feat(langchain): add ruff rules SLF

Author: cbornet

libs/langchain/langchain/agents/agent_iterator.py

@@ -190,14 +190,14 @@ def __iter__(self: AgentExecutorIterator) -> Iterator[AddableDict]:
             name=self.run_name,
         )
         try:
-            while self.agent_executor._should_continue(
+            while self.agent_executor._should_continue(  # noqa: SLF001
                 self.iterations,
                 self.time_elapsed,
             ):
                 # take the next step: this plans next action, executes it,
                 # yielding action and observation as they are generated
                 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.color_mapping,
                     self.inputs,
@@ -214,7 +214,7 @@ def __iter__(self: AgentExecutorIterator) -> Iterator[AddableDict]:
                             yield AddableDict(steps=[chunk], messages=chunk.messages)
 
                 # 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
                 self.update_iterations()
                 # decide if this is the final output
@@ -258,14 +258,14 @@ async def __aiter__(self) -> AsyncIterator[AddableDict]:
         )
         try:
             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.time_elapsed,
                 ):
                     # take the next step: this plans next action, executes it,
                     # yielding action and observation as they are generated
                     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.color_mapping,
                         self.inputs,
@@ -288,7 +288,7 @@ async def __aiter__(self) -> AsyncIterator[AddableDict]:
                                 )
 
                     # 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
                     self.update_iterations()
                     # decide if this is the final output
@@ -336,7 +336,7 @@ def _process_next_step_output(
         # Check for tool return
         if len(next_step_output) == 1:
             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:
                 return self._return(tool_return, run_manager=run_manager)
 
@@ -364,7 +364,7 @@ async def _aprocess_next_step_output(
         # Check for tool return
         if len(next_step_output) == 1:
             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:
                 return await self._areturn(tool_return, run_manager=run_manager)
 
@@ -376,7 +376,7 @@ def _stop(self, run_manager: CallbackManagerForChainRun) -> AddableDict:
         """
         logger.warning("Stopping agent prematurely due to triggering stop condition")
         # 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.intermediate_steps,
             **self.inputs,
@@ -389,7 +389,7 @@ async def _astop(self, run_manager: AsyncCallbackManagerForChainRun) -> AddableD
         the stopped response.
         """
         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.intermediate_steps,
             **self.inputs,
@@ -404,7 +404,7 @@ def _return(
         """
         Return the final output of the iterator.
         """
-        returned_output = self.agent_executor._return(
+        returned_output = self.agent_executor._return(  # noqa: SLF001
             output,
             self.intermediate_steps,
             run_manager=run_manager,
@@ -421,7 +421,7 @@ async def _areturn(
         """
         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,
             self.intermediate_steps,
             run_manager=run_manager,

libs/langchain/langchain/agents/initialize.py

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

libs/langchain/langchain/agents/openai_functions_agent/base.py

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

libs/langchain/langchain/agents/output_parsers/openai_functions.py

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

libs/langchain/langchain/chains/combine_documents/stuff.py

@@ -240,7 +240,7 @@ def prompt_length(self, docs: list[Document], **kwargs: Any) -> Optional[int]:
         """
         inputs = self._get_inputs(docs, **kwargs)
         prompt = self.llm_chain.prompt.format(**inputs)
-        return self.llm_chain._get_num_tokens(prompt)
+        return self.llm_chain.get_num_tokens(prompt)
 
     def combine_docs(
         self,

libs/langchain/langchain/chains/conversational_retrieval/base.py

@@ -388,7 +388,7 @@ def _reduce_tokens_below_limit(self, docs: list[Document]) -> list[Document]:
             StuffDocumentsChain,
         ):
             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)
                 for doc in docs
             ]
             token_count = sum(tokens[:num_docs])

libs/langchain/langchain/chains/llm.py

@@ -422,7 +422,7 @@ def from_string(cls, llm: BaseLanguageModel, template: str) -> LLMChain:
         prompt_template = PromptTemplate.from_template(template)
         return cls(llm=llm, prompt=prompt_template)
 
-    def _get_num_tokens(self, text: str) -> int:
+    def get_num_tokens(self, text: str) -> int:
         return _get_language_model(self.llm).get_num_tokens(text)
 
 

libs/langchain/langchain/chains/qa_with_sources/retrieval.py

@@ -33,7 +33,7 @@ def _reduce_tokens_below_limit(self, docs: list[Document]) -> list[Document]:
             StuffDocumentsChain,
         ):
             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)
                 for doc in docs
             ]
             token_count = sum(tokens[:num_docs])

libs/langchain/langchain/chains/qa_with_sources/vector_db.py

@@ -38,7 +38,7 @@ def _reduce_tokens_below_limit(self, docs: list[Document]) -> list[Document]:
             StuffDocumentsChain,
         ):
             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)
                 for doc in docs
             ]
             token_count = sum(tokens[:num_docs])

libs/langchain/langchain/output_parsers/combining.py

@@ -23,10 +23,10 @@ def validate_parsers(cls, values: dict[str, Any]) -> dict[str, Any]:
             msg = "Must have at least two parsers"
             raise ValueError(msg)
         for parser in parsers:
-            if parser._type == "combining":
+            if parser._type == "combining":  # noqa: SLF001
                 msg = "Cannot nest combining parsers"
                 raise ValueError(msg)
-            if parser._type == "list":
+            if parser._type == "list":  # noqa: SLF001
                 msg = "Cannot combine list parsers"
                 raise ValueError(msg)
         return values