from typing import List, Tuple from langchain.agents import AgentExecutor from langchain.agents.format_scratchpad import format_xml from langchain.schema import AIMessage, HumanMessage from langchain.tools import DuckDuckGoSearchRun from langchain.tools.render import render_text_description from langchain_community.chat_models import ChatAnthropic from langchain_core.pydantic_v1 import BaseModel, Field from xml_agent.prompts import conversational_prompt, parse_output def _format_chat_history(chat_history: List[Tuple[str, str]]): buffer = [] for human, ai in chat_history: buffer.append(HumanMessage(content=human)) buffer.append(AIMessage(content=ai)) return buffer model = ChatAnthropic(model="claude-2") 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"]), "chat_history": lambda x: _format_chat_history(x["chat_history"]), } | prompt | llm_with_stop | parse_output ) class AgentInput(BaseModel): question: str chat_history: List[Tuple[str, str]] = Field(..., extra={"widget": {"type": "chat"}}) 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"])