: str) -> List[float]:

2
libs/community/langchain_community/embeddings/mlflow.py
View File

@ -72,7 +72,7 @@ class MlflowEmbeddings(Embeddings, BaseModel):
for txt in _chunk(texts, 20):
resp = self._client.predict(
endpoint=self.endpoint,
inputs={"input": txt, **params}, # type: ignore[arg-type]
inputs={"input": txt, **params},
)
embeddings.extend(r["embedding"] for r in resp["data"])
return embeddings

4
libs/community/langchain_community/embeddings/octoai_embeddings.py
View File

@ -74,8 +74,8 @@ class OctoAIEmbeddings(OpenAIEmbeddings):
else:
values["openai_api_base"] = values["endpoint_url"]
values["openai_api_key"] = values["octoai_api_token"].get_secret_value()
values["client"] = openai.Embedding # type: ignore[attr-defined]
values["async_client"] = openai.Embedding # type: ignore[attr-defined]
values["client"] = openai.Embedding
values["async_client"] = openai.Embedding
except ImportError:
raise ImportError(

28
libs/community/langchain_community/embeddings/openai.py
View File

@ -58,11 +58,11 @@ def _create_retry_decorator(embeddings: OpenAIEmbeddings) -> Callable[[Any], Any
max=embeddings.retry_max_seconds,
),
retry=(
retry_if_exception_type(openai.error.Timeout) # type: ignore[attr-defined]
| retry_if_exception_type(openai.error.APIError) # type: ignore[attr-defined]
| retry_if_exception_type(openai.error.APIConnectionError) # type: ignore[attr-defined]
| retry_if_exception_type(openai.error.RateLimitError) # type: ignore[attr-defined]
| retry_if_exception_type(openai.error.ServiceUnavailableError) # type: ignore[attr-defined]
retry_if_exception_type(openai.error.Timeout)
| retry_if_exception_type(openai.error.APIError)
| retry_if_exception_type(openai.error.APIConnectionError)
| retry_if_exception_type(openai.error.RateLimitError)
| retry_if_exception_type(openai.error.ServiceUnavailableError)
),
before_sleep=before_sleep_log(logger, logging.WARNING),
)
@ -85,11 +85,11 @@ def _async_retry_decorator(embeddings: OpenAIEmbeddings) -> Any:
max=embeddings.retry_max_seconds,
),
retry=(
retry_if_exception_type(openai.error.Timeout) # type: ignore[attr-defined]
| retry_if_exception_type(openai.error.APIError) # type: ignore[attr-defined]
| retry_if_exception_type(openai.error.APIConnectionError) # type: ignore[attr-defined]
| retry_if_exception_type(openai.error.RateLimitError) # type: ignore[attr-defined]
| retry_if_exception_type(openai.error.ServiceUnavailableError) # type: ignore[attr-defined]
retry_if_exception_type(openai.error.Timeout)
| retry_if_exception_type(openai.error.APIError)
| retry_if_exception_type(openai.error.APIConnectionError)
| retry_if_exception_type(openai.error.RateLimitError)
| retry_if_exception_type(openai.error.ServiceUnavailableError)
),
before_sleep=before_sleep_log(logger, logging.WARNING),
)
@ -110,7 +110,7 @@ def _check_response(response: dict, skip_empty: bool = False) -> dict:
if any(len(d["embedding"]) == 1 for d in response["data"]) and not skip_empty:
import openai
raise openai.error.APIError("OpenAI API returned an empty embedding") # type: ignore[attr-defined]
raise openai.error.APIError("OpenAI API returned an empty embedding")
return response
@ -357,7 +357,7 @@ class OpenAIEmbeddings(BaseModel, Embeddings):
**client_params
).embeddings
elif not values.get("client"):
values["client"] = openai.Embedding # type: ignore[attr-defined]
values["client"] = openai.Embedding
else:
pass
return values
@ -390,10 +390,10 @@ class OpenAIEmbeddings(BaseModel, Embeddings):
"Please install it with `pip install openai`."
)
openai.proxy = { # type: ignore[attr-defined]
openai.proxy = {
"http": self.openai_proxy,
"https": self.openai_proxy,
} # type: ignore[assignment]
}
return openai_args
# please refer to

