chore(langchain): add ruff rules D (except D100 and D104)

libs/langchain/langchain/agents/__init__.py

@@ -1,5 +1,4 @@
-"""
-**Agent** is a class that uses an LLM to choose a sequence of actions to take.
+"""**Agent** is a class that uses an LLM to choose a sequence of actions to take.
 
 In Chains, a sequence of actions is hardcoded. In Agents,
 a language model is used as a reasoning engine to determine which actions

libs/langchain/langchain/agents/agent.py

@@ -366,7 +366,6 @@ def save(self, file_path: Union[Path, str]) -> None:
 
     def tool_run_logging_kwargs(self) -> builtins.dict:
         """Return logging kwargs for tool run."""
-
         return {}
 
 
@@ -1807,7 +1806,6 @@ async def astream(
         Yields:
             AddableDict: Addable dictionary.
         """
-
         config = ensure_config(config)
         iterator = AgentExecutorIterator(
             self,

libs/langchain/langchain/agents/agent_iterator.py

@@ -55,7 +55,8 @@ def __init__(
         include_run_info: bool = False,
         yield_actions: bool = False,
     ):
-        """
+        """Initialize the AgentExecutorIterator.
+
         Initialize the AgentExecutorIterator with the given AgentExecutor,
         inputs, and optional callbacks.
 
@@ -129,7 +130,8 @@ def color_mapping(self) -> dict[str, str]:
         )
 
     def reset(self) -> None:
-        """
+        """Reset the iterator to its initial state.
+
         Reset the iterator to its initial state, clearing intermediate steps,
         iterations, and time elapsed.
         """
@@ -141,9 +143,7 @@ def reset(self) -> None:
         self.start_time = time.time()
 
     def update_iterations(self) -> None:
-        """
-        Increment the number of iterations and update the time elapsed.
-        """
+        """Increment the number of iterations and update the time elapsed."""
         self.iterations += 1
         self.time_elapsed = time.time() - self.start_time
         logger.debug(
@@ -242,9 +242,10 @@ def __iter__(self: AgentExecutorIterator) -> Iterator[AddableDict]:
         yield self._stop(run_manager)
 
     async def __aiter__(self) -> AsyncIterator[AddableDict]:
-        """
+        """Create an async iterator for the AgentExecutor.
+
         N.B. __aiter__ must be a normal method, so need to initialize async run manager
-        on first __anext__ call where we can await it
+        on first __anext__ call where we can await it.
         """
         logger.debug("Initialising AgentExecutorIterator (async)")
         self.reset()
@@ -326,7 +327,8 @@ def _process_next_step_output(
         next_step_output: Union[AgentFinish, list[tuple[AgentAction, str]]],
         run_manager: CallbackManagerForChainRun,
     ) -> AddableDict:
-        """
+        """Process the output of the next step.
+
         Process the output of the next step,
         handling AgentFinish and tool return cases.
         """
@@ -354,7 +356,8 @@ async def _aprocess_next_step_output(
         next_step_output: Union[AgentFinish, list[tuple[AgentAction, str]]],
         run_manager: AsyncCallbackManagerForChainRun,
     ) -> AddableDict:
-        """
+        """Process the output of the next async step.
+
         Process the output of the next async step,
         handling AgentFinish and tool return cases.
         """
@@ -378,7 +381,8 @@ async def _aprocess_next_step_output(
         return AddableDict(intermediate_step=next_step_output)
 
     def _stop(self, run_manager: CallbackManagerForChainRun) -> AddableDict:
-        """
+        """Stop the iterator.
+
         Stop the iterator and raise a StopIteration exception with the stopped response.
         """
         logger.warning("Stopping agent prematurely due to triggering stop condition")
@@ -391,7 +395,8 @@ def _stop(self, run_manager: CallbackManagerForChainRun) -> AddableDict:
         return self._return(output, run_manager=run_manager)
 
