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125 lines
4.4 KiB
Python
125 lines
4.4 KiB
Python
"""An agent designed to hold a conversation in addition to using tools."""
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from __future__ import annotations
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from typing import Any, List, Optional, Sequence
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from pydantic import Field
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from langchain.agents.agent import Agent, AgentOutputParser
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from langchain.agents.agent_types import AgentType
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from langchain.agents.conversational.output_parser import ConvoOutputParser
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from langchain.agents.conversational.prompt import FORMAT_INSTRUCTIONS, PREFIX, SUFFIX
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from langchain.base_language import BaseLanguageModel
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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.prompts import PromptTemplate
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from langchain.tools.base import BaseTool
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class ConversationalAgent(Agent):
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"""An agent designed to hold a conversation in addition to using tools."""
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ai_prefix: str = "AI"
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output_parser: AgentOutputParser = Field(default_factory=ConvoOutputParser)
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@classmethod
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def _get_default_output_parser(
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cls, ai_prefix: str = "AI", **kwargs: Any
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) -> AgentOutputParser:
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return ConvoOutputParser(ai_prefix=ai_prefix)
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@property
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def _agent_type(self) -> str:
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"""Return Identifier of agent type."""
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return AgentType.CONVERSATIONAL_REACT_DESCRIPTION
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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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prefix: str = PREFIX,
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suffix: str = SUFFIX,
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format_instructions: str = FORMAT_INSTRUCTIONS,
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ai_prefix: str = "AI",
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human_prefix: str = "Human",
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input_variables: Optional[List[str]] = None,
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) -> PromptTemplate:
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"""Create prompt in the style of the zero shot agent.
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Args:
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tools: List of tools the agent will have access to, used to format the
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prompt.
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prefix: String to put before the list of tools.
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suffix: String to put after the list of tools.
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ai_prefix: String to use before AI output.
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human_prefix: String to use before human output.
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input_variables: List of input variables the final prompt will expect.
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Returns:
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A PromptTemplate with the template assembled from the pieces here.
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"""
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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 = format_instructions.format(
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tool_names=tool_names, ai_prefix=ai_prefix, human_prefix=human_prefix
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)
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template = "\n\n".join([prefix, tool_strings, format_instructions, suffix])
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if input_variables is None:
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input_variables = ["input", "chat_history", "agent_scratchpad"]
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return PromptTemplate(template=template, input_variables=input_variables)
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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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callback_manager: Optional[BaseCallbackManager] = None,
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output_parser: Optional[AgentOutputParser] = None,
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prefix: str = PREFIX,
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suffix: str = SUFFIX,
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format_instructions: str = FORMAT_INSTRUCTIONS,
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ai_prefix: str = "AI",
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human_prefix: str = "Human",
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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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ai_prefix=ai_prefix,
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human_prefix=human_prefix,
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prefix=prefix,
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suffix=suffix,
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format_instructions=format_instructions,
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input_variables=input_variables,
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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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_output_parser = output_parser or cls._get_default_output_parser(
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ai_prefix=ai_prefix
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)
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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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ai_prefix=ai_prefix,
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output_parser=_output_parser,
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**kwargs,
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)
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