76 lines
2.6 KiB
Python
76 lines
2.6 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 CompositeBackend, LocalShellBackend, FilesystemBackend
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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=0.7,
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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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# ---------- Sub‑agent for price generation ----------
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# The sub‑agent simply asks the LLM to produce a realistic price table.
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# It is wrapped in a tool so that the main agent can call it.
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@tool
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def get_price(product: str, city: str) -> str:
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"""Return a realistic price for a product in a given city.
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The response must be a Markdown table with columns: Продукт, Цена (руб.), Магазин.
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"""
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# Create a tiny agent that only generates the table.
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from langchain.agents import create_agent
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from langchain_core.messages import HumanMessage
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system_prompt = (
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"You are a market price generator. "
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"Given a product and a city, produce a realistic price table in Markdown. "
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"Use plausible Russian store names and prices."
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)
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sub_agent = create_agent(
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model=llm,
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tools=[],
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system_prompt=system_prompt,
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)
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prompt = f"Product: {product}\nCity: {city}"
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result = sub_agent.invoke({"messages": [HumanMessage(content=prompt)]})
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# The sub‑agent returns a dict with 'messages'; take the last content.
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return result["messages"][-1].content
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# ---------- Main agent ----------
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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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# ---------- Run ----------
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async def main():
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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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# Print all messages in order
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for msg in result["messages"]:
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if msg.content:
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print(msg.content)
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elif msg.tool_calls:
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for call in msg.tool_calls:
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print(f"{call['name']}({call['args']})")
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if __name__ == "__main__":
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asyncio.run(main())
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