     async def _astop(self, run_manager: AsyncCallbackManagerForChainRun) -> AddableDict:
-        """
+        """Stop the async iterator.
+
         Stop the async iterator and raise a StopAsyncIteration exception with
         the stopped response.
         """
@@ -408,9 +413,7 @@ def _return(
         output: AgentFinish,
         run_manager: CallbackManagerForChainRun,
     ) -> AddableDict:
-        """
-        Return the final output of the iterator.
-        """
+        """Return the final output of the iterator."""
         returned_output = self.agent_executor._return(  # noqa: SLF001
             output,
             self.intermediate_steps,
@@ -425,9 +428,7 @@ async def _areturn(
         output: AgentFinish,
         run_manager: AsyncCallbackManagerForChainRun,
     ) -> AddableDict:
-        """
-        Return the final output of the async iterator.
-        """
+        """Return the final output of the async iterator."""
         returned_output = await self.agent_executor._areturn(  # noqa: SLF001
             output,
             self.intermediate_steps,

libs/langchain/langchain/agents/agent_toolkits/conversational_retrieval/openai_functions.py

@@ -51,11 +51,11 @@ def create_conversational_retrieval_agent(
             defaults to False.
         max_token_limit: The max number of tokens to keep around in memory.
             Defaults to 2000.
+        **kwargs: Additional keyword arguments to pass to the AgentExecutor.
 
     Returns:
         An agent executor initialized appropriately
     """
-
     if remember_intermediate_steps:
         memory: BaseMemory = AgentTokenBufferMemory(
             memory_key=memory_key,

libs/langchain/langchain/agents/agent_toolkits/csv/__init__.py

@@ -3,7 +3,6 @@
 
 def __getattr__(name: str) -> Any:
     """Get attr name."""
-
     if name == "create_csv_agent":
         msg = (
             "This agent has been moved to langchain experiment. "

libs/langchain/langchain/agents/agent_toolkits/nasa/__init__.py

@@ -1 +1 @@
-"""NASA Toolkit"""
+"""NASA Toolkit."""

libs/langchain/langchain/agents/agent_toolkits/pandas/__init__.py

@@ -3,7 +3,6 @@
 
 def __getattr__(name: str) -> Any:
     """Get attr name."""
-
     if name == "create_pandas_dataframe_agent":
         msg = (
             "This agent has been moved to langchain experiment. "

libs/langchain/langchain/agents/agent_toolkits/python/__init__.py

@@ -3,7 +3,6 @@
 
 def __getattr__(name: str) -> Any:
     """Get attr name."""
-
     if name == "create_python_agent":
         msg = (
             "This agent has been moved to langchain experiment. "

libs/langchain/langchain/agents/agent_toolkits/spark/__init__.py

@@ -3,7 +3,6 @@
 
 def __getattr__(name: str) -> Any:
     """Get attr name."""
-
     if name == "create_spark_dataframe_agent":
         msg = (
             "This agent has been moved to langchain experiment. "

libs/langchain/langchain/agents/agent_toolkits/xorbits/__init__.py

@@ -3,7 +3,6 @@
 
 def __getattr__(name: str) -> Any:
     """Get attr name."""
-
     if name == "create_xorbits_agent":
         msg = (
             "This agent has been moved to langchain experiment. "

libs/langchain/langchain/agents/chat/base.py

@@ -105,7 +105,6 @@ def create_prompt(
         Returns:
             A prompt template.
         """
-
         tool_strings = "\n".join([f"{tool.name}: {tool.description}" for tool in tools])
         tool_names = ", ".join([tool.name for tool in tools])
         format_instructions = format_instructions.format(tool_names=tool_names)

libs/langchain/langchain/agents/chat/output_parser.py

@@ -26,8 +26,7 @@ def get_format_instructions(self) -> str:
         return self.format_instructions
 
     def parse(self, text: str) -> Union[AgentAction, AgentFinish]:
-        """Parse the output from the agent into
-        an AgentAction or AgentFinish object.
+        """Parse the output from the agent into an AgentAction or AgentFinish object.
 
