github/langchain
1
0
Fork 0
mirror of https://github.com/hwchase17/langchain.git synced 2025-07-03 11:47:49 +00:00
Code Issues Packages Projects Releases Wiki Activity

community: add mypy warn_unused_ignores rule (#30816)

Browse Source
This commit is contained in:
Christophe Bornet 2025-04-28 17:54:12 +02:00 committed by GitHub
parent a2863f8757
commit aee7988a94
No known key found for this signature in database
GPG Key ID: B5690EEEBB952194
346 changed files with 795 additions and 821 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

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"] additional_kwargs["name"] = _dict["name"]
return ToolMessage( return ToolMessage(
content=_dict.get("content", ""), 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, additional_kwargs=additional_kwargs,
) )
else: else:

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

@ -22,7 +22,7 @@ class AzureAiServicesToolkit(BaseToolkit):
tools: List[BaseTool] = [ tools: List[BaseTool] = [
AzureAiServicesDocumentIntelligenceTool(), # type: ignore[call-arg] AzureAiServicesDocumentIntelligenceTool(), # type: ignore[call-arg]
AzureAiServicesImageAnalysisTool(), # type: ignore[call-arg] AzureAiServicesImageAnalysisTool(),
AzureAiServicesSpeechToTextTool(), # type: ignore[call-arg] AzureAiServicesSpeechToTextTool(), # type: ignore[call-arg]
AzureAiServicesTextToSpeechTool(), # type: ignore[call-arg] AzureAiServicesTextToSpeechTool(), # type: ignore[call-arg]
AzureAiServicesTextAnalyticsForHealthTool(), # 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] = [] tools: List[BaseTool] = []
for tool in allowed_tools: for tool in allowed_tools:
tool_cls = _FILE_TOOLS_MAP[tool] 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 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 from langchain_community.utilities.requests import Requests
class NLATool(Tool): # type: ignore[override] class NLATool(Tool):
"""Natural Language API Tool.""" """Natural Language API Tool."""
@classmethod @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) 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.""" """Requests GET tool with LLM-instructed extraction of truncated responses."""
name: str = "requests_get" name: str = "requests_get"
@ -98,7 +98,7 @@ class RequestsGetToolWithParsing(BaseRequestsTool, BaseTool): # type: ignore[ov
raise NotImplementedError() raise NotImplementedError()
class RequestsPostToolWithParsing(BaseRequestsTool, BaseTool): # type: ignore[override] class RequestsPostToolWithParsing(BaseRequestsTool, BaseTool):
"""Requests POST tool with LLM-instructed extraction of truncated responses.""" """Requests POST tool with LLM-instructed extraction of truncated responses."""
name: str = "requests_post" name: str = "requests_post"
@ -129,7 +129,7 @@ class RequestsPostToolWithParsing(BaseRequestsTool, BaseTool): # type: ignore[o
raise NotImplementedError() raise NotImplementedError()
class RequestsPatchToolWithParsing(BaseRequestsTool, BaseTool): # type: ignore[override] class RequestsPatchToolWithParsing(BaseRequestsTool, BaseTool):
"""Requests PATCH tool with LLM-instructed extraction of truncated responses.""" """Requests PATCH tool with LLM-instructed extraction of truncated responses."""
name: str = "requests_patch" name: str = "requests_patch"
@ -162,7 +162,7 @@ class RequestsPatchToolWithParsing(BaseRequestsTool, BaseTool): # type: ignore[
raise NotImplementedError() raise NotImplementedError()
class RequestsPutToolWithParsing(BaseRequestsTool, BaseTool): # type: ignore[override] class RequestsPutToolWithParsing(BaseRequestsTool, BaseTool):
"""Requests PUT tool with LLM-instructed extraction of truncated responses.""" """Requests PUT tool with LLM-instructed extraction of truncated responses."""
name: str = "requests_put" name: str = "requests_put"
@ -193,7 +193,7 @@ class RequestsPutToolWithParsing(BaseRequestsTool, BaseTool): # type: ignore[ov
raise NotImplementedError() raise NotImplementedError()
class RequestsDeleteToolWithParsing(BaseRequestsTool, BaseTool): # type: ignore[override] class RequestsDeleteToolWithParsing(BaseRequestsTool, BaseTool):
"""Tool that sends a DELETE request and parses the response.""" """Tool that sends a DELETE request and parses the response."""
name: str = "requests_delete" name: str = "requests_delete"
@ -266,7 +266,7 @@ def _create_api_controller_agent(
if "GET" in allowed_operations: if "GET" in allowed_operations:
get_llm_chain = LLMChain(llm=llm, prompt=PARSING_GET_PROMPT) get_llm_chain = LLMChain(llm=llm, prompt=PARSING_GET_PROMPT)
tools.append( tools.append(
RequestsGetToolWithParsing( # type: ignore[call-arg] RequestsGetToolWithParsing(
requests_wrapper=requests_wrapper, requests_wrapper=requests_wrapper,
llm_chain=get_llm_chain, llm_chain=get_llm_chain,
allow_dangerous_requests=allow_dangerous_requests, allow_dangerous_requests=allow_dangerous_requests,
@ -275,7 +275,7 @@ def _create_api_controller_agent(
if "POST" in allowed_operations: if "POST" in allowed_operations:
post_llm_chain = LLMChain(llm=llm, prompt=PARSING_POST_PROMPT) post_llm_chain = LLMChain(llm=llm, prompt=PARSING_POST_PROMPT)
tools.append( tools.append(
RequestsPostToolWithParsing( # type: ignore[call-arg] RequestsPostToolWithParsing(
requests_wrapper=requests_wrapper, requests_wrapper=requests_wrapper,
llm_chain=post_llm_chain, llm_chain=post_llm_chain,
allow_dangerous_requests=allow_dangerous_requests, allow_dangerous_requests=allow_dangerous_requests,
@ -284,7 +284,7 @@ def _create_api_controller_agent(
if "PUT" in allowed_operations: if "PUT" in allowed_operations:
put_llm_chain = LLMChain(llm=llm, prompt=PARSING_PUT_PROMPT) put_llm_chain = LLMChain(llm=llm, prompt=PARSING_PUT_PROMPT)
tools.append( tools.append(
RequestsPutToolWithParsing( # type: ignore[call-arg] RequestsPutToolWithParsing(
requests_wrapper=requests_wrapper, requests_wrapper=requests_wrapper,
llm_chain=put_llm_chain, llm_chain=put_llm_chain,
allow_dangerous_requests=allow_dangerous_requests, allow_dangerous_requests=allow_dangerous_requests,
@ -293,7 +293,7 @@ def _create_api_controller_agent(
if "DELETE" in allowed_operations: if "DELETE" in allowed_operations:
delete_llm_chain = LLMChain(llm=llm, prompt=PARSING_DELETE_PROMPT) delete_llm_chain = LLMChain(llm=llm, prompt=PARSING_DELETE_PROMPT)
tools.append( tools.append(
RequestsDeleteToolWithParsing( # type: ignore[call-arg] RequestsDeleteToolWithParsing(
requests_wrapper=requests_wrapper, requests_wrapper=requests_wrapper,
llm_chain=delete_llm_chain, llm_chain=delete_llm_chain,
allow_dangerous_requests=allow_dangerous_requests, allow_dangerous_requests=allow_dangerous_requests,
@ -302,7 +302,7 @@ def _create_api_controller_agent(
if "PATCH" in allowed_operations: if "PATCH" in allowed_operations:
patch_llm_chain = LLMChain(llm=llm, prompt=PARSING_PATCH_PROMPT) patch_llm_chain = LLMChain(llm=llm, prompt=PARSING_PATCH_PROMPT)
tools.append( tools.append(
RequestsPatchToolWithParsing( # type: ignore[call-arg] RequestsPatchToolWithParsing(
requests_wrapper=requests_wrapper, requests_wrapper=requests_wrapper,
llm_chain=patch_llm_chain, llm_chain=patch_llm_chain,
allow_dangerous_requests=allow_dangerous_requests, 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, powerbi=self.powerbi,
examples=self.examples, examples=self.examples,
max_iterations=self.max_iterations, 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, tiktoken_model_name=self.tiktoken_model_name,
), ),
InfoPowerBITool(powerbi=self.powerbi), 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, name=name,
instructions=instructions, instructions=instructions,
tools=[_get_assistants_tool(tool) for tool in tools], tools=[_get_assistants_tool(tool) for tool in tools],
tool_resources=tool_resources, # type: ignore[arg-type] tool_resources=tool_resources,
model=model, model=model,
extra_body=extra_body, extra_body=extra_body,
**model_kwargs, **model_kwargs,
@ -431,7 +431,7 @@ class OpenAIAssistantV2Runnable(OpenAIAssistantRunnable):
name=name, name=name,
instructions=instructions, instructions=instructions,
tools=openai_tools, tools=openai_tools,
tool_resources=tool_resources, # type: ignore[arg-type] tool_resources=tool_resources,
model=model, model=model,
) )
return cls(assistant_id=assistant.id, async_client=async_client, **kwargs) 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: try:
async with self.redis.pipeline() as pipe: async with self.redis.pipeline() as pipe:
self._configure_pipeline_for_update(key, pipe, return_val, self.ttl) 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: except Exception as e:
logger.error(f"Redis async update failed: {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_key: str = "function",
output_parser: Optional[BaseLLMOutputParser] = None, output_parser: Optional[BaseLLMOutputParser] = None,
**kwargs: Any, **kwargs: Any,
) -> LLMChain: # type: ignore[valid-type] ) -> LLMChain:
"""[Legacy] Create an LLM chain that uses Ernie functions. """[Legacy] Create an LLM chain that uses Ernie functions.
Args: Args:
@ -455,7 +455,7 @@ def create_ernie_fn_chain(
} }
if len(ernie_functions) == 1: if len(ernie_functions) == 1:
llm_kwargs["function_call"] = {"name": ernie_functions[0]["name"]} llm_kwargs["function_call"] = {"name": ernie_functions[0]["name"]}
llm_chain = LLMChain( # type: ignore[misc] llm_chain = LLMChain(
llm=llm, llm=llm,
prompt=prompt, prompt=prompt,
output_parser=output_parser, output_parser=output_parser,
@ -474,7 +474,7 @@ def create_structured_output_chain(
output_key: str = "function", output_key: str = "function",
output_parser: Optional[BaseLLMOutputParser] = None, output_parser: Optional[BaseLLMOutputParser] = None,
**kwargs: Any, **kwargs: Any,
) -> LLMChain: # type: ignore[valid-type] ) -> LLMChain:
"""[Legacy] Create an LLMChain that uses an Ernie function to get a structured output. """[Legacy] Create an LLMChain that uses an Ernie function to get a structured output.
Args: Args:

