84 lines
3.2 KiB
Python
84 lines
3.2 KiB
Python
import asyncio
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import os
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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 FilesystemBackend, LocalShellBackend, CompositeBackend
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# --- LLM configuration -----------------------------------------------------
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# Connect to the local LM Studio server. Replace '<model_name>' with the exact
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# name of the model you have loaded in LM Studio.
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llm = ChatOpenAI(
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model='<model_name>',
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base_url='http://localhost:1234/v1',
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api_key=os.getenv('OPENAI_API_KEY', 'fake'),
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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 tool --------------------------------------------------------
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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 function internally creates a sub‑agent that asks the LLM to generate
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a price table. The sub‑agent is a lightweight wrapper around the same
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LLM instance to keep the example simple.
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"""
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# Create a sub‑agent that only has the task of generating a price table.
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sub_agent = create_deep_agent(
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model=llm,
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tools=[],
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backend=backend,
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system_prompt=f"You are a market analyst. Provide a realistic price for {product} in {city}. Output a markdown table with columns: Продукт, Цена (руб.), Магазин.",
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)
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# Invoke the sub‑agent with a simple prompt.
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result = asyncio.run(
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sub_agent.ainvoke(
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{"messages": [HumanMessage(content=f"Generate price for {product} in {city}")]},
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{"configurable": {"thread_id": f"price-{product}-{city}"}},
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)
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)
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# Return the content of the last message (the table).
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return result["messages"][-1].content
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# --- Main 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.",
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)
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# --- Helper to pretty‑print the conversation ------------------------------
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from langchain_core.messages import BaseMessage
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def format_message(msg: BaseMessage) -> str:
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if hasattr(msg, "content") and msg.content:
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return msg.content
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if hasattr(msg, "tool_calls") and msg.tool_calls:
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call = msg.tool_calls[0]
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return f"{call['name']}({call['args']})"
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return ""
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# --- Main entry point ------------------------------------------------------
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async def main():
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user_prompt = "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": [HumanMessage(content=user_prompt)]},
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{"configurable": {"thread_id": "shopping-session"}},
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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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print(format_message(msg))
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print("---")
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if __name__ == "__main__":
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asyncio.run(main())
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