from langchain.agents import AgentExecutor from langchain.agents.format_scratchpad import format_xml from langchain.llms import OpenAI from langchain.tools import DuckDuckGoSearchRun from langchain.tools.render import render_text_description from langchain_core.pydantic_v1 import BaseModel from solo_performance_prompting_agent.parser import parse_output from solo_performance_prompting_agent.prompts import conversational_prompt _model = OpenAI() _tools = [DuckDuckGoSearchRun()] _prompt = conversational_prompt.partial( tools=render_text_description(_tools), tool_names=", ".join([t.name for t in _tools]), ) _llm_with_stop = _model.bind(stop=["", ""]) agent = ( { "question": lambda x: x["question"], "agent_scratchpad": lambda x: format_xml(x["intermediate_steps"]), } | _prompt | _llm_with_stop | parse_output ) class AgentInput(BaseModel): question: str agent_executor = AgentExecutor( agent=agent, tools=_tools, verbose=True, handle_parsing_errors=True ).with_types(input_type=AgentInput) agent_executor = agent_executor | (lambda x: x["output"])