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

@ -324,7 +324,7 @@ class GraphCypherQAChain(Chain):
cypher_generation_chain = LLMChain( cypher_generation_chain = LLMChain(
llm=cypher_llm or llm, # type: ignore[arg-type] 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: 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 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: 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: else:
raise ValueError( raise ValueError(
"Missing required components for the cypher generation chain: " "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) sparql_generation_chain = LLMChain(llm=llm, prompt=sparql_prompt)
return cls( # type: ignore[call-arg] return cls(
qa_chain=qa_chain, qa_chain=qa_chain,
sparql_generation_chain=sparql_generation_chain, sparql_generation_chain=sparql_generation_chain,
examples=examples, 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. See https://python.langchain.com/docs/security for more information.
""" """
llm_chain: LLMChain # type: ignore[valid-type] llm_chain: LLMChain
requests_wrapper: TextRequestsWrapper = Field( requests_wrapper: TextRequestsWrapper = Field(
default_factory=lambda: TextRequestsWrapper(headers=DEFAULT_HEADERS), default_factory=lambda: TextRequestsWrapper(headers=DEFAULT_HEADERS),
exclude=True, exclude=True,
@ -88,7 +88,7 @@ class LLMRequestsChain(Chain):
# extract the text from the html # extract the text from the html
soup = BeautifulSoup(res, "html.parser") soup = BeautifulSoup(res, "html.parser")
other_keys[self.requests_key] = soup.get_text()[: self.text_length] 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 callbacks=_run_manager.get_child(), **other_keys
) )
return {self.output_key: result} return {self.output_key: result}

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

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

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

@ -52,7 +52,7 @@ class SlackChatLoader(BaseChatLoader):
) )
else: else:
results.append( results.append(
HumanMessage( # type: ignore[call-arg] HumanMessage(
role=sender, role=sender,
content=text, content=text,
additional_kwargs={ 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( message = AIMessage(
content=message.content, content=message.content,
additional_kwargs=message.additional_kwargs.copy(), additional_kwargs=message.additional_kwargs.copy(),
example=getattr(message, "example", None), # type: ignore[arg-type] example=getattr(message, "example", None),
) )
num_converted += 1 num_converted += 1
messages.append(message) 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() timestamp, sender, text = result.groups()
if not self._ignore_lines.match(text.strip()): if not self._ignore_lines.match(text.strip()):
results.append( results.append(
HumanMessage( # type: ignore[call-arg] HumanMessage(
role=sender, role=sender,
content=text, content=text,
additional_kwargs={ 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. # 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 __tablename__ = table_name
id = Column(Integer, primary_key=True) id = Column(Integer, primary_key=True)
session_id = Column(Text) session_id = Column(Text)

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

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

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

@ -304,7 +304,7 @@ class AzureMLChatOnlineEndpoint(BaseChatModel, AzureMLBaseEndpoint):
"http_client": None, "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 = [ message_dicts = [
CustomOpenAIChatContentFormatter._convert_message_to_dict(m) CustomOpenAIChatContentFormatter._convert_message_to_dict(m)
for m in messages for m in messages
@ -312,30 +312,30 @@ class AzureMLChatOnlineEndpoint(BaseChatModel, AzureMLBaseEndpoint):
params = {"stream": True, "stop": stop, "model": None, **kwargs} params = {"stream": True, "stop": stop, "model": None, **kwargs}
default_chunk_class = AIMessageChunk 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): if not isinstance(chunk, dict):
chunk = chunk.dict() # type: ignore[attr-defined] chunk = chunk.dict()
if len(chunk["choices"]) == 0: # type: ignore[call-overload] if len(chunk["choices"]) == 0:
continue continue
choice = chunk["choices"][0] # type: ignore[call-overload] choice = chunk["choices"][0]
chunk = _convert_delta_to_message_chunk( # type: ignore[assignment] chunk = _convert_delta_to_message_chunk(
choice["delta"], # type: ignore[arg-type, index] choice["delta"],
default_chunk_class, # type: ignore[arg-type, index] default_chunk_class,
) )
generation_info = {} 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 generation_info["finish_reason"] = finish_reason
logprobs = choice.get("logprobs") # type: ignore[union-attr] logprobs = choice.get("logprobs")
if logprobs: if logprobs:
generation_info["logprobs"] = logprobs generation_info["logprobs"] = logprobs
default_chunk_class = chunk.__class__ # type: ignore[assignment] default_chunk_class = chunk.__class__
chunk = ChatGenerationChunk( # type: ignore[assignment] chunk = ChatGenerationChunk(
message=chunk, # type: ignore[arg-type] message=chunk,
generation_info=generation_info or None, # type: ignore[arg-type] generation_info=generation_info or None,
) )
if run_manager: if run_manager:
run_manager.on_llm_new_token(chunk.text, chunk=chunk, logprobs=logprobs) # type: ignore[attr-defined, arg-type] run_manager.on_llm_new_token(chunk.text, chunk=chunk, logprobs=logprobs)
yield chunk # type: ignore[misc] yield chunk
async def _astream( async def _astream(
self, self,
@ -359,7 +359,7 @@ class AzureMLChatOnlineEndpoint(BaseChatModel, AzureMLBaseEndpoint):
"http_client": None, "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 = [ message_dicts = [
CustomOpenAIChatContentFormatter._convert_message_to_dict(m) CustomOpenAIChatContentFormatter._convert_message_to_dict(m)
for m in messages for m in messages
@ -367,9 +367,9 @@ class AzureMLChatOnlineEndpoint(BaseChatModel, AzureMLBaseEndpoint):
params = {"stream": True, "stop": stop, "model": None, **kwargs} params = {"stream": True, "stop": stop, "model": None, **kwargs}
default_chunk_class = AIMessageChunk default_chunk_class = AIMessageChunk
async for chunk in await async_client.chat.completions.create( # type: ignore[attr-defined] async for chunk in await async_client.chat.completions.create(
messages=message_dicts, # type: ignore[arg-type] messages=message_dicts,
**params, # type: ignore[arg-type] **params,
): ):
if not isinstance(chunk, dict): if not isinstance(chunk, dict):
chunk = 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( return AIMessage(
content=content, content=content,
additional_kwargs=additional_kwargs, additional_kwargs=additional_kwargs,
tool_calls=tool_calls, # type: ignore[arg-type] tool_calls=tool_calls,
invalid_tool_calls=invalid_tool_calls, invalid_tool_calls=invalid_tool_calls,
) )
elif role == "tool": elif role == "tool":
@ -137,7 +137,7 @@ def _convert_dict_to_message(_dict: Mapping[str, Any]) -> BaseMessage:
additional_kwargs["name"] = _dict["name"] additional_kwargs["name"] = _dict["name"]
return ToolMessage( return ToolMessage(
content=content, 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, additional_kwargs=additional_kwargs,
) )
elif role == "system": 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: if is_pydantic_schema:
output_parser: OutputParserLike = PydanticToolsParser( output_parser: OutputParserLike = PydanticToolsParser(
tools=[schema], # type: ignore[list-item] tools=[schema], # type: ignore[list-item]
first_tool_only=True, # type: ignore[list-item] first_tool_only=True,
) )
else: else:
key_name = convert_to_openai_tool(schema)["function"]["name"] 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: if is_pydantic_schema:
output_parser: OutputParserLike = PydanticToolsParser( output_parser: OutputParserLike = PydanticToolsParser(
tools=[schema], # type: ignore[list-item] tools=[schema], # type: ignore[list-item]
first_tool_only=True, # type: ignore[list-item] first_tool_only=True,
) )
else: else:
output_parser = JsonOutputKeyToolsParser( output_parser = JsonOutputKeyToolsParser(
@ -222,7 +222,7 @@ class ChatCloudflareWorkersAI(BaseChatModel):
elif method == "json_mode": elif method == "json_mode":
llm = self.bind(response_format={"type": "json_object"}) llm = self.bind(response_format={"type": "json_object"})
output_parser = ( output_parser = (
PydanticOutputParser(pydantic_object=schema) # type: ignore[type-var, arg-type] PydanticOutputParser(pydantic_object=schema) # type: ignore[arg-type]
if is_pydantic_schema if is_pydantic_schema
else JsonOutputParser() 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`.", "Please install it with `pip install openai`.",
) from e ) from e
try: try:
values["client"] = openai.ChatCompletion # type: ignore[attr-defined] values["client"] = openai.ChatCompletion
except AttributeError as exc: except AttributeError as exc:
raise ValueError( raise ValueError(
"`openai` has no `ChatCompletion` attribute, this is likely " "`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), stop=stop_after_attempt(llm.max_retries),
wait=wait_exponential(multiplier=1, min=min_seconds, max=max_seconds), wait=wait_exponential(multiplier=1, min=min_seconds, max=max_seconds),
retry=( retry=(
retry_if_exception_type(openai.error.Timeout) # type: ignore[attr-defined] retry_if_exception_type(openai.error.Timeout)
| retry_if_exception_type(openai.error.APIError) # type: ignore[attr-defined] | retry_if_exception_type(openai.error.APIError)
| retry_if_exception_type(openai.error.APIConnectionError) # type: ignore[attr-defined] | retry_if_exception_type(openai.error.APIConnectionError)
| retry_if_exception_type(openai.error.RateLimitError) # type: ignore[attr-defined] | retry_if_exception_type(openai.error.RateLimitError)
| retry_if_exception_type(openai.error.ServiceUnavailableError) # type: ignore[attr-defined] | retry_if_exception_type(openai.error.ServiceUnavailableError)
), ),
before_sleep=before_sleep_log(logger, logging.WARNING), before_sleep=before_sleep_log(logger, logging.WARNING),
) )
@ -234,7 +234,7 @@ class JinaChat(BaseChatModel):
"Please install it with `pip install openai`." "Please install it with `pip install openai`."
) )
try: try:
values["client"] = openai.ChatCompletion # type: ignore[attr-defined] values["client"] = openai.ChatCompletion
except AttributeError: except AttributeError:
raise ValueError( raise ValueError(
"`openai` has no `ChatCompletion` attribute, this is likely " "`openai` has no `ChatCompletion` attribute, this is likely "
@ -266,11 +266,11 @@ class JinaChat(BaseChatModel):
stop=stop_after_attempt(self.max_retries), stop=stop_after_attempt(self.max_retries),
wait=wait_exponential(multiplier=1, min=min_seconds, max=max_seconds), wait=wait_exponential(multiplier=1, min=min_seconds, max=max_seconds),
retry=( retry=(
retry_if_exception_type(openai.error.Timeout) # type: ignore[attr-defined] retry_if_exception_type(openai.error.Timeout)
| retry_if_exception_type(openai.error.APIError) # type: ignore[attr-defined] | retry_if_exception_type(openai.error.APIError)
| retry_if_exception_type(openai.error.APIConnectionError) # type: ignore[attr-defined] | retry_if_exception_type(openai.error.APIConnectionError)
| retry_if_exception_type(openai.error.RateLimitError) # type: ignore[attr-defined] | retry_if_exception_type(openai.error.RateLimitError)
| retry_if_exception_type(openai.error.ServiceUnavailableError) # type: ignore[attr-defined] | retry_if_exception_type(openai.error.ServiceUnavailableError)
), ),
before_sleep=before_sleep_log(logger, logging.WARNING), 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__) logger = logging.getLogger(__name__)
class ChatKonko(ChatOpenAI): # type: ignore[override] class ChatKonko(ChatOpenAI):
"""`ChatKonko` Chat large language models API. """`ChatKonko` Chat large language models API.
To use, you should have the ``konko`` python package installed, and the 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, additional_kwargs=additional_kwargs,
name=name, name=name,
id=id_, id=id_,
tool_calls=tool_calls, # type: ignore[arg-type] tool_calls=tool_calls,
invalid_tool_calls=invalid_tool_calls, invalid_tool_calls=invalid_tool_calls,
) )
elif role == "system": 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: if is_pydantic_schema:
output_parser: OutputParserLike = PydanticToolsParser( output_parser: OutputParserLike = PydanticToolsParser(
tools=[schema], # type: ignore[list-item] tools=[schema], # type: ignore[list-item]
first_tool_only=True, # type: ignore[list-item] first_tool_only=True,
) )
else: else:
key_name = convert_to_openai_tool(schema)["function"]["name"] 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 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. """Moonshot chat model integration.
Setup: 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": if method == "json_mode":
llm = self.bind(response_format={"type": "json_object"}) llm = self.bind(response_format={"type": "json_object"})
output_parser = ( output_parser = (
PydanticOutputParser(pydantic_object=schema) # type: ignore[type-var, arg-type] PydanticOutputParser(pydantic_object=schema) # type: ignore[arg-type]
if is_pydantic_schema if is_pydantic_schema
else JsonOutputParser() 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": elif method == "json_mode":
llm = self.bind(response_format={"type": "json_object"}) llm = self.bind(response_format={"type": "json_object"})
output_parser = ( output_parser = (
PydanticOutputParser(pydantic_object=schema) # type: ignore[type-var, arg-type] PydanticOutputParser(pydantic_object=schema) # type: ignore[arg-type]
if is_pydantic_schema if is_pydantic_schema
else JsonOutputParser() else JsonOutputParser()
) )