2
libs/community/langchain_community/embeddings/spacy_embeddings.py
View File

@ -54,7 +54,7 @@ class SpacyEmbeddings(BaseModel, Embeddings):
# Try to load the spaCy model
import spacy
values["nlp"] = spacy.load(model_name) # type: ignore[arg-type]
values["nlp"] = spacy.load(model_name)
except OSError:
# If the model is not found, raise a ValueError
raise ValueError(

6
libs/community/langchain_community/embeddings/vertexai.py
View File

@ -25,7 +25,7 @@ _MIN_BATCH_SIZE = 5
removal="1.0",
alternative_import="langchain_google_vertexai.VertexAIEmbeddings",
)
class VertexAIEmbeddings(_VertexAICommon, Embeddings): # type: ignore[override]
class VertexAIEmbeddings(_VertexAICommon, Embeddings):
"""Google Cloud VertexAI embedding models."""
# Instance context
@ -163,8 +163,8 @@ class VertexAIEmbeddings(_VertexAICommon, Embeddings): # type: ignore[override]
DeadlineExceeded,
]
retry_decorator = create_base_retry_decorator(
error_types=errors, # type: ignore[arg-type]
max_retries=self.max_retries, # type: ignore[arg-type]
error_types=errors,
max_retries=self.max_retries,
)
@retry_decorator

2
libs/community/langchain_community/embeddings/yandex.py
View File

@ -205,7 +205,7 @@ def _make_request(self: YandexGPTEmbeddings, texts: List[str], **kwargs): # typ
for text in texts:
request = TextEmbeddingRequest(model_uri=model_uri, text=text)
stub = EmbeddingsServiceStub(channel)
res = stub.TextEmbedding(request, metadata=self.grpc_metadata) # type: ignore[attr-defined]
res = stub.TextEmbedding(request, metadata=self.grpc_metadata)
result.append(list(res.embedding))
time.sleep(self.sleep_interval)

8
libs/community/langchain_community/graphs/neo4j_graph.py
View File

@ -481,20 +481,20 @@ class Neo4jGraph(GraphStore):
or e.code
== "Neo.DatabaseError.Transaction.TransactionStartFailed"
)
and "in an implicit transaction" in e.message # type: ignore[operator]
and "in an implicit transaction" in e.message
)
or ( # isPeriodicCommitError
e.code == "Neo.ClientError.Statement.SemanticError"
and (
"in an open transaction is not possible" in e.message # type: ignore[operator]
or "tried to execute in an explicit transaction" in e.message # type: ignore[operator]
"in an open transaction is not possible" in e.message
or "tried to execute in an explicit transaction" in e.message
)
)
):
raise
# fallback to allow implicit transactions
with self._driver.session(database=self._database) as session:
data = session.run(Query(text=query, timeout=self.timeout), params) # type: ignore[assignment]
data = session.run(Query(text=query, timeout=self.timeout), params)
json_data = [r.data() for r in data]
if self.sanitize:
json_data = [value_sanitize(el) for el in json_data]

