73 lines
2.7 KiB
Python
73 lines
2.7 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.tools import tool
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from deepagents import create_deep_agent
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from deepagents.backends import CompositeBackend, LocalShellBackend, FilesystemBackend
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from langchain.agents import create_agent
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# --- LLM configuration (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=0.7,
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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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# --- Tool with sub‑agent that returns a price table ---
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@tool
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def get_price(product: str, city: str) -> str:
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"""Retrieve price for product in city. Returns a Markdown table:
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| Продукт | Цена (руб.) | Магазин |
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"""
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# Sub‑agent to generate a realistic price table
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sub_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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sub_agent = create_agent(
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model=sub_llm,
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tools=[],
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system_prompt=f"Generate a realistic price for {product} in {city}. Return the result as a Markdown table with columns Продукт, Цена (руб.), Магазин.",
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)
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# Ask sub‑agent to produce the table
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res = sub_agent.invoke(
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{"messages": [{"role": "user", "content": "Provide the price table"}]}
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)
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return res["messages"][-1]["content"]
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# --- Main hierarchical agent ---
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main_agent = create_deep_agent(
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model=llm,
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tools=[get_price],
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backend=backend,
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system_prompt="Ты помощник по планированию покупок. Принимаешь список продуктов, узнаёшь цены и считаешь итоговую стоимость.",
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)
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async def main():
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question = "Помоги составить список покупок: молоко, хлеб, яблоки. Я нахожусь в Казани."
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result = await main_agent.ainvoke(
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{"messages": [{"role": "human", "content": question}]},
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{"configurable": {"thread_id": "shopping-session-1"}},
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)
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# Print all messages in order (tool calls and final answer)
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for msg in result["messages"]:
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if "content" in msg:
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print(msg["content"])
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if "tool_calls" in msg:
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for call in msg["tool_calls"]:
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print(f"{call['name']}({call['args']})")
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# Final answer (last message)
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print("\n--- Final answer ---")
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print(result["messages"][-1]["content"])
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if __name__ == "__main__":
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asyncio.run(main()) |