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127 lines
4.2 KiB
Python
127 lines
4.2 KiB
Python
from typing import Any, List, Optional, Sequence, Tuple
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from langchain.agents.agent import Agent
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from langchain.callbacks.base import BaseCallbackManager
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from langchain.chains import LLMChain
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from langchain.schema import BaseOutputParser
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from langchain.prompts.base import BasePromptTemplate
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from langchain.prompts.chat import (
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ChatPromptTemplate,
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HumanMessagePromptTemplate,
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MessagesPlaceholder,
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SystemMessagePromptTemplate,
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)
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from langchain.schema import (
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AgentAction,
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AIMessage,
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BaseLanguageModel,
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BaseMessage,
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HumanMessage,
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)
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from langchain.tools.base import BaseTool
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from core.prompts.input import EVAL_TOOL_RESPONSE
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class ConversationalChatAgent(Agent):
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"""An agent designed to hold a conversation in addition to using tools."""
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output_parser: BaseOutputParser
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@property
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def _agent_type(self) -> str:
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raise NotImplementedError
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@property
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def observation_prefix(self) -> str:
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"""Prefix to append the observation with."""
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return "Observation: "
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@property
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def llm_prefix(self) -> str:
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"""Prefix to append the llm call with."""
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return "Thought: "
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@classmethod
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def create_prompt(
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cls,
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tools: Sequence[BaseTool],
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system_message: str,
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human_message: str,
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output_parser: BaseOutputParser,
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input_variables: Optional[List[str]] = None,
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) -> BasePromptTemplate:
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tool_strings = "\n".join(
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[f"> {tool.name}: {tool.description}" for tool in tools]
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)
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tool_names = ", ".join([tool.name for tool in tools])
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format_instructions = human_message.format(
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format_instructions=output_parser.get_format_instructions()
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)
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final_prompt = format_instructions.format(
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tool_names=tool_names, tools=tool_strings
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)
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if input_variables is None:
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input_variables = ["input", "chat_history", "agent_scratchpad"]
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messages = [
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SystemMessagePromptTemplate.from_template(system_message),
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MessagesPlaceholder(variable_name="chat_history"),
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HumanMessagePromptTemplate.from_template(final_prompt),
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MessagesPlaceholder(variable_name="agent_scratchpad"),
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]
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return ChatPromptTemplate(input_variables=input_variables, messages=messages)
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def _extract_tool_and_input(self, llm_output: str) -> Optional[Tuple[str, str]]:
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try:
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response = self.output_parser.parse(llm_output)
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return response["action"], response["action_input"]
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except Exception:
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raise ValueError(f"Could not parse LLM output: {llm_output}")
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def _construct_scratchpad(
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self, intermediate_steps: List[Tuple[AgentAction, str]]
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) -> List[BaseMessage]:
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"""Construct the scratchpad that lets the agent continue its thought process."""
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thoughts: List[BaseMessage] = []
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for action, observation in intermediate_steps:
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thoughts.append(AIMessage(content=action.log))
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human_message = HumanMessage(
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content=EVAL_TOOL_RESPONSE.format(observation=observation)
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)
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thoughts.append(human_message)
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return thoughts
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@classmethod
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def from_llm_and_tools(
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cls,
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llm: BaseLanguageModel,
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tools: Sequence[BaseTool],
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system_message: str,
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human_message: str,
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output_parser: BaseOutputParser,
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callback_manager: Optional[BaseCallbackManager] = None,
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input_variables: Optional[List[str]] = None,
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**kwargs: Any,
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) -> 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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prompt = cls.create_prompt(
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tools,
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system_message=system_message,
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human_message=human_message,
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input_variables=input_variables,
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output_parser=output_parser,
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)
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llm_chain = LLMChain(
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llm=llm,
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prompt=prompt,
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callback_manager=callback_manager,
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)
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tool_names = [tool.name for tool in tools]
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return cls(
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llm_chain=llm_chain,
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allowed_tools=tool_names,
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output_parser=output_parser,
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**kwargs,
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)
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