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chore(langchain): add ruff rules SLF (#32112)

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See https://docs.astral.sh/ruff/rules/private-member-access/
This commit is contained in:
Christophe Bornet
2025-07-25 17:56:40 +02:00
committed by GitHub
parent 8f5ec20ccf
commit e1238b8085
10 changed files with 23 additions and 24 deletions
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24
libs/langchain/langchain/agents/agent_iterator.py
View File

@@ -190,14 +190,14 @@ class AgentExecutorIterator:
name=self.run_name, name=self.run_name,
) )
try: try:
while self.agent_executor._should_continue( while self.agent_executor._should_continue( # noqa: SLF001
self.iterations, self.iterations,
self.time_elapsed, self.time_elapsed,
): ):
# take the next step: this plans next action, executes it, # take the next step: this plans next action, executes it,
# yielding action and observation as they are generated # yielding action and observation as they are generated
next_step_seq: NextStepOutput = [] next_step_seq: NextStepOutput = []
for chunk in self.agent_executor._iter_next_step( for chunk in self.agent_executor._iter_next_step( # noqa: SLF001
self.name_to_tool_map, self.name_to_tool_map,
self.color_mapping, self.color_mapping,
self.inputs, self.inputs,
@@ -214,7 +214,7 @@ class AgentExecutorIterator:
yield AddableDict(steps=[chunk], messages=chunk.messages) yield AddableDict(steps=[chunk], messages=chunk.messages)
# convert iterator output to format handled by _process_next_step_output # convert iterator output to format handled by _process_next_step_output
next_step = self.agent_executor._consume_next_step(next_step_seq) next_step = self.agent_executor._consume_next_step(next_step_seq) # noqa: SLF001
# update iterations and time elapsed # update iterations and time elapsed
self.update_iterations() self.update_iterations()
# decide if this is the final output # decide if this is the final output
@@ -258,14 +258,14 @@ class AgentExecutorIterator:
) )
try: try:
async with asyncio_timeout(self.agent_executor.max_execution_time): async with asyncio_timeout(self.agent_executor.max_execution_time):
while self.agent_executor._should_continue( while self.agent_executor._should_continue( # noqa: SLF001
self.iterations, self.iterations,
self.time_elapsed, self.time_elapsed,
): ):
# take the next step: this plans next action, executes it, # take the next step: this plans next action, executes it,
# yielding action and observation as they are generated # yielding action and observation as they are generated
next_step_seq: NextStepOutput = [] next_step_seq: NextStepOutput = []
async for chunk in self.agent_executor._aiter_next_step( async for chunk in self.agent_executor._aiter_next_step( # noqa: SLF001
self.name_to_tool_map, self.name_to_tool_map,
self.color_mapping, self.color_mapping,
self.inputs, self.inputs,
@@ -288,7 +288,7 @@ class AgentExecutorIterator:
) )
# convert iterator output to format handled by _process_next_step # convert iterator output to format handled by _process_next_step
next_step = self.agent_executor._consume_next_step(next_step_seq) next_step = self.agent_executor._consume_next_step(next_step_seq) # noqa: SLF001
# update iterations and time elapsed # update iterations and time elapsed
self.update_iterations() self.update_iterations()
# decide if this is the final output # decide if this is the final output
@@ -336,7 +336,7 @@ class AgentExecutorIterator:
# Check for tool return # Check for tool return
if len(next_step_output) == 1: if len(next_step_output) == 1:
next_step_action = next_step_output[0] next_step_action = next_step_output[0]
tool_return = self.agent_executor._get_tool_return(next_step_action) tool_return = self.agent_executor._get_tool_return(next_step_action) # noqa: SLF001
if tool_return is not None: if tool_return is not None:
return self._return(tool_return, run_manager=run_manager) return self._return(tool_return, run_manager=run_manager)
@@ -364,7 +364,7 @@ class AgentExecutorIterator:
# Check for tool return # Check for tool return
if len(next_step_output) == 1: if len(next_step_output) == 1:
next_step_action = next_step_output[0] next_step_action = next_step_output[0]
tool_return = self.agent_executor._get_tool_return(next_step_action) tool_return = self.agent_executor._get_tool_return(next_step_action) # noqa: SLF001
if tool_return is not None: if tool_return is not None:
return await self._areturn(tool_return, run_manager=run_manager) return await self._areturn(tool_return, run_manager=run_manager)
@@ -376,7 +376,7 @@ class AgentExecutorIterator:
""" """
logger.warning("Stopping agent prematurely due to triggering stop condition") logger.warning("Stopping agent prematurely due to triggering stop condition")
# this manually constructs agent finish with output key # this manually constructs agent finish with output key
output = self.agent_executor._action_agent.return_stopped_response( output = self.agent_executor._action_agent.return_stopped_response( # noqa: SLF001
self.agent_executor.early_stopping_method, self.agent_executor.early_stopping_method,
self.intermediate_steps, self.intermediate_steps,
**self.inputs, **self.inputs,
@@ -389,7 +389,7 @@ class AgentExecutorIterator:
the stopped response. the stopped response.
""" """
logger.warning("Stopping agent prematurely due to triggering stop condition") logger.warning("Stopping agent prematurely due to triggering stop condition")
output = self.agent_executor._action_agent.return_stopped_response( output = self.agent_executor._action_agent.return_stopped_response( # noqa: SLF001
self.agent_executor.early_stopping_method, self.agent_executor.early_stopping_method,
self.intermediate_steps, self.intermediate_steps,
**self.inputs, **self.inputs,
@@ -404,7 +404,7 @@ class AgentExecutorIterator:
""" """
Return the final output of the iterator. Return the final output of the iterator.
""" """
returned_output = self.agent_executor._return( returned_output = self.agent_executor._return( # noqa: SLF001
output, output,
self.intermediate_steps, self.intermediate_steps,
run_manager=run_manager, run_manager=run_manager,
@@ -421,7 +421,7 @@ class AgentExecutorIterator:
""" """
Return the final output of the async iterator. Return the final output of the async iterator.
""" """
returned_output = await self.agent_executor._areturn( returned_output = await self.agent_executor._areturn( # noqa: SLF001
output, output,
self.intermediate_steps, self.intermediate_steps,
run_manager=run_manager, run_manager=run_manager,

