113 lines
4.5 KiB
Python
113 lines
4.5 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 ------------------------------------------------------------
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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 --------------------------------------------------------
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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 ------------------------------------------------------------
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@tool
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def get_weather(city: str, date: str = "today") -> str:
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"""Return a mock weather report for the given city and date."""
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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 ----------------------------------------------------------
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# DESIGN DECISION: Using HumanInTheLoopMiddleware with interrupt_on for get_weather
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# NECESSITY: Middleware automatically pauses before tool execution and asks for approval.
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# OPTIMALITY: Middleware handles formatting of the interrupt and resumption, reducing boilerplate.
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# ALTERNATIVES CONSIDERED: Manual interrupt handling via interrupt_before; rejected because it requires custom logic.
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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 invoke_agent(message: str, thread_id: str):
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config = {"configurable": {"thread_id": thread_id}}
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result = await agent.ainvoke(
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{"messages": [HumanMessage(content=message)]},
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config=config,
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)
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return result, config
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async def resume_agent(decisions, config):
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result = await agent.ainvoke(Command(resume={"decisions": decisions}), config=config)
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return result
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def print_interrupt(interrupt):
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action_requests = interrupt['action_requests']
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review_configs = interrupt['review_configs']
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print("\n--- Подтверждение ---")
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for idx, action in enumerate(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"{idx+1}. 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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return action_requests, review_configs
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async def main():
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thread_id = "session-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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result, config = await invoke_agent(user_input, thread_id)
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# Loop until no interrupt
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while "__interrupt__" in result:
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interrupt_value = result["__interrupt__"][0].value
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action_requests, review_configs = print_interrupt(interrupt_value)
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decisions = []
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for idx, action in enumerate(action_requests):
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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("Сообщение для агента (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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result = await resume_agent(decisions, config)
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# No more interrupts – print final answer
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final_message = result["messages"][-1].content
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print(f"\nАгент: {final_message}\n")
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# Ask if user wants another query in the same session
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again = input("Хотите задать ещё вопрос? (y/n): ").strip().lower()
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if again != "y":
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break
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if __name__ == "__main__":
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asyncio.run(main())
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