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

@ -98,7 +98,7 @@ class ChatOctoAI(ChatOpenAI):
else: else:
values["openai_api_base"] = values["octoai_api_base"] values["openai_api_base"] = values["octoai_api_base"]
values["openai_api_key"] = values["octoai_api_token"].get_secret_value() 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: except ImportError:
raise ImportError( raise ImportError(
"Could not import openai python package. " "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 import openai
errors = [ errors = [
openai.error.Timeout, # type: ignore[attr-defined] openai.error.Timeout,
openai.error.APIError, # type: ignore[attr-defined] openai.error.APIError,
openai.error.APIConnectionError, # type: ignore[attr-defined] openai.error.APIConnectionError,
openai.error.RateLimitError, # type: ignore[attr-defined] openai.error.RateLimitError,
openai.error.ServiceUnavailableError, # type: ignore[attr-defined] openai.error.ServiceUnavailableError,
] ]
return create_base_retry_decorator( return create_base_retry_decorator(
error_types=errors, max_retries=llm.max_retries, run_manager=run_manager error_types=errors, max_retries=llm.max_retries, run_manager=run_manager
@ -358,7 +358,7 @@ class ChatOpenAI(BaseChatModel):
**client_params **client_params
).chat.completions ).chat.completions
elif not values.get("client"): elif not values.get("client"):
values["client"] = openai.ChatCompletion # type: ignore[attr-defined] values["client"] = openai.ChatCompletion
else: else:
pass pass
return values return values
@ -595,7 +595,7 @@ class ChatOpenAI(BaseChatModel):
if self.openai_proxy: if self.openai_proxy:
import openai 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} return {**self._default_params, **openai_creds}
def _get_invocation_params( 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( if is_pydantic_schema and hasattr(
schema, "model_json_schema" schema, "model_json_schema"
): # accounting for pydantic v1 and v2 ): # 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: elif is_pydantic_schema:
response_format = schema.schema() # type: ignore[union-attr] response_format = schema.schema() # type: ignore[union-attr]
elif isinstance(schema, dict): 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: if is_pydantic_schema:
output_parser: OutputParserLike[Any] = PydanticToolsParser( output_parser: OutputParserLike[Any] = PydanticToolsParser(
tools=[schema], # type: ignore[list-item] tools=[schema], # type: ignore[list-item]
first_tool_only=True, # type: ignore[list-item] first_tool_only=True,
) )
else: else:
output_parser = JsonOutputKeyToolsParser( output_parser = JsonOutputKeyToolsParser(
@ -648,7 +648,7 @@ class ChatSambaNovaCloud(BaseChatModel):
# llm = self.bind(response_format={"type": "json_object"}) # llm = self.bind(response_format={"type": "json_object"})
if is_pydantic_schema: if is_pydantic_schema:
schema = cast(Type[BaseModel], 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: else:
output_parser = JsonOutputParser() output_parser = JsonOutputParser()
@ -666,7 +666,7 @@ class ChatSambaNovaCloud(BaseChatModel):
# ) # )
if is_pydantic_schema: if is_pydantic_schema:
schema = cast(Type[BaseModel], 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: else:
output_parser = JsonOutputParser() output_parser = JsonOutputParser()
else: 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] @deprecated( # type: ignore[arg-type]
since="0.0.34", removal="1.0", alternative_import="langchain_upstage.ChatUpstage" 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. """Wrapper around Solar large language models.
To use, you should have the ``openai`` python package installed, and the To use, you should have the ``openai`` python package installed, and the
environment variable ``SOLAR_API_KEY`` set with your API key. 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) json_payload = json.dumps(payload)
async with ClientSession() as session: 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 url, data=json_payload, headers=headers, stream=True
) as response: ) as response:
response.raise_for_status() 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( else AIMessage(
content=content, content=content,
additional_kwargs=additional_kwargs, additional_kwargs=additional_kwargs,
tool_calls=tool_calls, # type: ignore[arg-type] tool_calls=tool_calls,
invalid_tool_calls=invalid_tool_calls, invalid_tool_calls=invalid_tool_calls,
) )
) )
@ -163,7 +163,7 @@ def convert_dict_to_message(
if is_chunk if is_chunk
else ToolMessage( else ToolMessage(
content=_dict.get("content", ""), 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, additional_kwargs=additional_kwargs,
) )
) )
@ -894,7 +894,7 @@ class ChatTongyi(BaseChatModel):
if is_pydantic_schema: if is_pydantic_schema:
output_parser: OutputParserLike = PydanticToolsParser( output_parser: OutputParserLike = PydanticToolsParser(
tools=[schema], # type: ignore[list-item] tools=[schema], # type: ignore[list-item]
first_tool_only=True, # type: ignore[list-item] first_tool_only=True,
) )
else: else:
key_name = convert_to_openai_tool(schema)["function"]["name"] 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", removal="1.0",
alternative_import="langchain_google_vertexai.ChatVertexAI", alternative_import="langchain_google_vertexai.ChatVertexAI",
) )
class ChatVertexAI(_VertexAICommon, BaseChatModel): # type: ignore[override] class ChatVertexAI(_VertexAICommon, BaseChatModel):
"""`Vertex AI` Chat large language models API.""" """`Vertex AI` Chat large language models API."""
model_name: str = "chat-bison" 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"] additional_kwargs["name"] = dct["name"]
return ToolMessage( return ToolMessage(
content=content, 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, additional_kwargs=additional_kwargs,
) )
return ChatMessage(role=role, content=content) # type: ignore[arg-type] return ChatMessage(role=role, content=content) # type: ignore[arg-type]
@ -861,7 +861,7 @@ class ChatZhipuAI(BaseChatModel):
if is_pydantic_schema: if is_pydantic_schema:
output_parser: OutputParserLike = PydanticToolsParser( output_parser: OutputParserLike = PydanticToolsParser(
tools=[schema], # type: ignore[list-item] tools=[schema], # type: ignore[list-item]
first_tool_only=True, # type: ignore[list-item] first_tool_only=True,
) )
else: else:
output_parser = JsonOutputKeyToolsParser( 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. # in a separate loop, in a separate thread.
with ThreadPoolExecutor(max_workers=1) as executor: with ThreadPoolExecutor(max_workers=1) as executor:
future: Future[List[str]] = executor.submit( future: Future[List[str]] = executor.submit(
asyncio.run, # type: ignore[arg-type] asyncio.run,
self.fetch_all(self.web_paths), # type: ignore[arg-type] self.fetch_all(self.web_paths),
) )
results = future.result() results = future.result()
except RuntimeError: 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 yield self.path
return return
paths = self.path.glob(self.glob) # type: ignore[attr-defined] paths = self.path.glob(self.glob)
for path in paths: for path in paths:
if self.exclude: if self.exclude:
if any(path.match(glob) for glob in 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__( def __init__(
self, self,
blob_loader: BlobLoader, # type: ignore[valid-type] blob_loader: BlobLoader,
blob_parser: BaseBlobParser, blob_parser: BaseBlobParser,
num_workers: int = 4, # type: ignore[valid-type] num_workers: int = 4,
) -> None: ) -> None:
super().__init__(blob_loader, blob_parser) super().__init__(blob_loader, blob_parser)
self.num_workers = num_workers self.num_workers = num_workers
@ -40,7 +40,7 @@ class ConcurrentLoader(GenericLoader):
) as executor: ) as executor:
futures = { futures = {
executor.submit(self.blob_parser.lazy_parse, blob) 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): for future in concurrent.futures.as_completed(futures):
yield from future.result() yield from future.result()
@ -72,7 +72,7 @@ class ConcurrentLoader(GenericLoader):
num_workers: Max number of concurrent workers to use. num_workers: Max number of concurrent workers to use.
parser_kwargs: Keyword arguments to pass to the parser. parser_kwargs: Keyword arguments to pass to the parser.
""" """
blob_loader = FileSystemBlobLoader( # type: ignore[attr-defined, misc] blob_loader = FileSystemBlobLoader(
path, path,
glob=glob, glob=glob,
exclude=exclude, 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] self.number_of_retries # type: ignore[arg-type]
), ),
wait=wait_exponential( wait=wait_exponential(
multiplier=1, # type: ignore[arg-type] multiplier=1,
min=self.min_retry_seconds, # type: ignore[arg-type] min=self.min_retry_seconds, # type: ignore[arg-type]
max=self.max_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: def _get_elements(self) -> List:
from unstructured.partition.csv import partition_csv 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.url_path = url_path
self.bytes_source = bytes_source self.bytes_source = bytes_source
self.parser = AzureAIDocumentIntelligenceParser( # type: ignore[misc] self.parser = AzureAIDocumentIntelligenceParser(
api_endpoint=api_endpoint, api_endpoint=api_endpoint,
api_key=api_key, api_key=api_key,
api_version=api_version, api_version=api_version,
@ -116,10 +116,10 @@ class AzureAIDocumentIntelligenceLoader(BaseLoader):
) -> Iterator[Document]: ) -> Iterator[Document]:
"""Lazy load the document as pages.""" """Lazy load the document as pages."""
if self.file_path is not None: 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) yield from self.parser.parse(blob)
elif self.url_path is not None: 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: elif self.bytes_source is not None:
yield from self.parser.parse_bytes(self.bytes_source) yield from self.parser.parse_bytes(self.bytes_source)
else: else:

