106 lines
4.3 KiB
Python
106 lines
4.3 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 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 initialization (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 setup -------------------------------------------------
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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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# --- Tool definition -------------------------------------------------
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@tool
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def get_weather(city: str, date: str) -> str:
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"""Return a mock weather report for the given city and date."""
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# In a real scenario this would call an external API.
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return f"The weather in {city} on {date} is sunny with a high of 25°C."
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# --- Agent creation -------------------------------------------------
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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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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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checkpointer=MemorySaver(),
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)
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# --- Helper functions -------------------------------------------------
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async def prompt_user_for_decisions(action_requests, review_configs):
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decisions = []
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for idx, action in enumerate(action_requests):
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print(f"\n--- Подтверждение ---")
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print(f"Инструмент: {action.get('name')}\n")
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print(f"Аргументы: {action.get('args')}\n")
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if "description" in action:
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print(f"Описание: {action['description']}\n")
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allowed = review_configs[idx].get("allowed_decisions", ["approve", "reject", "edit"])
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prompt = f"a = approve, r = reject{', e = edit' if 'edit' in allowed else ''}: "
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while True:
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choice = input(prompt).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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msg = input("Сообщение для агента (причина отказа): ")
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decisions.append({"type": "reject", "message": msg})
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break
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elif choice == "e" and "edit" in allowed:
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# Simple edit: ask for new JSON args
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new_args = input("Введите отредактированные аргументы в формате JSON: ")
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try:
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import json
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edited = json.loads(new_args)
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decisions.append({"type": "edit", "edited_action": {"name": action['name'], "args": edited}})
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break
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except json.JSONDecodeError:
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print("Неверный JSON. Попробуйте снова.")
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else:
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print("Неверный выбор. Попробуйте снова.")
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return decisions
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# --- Main interaction loop -------------------------------------------------
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async def main():
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config = {"configurable": {"thread_id": "session-1"}}
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# Initial user message
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user_input = input("Вы: ")
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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 possible 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 = await prompt_user_for_decisions(action_requests, review_configs)
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result = await agent.ainvoke(Command(resume={"decisions": decisions}), config)
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# Final answer
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final_message = result["messages"][-1].content
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print(f"\nАгент: {final_message}")
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if __name__ == "__main__":
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asyncio.run(main())
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