"""Chain that takes in an input and produces an action and action input.""" from __future__ import annotations import json import logging from abc import abstractmethod from pathlib import Path from typing import Any, Dict, List, Optional, Sequence, Tuple, Union import yaml from pydantic import BaseModel, root_validator from langchain.agents.tools import InvalidTool from langchain.callbacks.base import BaseCallbackManager from langchain.chains.base import Chain from langchain.chains.llm import LLMChain from langchain.input import get_color_mapping from langchain.llms.base import BaseLLM from langchain.prompts.base import BasePromptTemplate from langchain.prompts.few_shot import FewShotPromptTemplate from langchain.prompts.prompt import PromptTemplate from langchain.schema import AgentAction, AgentFinish from langchain.tools.base import BaseTool logger = logging.getLogger() class Agent(BaseModel): """Class responsible for calling the language model and deciding the action. This is driven by an LLMChain. The prompt in the LLMChain MUST include a variable called "agent_scratchpad" where the agent can put its intermediary work. """ llm_chain: LLMChain allowed_tools: Optional[List[str]] = None return_values: List[str] = ["output"] @abstractmethod def _extract_tool_and_input(self, text: str) -> Optional[Tuple[str, str]]: """Extract tool and tool input from llm output.""" def _fix_text(self, text: str) -> str: """Fix the text.""" raise ValueError("fix_text not implemented for this agent.") @property def _stop(self) -> List[str]: return [f"\n{self.observation_prefix}"] def _construct_scratchpad( self, intermediate_steps: List[Tuple[AgentAction, str]] ) -> str: """Construct the scratchpad that lets the agent continue its thought process.""" thoughts = "" for action, observation in intermediate_steps: thoughts += action.log thoughts += f"\n{self.observation_prefix}{observation}\n{self.llm_prefix}" return thoughts def _get_next_action(self, full_inputs: Dict[str, str]) -> AgentAction: full_output = self.llm_chain.predict(**full_inputs) parsed_output = self._extract_tool_and_input(full_output) while parsed_output is None: full_output = self._fix_text(full_output) full_inputs["agent_scratchpad"] += full_output output = self.llm_chain.predict(**full_inputs) full_output += output parsed_output = self._extract_tool_and_input(full_output) return AgentAction( tool=parsed_output[0], tool_input=parsed_output[1], log=full_output ) async def _aget_next_action(self, full_inputs: Dict[str, str]) -> AgentAction: full_output = await self.llm_chain.apredict(**full_inputs) parsed_output = self._extract_tool_and_input(full_output) while parsed_output is None: full_output = self._fix_text(full_output) full_inputs["agent_scratchpad"] += full_output output = await self.llm_chain.apredict(**full_inputs) full_output += output parsed_output = self._extract_tool_and_input(full_output) return AgentAction( tool=parsed_output[0], tool_input=parsed_output[1], log=full_output ) def plan( self, intermediate_steps: List[Tuple[AgentAction, str]], **kwargs: Any ) -> Union[AgentAction, AgentFinish]: """Given input, decided what to do. Args: intermediate_steps: Steps the LLM has taken to date, along with observations **kwargs: User inputs. Returns: Action specifying what tool to use. """ full_inputs = self.get_full_inputs(intermediate_steps, **kwargs) action = self._get_next_action(full_inputs) if action.tool == self.finish_tool_name: return AgentFinish({"output": action.tool_input}, action.log) return action async def aplan( self, intermediate_steps: List[Tuple[AgentAction, str]], **kwargs: Any ) -> Union[AgentAction, AgentFinish]: """Given input, decided what to do. Args: intermediate_steps: Steps the LLM has taken to date, along with observations **kwargs: User inputs. Returns: Action specifying what tool to use. """ full_inputs = self.get_full_inputs(intermediate_steps, **kwargs) action = await self._aget_next_action(full_inputs) if action.tool == self.finish_tool_name: return AgentFinish({"output": action.tool_input}, action.log) return action def get_full_inputs( self, intermediate_steps: List[Tuple[AgentAction, str]], **kwargs: Any ) -> Dict[str, Any]: """Create the full inputs for the LLMChain from intermediate steps.""" thoughts = self._construct_scratchpad(intermediate_steps) new_inputs = {"agent_scratchpad": thoughts, "stop": self._stop} full_inputs = {**kwargs, **new_inputs} return full_inputs def prepare_for_new_call(self) -> None: """Prepare the agent for new call, if needed.""" pass @property def finish_tool_name(self) -> str: """Name of the tool to use to finish the chain.""" return "Final Answer" @property def input_keys(self) -> List[str]: """Return the input keys. :meta private: """ return list(set(self.llm_chain.input_keys) - {"agent_scratchpad"}) @root_validator() def validate_prompt(cls, values: Dict) -> Dict: """Validate that prompt matches format.""" prompt = values["llm_chain"].prompt if "agent_scratchpad" not in prompt.input_variables: logger.warning( "`agent_scratchpad` should be a variable in prompt.input_variables." " Did not find it, so adding it at the end." ) prompt.input_variables.append("agent_scratchpad") if isinstance(prompt, PromptTemplate): prompt.template += "\n{agent_scratchpad}" elif isinstance(prompt, FewShotPromptTemplate): prompt.suffix += "\n{agent_scratchpad}" else: raise ValueError(f"Got unexpected prompt type {type(prompt)}") return values @property @abstractmethod def observation_prefix(self) -> str: """Prefix to append the observation with.""" @property @abstractmethod def llm_prefix(self) -> str: """Prefix to append the LLM call with.""" @classmethod @abstractmethod def create_prompt(cls, tools: Sequence[BaseTool]) -> BasePromptTemplate: """Create a prompt for this class.""" @classmethod def _validate_tools(cls, tools: Sequence[BaseTool]) -> None: """Validate that appropriate tools are passed in.""" pass @classmethod def from_llm_and_tools( cls, llm: BaseLLM, tools: Sequence[BaseTool], callback_manager: Optional[BaseCallbackManager] = None, **kwargs: Any, ) -> Agent: """Construct an agent from an LLM and tools.""" cls._validate_tools(tools) llm_chain = LLMChain( llm=llm, prompt=cls.create_prompt(tools), callback_manager=callback_manager, ) tool_names = [tool.name for tool in tools] return cls(llm_chain=llm_chain, allowed_tools=tool_names, **kwargs) def return_stopped_response( self, early_stopping_method: str, intermediate_steps: List[Tuple[AgentAction, str]], **kwargs: Any, ) -> AgentFinish: """Return response when agent has been stopped due to max iterations.""" if early_stopping_method == "force": # `force` just returns a constant string return AgentFinish({"output": "Agent stopped due to max iterations."}, "") elif early_stopping_method == "generate": # Generate does one final forward pass thoughts = "" for action, observation in intermediate_steps: thoughts += action.log thoughts += ( f"\n{self.observation_prefix}{observation}\n{self.llm_prefix}" ) # Adding to the previous steps, we now tell the LLM to make a final pred thoughts += ( "\n\nI now need to return a final answer based on the previous steps:" ) new_inputs = {"agent_scratchpad": thoughts, "stop": self._stop} full_inputs = {**kwargs, **new_inputs} full_output = self.llm_chain.predict(**full_inputs) # We try to extract a final answer parsed_output = self._extract_tool_and_input(full_output) if parsed_output is None: # If we cannot extract, we just return the full output return AgentFinish({"output": full_output}, full_output) tool, tool_input = parsed_output if tool == self.finish_tool_name: # If we can extract, we send the correct stuff return AgentFinish({"output": tool_input}, full_output) else: # If we can extract, but the tool is not the final tool, # we just return the full output return AgentFinish({"output": full_output}, full_output) else: raise ValueError( "early_stopping_method should be one of `force` or `generate`, " f"got {early_stopping_method}" ) @property @abstractmethod def _agent_type(self) -> str: """Return Identifier of agent type.""" def dict(self, **kwargs: Any) -> Dict: """Return dictionary representation of agent.""" _dict = super().dict() _dict["_type"] = self._agent_type return _dict def save(self, file_path: Union[Path, str]) -> None: """Save the agent. Args: