95 lines
3.2 KiB
Python
95 lines
3.2 KiB
Python
import os
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from langchain_openai import ChatOpenAI
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from langchain.agents import create_agent
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from langchain.agents.middleware import HumanInTheLoopMiddleware
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from langgraph.checkpoint.memory import MemorySaver
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from langgraph.types import Command
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# LLM setup
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llm = ChatOpenAI(
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model="openai/gpt-oss-20b:free",
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base_url="https://openrouter.ai/api/v1",
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api_key=os.getenv("OPENAI_API_KEY"),
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temperature=0.0,
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)
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# Simple tool: get_weather
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from langchain.tools import tool
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@tool
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def get_weather(city: str, date: str = "сегодня") -> str:
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"""Return a mock weather description for the given city and date."""
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# In a real scenario, call an API. Here we return a placeholder.
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return f"Погода в {city} на {date}: солнечно, 25°C."
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# Memory for middleware
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memory = MemorySaver()
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# Agent with HumanInTheLoopMiddleware
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agent = create_agent(
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model=llm,
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tools=[get_weather],
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system_prompt="Ты полезный ассистент.",
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middleware=[
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HumanInTheLoopMiddleware(
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interrupt_on={
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"get_weather": True, # allow approve, edit, reject
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},
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description_prefix="Подтвердите вызов инструмента",
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),
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],
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checkpointer=memory,
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)
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# Helper to process interrupt and get decisions
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def handle_interrupt(interrupt_value):
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action_requests = interrupt_value["action_requests"]
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decisions = []
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for idx, action in enumerate(action_requests, 1):
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name = action.get("name")
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args = action.get("args")
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description = action.get("description", "")
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print(f"\n--- Подтверждение {idx} ---")
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print(f"Инструмент: {name}")
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print(f"Аргументы: {args}")
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if description:
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print(f"Описание: {description}")
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while True:
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choice = input("a = approve, r = reject: ").strip().lower()
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if choice == "a":
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decisions.append({"type": "approve"})
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break
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elif choice == "r":
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msg = input("Причина отказа: ")
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decisions.append({"type": "reject", "message": msg})
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break
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else:
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print("Неверный ввод. Попробуйте снова.")
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return decisions
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# Main interaction loop
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if __name__ == "__main__":
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config = {"configurable": {"thread_id": "сессия-1"}}
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while True:
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user_input = input("Вы: ")
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if not user_input:
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continue
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# Initial invoke
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result = agent.invoke(
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{"messages": [{"role": "human", "content": user_input}]},
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config=config,
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)
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# Process interrupts
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while "__interrupt__" in result:
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interrupt = result["__interrupt__"][0].value
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decisions = handle_interrupt(interrupt)
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result = agent.invoke(Command(resume={"decisions": decisions}), config=config)
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# Final answer
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if result.get("messages"):
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answer = result["messages"][-1].content
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print(f"\nАгент: {answer}\n")
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else:
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print("\nАгент не ответил.\n")
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# Continue loop for next user query
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