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

@ -60,16 +60,16 @@ class UnstructuredEmailLoader(UnstructuredFileLoader):
def _get_elements(self) -> List: def _get_elements(self) -> List:
from unstructured.file_utils.filetype import FileType, detect_filetype 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: if filetype == FileType.EML:
from unstructured.partition.email import partition_email 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: elif satisfies_min_unstructured_version("0.5.8") and filetype == FileType.MSG:
from unstructured.partition.msg import partition_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: else:
raise ValueError( raise ValueError(
f"Filetype {filetype} is not supported in UnstructuredEmailLoader." 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: def _get_elements(self) -> List:
from unstructured.partition.epub import partition_epub 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: def _get_elements(self) -> List:
from unstructured.partition.xlsx import partition_xlsx 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__( def __init__(
self, self,
blob_loader: BlobLoader, # type: ignore[valid-type] blob_loader: BlobLoader,
blob_parser: BaseBlobParser, blob_parser: BaseBlobParser,
) -> None: ) -> None:
"""A generic document loader. """A generic document loader.
@ -112,7 +112,7 @@ class GenericLoader(BaseLoader):
self, self,
) -> Iterator[Document]: ) -> Iterator[Document]:
"""Load documents lazily. Use this when working at a large scale.""" """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) yield from self.blob_parser.lazy_parse(blob)
def load_and_split( def load_and_split(
@ -159,7 +159,7 @@ class GenericLoader(BaseLoader):
Returns: Returns:
A generic document loader. A generic document loader.
""" """
blob_loader = FileSystemBlobLoader( # type: ignore[attr-defined, misc] blob_loader = FileSystemBlobLoader(
path, path,
glob=glob, glob=glob,
exclude=exclude, 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) 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): if len(ignored_files):
continue continue

