Source code for langchain.agents.mrkl.base

"""Attempt to implement MRKL systems as described in"""
from __future__ import annotations

from typing import Any, Callable, List, NamedTuple, 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 import BaseTool, Tool

from langchain.agents.agent import Agent, AgentExecutor, AgentOutputParser
from langchain.agents.agent_types import AgentType
from langchain.agents.mrkl.output_parser import MRKLOutputParser
from langchain.agents.mrkl.prompt import FORMAT_INSTRUCTIONS, PREFIX, SUFFIX
from langchain.agents.utils import validate_tools_single_input
from langchain.chains import LLMChain
from import render_text_description

[docs]class ChainConfig(NamedTuple): """Configuration for chain to use in MRKL system. Args: action_name: Name of the action. action: Action function to call. action_description: Description of the action. """ action_name: str action: Callable action_description: str
[docs]@deprecated("0.1.0", alternative="create_react_agent", removal="0.3.0") class ZeroShotAgent(Agent): """Agent for the MRKL chain.""" output_parser: AgentOutputParser = Field(default_factory=MRKLOutputParser) @classmethod def _get_default_output_parser(cls, **kwargs: Any) -> AgentOutputParser: return MRKLOutputParser() @property def _agent_type(self) -> str: """Return Identifier of agent type.""" return AgentType.ZERO_SHOT_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, 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. input_variables: List of input variables the final prompt will expect. Returns: A PromptTemplate with the template assembled from the pieces here. """ tool_strings = render_text_description(list(tools)) tool_names = ", ".join([ for tool in tools]) format_instructions = format_instructions.format(tool_names=tool_names) template = "\n\n".join([prefix, tool_strings, format_instructions, suffix]) if input_variables: return PromptTemplate(template=template, input_variables=input_variables) return PromptTemplate.from_template(template)
[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, 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, 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 = [ for tool in tools] _output_parser = output_parser or cls._get_default_output_parser() return cls( llm_chain=llm_chain, allowed_tools=tool_names, output_parser=_output_parser, **kwargs, )
@classmethod def _validate_tools(cls, tools: Sequence[BaseTool]) -> None: validate_tools_single_input(cls.__name__, tools) if len(tools) == 0: raise ValueError( f"Got no tools for {cls.__name__}. At least one tool must be provided." ) for tool in tools: if tool.description is None: raise ValueError( f"Got a tool {} without a description. For this agent, " f"a description must always be provided." ) super()._validate_tools(tools)
[docs]@deprecated("0.1.0", removal="0.3.0") class MRKLChain(AgentExecutor): """[Deprecated] Chain that implements the MRKL system."""
[docs] @classmethod def from_chains( cls, llm: BaseLanguageModel, chains: List[ChainConfig], **kwargs: Any ) -> AgentExecutor: """User friendly way to initialize the MRKL chain. This is intended to be an easy way to get up and running with the MRKL chain. Args: llm: The LLM to use as the agent LLM. chains: The chains the MRKL system has access to. **kwargs: parameters to be passed to initialization. Returns: An initialized MRKL chain. """ tools = [ Tool( name=c.action_name, func=c.action, description=c.action_description, ) for c in chains ] agent = ZeroShotAgent.from_llm_and_tools(llm, tools) return cls(agent=agent, tools=tools, **kwargs)