42
libs/community/langchain_community/indexes/_sql_record_manager.py
View File

@ -304,7 +304,7 @@ class SQLRecordManager(RecordManager):
# Note: uses SQLite insert to make on_conflict_do_update work.
# This code needs to be generalized a bit to work with more dialects.
insert_stmt = sqlite_insert(UpsertionRecord).values(records_to_upsert)
stmt = insert_stmt.on_conflict_do_update( # type: ignore[attr-defined]
stmt = insert_stmt.on_conflict_do_update(
[UpsertionRecord.key, UpsertionRecord.namespace],
set_=dict(
# attr-defined type ignore
@ -318,7 +318,7 @@ class SQLRecordManager(RecordManager):
# Note: uses SQLite insert to make on_conflict_do_update work.
# This code needs to be generalized a bit to work with more dialects.
insert_stmt = pg_insert(UpsertionRecord).values(records_to_upsert) # type: ignore[assignment]
stmt = insert_stmt.on_conflict_do_update( # type: ignore[attr-defined]
stmt = insert_stmt.on_conflict_do_update(
"uix_key_namespace", # Name of constraint
set_=dict(
# attr-defined type ignore
@ -379,7 +379,7 @@ class SQLRecordManager(RecordManager):
# Note: uses SQLite insert to make on_conflict_do_update work.
# This code needs to be generalized a bit to work with more dialects.
insert_stmt = sqlite_insert(UpsertionRecord).values(records_to_upsert)
stmt = insert_stmt.on_conflict_do_update( # type: ignore[attr-defined]
stmt = insert_stmt.on_conflict_do_update(
[UpsertionRecord.key, UpsertionRecord.namespace],
set_=dict(
# attr-defined type ignore
@ -393,7 +393,7 @@ class SQLRecordManager(RecordManager):
# Note: uses SQLite insert to make on_conflict_do_update work.
# This code needs to be generalized a bit to work with more dialects.
insert_stmt = pg_insert(UpsertionRecord).values(records_to_upsert) # type: ignore[assignment]
stmt = insert_stmt.on_conflict_do_update( # type: ignore[attr-defined]
stmt = insert_stmt.on_conflict_do_update(
"uix_key_namespace", # Name of constraint
set_=dict(
# attr-defined type ignore
@ -412,7 +412,7 @@ class SQLRecordManager(RecordManager):
with self._make_session() as session:
records = (
# mypy does not recognize .all()
session.query(UpsertionRecord.key) # type: ignore[attr-defined]
session.query(UpsertionRecord.key)
.filter(
and_(
UpsertionRecord.key.in_(keys),
@ -460,21 +460,15 @@ class SQLRecordManager(RecordManager):
# mypy does not recognize .all() or .filter()
if after:
query = query.filter( # type: ignore[attr-defined]
UpsertionRecord.updated_at > after
)
query = query.filter(UpsertionRecord.updated_at > after)
if before:
query = query.filter( # type: ignore[attr-defined]
UpsertionRecord.updated_at < before
)
query = query.filter(UpsertionRecord.updated_at < before)
if group_ids:
query = query.filter( # type: ignore[attr-defined]
UpsertionRecord.group_id.in_(group_ids)
)
query = query.filter(UpsertionRecord.group_id.in_(group_ids))
if limit:
query = query.limit(limit) # type: ignore[attr-defined]
records = query.all() # type: ignore[attr-defined]
query = query.limit(limit)
records = query.all()
return [r.key for r in records] # type: ignore[misc]
async def alist_keys(
@ -493,20 +487,14 @@ class SQLRecordManager(RecordManager):
# mypy does not recognize .all() or .filter()
if after:
query = query.filter( # type: ignore[attr-defined]
UpsertionRecord.updated_at > after
)
query = query.filter(UpsertionRecord.updated_at > after)
if before:
query = query.filter( # type: ignore[attr-defined]
UpsertionRecord.updated_at < before
)
query = query.filter(UpsertionRecord.updated_at < before)
if group_ids:
query = query.filter( # type: ignore[attr-defined]
UpsertionRecord.group_id.in_(group_ids)
)
query = query.filter(UpsertionRecord.group_id.in_(group_ids))
if limit:
query = query.limit(limit) # type: ignore[attr-defined]
query = query.limit(limit)
records = (await session.execute(query)).scalars().all()
return list(records)
@ -519,7 +507,7 @@ class SQLRecordManager(RecordManager):
UpsertionRecord.key.in_(keys),
UpsertionRecord.namespace == self.namespace,
)
).delete() # type: ignore[attr-defined]
).delete()
session.commit()
async def adelete_keys(self, keys: Sequence[str]) -> None:

2
libs/community/langchain_community/llms/aleph_alpha.py
View File

@ -281,6 +281,6 @@ class AlephAlpha(LLM):
if __name__ == "__main__":
aa = AlephAlpha() # type: ignore[call-arg]
aa = AlephAlpha()
print(aa.invoke("How are you?")) # noqa: T201