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

@ -48,4 +48,4 @@ class UnstructuredHTMLLoader(UnstructuredFileLoader):
def _get_elements(self) -> List: def _get_elements(self) -> List:
from unstructured.partition.html import partition_html 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: def _get_elements(self) -> List:
from unstructured.partition.image import partition_image 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: try:
if isinstance(image, bytes): 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 ( elif isinstance(image, str) and (
image.startswith("http://") or image.startswith("https://") 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: else:
image = Image.open(image).convert("RGB") # type: ignore[assignment] image = Image.open(image).convert("RGB")
except Exception: except Exception:
if isinstance(image_source, bytes): if isinstance(image_source, bytes):
msg = "Could not get image data from 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: def _get_elements(self) -> List:
from unstructured.partition.md import partition_md 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: def _get_elements(self) -> List:
from unstructured.partition.odt import partition_odt 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: def _get_elements(self) -> List:
from unstructured.partition.org import partition_org 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 model=self.model, file=file_obj, **self._create_params
) )
else: else:
transcript = openai.Audio.transcribe(self.model, file_obj) # type: ignore[attr-defined] transcript = openai.Audio.transcribe(self.model, file_obj)
break break
except Exception as e: except Exception as e:
attempts += 1 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): class MsWordParser(BaseBlobParser):
"""Parse the Microsoft Word documents from a blob.""" """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. """Parse a Microsoft Word document into the Document iterator.
Args: Args:
@ -33,13 +33,13 @@ class MsWordParser(BaseBlobParser):
partition_docx partition_docx
), ),
} }
if blob.mimetype not in ( # type: ignore[attr-defined] if blob.mimetype not in (
"application/msword", "application/msword",
"application/vnd.openxmlformats-officedocument.wordprocessingml.document", "application/vnd.openxmlformats-officedocument.wordprocessingml.document",
): ):
raise ValueError("This blob type is not supported for this parser.") raise ValueError("This blob type is not supported for this parser.")
with blob.as_bytes_io() as word_document: # type: ignore[attr-defined] with blob.as_bytes_io() as word_document:
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] elements = mime_type_parser[blob.mimetype](file=word_document)
text = "\n\n".join([str(el) for el in elements]) 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) 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_mode = extraction_mode
self.extraction_kwargs = extraction_kwargs or {} 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. Lazily parse the blob.
Insert image, if possible, between two paragraphs. Insert image, if possible, between two paragraphs.
@ -380,7 +380,7 @@ class PyPDFParser(BaseBlobParser):
**self.extraction_kwargs, **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) pdf_reader = pypdf.PdfReader(pdf_file_obj, password=self.password)
doc_metadata = _purge_metadata( doc_metadata = _purge_metadata(
@ -434,7 +434,7 @@ class PyPDFParser(BaseBlobParser):
if "/XObject" not in cast(dict, page["/Resources"]).keys(): if "/XObject" not in cast(dict, page["/Resources"]).keys():
return "" return ""
xObject = page["/Resources"]["/XObject"].get_object() # type: ignore[index] xObject = page["/Resources"]["/XObject"].get_object()
images = [] images = []
for obj in xObject: for obj in xObject:
np_image: Any = None np_image: Any = None
@ -677,7 +677,7 @@ class PDFMinerParser(BaseBlobParser):
return metadata 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. Lazily parse the blob.
Insert image, if possible, between two paragraphs. Insert image, if possible, between two paragraphs.
@ -919,7 +919,7 @@ class PyMuPDFParser(BaseBlobParser):
self.extract_tables = extract_tables self.extract_tables = extract_tables
self.extract_tables_settings = extract_tables_settings 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( return self._lazy_parse(
blob, blob,
) )
@ -930,7 +930,7 @@ class PyMuPDFParser(BaseBlobParser):
# text-kwargs is present for backwards compatibility. # text-kwargs is present for backwards compatibility.
# Users should not use it directly. # Users should not use it directly.
text_kwargs: Optional[dict[str, Any]] = None, text_kwargs: Optional[dict[str, Any]] = None,
) -> Iterator[Document]: # type: ignore[valid-type] ) -> Iterator[Document]:
"""Lazily parse the blob. """Lazily parse the blob.
Insert image, if possible, between two paragraphs. Insert image, if possible, between two paragraphs.
In this way, a paragraph can be continued on the next page. In this way, a paragraph can be continued on the next page.
@ -990,8 +990,8 @@ class PyMuPDFParser(BaseBlobParser):
) )
with PyMuPDFParser._lock: with PyMuPDFParser._lock:
with blob.as_bytes_io() as file_path: # type: ignore[attr-defined] with blob.as_bytes_io() as file_path:
if blob.data is None: # type: ignore[attr-defined] if blob.data is None:
doc = pymupdf.open(file_path) doc = pymupdf.open(file_path)
else: else:
doc = pymupdf.open(stream=file_path, filetype="pdf") doc = pymupdf.open(stream=file_path, filetype="pdf")
@ -1066,8 +1066,8 @@ class PyMuPDFParser(BaseBlobParser):
"producer": "PyMuPDF", "producer": "PyMuPDF",
"creator": "PyMuPDF", "creator": "PyMuPDF",
"creationdate": "", "creationdate": "",
"source": blob.source, # type: ignore[attr-defined] "source": blob.source,
"file_path": blob.source, # type: ignore[attr-defined] "file_path": blob.source,
"total_pages": len(doc), "total_pages": len(doc),
}, },
**{ **{
@ -1273,7 +1273,7 @@ class PyPDFium2Parser(BaseBlobParser):
self.mode = mode self.mode = mode
self.pages_delimiter = pages_delimiter 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. Lazily parse the blob.
Insert image, if possible, between two paragraphs. Insert image, if possible, between two paragraphs.
@ -1299,7 +1299,7 @@ class PyPDFium2Parser(BaseBlobParser):
# pypdfium2 is really finicky with respect to closing things, # pypdfium2 is really finicky with respect to closing things,
# if done incorrectly creates seg faults. # if done incorrectly creates seg faults.
with PyPDFium2Parser._lock: 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 pdf_reader = None
try: try:
pdf_reader = pypdfium2.PdfDocument( pdf_reader = pypdfium2.PdfDocument(
@ -1410,11 +1410,11 @@ class PDFPlumberParser(BaseBlobParser):
self.dedupe = dedupe self.dedupe = dedupe
self.extract_images = extract_images 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.""" """Lazily parse the blob."""
import pdfplumber 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 doc = pdfplumber.open(file_path) # open document
yield from [ yield from [
@ -1424,8 +1424,8 @@ class PDFPlumberParser(BaseBlobParser):
+ self._extract_images_from_page(page), + self._extract_images_from_page(page),
metadata=dict( metadata=dict(
{ {
"source": blob.source, # type: ignore[attr-defined] "source": blob.source,
"file_path": blob.source, # type: ignore[attr-defined] "file_path": blob.source,
"page": page.page_number - 1, "page": page.page_number - 1,
"total_pages": len(doc.pages), "total_pages": len(doc.pages),
}, },
@ -1593,14 +1593,14 @@ class AmazonTextractPDFParser(BaseBlobParser):
else: else:
self.boto3_textract_client = client 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 """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 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 has to be set to the S3 URI and for single page docs
the blob.data is taken 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) # Either call with S3 path (multi-page) or with bytes (single-page)
if ( if (
url_parse_result url_parse_result
@ -1608,13 +1608,13 @@ class AmazonTextractPDFParser(BaseBlobParser):
and url_parse_result.netloc and url_parse_result.netloc
): ):
textract_response_json = self.tc.call_textract( 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, features=self.textract_features,
boto3_textract_client=self.boto3_textract_client, boto3_textract_client=self.boto3_textract_client,
) )
else: else:
textract_response_json = self.tc.call_textract( 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, features=self.textract_features,
call_mode=self.tc.Textract_Call_Mode.FORCE_SYNC, call_mode=self.tc.Textract_Call_Mode.FORCE_SYNC,
boto3_textract_client=self.boto3_textract_client, boto3_textract_client=self.boto3_textract_client,
@ -1625,7 +1625,7 @@ class AmazonTextractPDFParser(BaseBlobParser):
for idx, page in enumerate(document.pages): for idx, page in enumerate(document.pages):
yield Document( yield Document(
page_content=page.get_text(config=self.linearization_config), 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.client = client
self.model = model 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: for p in result.pages:
content = " ".join([line.content for line in p.lines]) content = " ".join([line.content for line in p.lines])
d = Document( d = Document(
page_content=content, page_content=content,
metadata={ metadata={
"source": blob.source, # type: ignore[attr-defined] "source": blob.source,
"page": p.page_number, "page": p.page_number,
}, },
) )
yield d 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.""" """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) poller = self.client.begin_analyze_document(self.model, file_obj)
result = poller.result() 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): class TextParser(BaseBlobParser):
"""Parser for text blobs.""" """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.""" """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: def _get_elements(self) -> list:
from unstructured.partition.pdf import partition_pdf 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): class BasePDFLoader(BaseLoader, ABC):
@ -299,11 +299,9 @@ class PyPDFLoader(BasePDFLoader):
In this way, a paragraph can be continued on the next page. In this way, a paragraph can be continued on the next page.
""" """
if self.web_path: if self.web_path:
blob = Blob.from_data( # type: ignore[attr-defined] blob = Blob.from_data(open(self.file_path, "rb").read(), path=self.web_path)
open(self.file_path, "rb").read(), path=self.web_path
)
else: 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) 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. In this way, a paragraph can be continued on the next page.
""" """
if self.web_path: if self.web_path:
blob = Blob.from_data( # type: ignore[attr-defined] blob = Blob.from_data(open(self.file_path, "rb").read(), path=self.web_path)
open(self.file_path, "rb").read(), path=self.web_path
)
else: 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) 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. In this way, a paragraph can be continued on the next page.
""" """
if self.web_path: if self.web_path:
blob = Blob.from_data( # type: ignore[attr-defined] blob = Blob.from_data(open(self.file_path, "rb").read(), path=self.web_path)
open(self.file_path, "rb").read(), path=self.web_path
)
else: 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) yield from self.parser.lazy_parse(blob)
@ -850,9 +844,9 @@ class PyMuPDFLoader(BasePDFLoader):
) )
parser = self.parser parser = self.parser
if self.web_path: 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: 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) yield from parser._lazy_parse(blob, text_kwargs=kwargs)
def load(self, **kwargs: Any) -> list[Document]: def load(self, **kwargs: Any) -> list[Document]:
@ -1046,9 +1040,9 @@ class PDFPlumberLoader(BasePDFLoader):
extract_images=self.extract_images, extract_images=self.extract_images,
) )
if self.web_path: 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: else:
blob = Blob.from_path(self.file_path) # type: ignore[attr-defined] blob = Blob.from_path(self.file_path)
return parser.parse(blob) return parser.parse(blob)
@ -1163,7 +1157,7 @@ class AmazonTextractPDFLoader(BasePDFLoader):
# raises ValueError when multipage and not on S3""" # raises ValueError when multipage and not on S3"""
if self.web_path and self._is_s3_url(self.web_path): 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: else:
blob = Blob.from_path(self.file_path) blob = Blob.from_path(self.file_path)
if AmazonTextractPDFLoader._get_number_of_pages(blob) > 1: if AmazonTextractPDFLoader._get_number_of_pages(blob) > 1:
@ -1176,7 +1170,7 @@ class AmazonTextractPDFLoader(BasePDFLoader):
yield from self.parser.parse(blob) yield from self.parser.parse(blob)
@staticmethod @staticmethod
def _get_number_of_pages(blob: Blob) -> int: # type: ignore[valid-type] def _get_number_of_pages(blob: Blob) -> int:
try: try:
import pypdf import pypdf
from PIL import Image, ImageSequence from PIL import Image, ImageSequence
@ -1186,22 +1180,20 @@ class AmazonTextractPDFLoader(BasePDFLoader):
"Could not import pypdf or Pilloe python package. " "Could not import pypdf or Pilloe python package. "
"Please install it with `pip install pypdf Pillow`." "Please install it with `pip install pypdf Pillow`."
) )
if blob.mimetype == "application/pdf": # type: ignore[attr-defined] if blob.mimetype == "application/pdf":
with blob.as_bytes_io() as input_pdf_file: # type: ignore[attr-defined] with blob.as_bytes_io() as input_pdf_file:
pdf_reader = pypdf.PdfReader(input_pdf_file) pdf_reader = pypdf.PdfReader(input_pdf_file)
return len(pdf_reader.pages) return len(pdf_reader.pages)
elif blob.mimetype == "image/tiff": # type: ignore[attr-defined] elif blob.mimetype == "image/tiff":
num_pages = 0 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)): for _, _ in enumerate(ImageSequence.Iterator(img)):
num_pages += 1 num_pages += 1
return num_pages 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 return 1
else: else:
raise ValueError( # type: ignore[attr-defined] raise ValueError(f"unsupported mime type: {blob.mimetype}")
f"unsupported mime type: {blob.mimetype}"
)
class DedocPDFLoader(DedocBaseLoader): class DedocPDFLoader(DedocBaseLoader):
@ -1348,7 +1340,7 @@ class DocumentIntelligenceLoader(BasePDFLoader):
self, self,
) -> Iterator[Document]: ) -> Iterator[Document]:
"""Lazy load given path as pages.""" """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) 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: try:
import magic # noqa: F401 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: except ImportError:
_, extension = os.path.splitext(str(self.file_path)) _, extension = os.path.splitext(str(self.file_path))
is_ppt = extension == ".ppt" is_ppt = extension == ".ppt"
@ -70,8 +70,8 @@ class UnstructuredPowerPointLoader(UnstructuredFileLoader):
if is_ppt: if is_ppt:
from unstructured.partition.ppt import partition_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: else:
from unstructured.partition.pptx import partition_pptx 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: def _get_elements(self) -> List:
from unstructured.partition.rst import partition_rst 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: def _get_elements(self) -> List:
from unstructured.partition.rtf import partition_rtf 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: def _get_elements(self) -> List:
from unstructured.partition.tsv import partition_tsv 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): elif not os.path.isfile(self.file_path):
raise ValueError("File path %s is not a valid file or url" % 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: def __del__(self) -> None:
if hasattr(self, "temp_file"): if hasattr(self, "temp_file"):
@ -50,5 +50,5 @@ class VsdxLoader(BaseLoader, ABC):
return bool(parsed.netloc) and bool(parsed.scheme) return bool(parsed.netloc) and bool(parsed.scheme)
def load(self) -> List[Document]: 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)) 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( def from_params(
cls, places: Sequence[str], *, openweathermap_api_key: Optional[str] = None cls, places: Sequence[str], *, openweathermap_api_key: Optional[str] = None
) -> WeatherDataLoader: ) -> 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) return cls(client, places)
def lazy_load( def lazy_load(