2
libs/langchain/langchain/agents/initialize.py
View File

@@ -90,7 +90,7 @@ def initialize_agent(
) )
with contextlib.suppress(NotImplementedError): with contextlib.suppress(NotImplementedError):
# TODO: Add tags from the serialized object directly. # TODO: Add tags from the serialized object directly.
tags_.append(agent_obj._agent_type) tags_.append(agent_obj._agent_type) # noqa: SLF001
else: else:
msg = ( msg = (
"Somehow both `agent` and `agent_path` are None, this should never happen." "Somehow both `agent` and `agent_path` are None, this should never happen."

4
libs/langchain/langchain/agents/openai_functions_agent/base.py
View File

@@ -134,7 +134,7 @@ class OpenAIFunctionsAgent(BaseSingleActionAgent):
messages, messages,
callbacks=callbacks, callbacks=callbacks,
) )
return self.output_parser._parse_ai_message(predicted_message) return self.output_parser.parse_ai_message(predicted_message)
async def aplan( async def aplan(
self, self,
@@ -167,7 +167,7 @@ class OpenAIFunctionsAgent(BaseSingleActionAgent):
functions=self.functions, functions=self.functions,
callbacks=callbacks, callbacks=callbacks,
) )
return self.output_parser._parse_ai_message(predicted_message) return self.output_parser.parse_ai_message(predicted_message)
def return_stopped_response( def return_stopped_response(
self, self,

4
libs/langchain/langchain/agents/output_parsers/openai_functions.py
View File

@@ -30,7 +30,7 @@ class OpenAIFunctionsAgentOutputParser(AgentOutputParser):
return "openai-functions-agent" return "openai-functions-agent"
@staticmethod @staticmethod
def _parse_ai_message(message: BaseMessage) -> Union[AgentAction, AgentFinish]: def parse_ai_message(message: BaseMessage) -> Union[AgentAction, AgentFinish]:
"""Parse an AI message.""" """Parse an AI message."""
if not isinstance(message, AIMessage): if not isinstance(message, AIMessage):
msg = f"Expected an AI message got {type(message)}" msg = f"Expected an AI message got {type(message)}"
@@ -89,7 +89,7 @@ class OpenAIFunctionsAgentOutputParser(AgentOutputParser):
msg = "This output parser only works on ChatGeneration output" msg = "This output parser only works on ChatGeneration output"
raise ValueError(msg) # noqa: TRY004 raise ValueError(msg) # noqa: TRY004
message = result[0].message message = result[0].message
return self._parse_ai_message(message) return self.parse_ai_message(message)
def parse(self, text: str) -> Union[AgentAction, AgentFinish]: def parse(self, text: str) -> Union[AgentAction, AgentFinish]:
msg = "Can only parse messages" msg = "Can only parse messages"

