108 lines
4.5 KiB
Python
108 lines
4.5 KiB
Python
import os
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import asyncio
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from typing import Any, Dict, List
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from pydantic import SecretStr
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from langchain_openai import ChatOpenAI
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from langchain_core.messages import HumanMessage, AIMessage, ToolMessage
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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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# DESIGN DECISION: Use OpenRouter LLM as required by the technical constraints.
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# NECESSITY: The course forbids local LM endpoints and mandates OpenRouter for all LLM calls.
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# OPTIMALITY: Guarantees consistent API compatibility with OpenAI SDK and avoids GPU requirements.
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# ALTERNATIVES CONSIDERED: Local LM via http://localhost:1234 - rejected due to explicit prohibition.
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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 required by deepagents - combines a shell and filesystem workspace.
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backend = CompositeBackend(
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[
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LocalShellBackend(workspace_dir="./workspace"),
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FilesystemBackend(),
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]
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)
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@tool
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def get_price(product: str, city: str) -> str:
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"""
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Получить примерную цену продукта в указанном городе.
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Возвращает таблицу в markdown-формате:
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| Продукт | Цена (руб.) | Магазин |
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"""
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# DESIGN DECISION: Sub-agent is created inside the tool using the same LLM.
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# NECESSITY: The assignment explicitly requires a hierarchical agent where a tool
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# invokes its own agent to generate realistic prices.
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# OPTIMALITY: Re-using the same LLM and backend keeps the environment consistent
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# and avoids additional dependencies.
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# ALTERNATIVES CONSIDERED: Calling an external API for prices - rejected because
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# it would break the self-contained requirement.
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sub_agent = create_deep_agent(
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model=llm,
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tools=[], # No further tools needed for price generation
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backend=backend,
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system_prompt=(
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"Ты суб-агент, который генерирует реалистичную цену продукта в заданном городе. "
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"Ответ дай в виде markdown-таблицы с колонками: Продукт, Цена (руб.), Магазин."
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),
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)
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# Формируем запрос к суб-агенту
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query = f"Сгенерируй цену для продукта '{product}' в городе '{city}'."
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# Асинхронный вызов суб-агента
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async def invoke_sub() -> Dict[str, Any]:
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return await sub_agent.ainvoke(
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{"messages": [HumanMessage(content=query)]},
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{"configurable": {"thread_id": f"price-{product}-{city}"}},
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)
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# Запускаем цикл событий, если уже внутри async контекста
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try:
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loop = asyncio.get_running_loop()
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result = loop.create_task(invoke_sub())
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sub_result = asyncio.run(invoke_sub())
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except RuntimeError:
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# No running loop - create one
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sub_result = asyncio.run(invoke_sub())
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# Последнее сообщение суб-агента содержит таблицу
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price_table = sub_result["messages"][-1].content
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return price_table
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# Главный агент
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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.",
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)
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def format_message(msg: Any) -> str:
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"""Привести сообщение к читаемому виду."""
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if isinstance(msg, AIMessage) or isinstance(msg, HumanMessage):
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return f"{msg.type.upper()}: {msg.content}"
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if isinstance(msg, ToolMessage):
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return f"TOOL CALL: {msg.name}({msg.args}) -> {msg.content}"
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# Fallback
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return str(msg)
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async def main() -> None:
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user_query = "Помоги составить список покупок: молоко, хлеб, яблоки. Я нахожусь в Казани."
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result = await agent.ainvoke(
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{"messages": [HumanMessage(content=user_query)]},
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{"configurable": {"thread_id": "session-1"}},
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)
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# Вывод всей цепочки сообщений
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for i, message in enumerate(result["messages"]):
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print(f"--- Message {i + 1} ---")
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print(format_message(message))
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print()
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if __name__ == "__main__":
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asyncio.run(main()) |