         Args:
             text: The text to parse.
@@ -39,7 +38,6 @@ def parse(self, text: str) -> Union[AgentAction, AgentFinish]:
             OutputParserException: If the output could not be parsed.
             ValueError: If the action could not be found.
         """
-
         includes_answer = FINAL_ANSWER_ACTION in text
         try:
             found = self.pattern.search(text)

libs/langchain/langchain/agents/conversational/output_parser.py

@@ -22,16 +22,14 @@ def get_format_instructions(self) -> str:
         return self.format_instructions
 
     def parse(self, text: str) -> Union[AgentAction, AgentFinish]:
-        """Parse the output from the agent into
-        an AgentAction or AgentFinish object.
+        """Parse the output from the agent into an AgentAction or AgentFinish object.
 
         Args:
             text: The text to parse.
 
         Returns:
             An AgentAction or AgentFinish object.
         """
-
         if f"{self.ai_prefix}:" in text:
             return AgentFinish(
                 {"output": text.split(f"{self.ai_prefix}:")[-1].strip()},

libs/langchain/langchain/agents/format_scratchpad/openai_functions.py

@@ -16,6 +16,7 @@ def _convert_agent_action_to_messages(
 
     Args:
         agent_action: Agent action to convert.
+        observation: The result of the tool invocation.
 
     Returns:
         AIMessage or the previous messages plus a FunctionMessage that corresponds to
@@ -34,9 +35,11 @@ def _create_function_message(
     observation: Any,
 ) -> FunctionMessage:
     """Convert agent action and observation into a function message.
+
     Args:
         agent_action: the tool invocation request from the agent.
         observation: the result of the tool invocation.
+
     Returns:
         FunctionMessage that corresponds to the original tool invocation.
 

libs/langchain/langchain/agents/format_scratchpad/tools.py

@@ -21,6 +21,7 @@ def _create_tool_message(
     Args:
         agent_action: the tool invocation request from the agent.
         observation: the result of the tool invocation.
+
     Returns:
         ToolMessage that corresponds to the original tool invocation.
 

libs/langchain/langchain/agents/json_chat/base.py

@@ -20,7 +20,7 @@ def create_json_chat_agent(
     tools_renderer: ToolsRenderer = render_text_description,
     template_tool_response: str = TEMPLATE_TOOL_RESPONSE,
 ) -> Runnable:
-    """Create an agent that uses JSON to format its logic, build for Chat Models.
+    r"""Create an agent that uses JSON to format its logic, build for Chat Models.
 
     Args:
         llm: LLM to use as the agent.
@@ -93,27 +93,27 @@ def create_json_chat_agent(
 
             system = '''Assistant is a large language model trained by OpenAI.
 
-            Assistant is designed to be able to assist with a wide range of tasks, from answering \
-            simple questions to providing in-depth explanations and discussions on a wide range of \
-            topics. As a language model, Assistant is able to generate human-like text based on \
-            the input it receives, allowing it to engage in natural-sounding conversations and \
+            Assistant is designed to be able to assist with a wide range of tasks, from answering
+            simple questions to providing in-depth explanations and discussions on a wide range of
+            topics. As a language model, Assistant is able to generate human-like text based on
+            the input it receives, allowing it to engage in natural-sounding conversations and
             provide responses that are coherent and relevant to the topic at hand.
 
-            Assistant is constantly learning and improving, and its capabilities are constantly \
-            evolving. It is able to process and understand large amounts of text, and can use this \
-            knowledge to provide accurate and informative responses to a wide range of questions. \
-            Additionally, Assistant is able to generate its own text based on the input it \
-            receives, allowing it to engage in discussions and provide explanations and \
+            Assistant is constantly learning and improving, and its capabilities are constantly
+            evolving. It is able to process and understand large amounts of text, and can use this
+            knowledge to provide accurate and informative responses to a wide range of questions.
+            Additionally, Assistant is able to generate its own text based on the input it
+            receives, allowing it to engage in discussions and provide explanations and
             descriptions on a wide range of topics.
 
