79 lines
3.1 KiB
Python
79 lines
3.1 KiB
Python
"""
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Agent configuration for the Human‑in‑the‑Loop example.
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The agent uses :class:`langchain.agents.middleware.HumanInTheLoopMiddleware` to pause whenever a tool is called. The middleware automatically builds an interrupt payload that contains the name of the tool, its arguments and the allowed decisions (approve / reject / edit). The caller can then resume execution by sending a ``Command`` with the chosen decisions.
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"""
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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 – BroJS
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llm = ChatOpenAI(
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model="openai/gpt-oss-20b:free",
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base_url="https://platform.brojs.ru/jrnl-bh/api/inference/v1",
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api_key=os.getenv("JOURNAL_MCP_PAT"),
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temperature=0.5,
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)
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# Tool import – defined in tools.py
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from tools import get_weather
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memory = MemorySaver()
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agent = create_agent(
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llm=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={"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=memory,
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)
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# Helper to run a single user query with HIL loop
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def run_query(user_msg: str, thread_id: str = "session-1"):
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config = {"configurable": {"thread_id": thread_id}}
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result = agent.invoke({"messages": [{"role": "human", "content": user_msg}]}, config)
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# Loop while the agent is paused for a decision
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while "__interrupt__" in result:
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interrupt_value = result["__interrupt__"][0].value
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action_requests = interrupt_value.get("action_requests", [])
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decisions = []
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print("\n--- Подтверждение вызова инструмента ---")
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for idx, act in enumerate(action_requests):
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name = act["name"]
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args = act.get("args", {})
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desc = act.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 desc:
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print(f" Описание: {desc}")
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# Simple approve/reject per action
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for idx, act in enumerate(action_requests):
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while True:
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choice = input("a=approve, r=reject (e=edit не поддерживается): ").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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# Resume execution with collected decisions
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result = agent.invoke(Command(resume={"decisions": decisions}), config)
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# Final answer
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final_msg = result["messages"][-1].content
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print("\nОтвет агента:")
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print(final_msg)
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return final_msg
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if __name__ == "__main__":
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run_query("Какая погода в Казани сегодня?")
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