Source code for langchain.agents.conversational.base

"""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.pydantic_v1 import Field
from langchain_core.tools import BaseTool

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", alternative="create_react_agent", removal="0.3.0") 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.""" return "Observation: " @property def llm_prefix(self) -> str: """Prefix to append the llm call with.""" 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. suffix: String to put after the list of tools. ai_prefix: String to use before AI output. human_prefix: String to use before human output. input_variables: List of input variables the final prompt will expect. 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)
[docs] @classmethod def from_llm_and_tools( cls, llm: BaseLanguageModel, tools: Sequence[BaseTool], callback_manager: Optional[BaseCallbackManager] = None, output_parser: Optional[AgentOutputParser] = None, 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, **kwargs: Any, ) -> Agent: """Construct an agent from an LLM and tools.""" cls._validate_tools(tools) prompt = cls.create_prompt( tools, ai_prefix=ai_prefix, human_prefix=human_prefix, prefix=prefix, suffix=suffix, format_instructions=format_instructions, input_variables=input_variables, ) llm_chain = LLMChain( # type: ignore[misc] llm=llm, prompt=prompt, callback_manager=callback_manager, ) tool_names = [tool.name for tool in tools] _output_parser = output_parser or cls._get_default_output_parser( ai_prefix=ai_prefix ) return cls( llm_chain=llm_chain, allowed_tools=tool_names, ai_prefix=ai_prefix, output_parser=_output_parser, **kwargs, )