98 lines
3.4 KiB
Python
98 lines
3.4 KiB
Python
import os
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import asyncio
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from langchain_openai import ChatOpenAI
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from langchain_core.messages import HumanMessage
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from langchain.tools import tool
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from deepagents import create_deep_agent
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from deepagents.backends import FilesystemBackend, LocalShellBackend, CompositeBackend
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from langgraph.checkpoint.memory import MemorySaver
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from langchain.agents.middleware import HumanInTheLoopMiddleware
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from langgraph.types import Command
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# LLM via OpenRouter
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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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# Backend for deepagents
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backend = CompositeBackend([
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LocalShellBackend(workspace_dir="./workspace"),
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FilesystemBackend(),
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])
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# Example tool – get_weather
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@tool
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def get_weather(city: str, date: str) -> str:
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"""Return the weather for a given city and date."""
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# Dummy implementation – replace with real API call if needed
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return f"Weather in {city} on {date} is sunny and 25°C."
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# Create the agent with Human‑in‑the‑Loop middleware
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agent = create_deep_agent(
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model=llm,
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tools=[get_weather],
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backend=backend,
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system_prompt="You are a helpful assistant.",
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checkpointer=MemorySaver(),
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middleware=[
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HumanInTheLoopMiddleware(
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interrupt_on={"get_weather": True},
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description_prefix="Подтвердите вызов инструмента",
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),
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],
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)
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async def main():
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config = {"configurable": {"thread_id": "session-1"}}
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user_input = input("Вы: ")
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# Initial invoke
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result = await agent.ainvoke(
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{"messages": [HumanMessage(content=user_input)]},
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config,
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)
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# Process Human‑in‑the‑Loop interrupts
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while "__interrupt__" in result:
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interrupt = result["__interrupt__"][0].value
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action_requests = interrupt.get("action_requests", [])
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review_configs = interrupt.get("review_configs", {})
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decisions = []
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print("\n--- Подтверждение ---")
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for action in action_requests:
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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"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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allowed = review_configs.get(name, {}).get("allowed_decisions", ["approve", "reject"])
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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" and "approve" in allowed:
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decisions.append({"type": "approve"})
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break
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elif choice == "r" and "reject" in allowed:
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reason = input("Сообщение для агента (причина отказа): ").strip()
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decisions.append({"type": "reject", "message": reason})
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break
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else:
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print("Неверный ввод. Попробуйте снова.")
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# Resume agent after decisions
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result = await agent.ainvoke(
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Command(resume={"decisions": decisions}),
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config,
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)
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# Final answer
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final_message = result["messages"][-1].content
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print("\nАгент:", final_message)
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if __name__ == "__main__":
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asyncio.run(main()) |