"""An agent designed to hold a conversation in addition to using tools."""
from __future__ import annotations
from typing import Any, List, Optional, Sequence
from langchain_core._api import deprecated
from langchain_core.callbacks import BaseCallbackManager
from langchain_core.language_models import BaseLanguageModel
from langchain_core.prompts import PromptTemplate
from langchain_core.tools import BaseTool
from pydantic import Field
from langchain._api.deprecation import AGENT_DEPRECATION_WARNING
from langchain.agents.agent import Agent, AgentOutputParser
from langchain.agents.agent_types import AgentType
from langchain.agents.conversational.output_parser import ConvoOutputParser
from langchain.agents.conversational.prompt import FORMAT_INSTRUCTIONS, PREFIX, SUFFIX
from langchain.agents.utils import validate_tools_single_input
from langchain.chains import LLMChain
[docs]
@deprecated(
"0.1.0",
message=AGENT_DEPRECATION_WARNING,
removal="1.0",
)
class ConversationalAgent(Agent):
"""An agent that holds a conversation in addition to using tools."""
ai_prefix: str = "AI"
"""Prefix to use before AI output."""
output_parser: AgentOutputParser = Field(default_factory=ConvoOutputParser)
"""Output parser for the agent."""
@classmethod
def _get_default_output_parser(
cls, ai_prefix: str = "AI", **kwargs: Any
) -> AgentOutputParser:
return ConvoOutputParser(ai_prefix=ai_prefix)
@property
def _agent_type(self) -> str:
"""Return Identifier of agent type."""
return AgentType.CONVERSATIONAL_REACT_DESCRIPTION
@property
def observation_prefix(self) -> str:
"""Prefix to append the observation with.
Returns:
"Observation: "
"""
return "Observation: "
@property
def llm_prefix(self) -> str:
"""Prefix to append the llm call with.
Returns:
"Thought: "
"""
return "Thought:"
[docs]
@classmethod
def create_prompt(
cls,
tools: Sequence[BaseTool],
prefix: str = PREFIX,
suffix: str = SUFFIX,
format_instructions: str = FORMAT_INSTRUCTIONS,
ai_prefix: str = "AI",
human_prefix: str = "Human",
input_variables: Optional[List[str]] = None,
) -> PromptTemplate:
"""Create prompt in the style of the zero-shot agent.
Args:
tools: List of tools the agent will have access to, used to format the
prompt.
prefix: String to put before the list of tools. Defaults to PREFIX.
suffix: String to put after the list of tools. Defaults to SUFFIX.
format_instructions: Instructions on how to use the tools. Defaults to
FORMAT_INSTRUCTIONS
ai_prefix: String to use before AI output. Defaults to "AI".
human_prefix: String to use before human output.
Defaults to "Human".
input_variables: List of input variables the final prompt will expect.
Defaults to ["input", "chat_history", "agent_scratchpad"].
Returns:
A PromptTemplate with the template assembled from the pieces here.
"""
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, ai_prefix=ai_prefix, human_prefix=human_prefix
)
template = "\n\n".join([prefix, tool_strings, format_instructions, suffix])
if input_variables is None:
input_variables = ["input", "chat_history", "agent_scratchpad"]
return PromptTemplate(template=template, input_variables=input_variables)
@classmethod
def _validate_tools(cls, tools: Sequence[BaseTool]) -> None:
super()._validate_tools(tools)
validate_tools_single_input(cls.__name__, tools)