4
libs/community/langchain_community/llms/anyscale.py
View File

@ -62,7 +62,7 @@ def create_llm_result(
return LLMResult(generations=generations, llm_output=llm_output)
class Anyscale(BaseOpenAI): # type: ignore[override]
class Anyscale(BaseOpenAI):
"""Anyscale large language models.
To use, you should have the environment variable ``ANYSCALE_API_KEY``set with your
@ -136,7 +136,7 @@ class Anyscale(BaseOpenAI): # type: ignore[override]
else:
values["openai_api_base"] = values["anyscale_api_base"]
values["openai_api_key"] = values["anyscale_api_key"].get_secret_value()
values["client"] = openai.Completion # type: ignore[attr-defined]
values["client"] = openai.Completion
except ImportError:
raise ImportError(
"Could not import openai python package. "

10
libs/community/langchain_community/llms/azureml_endpoint.py
View File

@ -194,7 +194,7 @@ class GPT2ContentFormatter(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 # type: ignore[union-attr]
raise ValueError(self.format_error_msg.format(api_type=api_type)) from e
return Generation(text=choice)
@ -239,7 +239,7 @@ class HFContentFormatter(ContentFormatterBase):
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 # type: ignore[union-attr]
raise ValueError(self.format_error_msg.format(api_type=api_type)) from e
return Generation(text=choice)
@ -268,7 +268,7 @@ class DollyContentFormatter(ContentFormatterBase):
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 # type: ignore[union-attr]
raise ValueError(self.format_error_msg.format(api_type=api_type)) from e
return Generation(text=choice)
@ -315,7 +315,7 @@ class CustomOpenAIContentFormatter(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 # type: ignore[union-attr]
raise ValueError(self.format_error_msg.format(api_type=api_type)) from e
return Generation(text=choice)
if api_type == AzureMLEndpointApiType.serverless:
try:
@ -327,7 +327,7 @@ class CustomOpenAIContentFormatter(ContentFormatterBase):
"received."
)
except (KeyError, IndexError, TypeError) as e:
raise ValueError(self.format_error_msg.format(api_type=api_type)) from e # type: ignore[union-attr]
raise ValueError(self.format_error_msg.format(api_type=api_type)) from e
return Generation(
text=choice["text"].strip(),
generation_info=dict(

2
libs/community/langchain_community/llms/beam.py
View File

@ -19,7 +19,7 @@ DEFAULT_NUM_TRIES = 10
DEFAULT_SLEEP_TIME = 4
class Beam(LLM): # type: ignore[override, override, override, override]
class Beam(LLM):
"""Beam API for gpt2 large language model.
To use, you should have the ``beam-sdk`` python package installed,

4
libs/community/langchain_community/llms/gooseai.py
View File

@ -97,8 +97,8 @@ class GooseAI(LLM):
import openai
openai.api_key = gooseai_api_key.get_secret_value()
openai.api_base = "https://api.goose.ai/v1" # type: ignore[attr-defined]
values["client"] = openai.Completion # type: ignore[attr-defined]
openai.api_base = "https://api.goose.ai/v1"
values["client"] = openai.Completion
except ImportError:
raise ImportError(
"Could not import openai python package. "

2
libs/community/langchain_community/llms/javelin_ai_gateway.py
View File

@ -12,7 +12,7 @@ from pydantic import BaseModel
# Ignoring type because below is valid pydantic code
# Unexpected keyword argument "extra" for "__init_subclass__" of "object"
class Params(BaseModel, extra="allow"): # type: ignore[call-arg]
class Params(BaseModel, extra="allow"):
"""Parameters for the Javelin AI Gateway LLM."""
temperature: float = 0.0

2
libs/community/langchain_community/llms/mlflow_ai_gateway.py
View File

@ -10,7 +10,7 @@ from pydantic import BaseModel
# Ignoring type because below is valid pydantic code
# Unexpected keyword argument "extra" for "__init_subclass__" of "object"
class Params(BaseModel, extra="allow"): # type: ignore[call-arg]
class Params(BaseModel, extra="allow"):
"""Parameters for the MLflow AI Gateway LLM."""
temperature: float = 0.0

