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217 lines
7.9 KiB
Python
217 lines
7.9 KiB
Python
"""Chain that takes in an input and produces an action and action input."""
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from __future__ import annotations
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import logging
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from abc import abstractmethod
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from typing import Any, Dict, List, Optional, Tuple, Union
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from pydantic import BaseModel, root_validator
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import langchain
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from langchain.agents.tools import Tool
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from langchain.chains.base import Chain
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from langchain.chains.llm import LLMChain
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from langchain.input import get_color_mapping
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from langchain.llms.base import BaseLLM
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from langchain.prompts.base import BasePromptTemplate
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from langchain.prompts.few_shot import FewShotPromptTemplate
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from langchain.prompts.prompt import PromptTemplate
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from langchain.schema import AgentAction, AgentFinish
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logger = logging.getLogger()
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class Agent(BaseModel):
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"""Class responsible for calling the language model and deciding the action.
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This is driven by an LLMChain. The prompt in the LLMChain MUST include
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a variable called "agent_scratchpad" where the agent can put its
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intermediary work.
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"""
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llm_chain: LLMChain
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return_values: List[str] = ["output"]
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@abstractmethod
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def _extract_tool_and_input(self, text: str) -> Optional[Tuple[str, str]]:
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"""Extract tool and tool input from llm output."""
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def _fix_text(self, text: str) -> str:
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"""Fix the text."""
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raise ValueError("fix_text not implemented for this agent.")
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@property
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def _stop(self) -> List[str]:
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return [f"\n{self.observation_prefix}"]
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def plan(
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self, intermediate_steps: List[Tuple[AgentAction, str]], **kwargs: Any
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) -> Union[AgentFinish, AgentAction]:
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"""Given input, decided what to do.
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Args:
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intermediate_steps: Steps the LLM has taken to date,
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along with observations
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**kwargs: User inputs.
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Returns:
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Action specifying what tool to use.
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"""
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thoughts = ""
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for action, observation in intermediate_steps:
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thoughts += action.log
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thoughts += f"\n{self.observation_prefix}{observation}\n{self.llm_prefix}"
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new_inputs = {"agent_scratchpad": thoughts, "stop": self._stop}
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full_inputs = {**kwargs, **new_inputs}
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full_output = self.llm_chain.predict(**full_inputs)
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parsed_output = self._extract_tool_and_input(full_output)
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while parsed_output is None:
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full_output = self._fix_text(full_output)
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full_inputs["agent_scratchpad"] += full_output
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output = self.llm_chain.predict(**full_inputs)
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full_output += output
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parsed_output = self._extract_tool_and_input(full_output)
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tool, tool_input = parsed_output
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if tool == self.finish_tool_name:
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return AgentFinish({"output": tool_input}, full_output)
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return AgentAction(tool, tool_input, full_output)
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def prepare_for_new_call(self) -> None:
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"""Prepare the agent for new call, if needed."""
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pass
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@property
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def finish_tool_name(self) -> str:
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"""Name of the tool to use to finish the chain."""
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return "Final Answer"
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@property
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def input_keys(self) -> List[str]:
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"""Return the input keys.
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:meta private:
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"""
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return list(set(self.llm_chain.input_keys) - {"agent_scratchpad"})
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@root_validator()
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def validate_prompt(cls, values: Dict) -> Dict:
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"""Validate that prompt matches format."""
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prompt = values["llm_chain"].prompt
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if "agent_scratchpad" not in prompt.input_variables:
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logger.warning(
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"`agent_scratchpad` should be a variable in prompt.input_variables."
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" Did not find it, so adding it at the end."
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)
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prompt.input_variables.append("agent_scratchpad")
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if isinstance(prompt, PromptTemplate):
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prompt.template += "\n{agent_scratchpad}"
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elif isinstance(prompt, FewShotPromptTemplate):
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prompt.suffix += "\n{agent_scratchpad}"
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else:
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raise ValueError(f"Got unexpected prompt type {type(prompt)}")
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return values
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@property
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@abstractmethod
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def observation_prefix(self) -> str:
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"""Prefix to append the observation with."""
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@property
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@abstractmethod
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def llm_prefix(self) -> str:
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"""Prefix to append the LLM call with."""
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@classmethod
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@abstractmethod
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def create_prompt(cls, tools: List[Tool]) -> BasePromptTemplate:
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"""Create a prompt for this class."""
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@classmethod
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def _validate_tools(cls, tools: List[Tool]) -> None:
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"""Validate that appropriate tools are passed in."""
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pass
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@classmethod
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def from_llm_and_tools(cls, llm: BaseLLM, tools: List[Tool]) -> Agent:
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"""Construct an agent from an LLM and tools."""
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cls._validate_tools(tools)
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llm_chain = LLMChain(llm=llm, prompt=cls.create_prompt(tools))
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return cls(llm_chain=llm_chain)
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class AgentExecutor(Chain, BaseModel):
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"""Consists of an agent using tools."""
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agent: Agent
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tools: List[Tool]
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return_intermediate_steps: bool = False
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@classmethod
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def from_agent_and_tools(
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cls, agent: Agent, tools: List[Tool], **kwargs: Any
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) -> AgentExecutor:
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"""Create from agent and tools."""
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return cls(agent=agent, tools=tools, **kwargs)
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@property
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def input_keys(self) -> List[str]:
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"""Return the input keys.
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:meta private:
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"""
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return self.agent.input_keys
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@property
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def output_keys(self) -> List[str]:
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"""Return the singular output key.
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:meta private:
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"""
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if self.return_intermediate_steps:
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return self.agent.return_values + ["intermediate_steps"]
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else:
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return self.agent.return_values
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def _call(self, inputs: Dict[str, str]) -> Dict[str, Any]:
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"""Run text through and get agent response."""
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# Do any preparation necessary when receiving a new input.
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self.agent.prepare_for_new_call()
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# Construct a mapping of tool name to tool for easy lookup
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name_to_tool_map = {tool.name: tool.func for tool in self.tools}
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# We construct a mapping from each tool to a color, used for logging.
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color_mapping = get_color_mapping(
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[tool.name for tool in self.tools], excluded_colors=["green"]
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)
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intermediate_steps: List[Tuple[AgentAction, str]] = []
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# We now enter the agent loop (until it returns something).
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while True:
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# Call the LLM to see what to do.
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output = self.agent.plan(intermediate_steps, **inputs)
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# If the tool chosen is the finishing tool, then we end and return.
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if isinstance(output, AgentFinish):
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if self.verbose:
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langchain.logger.log_agent_end(output, color="green")
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final_output = output.return_values
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if self.return_intermediate_steps:
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final_output["intermediate_steps"] = intermediate_steps
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return final_output
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if self.verbose:
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langchain.logger.log_agent_action(output, color="green")
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# And then we lookup the tool
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if output.tool in name_to_tool_map:
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chain = name_to_tool_map[output.tool]
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# We then call the tool on the tool input to get an observation
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observation = chain(output.tool_input)
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color = color_mapping[output.tool]
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else:
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observation = f"{output.tool} is not a valid tool, try another one."
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color = None
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if self.verbose:
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langchain.logger.log_agent_observation(
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observation,
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color=color,
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observation_prefix=self.agent.observation_prefix,
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llm_prefix=self.agent.llm_prefix,
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)
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intermediate_steps.append((output, observation))
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