-            Overall, Assistant is a powerful system that can help with a wide range of tasks \
-            and provide valuable insights and information on a wide range of topics. Whether \
-            you need help with a specific question or just want to have a conversation about \
+            Overall, Assistant is a powerful system that can help with a wide range of tasks
+            and provide valuable insights and information on a wide range of topics. Whether
+            you need help with a specific question or just want to have a conversation about
             a particular topic, Assistant is here to assist.'''
 
             human = '''TOOLS
             ------
-            Assistant can ask the user to use tools to look up information that may be helpful in \
+            Assistant can ask the user to use tools to look up information that may be helpful in
             answering the users original question. The tools the human can use are:
 
             {tools}
@@ -129,25 +129,25 @@ def create_json_chat_agent(
 
             ```json
             {{
-                "action": string, \\ The action to take. Must be one of {tool_names}
-                "action_input": string \\ The input to the action
+                "action": string, \\\\ The action to take. Must be one of {tool_names}
+                "action_input": string \\\\ The input to the action
             }}
             ```
 
             **Option #2:**
-            Use this if you want to respond directly to the human. Markdown code snippet formatted \
+            Use this if you want to respond directly to the human. Markdown code snippet formatted
             in the following schema:
 
             ```json
             {{
                 "action": "Final Answer",
-                "action_input": string \\ You should put what you want to return to use here
+                "action_input": string \\\\ You should put what you want to return to use here
             }}
             ```
 
             USER'S INPUT
             --------------------
-            Here is the user's input (remember to respond with a markdown code snippet of a json \
+            Here is the user's input (remember to respond with a markdown code snippet of a json
             blob with a single action, and NOTHING else):
 
             {input}'''

libs/langchain/langchain/agents/mrkl/output_parser.py

@@ -30,8 +30,7 @@ def get_format_instructions(self) -> str:
         return self.format_instructions
 
     def parse(self, text: str) -> Union[AgentAction, AgentFinish]:
-        """Parse the output from the agent into
-        an AgentAction or AgentFinish object.
+        """Parse the output from the agent into an AgentAction or AgentFinish object.
 
         Args:
             text: The text to parse.

libs/langchain/langchain/agents/openai_assistant/base.py

@@ -302,14 +302,14 @@ def invoke(
                 attachments: A list of files attached to the message, and the
                     tools they should be added to.
             config: Runnable config. Defaults to None.
+            **kwargs: Additional arguments.
 
         Return:
             If self.as_agent, will return
                 Union[List[OpenAIAssistantAction], OpenAIAssistantFinish].
                 Otherwise, will return OpenAI types
                 Union[List[ThreadMessage], List[RequiredActionFunctionToolCall]].
         """
-
         config = ensure_config(config)
         callback_manager = CallbackManager.configure(
             inheritable_callbacks=config.get("callbacks"),
@@ -390,6 +390,7 @@ async def acreate_assistant(
             model: Assistant model to use.
             async_client: AsyncOpenAI client.
                 Will create default async_client if not specified.
+            **kwargs: Additional arguments.
 
         Returns:
             AsyncOpenAIAssistantRunnable configured to run using the created assistant.
@@ -442,7 +443,6 @@ async def ainvoke(
                 Otherwise, will return OpenAI types
                 Union[List[ThreadMessage], List[RequiredActionFunctionToolCall]].
         """
-
         config = config or {}
         callback_manager = CallbackManager.configure(
             inheritable_callbacks=config.get("callbacks"),

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

@@ -92,7 +92,6 @@ def input_keys(self) -> list[str]:
     @property
     def functions(self) -> list[dict]:
         """Get functions."""
-
         return [dict(convert_to_openai_function(t)) for t in self.tools]
 
     def plan(
@@ -309,7 +308,6 @@ def create_openai_functions_agent(
         ValueError: If `agent_scratchpad` is not in the prompt.
 
     Example:
-
         Creating an agent with no memory
 
         .. code-block:: python