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

@ -121,7 +121,7 @@ class UnstructuredWordDocumentLoader(UnstructuredFileLoader):
try: try:
import magic # noqa: F401 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: except ImportError:
_, extension = os.path.splitext(str(self.file_path)) _, extension = os.path.splitext(str(self.file_path))
is_doc = extension == ".doc" is_doc = extension == ".doc"
@ -132,8 +132,8 @@ class UnstructuredWordDocumentLoader(UnstructuredFileLoader):
if is_doc: if is_doc:
from unstructured.partition.doc import partition_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: else:
from unstructured.partition.docx import partition_docx 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: def _get_elements(self) -> List:
from unstructured.partition.xml import partition_xml 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: try:
from google.api_core.client_options import ClientOptions 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: except ImportError as exc:
raise ImportError( raise ImportError(
"Install Google Cloud Translate to use this parser." "Install Google Cloud Translate to use this parser."
@ -76,7 +76,7 @@ class GoogleTranslateTransformer(BaseDocumentTransformer):
Options: `text/plain`, `text/html` Options: `text/plain`, `text/html`
""" """
try: try:
from google.cloud import translate # type: ignore[attr-defined] from google.cloud import translate
except ImportError as exc: except ImportError as exc:
raise ImportError( raise ImportError(
"Install Google Cloud Translate to use this parser." "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 = [] new_documents = []
for document in 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( new_document = Document(
page_content=document.page_content, page_content=document.page_content,
metadata={**extracted_metadata, **document.metadata}, metadata={**extracted_metadata, **document.metadata},

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

@ -68,7 +68,7 @@ class AnyscaleEmbeddings(OpenAIEmbeddings):
else: else:
values["openai_api_base"] = values["anyscale_api_base"] values["openai_api_base"] = values["anyscale_api_base"]
values["openai_api_key"] = values["anyscale_api_key"].get_secret_value() 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 return values
@property @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", removal="1.0",
alternative_import="langchain_openai.AzureOpenAIEmbeddings", alternative_import="langchain_openai.AzureOpenAIEmbeddings",
) )
class AzureOpenAIEmbeddings(OpenAIEmbeddings): # type: ignore[override] class AzureOpenAIEmbeddings(OpenAIEmbeddings):
"""`Azure OpenAI` Embeddings API.""" """`Azure OpenAI` Embeddings API."""
azure_endpoint: Union[str, None] = None azure_endpoint: Union[str, None] = None
@ -170,16 +170,16 @@ class AzureOpenAIEmbeddings(OpenAIEmbeddings): # type: ignore[override]
"default_query": self.default_query, "default_query": self.default_query,
"http_client": self.http_client, "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: if self.azure_ad_async_token_provider:
client_params["azure_ad_token_provider"] = ( client_params["azure_ad_token_provider"] = (
self.azure_ad_async_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: else:
self.client = openai.Embedding # type: ignore[attr-defined] self.client = openai.Embedding
return self return self
@property @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), stop=stop_after_attempt(embeddings.max_retries),
wait=wait_exponential(multiplier=1, min=min_seconds, max=max_seconds), wait=wait_exponential(multiplier=1, min=min_seconds, max=max_seconds),
retry=( retry=(
retry_if_exception_type(openai.error.Timeout) # type: ignore[attr-defined] retry_if_exception_type(openai.error.Timeout)
| retry_if_exception_type(openai.error.APIError) # type: ignore[attr-defined] | retry_if_exception_type(openai.error.APIError)
| retry_if_exception_type(openai.error.APIConnectionError) # type: ignore[attr-defined] | retry_if_exception_type(openai.error.APIConnectionError)
| retry_if_exception_type(openai.error.RateLimitError) # type: ignore[attr-defined] | retry_if_exception_type(openai.error.RateLimitError)
| retry_if_exception_type(openai.error.ServiceUnavailableError) # type: ignore[attr-defined] | retry_if_exception_type(openai.error.ServiceUnavailableError)
), ),
before_sleep=before_sleep_log(logger, logging.WARNING), 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), stop=stop_after_attempt(embeddings.max_retries),
wait=wait_exponential(multiplier=1, min=min_seconds, max=max_seconds), wait=wait_exponential(multiplier=1, min=min_seconds, max=max_seconds),
retry=( retry=(
retry_if_exception_type(openai.error.Timeout) # type: ignore[attr-defined] retry_if_exception_type(openai.error.Timeout)
| retry_if_exception_type(openai.error.APIError) # type: ignore[attr-defined] | retry_if_exception_type(openai.error.APIError)
| retry_if_exception_type(openai.error.APIConnectionError) # type: ignore[attr-defined] | retry_if_exception_type(openai.error.APIConnectionError)
| retry_if_exception_type(openai.error.RateLimitError) # type: ignore[attr-defined] | retry_if_exception_type(openai.error.RateLimitError)
| retry_if_exception_type(openai.error.ServiceUnavailableError) # type: ignore[attr-defined] | retry_if_exception_type(openai.error.ServiceUnavailableError)
), ),
before_sleep=before_sleep_log(logger, logging.WARNING), 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"]): if any(len(d["embedding"]) == 1 for d in response["data"]):
import openai 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 return response
@ -230,7 +230,7 @@ class LocalAIEmbeddings(BaseModel, Embeddings):
try: try:
import openai import openai
values["client"] = openai.Embedding # type: ignore[attr-defined] values["client"] = openai.Embedding
except ImportError: except ImportError:
raise ImportError( raise ImportError(
"Could not import openai python package. " "Could not import openai python package. "
@ -253,10 +253,10 @@ class LocalAIEmbeddings(BaseModel, Embeddings):
if self.openai_proxy: if self.openai_proxy:
import openai import openai
openai.proxy = { # type: ignore[attr-defined] openai.proxy = {
"http": self.openai_proxy, "http": self.openai_proxy,
"https": self.openai_proxy, "https": self.openai_proxy,
} # type: ignore[assignment] }
return openai_args return openai_args
def _embedding_func(self, text: str, *, engine: str) -> List[float]: 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): for txt in _chunk(texts, 20):
resp = self._client.predict( resp = self._client.predict(
endpoint=self.endpoint, endpoint=self.endpoint,
inputs={"input": txt, **params}, # type: ignore[arg-type] inputs={"input": txt, **params},
) )
embeddings.extend(r["embedding"] for r in resp["data"]) embeddings.extend(r["embedding"] for r in resp["data"])
return embeddings return embeddings

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

@ -74,8 +74,8 @@ class OctoAIEmbeddings(OpenAIEmbeddings):
else: else:
values["openai_api_base"] = values["endpoint_url"] values["openai_api_base"] = values["endpoint_url"]
values["openai_api_key"] = values["octoai_api_token"].get_secret_value() values["openai_api_key"] = values["octoai_api_token"].get_secret_value()
values["client"] = openai.Embedding # type: ignore[attr-defined] values["client"] = openai.Embedding
values["async_client"] = openai.Embedding # type: ignore[attr-defined] values["async_client"] = openai.Embedding
except ImportError: except ImportError:
raise 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, max=embeddings.retry_max_seconds,
), ),
retry=( retry=(
retry_if_exception_type(openai.error.Timeout) # type: ignore[attr-defined] retry_if_exception_type(openai.error.Timeout)
| retry_if_exception_type(openai.error.APIError) # type: ignore[attr-defined] | retry_if_exception_type(openai.error.APIError)
| retry_if_exception_type(openai.error.APIConnectionError) # type: ignore[attr-defined] | retry_if_exception_type(openai.error.APIConnectionError)
| retry_if_exception_type(openai.error.RateLimitError) # type: ignore[attr-defined] | retry_if_exception_type(openai.error.RateLimitError)
| retry_if_exception_type(openai.error.ServiceUnavailableError) # type: ignore[attr-defined] | retry_if_exception_type(openai.error.ServiceUnavailableError)
), ),
before_sleep=before_sleep_log(logger, logging.WARNING), before_sleep=before_sleep_log(logger, logging.WARNING),
) )
@ -85,11 +85,11 @@ def _async_retry_decorator(embeddings: OpenAIEmbeddings) -> Any:
max=embeddings.retry_max_seconds, max=embeddings.retry_max_seconds,
), ),
retry=( retry=(
retry_if_exception_type(openai.error.Timeout) # type: ignore[attr-defined] retry_if_exception_type(openai.error.Timeout)
| retry_if_exception_type(openai.error.APIError) # type: ignore[attr-defined] | retry_if_exception_type(openai.error.APIError)
| retry_if_exception_type(openai.error.APIConnectionError) # type: ignore[attr-defined] | retry_if_exception_type(openai.error.APIConnectionError)
| retry_if_exception_type(openai.error.RateLimitError) # type: ignore[attr-defined] | retry_if_exception_type(openai.error.RateLimitError)
| retry_if_exception_type(openai.error.ServiceUnavailableError) # type: ignore[attr-defined] | retry_if_exception_type(openai.error.ServiceUnavailableError)
), ),
before_sleep=before_sleep_log(logger, logging.WARNING), 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: if any(len(d["embedding"]) == 1 for d in response["data"]) and not skip_empty:
import openai 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 return response
@ -357,7 +357,7 @@ class OpenAIEmbeddings(BaseModel, Embeddings):
**client_params **client_params
).embeddings ).embeddings
elif not values.get("client"): elif not values.get("client"):
values["client"] = openai.Embedding # type: ignore[attr-defined] values["client"] = openai.Embedding
else: else:
pass pass
return values return values
@ -390,10 +390,10 @@ class OpenAIEmbeddings(BaseModel, Embeddings):
"Please install it with `pip install openai`." "Please install it with `pip install openai`."
) )
openai.proxy = { # type: ignore[attr-defined] openai.proxy = {
"http": self.openai_proxy, "http": self.openai_proxy,
"https": self.openai_proxy, "https": self.openai_proxy,
} # type: ignore[assignment] }
return openai_args return openai_args
# please refer to # 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 # Try to load the spaCy model
import spacy import spacy
values["nlp"] = spacy.load(model_name) # type: ignore[arg-type] values["nlp"] = spacy.load(model_name)
except OSError: except OSError:
# If the model is not found, raise a ValueError # If the model is not found, raise a ValueError
raise ValueError( raise ValueError(