4
libs/community/langchain_community/llms/octoai_endpoint.py
View File

@ -10,7 +10,7 @@ DEFAULT_BASE_URL = "https://text.octoai.run/v1/"
DEFAULT_MODEL = "codellama-7b-instruct"
class OctoAIEndpoint(BaseOpenAI): # type: ignore[override]
class OctoAIEndpoint(BaseOpenAI):
"""OctoAI LLM Endpoints - OpenAI compatible.
OctoAIEndpoint is a class to interact with OctoAI Compute Service large
@ -102,7 +102,7 @@ class OctoAIEndpoint(BaseOpenAI): # type: ignore[override]
else:
values["openai_api_base"] = values["octoai_api_base"]
values["openai_api_key"] = values["octoai_api_token"].get_secret_value()
values["client"] = openai.Completion # type: ignore[attr-defined]
values["client"] = openai.Completion
except ImportError:
raise ImportError(
"Could not import openai python package. "

4
libs/community/langchain_community/llms/ollama.py
View File

@ -319,9 +319,9 @@ class _OllamaCommon(BaseLanguageModel):
"Content-Type": "application/json",
**(self.headers if isinstance(self.headers, dict) else {}),
},
auth=self.auth, # type: ignore[arg-type]
auth=self.auth, # type: ignore[arg-type,unused-ignore]
json=request_payload,
timeout=self.timeout, # type: ignore[arg-type]
timeout=self.timeout, # type: ignore[arg-type,unused-ignore]
) as response:
if response.status != 200:
if response.status == 404:

22
libs/community/langchain_community/llms/openai.py
View File

@ -100,11 +100,11 @@ def _create_retry_decorator(
import openai
errors = [
openai.error.Timeout, # type: ignore[attr-defined]
openai.error.APIError, # type: ignore[attr-defined]
openai.error.APIConnectionError, # type: ignore[attr-defined]
openai.error.RateLimitError, # type: ignore[attr-defined]
openai.error.ServiceUnavailableError, # type: ignore[attr-defined]
openai.error.Timeout,
openai.error.APIError,
openai.error.APIConnectionError,
openai.error.RateLimitError,
openai.error.ServiceUnavailableError,
]
return create_base_retry_decorator(
error_types=errors, max_retries=llm.max_retries, run_manager=run_manager
@ -323,7 +323,7 @@ class BaseOpenAI(BaseLLM):
if not values.get("async_client"):
values["async_client"] = openai.AsyncOpenAI(**client_params).completions
elif not values.get("client"):
values["client"] = openai.Completion # type: ignore[attr-defined]
values["client"] = openai.Completion
else:
pass
@ -607,7 +607,7 @@ class BaseOpenAI(BaseLLM):
if self.openai_proxy:
import openai
openai.proxy = {"http": self.openai_proxy, "https": self.openai_proxy} # type: ignore[assignment] # type: ignore[attr-defined] # type: ignore[attr-defined] # type: ignore[attr-defined] # type: ignore[attr-defined] # type: ignore[attr-defined] # type: ignore[attr-defined]
openai.proxy = {"http": self.openai_proxy, "https": self.openai_proxy}
return {**openai_creds, **self._default_params}
@property
@ -943,7 +943,7 @@ class AzureOpenAI(BaseOpenAI):
).completions
else:
values["client"] = openai.Completion # type: ignore[attr-defined]
values["client"] = openai.Completion
return values
@ -1068,18 +1068,18 @@ class OpenAIChat(BaseLLM):
openai.api_key = openai_api_key
if openai_api_base:
openai.api_base = openai_api_base # type: ignore[attr-defined]
openai.api_base = openai_api_base
if openai_organization:
openai.organization = openai_organization
if openai_proxy:
openai.proxy = {"http": openai_proxy, "https": openai_proxy} # type: ignore[assignment] # type: ignore[attr-defined] # type: ignore[attr-defined] # type: ignore[attr-defined] # type: ignore[attr-defined] # type: ignore[attr-defined] # type: ignore[attr-defined]
openai.proxy = {"http": openai_proxy, "https": openai_proxy}
except ImportError:
raise ImportError(
"Could not import openai python package. "
"Please install it with `pip install openai`."
)
try:
values["client"] = openai.ChatCompletion # type: ignore[attr-defined]
values["client"] = openai.ChatCompletion
except AttributeError:
raise ValueError(
"`openai` has no `ChatCompletion` attribute, this is likely "