2
libs/langchain/langchain/chains/combine_documents/stuff.py
View File

@@ -239,7 +239,7 @@ class StuffDocumentsChain(BaseCombineDocumentsChain):
""" """
inputs = self._get_inputs(docs, **kwargs) inputs = self._get_inputs(docs, **kwargs)
prompt = self.llm_chain.prompt.format(**inputs) prompt = self.llm_chain.prompt.format(**inputs)
return self.llm_chain._get_num_tokens(prompt) return self.llm_chain._get_num_tokens(prompt) # noqa: SLF001
def combine_docs( def combine_docs(
self, self,

2
libs/langchain/langchain/chains/conversational_retrieval/base.py
View File

@@ -388,7 +388,7 @@ class ConversationalRetrievalChain(BaseConversationalRetrievalChain):
StuffDocumentsChain, StuffDocumentsChain,
): ):
tokens = [ tokens = [
self.combine_docs_chain.llm_chain._get_num_tokens(doc.page_content) self.combine_docs_chain.llm_chain._get_num_tokens(doc.page_content) # noqa: SLF001
for doc in docs for doc in docs
] ]
token_count = sum(tokens[:num_docs]) token_count = sum(tokens[:num_docs])

2
libs/langchain/langchain/chains/qa_with_sources/retrieval.py
View File

@@ -33,7 +33,7 @@ class RetrievalQAWithSourcesChain(BaseQAWithSourcesChain):
StuffDocumentsChain, StuffDocumentsChain,
): ):
tokens = [ tokens = [
self.combine_documents_chain.llm_chain._get_num_tokens(doc.page_content) self.combine_documents_chain.llm_chain._get_num_tokens(doc.page_content) # noqa: SLF001
for doc in docs for doc in docs
] ]
token_count = sum(tokens[:num_docs]) token_count = sum(tokens[:num_docs])

2
libs/langchain/langchain/chains/qa_with_sources/vector_db.py
View File

@@ -38,7 +38,7 @@ class VectorDBQAWithSourcesChain(BaseQAWithSourcesChain):
StuffDocumentsChain, StuffDocumentsChain,
): ):
tokens = [ tokens = [
self.combine_documents_chain.llm_chain._get_num_tokens(doc.page_content) self.combine_documents_chain.llm_chain._get_num_tokens(doc.page_content) # noqa: SLF001
for doc in docs for doc in docs
] ]
token_count = sum(tokens[:num_docs]) token_count = sum(tokens[:num_docs])

4
libs/langchain/langchain/output_parsers/combining.py
View File

@@ -25,10 +25,10 @@ class CombiningOutputParser(BaseOutputParser[dict[str, Any]]):
msg = "Must have at least two parsers" msg = "Must have at least two parsers"
raise ValueError(msg) raise ValueError(msg)
for parser in parsers: for parser in parsers:
if parser._type == "combining": if parser._type == "combining": # noqa: SLF001
msg = "Cannot nest combining parsers" msg = "Cannot nest combining parsers"
raise ValueError(msg) raise ValueError(msg)
if parser._type == "list": if parser._type == "list": # noqa: SLF001
msg = "Cannot combine list parsers" msg = "Cannot combine list parsers"
raise ValueError(msg) raise ValueError(msg)
return values return values

1
libs/langchain/pyproject.toml
View File

@@ -200,7 +200,6 @@ ignore = [
"PLR09", # Too many something (args, statements, etc) "PLR09", # Too many something (args, statements, etc)
"S112", # Rarely useful "S112", # Rarely useful
"RUF012", # Doesn't play well with Pydantic "RUF012", # Doesn't play well with Pydantic
"SLF001", # Private member access
"UP007", # pyupgrade: non-pep604-annotation-union "UP007", # pyupgrade: non-pep604-annotation-union
# TODO rules # TODO rules