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

@ -25,7 +25,7 @@ _MIN_BATCH_SIZE = 5
removal="1.0", removal="1.0",
alternative_import="langchain_google_vertexai.VertexAIEmbeddings", alternative_import="langchain_google_vertexai.VertexAIEmbeddings",
) )
class VertexAIEmbeddings(_VertexAICommon, Embeddings): # type: ignore[override] class VertexAIEmbeddings(_VertexAICommon, Embeddings):
"""Google Cloud VertexAI embedding models.""" """Google Cloud VertexAI embedding models."""
# Instance context # Instance context
@ -163,8 +163,8 @@ class VertexAIEmbeddings(_VertexAICommon, Embeddings): # type: ignore[override]
DeadlineExceeded, DeadlineExceeded,
] ]
retry_decorator = create_base_retry_decorator( retry_decorator = create_base_retry_decorator(
error_types=errors, # type: ignore[arg-type] error_types=errors,
max_retries=self.max_retries, # type: ignore[arg-type] max_retries=self.max_retries,
) )
@retry_decorator @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: for text in texts:
request = TextEmbeddingRequest(model_uri=model_uri, text=text) request = TextEmbeddingRequest(model_uri=model_uri, text=text)
stub = EmbeddingsServiceStub(channel) 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)) result.append(list(res.embedding))
time.sleep(self.sleep_interval) 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 or e.code
== "Neo.DatabaseError.Transaction.TransactionStartFailed" == "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 or ( # isPeriodicCommitError
e.code == "Neo.ClientError.Statement.SemanticError" e.code == "Neo.ClientError.Statement.SemanticError"
and ( and (
"in an open transaction is not possible" 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 # type: ignore[operator] or "tried to execute in an explicit transaction" in e.message
) )
) )
): ):
raise raise
# fallback to allow implicit transactions # fallback to allow implicit transactions
with self._driver.session(database=self._database) as session: 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] json_data = [r.data() for r in data]
if self.sanitize: if self.sanitize:
json_data = [value_sanitize(el) for el in json_data] 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. # Note: uses SQLite insert to make on_conflict_do_update work.
# This code needs to be generalized a bit to work with more dialects. # This code needs to be generalized a bit to work with more dialects.
insert_stmt = sqlite_insert(UpsertionRecord).values(records_to_upsert) 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], [UpsertionRecord.key, UpsertionRecord.namespace],
set_=dict( set_=dict(
# attr-defined type ignore # attr-defined type ignore
@ -318,7 +318,7 @@ class SQLRecordManager(RecordManager):
# Note: uses SQLite insert to make on_conflict_do_update work. # Note: uses SQLite insert to make on_conflict_do_update work.
# This code needs to be generalized a bit to work with more dialects. # 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] 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 "uix_key_namespace", # Name of constraint
set_=dict( set_=dict(
# attr-defined type ignore # attr-defined type ignore
@ -379,7 +379,7 @@ class SQLRecordManager(RecordManager):
# Note: uses SQLite insert to make on_conflict_do_update work. # Note: uses SQLite insert to make on_conflict_do_update work.
# This code needs to be generalized a bit to work with more dialects. # This code needs to be generalized a bit to work with more dialects.
insert_stmt = sqlite_insert(UpsertionRecord).values(records_to_upsert) 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], [UpsertionRecord.key, UpsertionRecord.namespace],
set_=dict( set_=dict(
# attr-defined type ignore # attr-defined type ignore
@ -393,7 +393,7 @@ class SQLRecordManager(RecordManager):
# Note: uses SQLite insert to make on_conflict_do_update work. # Note: uses SQLite insert to make on_conflict_do_update work.
# This code needs to be generalized a bit to work with more dialects. # 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] 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 "uix_key_namespace", # Name of constraint
set_=dict( set_=dict(
# attr-defined type ignore # attr-defined type ignore
@ -412,7 +412,7 @@ class SQLRecordManager(RecordManager):
with self._make_session() as session: with self._make_session() as session:
records = ( records = (
# mypy does not recognize .all() # mypy does not recognize .all()
session.query(UpsertionRecord.key) # type: ignore[attr-defined] session.query(UpsertionRecord.key)
.filter( .filter(
and_( and_(
UpsertionRecord.key.in_(keys), UpsertionRecord.key.in_(keys),
@ -460,21 +460,15 @@ class SQLRecordManager(RecordManager):
# mypy does not recognize .all() or .filter() # mypy does not recognize .all() or .filter()
if after: if after:
query = query.filter( # type: ignore[attr-defined] query = query.filter(UpsertionRecord.updated_at > after)
UpsertionRecord.updated_at > after
)
if before: if before:
query = query.filter( # type: ignore[attr-defined] query = query.filter(UpsertionRecord.updated_at < before)
UpsertionRecord.updated_at < before
)
if group_ids: if group_ids:
query = query.filter( # type: ignore[attr-defined] query = query.filter(UpsertionRecord.group_id.in_(group_ids))
UpsertionRecord.group_id.in_(group_ids)
)
if limit: if limit:
query = query.limit(limit) # type: ignore[attr-defined] query = query.limit(limit)
records = query.all() # type: ignore[attr-defined] records = query.all()
return [r.key for r in records] # type: ignore[misc] return [r.key for r in records] # type: ignore[misc]
async def alist_keys( async def alist_keys(
@ -493,20 +487,14 @@ class SQLRecordManager(RecordManager):
# mypy does not recognize .all() or .filter() # mypy does not recognize .all() or .filter()
if after: if after:
query = query.filter( # type: ignore[attr-defined] query = query.filter(UpsertionRecord.updated_at > after)
UpsertionRecord.updated_at > after
)
if before: if before:
query = query.filter( # type: ignore[attr-defined] query = query.filter(UpsertionRecord.updated_at < before)
UpsertionRecord.updated_at < before
)
if group_ids: if group_ids:
query = query.filter( # type: ignore[attr-defined] query = query.filter(UpsertionRecord.group_id.in_(group_ids))
UpsertionRecord.group_id.in_(group_ids)
)
if limit: if limit:
query = query.limit(limit) # type: ignore[attr-defined] query = query.limit(limit)
records = (await session.execute(query)).scalars().all() records = (await session.execute(query)).scalars().all()
return list(records) return list(records)
@ -519,7 +507,7 @@ class SQLRecordManager(RecordManager):
UpsertionRecord.key.in_(keys), UpsertionRecord.key.in_(keys),
UpsertionRecord.namespace == self.namespace, UpsertionRecord.namespace == self.namespace,
) )
).delete() # type: ignore[attr-defined] ).delete()
session.commit() session.commit()
async def adelete_keys(self, keys: Sequence[str]) -> None: 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__": if __name__ == "__main__":
aa = AlephAlpha() # type: ignore[call-arg] aa = AlephAlpha()
print(aa.invoke("How are you?")) # noqa: T201 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) return LLMResult(generations=generations, llm_output=llm_output)
class Anyscale(BaseOpenAI): # type: ignore[override] class Anyscale(BaseOpenAI):
"""Anyscale large language models. """Anyscale large language models.
To use, you should have the environment variable ``ANYSCALE_API_KEY``set with your 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: else:
values["openai_api_base"] = values["anyscale_api_base"] values["openai_api_base"] = values["anyscale_api_base"]
values["openai_api_key"] = values["anyscale_api_key"].get_secret_value() 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: except ImportError:
raise ImportError( raise ImportError(
"Could not import openai python package. " "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: try:
choice = json.loads(output)[0]["0"] choice = json.loads(output)[0]["0"]
except (KeyError, IndexError, TypeError) as e: 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) return Generation(text=choice)
@ -239,7 +239,7 @@ class HFContentFormatter(ContentFormatterBase):
try: try:
choice = json.loads(output)[0]["0"]["generated_text"] choice = json.loads(output)[0]["0"]["generated_text"]
except (KeyError, IndexError, TypeError) as e: 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) return Generation(text=choice)
@ -268,7 +268,7 @@ class DollyContentFormatter(ContentFormatterBase):
try: try:
choice = json.loads(output)[0] choice = json.loads(output)[0]
except (KeyError, IndexError, TypeError) as e: 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) return Generation(text=choice)
@ -315,7 +315,7 @@ class CustomOpenAIContentFormatter(ContentFormatterBase):
try: try:
choice = json.loads(output)[0]["0"] choice = json.loads(output)[0]["0"]
except (KeyError, IndexError, TypeError) as e: 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) return Generation(text=choice)
if api_type == AzureMLEndpointApiType.serverless: if api_type == AzureMLEndpointApiType.serverless:
try: try:
@ -327,7 +327,7 @@ class CustomOpenAIContentFormatter(ContentFormatterBase):
"received." "received."
) )
except (KeyError, IndexError, TypeError) as e: 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( return Generation(
text=choice["text"].strip(), text=choice["text"].strip(),
generation_info=dict( 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 DEFAULT_SLEEP_TIME = 4
class Beam(LLM): # type: ignore[override, override, override, override] class Beam(LLM):
"""Beam API for gpt2 large language model. """Beam API for gpt2 large language model.
To use, you should have the ``beam-sdk`` python package installed, 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 import openai
openai.api_key = gooseai_api_key.get_secret_value() openai.api_key = gooseai_api_key.get_secret_value()
openai.api_base = "https://api.goose.ai/v1" # type: ignore[attr-defined] openai.api_base = "https://api.goose.ai/v1"
values["client"] = openai.Completion # type: ignore[attr-defined] values["client"] = openai.Completion
except ImportError: except ImportError:
raise ImportError( raise ImportError(
"Could not import openai python package. " "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 # Ignoring type because below is valid pydantic code
# Unexpected keyword argument "extra" for "__init_subclass__" of "object" # 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.""" """Parameters for the Javelin AI Gateway LLM."""
temperature: float = 0.0 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 # Ignoring type because below is valid pydantic code
# Unexpected keyword argument "extra" for "__init_subclass__" of "object" # 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.""" """Parameters for the MLflow AI Gateway LLM."""
temperature: float = 0.0 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" DEFAULT_MODEL = "codellama-7b-instruct"
class OctoAIEndpoint(BaseOpenAI): # type: ignore[override] class OctoAIEndpoint(BaseOpenAI):
"""OctoAI LLM Endpoints - OpenAI compatible. """OctoAI LLM Endpoints - OpenAI compatible.
OctoAIEndpoint is a class to interact with OctoAI Compute Service large OctoAIEndpoint is a class to interact with OctoAI Compute Service large
@ -102,7 +102,7 @@ class OctoAIEndpoint(BaseOpenAI): # type: ignore[override]
else: else:
values["openai_api_base"] = values["octoai_api_base"] values["openai_api_base"] = values["octoai_api_base"]
values["openai_api_key"] = values["octoai_api_token"].get_secret_value() 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: except ImportError:
raise ImportError( raise ImportError(
"Could not import openai python package. " "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", "Content-Type": "application/json",
**(self.headers if isinstance(self.headers, dict) else {}), **(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, json=request_payload,
timeout=self.timeout, # type: ignore[arg-type] timeout=self.timeout, # type: ignore[arg-type,unused-ignore]
) as response: ) as response:
if response.status != 200: if response.status != 200:
if response.status == 404: if response.status == 404:

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

@ -100,11 +100,11 @@ def _create_retry_decorator(
import openai import openai
errors = [ errors = [
openai.error.Timeout, # type: ignore[attr-defined] openai.error.Timeout,
openai.error.APIError, # type: ignore[attr-defined] openai.error.APIError,
openai.error.APIConnectionError, # type: ignore[attr-defined] openai.error.APIConnectionError,
openai.error.RateLimitError, # type: ignore[attr-defined] openai.error.RateLimitError,
openai.error.ServiceUnavailableError, # type: ignore[attr-defined] openai.error.ServiceUnavailableError,
] ]
return create_base_retry_decorator( return create_base_retry_decorator(
error_types=errors, max_retries=llm.max_retries, run_manager=run_manager 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"): if not values.get("async_client"):
values["async_client"] = openai.AsyncOpenAI(**client_params).completions values["async_client"] = openai.AsyncOpenAI(**client_params).completions
elif not values.get("client"): elif not values.get("client"):
values["client"] = openai.Completion # type: ignore[attr-defined] values["client"] = openai.Completion
else: else:
pass pass
@ -607,7 +607,7 @@ class BaseOpenAI(BaseLLM):
if self.openai_proxy: if self.openai_proxy:
import openai 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} return {**openai_creds, **self._default_params}
@property @property
@ -943,7 +943,7 @@ class AzureOpenAI(BaseOpenAI):
).completions ).completions
else: else:
values["client"] = openai.Completion # type: ignore[attr-defined] values["client"] = openai.Completion
return values return values
@ -1068,18 +1068,18 @@ class OpenAIChat(BaseLLM):
openai.api_key = openai_api_key openai.api_key = openai_api_key
if openai_api_base: if openai_api_base:
openai.api_base = openai_api_base # type: ignore[attr-defined] openai.api_base = openai_api_base
if openai_organization: if openai_organization:
openai.organization = openai_organization openai.organization = openai_organization
if openai_proxy: 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: except ImportError:
raise ImportError( raise ImportError(
"Could not import openai python package. " "Could not import openai python package. "
"Please install it with `pip install openai`." "Please install it with `pip install openai`."
) )
try: try:
values["client"] = openai.ChatCompletion # type: ignore[attr-defined] values["client"] = openai.ChatCompletion
except AttributeError: except AttributeError:
raise ValueError( raise ValueError(
"`openai` has no `ChatCompletion` attribute, this is likely " "`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 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, llm: VertexAI,
prompt: List[Union[str, "Image"]], prompt: List[Union[str, "Image"]],
stream: bool = False, stream: bool = False,
@ -124,7 +124,7 @@ class _VertexAIBase(BaseModel):
return cls.task_executor return cls.task_executor
class _VertexAICommon(_VertexAIBase): # type: ignore[override] class _VertexAICommon(_VertexAIBase):
client: "_LanguageModel" = None #: :meta private: client: "_LanguageModel" = None #: :meta private:
client_preview: "_LanguageModel" = None #: :meta private: client_preview: "_LanguageModel" = None #: :meta private:
model_name: str model_name: str
@ -208,7 +208,7 @@ class _VertexAICommon(_VertexAIBase): # type: ignore[override]
removal="1.0", removal="1.0",
alternative_import="langchain_google_vertexai.VertexAI", alternative_import="langchain_google_vertexai.VertexAI",
) )
class VertexAI(_VertexAICommon, BaseLLM): # type: ignore[override] class VertexAI(_VertexAICommon, BaseLLM):
"""Google Vertex AI large language models.""" """Google Vertex AI large language models."""
model_name: str = "text-bison" model_name: str = "text-bison"
@ -332,7 +332,7 @@ class VertexAI(_VertexAICommon, BaseLLM): # type: ignore[override]
generation += chunk generation += chunk
generations.append([generation]) generations.append([generation])
else: else:
res = completion_with_retry( # type: ignore[misc] res = completion_with_retry(
self, self,
[prompt], [prompt],
stream=should_stream, stream=should_stream,
@ -375,7 +375,7 @@ class VertexAI(_VertexAICommon, BaseLLM): # type: ignore[override]
**kwargs: Any, **kwargs: Any,
) -> Iterator[GenerationChunk]: ) -> Iterator[GenerationChunk]:
params = self._prepare_params(stop=stop, stream=True, **kwargs) 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, self,
[prompt], [prompt],
stream=True, stream=True,
@ -448,7 +448,7 @@ class VertexAIModelGarden(_VertexAIBase, BaseLLM):
@property @property
def endpoint_path(self) -> str: def endpoint_path(self) -> str:
return self.client.endpoint_path( return self.client.endpoint_path(
project=self.project, # type: ignore[arg-type] project=self.project,
location=self.location, location=self.location,
endpoint=self.endpoint_id, 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)], messages=[Message(role="user", text=prompt)],
) )
stub = TextGenerationServiceStub(channel) 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 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)], messages=[Message(role="user", text=prompt)],
) )
stub = TextGenerationAsyncServiceStub(channel) 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( async with grpc.aio.secure_channel(
operation_api_url, channel_credentials operation_api_url, channel_credentials
) as operation_channel: ) as operation_channel:
@ -301,7 +301,7 @@ async def _amake_request(self: YandexGPT, prompt: str) -> str:
operation_request = GetOperationRequest(operation_id=operation.id) operation_request = GetOperationRequest(operation_id=operation.id)
operation = await operation_stub.Get( operation = await operation_stub.Get(
operation_request, operation_request,
metadata=self.grpc_metadata, # type: ignore[attr-defined] metadata=self.grpc_metadata,
) )
completion_response = CompletionResponse() 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 langchain.memory import ConversationBufferMemory
from zep_cloud import MemoryGetRequestMemoryType from zep_cloud import MemoryGetRequestMemoryType
class ZepCloudMemory(ConversationBufferMemory): # type: ignore[override] class ZepCloudMemory(ConversationBufferMemory):
"""Persist your chain history to the Zep MemoryStore. """Persist your chain history to the Zep MemoryStore.
Documentation: https://help.getzep.com 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: try:
from langchain.memory import ConversationBufferMemory from langchain.memory import ConversationBufferMemory
class ZepMemory(ConversationBufferMemory): # type: ignore[override] class ZepMemory(ConversationBufferMemory):
"""Persist your chain history to the Zep MemoryStore. """Persist your chain history to the Zep MemoryStore.
The number of messages returned by Zep and when the Zep server summarizes chat 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 from pydantic import BaseModel, model_validator
class VectorSearchConfig(BaseModel, extra="allow"): # type: ignore[call-arg] class VectorSearchConfig(BaseModel, extra="allow"):
"""Configuration for vector search.""" """Configuration for vector search."""
numberOfResults: int = 4 numberOfResults: int = 4
class RetrievalConfig(BaseModel, extra="allow"): # type: ignore[call-arg] class RetrievalConfig(BaseModel, extra="allow"):
"""Configuration for retrieval.""" """Configuration for retrieval."""
vectorSearchConfiguration: VectorSearchConfig 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" # 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.""" """Information that highlights the keywords in the excerpt."""
BeginOffset: int BeginOffset: int
@ -82,7 +82,7 @@ class Highlight(BaseModel, extra="allow"): # type: ignore[call-arg]
# Unexpected keyword argument "extra" for "__init_subclass__" of "object" # 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 with highlights."""
Text: str Text: str
@ -92,9 +92,7 @@ class TextWithHighLights(BaseModel, extra="allow"): # type: ignore[call-arg]
# Unexpected keyword argument "extra" for "__init_subclass__" of "object" # Unexpected keyword argument "extra" for "__init_subclass__" of "object"
class AdditionalResultAttributeValue( # type: ignore[call-arg] class AdditionalResultAttributeValue(BaseModel, extra="allow"):
BaseModel, extra="allow"
):
"""Value of an additional result attribute.""" """Value of an additional result attribute."""
TextWithHighlightsValue: TextWithHighLights TextWithHighlightsValue: TextWithHighLights
@ -102,7 +100,7 @@ class AdditionalResultAttributeValue( # type: ignore[call-arg]
# Unexpected keyword argument "extra" for "__init_subclass__" of "object" # 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.""" """Additional result attribute."""
Key: str Key: str
@ -117,7 +115,7 @@ class AdditionalResultAttribute(BaseModel, extra="allow"): # type: ignore[call-
# Unexpected keyword argument "extra" for "__init_subclass__" of "object" # 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.""" """Value of a document attribute."""
DateValue: Optional[str] = None 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" # 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.""" """Document attribute."""
Key: str Key: str
@ -158,7 +156,7 @@ class DocumentAttribute(BaseModel, extra="allow"): # type: ignore[call-arg]
# Unexpected keyword argument "extra" for "__init_subclass__" of "object" # 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.""" """Base class of a result item."""
Id: Optional[str] Id: Optional[str]
@ -288,7 +286,7 @@ class RetrieveResultItem(ResultItem):
# Unexpected keyword argument "extra" for "__init_subclass__" of "object" # 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. """`Amazon Kendra Query API` search result.
It is composed of: 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" # 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. """`Amazon Kendra Retrieve API` search result.
It is composed of: It is composed of:

Some files were not shown because too many files have changed in this diff Show More