file_path: Path to file to save the agent to. Example: .. code-block:: python # If working with agent executor agent.agent.save(file_path="path/agent.yaml") """ # Convert file to Path object. if isinstance(file_path, str): save_path = Path(file_path) else: save_path = file_path directory_path = save_path.parent directory_path.mkdir(parents=True, exist_ok=True) # Fetch dictionary to save agent_dict = self.dict() if save_path.suffix == ".json": with open(file_path, "w") as f: json.dump(agent_dict, f, indent=4) elif save_path.suffix == ".yaml": with open(file_path, "w") as f: yaml.dump(agent_dict, f, default_flow_style=False) else: raise ValueError(f"{save_path} must be json or yaml") class AgentExecutor(Chain, BaseModel): """Consists of an agent using tools.""" agent: Agent tools: Sequence[BaseTool] return_intermediate_steps: bool = False max_iterations: Optional[int] = 15 early_stopping_method: str = "force" @classmethod def from_agent_and_tools( cls, agent: Agent, tools: Sequence[BaseTool], callback_manager: Optional[BaseCallbackManager] = None, **kwargs: Any, ) -> AgentExecutor: """Create from agent and tools.""" return cls( agent=agent, tools=tools, callback_manager=callback_manager, **kwargs ) @root_validator() def validate_tools(cls, values: Dict) -> Dict: """Validate that tools are compatible with agent.""" agent = values["agent"] tools = values["tools"] if agent.allowed_tools is not None: if set(agent.allowed_tools) != set([tool.name for tool in tools]): raise ValueError( f"Allowed tools ({agent.allowed_tools}) different than " f"provided tools ({[tool.name for tool in tools]})" ) return values def save(self, file_path: Union[Path, str]) -> None: """Raise error - saving not supported for Agent Executors.""" raise ValueError( "Saving not supported for agent executors. " "If you are trying to save the agent, please use the " "`.save_agent(...)`" ) def save_agent(self, file_path: Union[Path, str]) -> None: """Save the underlying agent.""" return self.agent.save(file_path) @property def input_keys(self) -> List[str]: """Return the input keys. :meta private: """ return self.agent.input_keys @property def output_keys(self) -> List[str]: """Return the singular output key. :meta private: """ if self.return_intermediate_steps: return self.agent.return_values + ["intermediate_steps"] else: return self.agent.return_values def _should_continue(self, iterations: int) -> bool: if self.max_iterations is None: return True else: return iterations < self.max_iterations def _return(self, output: AgentFinish, intermediate_steps: list) -> Dict[str, Any]: self.callback_manager.on_agent_finish( output, color="green", verbose=self.verbose ) final_output = output.return_values if self.return_intermediate_steps: final_output["intermediate_steps"] = intermediate_steps return final_output async def _areturn( self, output: AgentFinish, intermediate_steps: list ) -> Dict[str, Any]: if self.callback_manager.is_async: await self.callback_manager.on_agent_finish( output, color="green", verbose=self.verbose ) else: self.callback_manager.on_agent_finish( output, color="green", verbose=self.verbose ) final_output = output.return_values if self.return_intermediate_steps: final_output["intermediate_steps"] = intermediate_steps return final_output def _take_next_step( self, name_to_tool_map: Dict[str, BaseTool], color_mapping: Dict[str, str], inputs: Dict[str, str], intermediate_steps: List[Tuple[AgentAction, str]], ) -> Union[AgentFinish, Tuple[AgentAction, str]]: """Take a single step in the thought-action-observation loop. Override this to take control of how the agent makes and acts on choices. """ # Call the LLM to see what to do. output = self.agent.plan(intermediate_steps, **inputs) # If the tool chosen is the finishing tool, then we end and return. if isinstance(output, AgentFinish): return output # Otherwise we lookup the tool if output.tool in name_to_tool_map: tool = name_to_tool_map[output.tool] return_direct = tool.return_direct color = color_mapping[output.tool] llm_prefix = "" if return_direct else self.agent.llm_prefix # We then call the tool on the tool input to get an observation observation = tool.run( output, verbose=self.verbose, color=color, llm_prefix=llm_prefix, observation_prefix=self.agent.observation_prefix, ) else: observation = InvalidTool().run( output, verbose=self.verbose, color=None, llm_prefix="", observation_prefix=self.agent.observation_prefix, ) return_direct = False if return_direct: # Set the log to "" because we do not want to log it. return AgentFinish({self.agent.return_values[0]: observation}, "") return output, observation async def _atake_next_step( self, name_to_tool_map: Dict[str, BaseTool], color_mapping: Dict[str, str], inputs: Dict[str, str], intermediate_steps: List[Tuple[AgentAction, str]], ) -> Union[AgentFinish, Tuple[AgentAction, str]]: """Take a single step in the thought-action-observation loop. Override this to take control of how the agent makes and acts on choices. """ # Call the LLM to see what to do. output = await self.agent.aplan(intermediate_steps, **inputs) # If the tool chosen is the finishing tool, then we end and return. if isinstance(output, AgentFinish): return output # Otherwise we lookup the tool if output.tool in name_to_tool_map: tool = name_to_tool_map[output.tool] return_direct = tool.return_direct color = color_mapping[output.tool] llm_prefix = "" if return_direct else self.agent.llm_prefix # We then call the tool on the tool input to get an observation observation = await tool.arun( output, verbose=self.verbose, color=color, llm_prefix=llm_prefix, observation_prefix=self.agent.observation_prefix, ) else: observation = await InvalidTool().arun( output, verbose=self.verbose, color=None, llm_prefix="", observation_prefix=self.agent.observation_prefix, ) return_direct = False if return_direct: # Set the log to "" because we do not want to log it. return AgentFinish({self.agent.return_values[0]: observation}, "") return output, observation def _call(self, inputs: Dict[str, str]) -> Dict[str, Any]: """Run text through and get agent response.""" # Do any preparation necessary when receiving a new input. self.agent.prepare_for_new_call() # Construct a mapping of tool name to tool for easy lookup name_to_tool_map = {tool.name: tool for tool in self.tools} # We construct a mapping from each tool to a color, used for logging. color_mapping = get_color_mapping( [tool.name for tool in self.tools], excluded_colors=["green"] ) intermediate_steps: List[Tuple[AgentAction, str]] = [] # Let's start tracking the iterations the agent has gone through iterations = 0 # We now enter the agent loop (until it returns something). while self._should_continue(iterations): next_step_output = self._take_next_step( name_to_tool_map, color_mapping, inputs, intermediate_steps ) if isinstance(next_step_output, AgentFinish): return self._return(next_step_output, intermediate_steps) intermediate_steps.append(next_step_output) iterations += 1 output = self.agent.return_stopped_response( self.early_stopping_method, intermediate_steps, **inputs ) return self._return(output, intermediate_steps) async def _acall(self, inputs: Dict[str, str]) -> Dict[str, str]: """Run text through and get agent response.""" # Do any preparation necessary when receiving a new input. self.agent.prepare_for_new_call() # Construct a mapping of tool name to tool for easy lookup name_to_tool_map = {tool.name: tool for tool in self.tools} # We construct a mapping from each tool to a color, used for logging. color_mapping = get_color_mapping( [tool.name for tool in self.tools], excluded_colors=["green"] ) intermediate_steps: List[Tuple[AgentAction, str]] = [] # Let's start tracking the iterations the agent has gone through iterations = 0 # We now enter the agent loop (until it returns something). while self._should_continue(iterations): next_step_output = await self._atake_next_step( name_to_tool_map, color_mapping, inputs, intermediate_steps ) if isinstance(next_step_output, AgentFinish): return await self._areturn(next_step_output, intermediate_steps) intermediate_steps.append(next_step_output) iterations += 1 output = self.agent.return_stopped_response( self.early_stopping_method, intermediate_steps, **inputs ) return await self._areturn(output, intermediate_steps)