90 lines
3.2 KiB
Python
90 lines
3.2 KiB
Python
import os
|
|
import json
|
|
from langchain_openai import ChatOpenAI
|
|
from langchain.agents import create_agent
|
|
from langchain.agents.middleware import HumanInTheLoopMiddleware
|
|
from langgraph.checkpoint.memory import MemorySaver
|
|
from langgraph.types import Command
|
|
|
|
# LLM setup
|
|
llm = ChatOpenAI(
|
|
model="openai/gpt-oss-20b:free",
|
|
base_url="https://openrouter.ai/api/v1",
|
|
api_key=os.getenv("OPENAI_API_KEY"),
|
|
temperature=0.0,
|
|
)
|
|
|
|
# Simple tool: get_weather
|
|
from langchain.tools import tool
|
|
|
|
@tool
|
|
def get_weather(city: str, date: str = "сегодня") -> str:
|
|
"""Return a mock weather description for the given city and date."""
|
|
# In a real scenario, call an API. Here we return a deterministic string.
|
|
return f"Погода в {city} на {date}: солнечно, 25°C."
|
|
|
|
# Agent with HumanInTheLoopMiddleware
|
|
memory = MemorySaver()
|
|
|
|
agent = create_agent(
|
|
model=llm,
|
|
tools=[get_weather],
|
|
system_prompt="Ты полезный ассистент.",
|
|
middleware=[
|
|
HumanInTheLoopMiddleware(
|
|
interrupt_on={"get_weather": True},
|
|
description_prefix="Подтвердите вызов инструмента",
|
|
),
|
|
],
|
|
checkpointer=memory,
|
|
)
|
|
|
|
# Helper to process interrupt and collect decisions
|
|
|
|
def handle_interrupt(interrupt_value):
|
|
action_requests = interrupt_value["action_requests"]
|
|
decisions = []
|
|
for idx, action in enumerate(action_requests, 1):
|
|
name = action.get("name")
|
|
args = action.get("args", {})
|
|
description = action.get("description", "")
|
|
print(f"\n--- Подтверждение {idx} ---")
|
|
print(f"Инструмент: {name}")
|
|
print(f"Аргументы: {args}")
|
|
if description:
|
|
print(f"Описание: {description}")
|
|
while True:
|
|
choice = input("a = approve, r = reject: ").strip().lower()
|
|
if choice == "a":
|
|
decisions.append({"type": "approve"})
|
|
break
|
|
elif choice == "r":
|
|
msg = input("Сообщение для агента (причина отказа): ")
|
|
decisions.append({"type": "reject", "message": msg})
|
|
break
|
|
else:
|
|
print("Неверный ввод. Попробуйте снова.")
|
|
return decisions
|
|
|
|
# Main interaction loop
|
|
if __name__ == "__main__":
|
|
config = {"configurable": {"thread_id": "сессия-1"}}
|
|
while True:
|
|
user_input = input("\nВы: ")
|
|
if user_input.lower() in {"exit", "quit"}:
|
|
print("Завершение.")
|
|
break
|
|
# Initial invoke
|
|
result = agent.invoke({"messages": [{"role": "human", "content": user_input}]}, config=config)
|
|
# Process interrupts
|
|
while "__interrupt__" in result:
|
|
interrupt = result["__interrupt__"][0].value
|
|
decisions = handle_interrupt(interrupt)
|
|
result = agent.invoke(Command(resume={"decisions": decisions}), config=config)
|
|
# Final answer
|
|
if result.get("messages"):
|
|
final_msg = result["messages"][-1].content
|
|
print(f"\nАгент: {final_msg}")
|
|
else:
|
|
print("\nАгент не вернул ответа.")
|