12
libs/community/langchain_community/llms/vertexai.py
View File

@ -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( # type: ignore[no-redef]
def completion_with_retry(
llm: VertexAI,
prompt: List[Union[str, "Image"]],
stream: bool = False,
@ -124,7 +124,7 @@ class _VertexAIBase(BaseModel):
return cls.task_executor
class _VertexAICommon(_VertexAIBase): # type: ignore[override]
class _VertexAICommon(_VertexAIBase):
client: "_LanguageModel" = None #: :meta private:
client_preview: "_LanguageModel" = None #: :meta private:
model_name: str
@ -208,7 +208,7 @@ class _VertexAICommon(_VertexAIBase): # type: ignore[override]
removal="1.0",
alternative_import="langchain_google_vertexai.VertexAI",
)
class VertexAI(_VertexAICommon, BaseLLM): # type: ignore[override]
class VertexAI(_VertexAICommon, BaseLLM):
"""Google Vertex AI large language models."""
model_name: str = "text-bison"
@ -332,7 +332,7 @@ class VertexAI(_VertexAICommon, BaseLLM): # type: ignore[override]
generation += chunk
generations.append([generation])
else:
res = completion_with_retry( # type: ignore[misc]
res = completion_with_retry(
self,
[prompt],
stream=should_stream,
@ -375,7 +375,7 @@ class VertexAI(_VertexAICommon, BaseLLM): # type: ignore[override]
**kwargs: Any,
) -> Iterator[GenerationChunk]:
params = self._prepare_params(stop=stop, stream=True, **kwargs)
for stream_resp in completion_with_retry( # type: ignore[misc]
for stream_resp in completion_with_retry(
self,
[prompt],
stream=True,
@ -448,7 +448,7 @@ class VertexAIModelGarden(_VertexAIBase, BaseLLM):
@property
def endpoint_path(self) -> str:
return self.client.endpoint_path(
project=self.project, # type: ignore[arg-type]
project=self.project,
location=self.location,
endpoint=self.endpoint_id,
)

6
libs/community/langchain_community/llms/yandex.py
View File

@ -235,7 +235,7 @@ def _make_request(
messages=[Message(role="user", text=prompt)],
)
stub = TextGenerationServiceStub(channel)
res = stub.Completion(request, metadata=self.grpc_metadata) # type: ignore[attr-defined]
res = stub.Completion(request, metadata=self.grpc_metadata)
return list(res)[0].alternatives[0].message.text
@ -291,7 +291,7 @@ 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) # type: ignore[attr-defined]
operation = await stub.Completion(request, metadata=self.grpc_metadata)
async with grpc.aio.secure_channel(
operation_api_url, channel_credentials
) as operation_channel:
@ -301,7 +301,7 @@ async def _amake_request(self: YandexGPT, prompt: str) -> str:
operation_request = GetOperationRequest(operation_id=operation.id)
operation = await operation_stub.Get(
operation_request,
metadata=self.grpc_metadata, # type: ignore[attr-defined]
metadata=self.grpc_metadata,
)
completion_response = CompletionResponse()

2
libs/community/langchain_community/memory/zep_cloud_memory.py
View File

@ -8,7 +8,7 @@ try:
from langchain.memory import ConversationBufferMemory
from zep_cloud import MemoryGetRequestMemoryType
class ZepCloudMemory(ConversationBufferMemory): # type: ignore[override]
class ZepCloudMemory(ConversationBufferMemory):
"""Persist your chain history to the Zep MemoryStore.
Documentation: https://help.getzep.com

2
libs/community/langchain_community/memory/zep_memory.py
View File

@ -7,7 +7,7 @@ from langchain_community.chat_message_histories import ZepChatMessageHistory
try:
from langchain.memory import ConversationBufferMemory
class ZepMemory(ConversationBufferMemory): # type: ignore[override]
class ZepMemory(ConversationBufferMemory):
"""Persist your chain history to the Zep MemoryStore.
The number of messages returned by Zep and when the Zep server summarizes chat

4
libs/community/langchain_community/retrievers/bedrock.py
View File

@ -7,13 +7,13 @@ from langchain_core.retrievers import BaseRetriever
from pydantic import BaseModel, model_validator
class VectorSearchConfig(BaseModel, extra="allow"): # type: ignore[call-arg]
class VectorSearchConfig(BaseModel, extra="allow"):
"""Configuration for vector search."""
numberOfResults: int = 4
class RetrievalConfig(BaseModel, extra="allow"): # type: ignore[call-arg]
class RetrievalConfig(BaseModel, extra="allow"):
"""Configuration for retrieval."""
vectorSearchConfiguration: VectorSearchConfig

20
libs/community/langchain_community/retrievers/kendra.py
View File

@ -68,7 +68,7 @@ Dates are also represented as str.
# Unexpected keyword argument "extra" for "__init_subclass__" of "object"
class Highlight(BaseModel, extra="allow"): # type: ignore[call-arg]
class Highlight(BaseModel, extra="allow"):
"""Information that highlights the keywords in the excerpt."""
BeginOffset: int
@ -82,7 +82,7 @@ class Highlight(BaseModel, extra="allow"): # type: ignore[call-arg]
# Unexpected keyword argument "extra" for "__init_subclass__" of "object"
class TextWithHighLights(BaseModel, extra="allow"): # type: ignore[call-arg]
class TextWithHighLights(BaseModel, extra="allow"):
"""Text with highlights."""
Text: str
@ -92,9 +92,7 @@ class TextWithHighLights(BaseModel, extra="allow"): # type: ignore[call-arg]
# Unexpected keyword argument "extra" for "__init_subclass__" of "object"
class AdditionalResultAttributeValue( # type: ignore[call-arg]
BaseModel, extra="allow"
):
class AdditionalResultAttributeValue(BaseModel, extra="allow"):
"""Value of an additional result attribute."""
TextWithHighlightsValue: TextWithHighLights
@ -102,7 +100,7 @@ class AdditionalResultAttributeValue( # type: ignore[call-arg]
# Unexpected keyword argument "extra" for "__init_subclass__" of "object"
class AdditionalResultAttribute(BaseModel, extra="allow"): # type: ignore[call-arg]
class AdditionalResultAttribute(BaseModel, extra="allow"):
"""Additional result attribute."""
Key: str
@ -117,7 +115,7 @@ class AdditionalResultAttribute(BaseModel, extra="allow"): # type: ignore[call-
# Unexpected keyword argument "extra" for "__init_subclass__" of "object"
class DocumentAttributeValue(BaseModel, extra="allow"): # type: ignore[call-arg]
class DocumentAttributeValue(BaseModel, extra="allow"):
"""Value of a document attribute."""
DateValue: Optional[str] = None
@ -148,7 +146,7 @@ class DocumentAttributeValue(BaseModel, extra="allow"): # type: ignore[call-arg
# Unexpected keyword argument "extra" for "__init_subclass__" of "object"
class DocumentAttribute(BaseModel, extra="allow"): # type: ignore[call-arg]
class DocumentAttribute(BaseModel, extra="allow"):
"""Document attribute."""
Key: str
@ -158,7 +156,7 @@ class DocumentAttribute(BaseModel, extra="allow"): # type: ignore[call-arg]
# Unexpected keyword argument "extra" for "__init_subclass__" of "object"
class ResultItem(BaseModel, ABC, extra="allow"): # type: ignore[call-arg]
class ResultItem(BaseModel, ABC, extra="allow"):
"""Base class of a result item."""
Id: Optional[str]
@ -288,7 +286,7 @@ class RetrieveResultItem(ResultItem):
# Unexpected keyword argument "extra" for "__init_subclass__" of "object"
class QueryResult(BaseModel, extra="allow"): # type: ignore[call-arg]
class QueryResult(BaseModel, extra="allow"):
"""`Amazon Kendra Query API` search result.
It is composed of:
@ -302,7 +300,7 @@ class QueryResult(BaseModel, extra="allow"): # type: ignore[call-arg]
# Unexpected keyword argument "extra" for "__init_subclass__" of "object"
class RetrieveResult(BaseModel, extra="allow"): # type: ignore[call-arg]
class RetrieveResult(BaseModel, extra="allow"):
"""`Amazon Kendra Retrieve API` search result.
It is composed of:

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