110 lines
3.6 KiB
Python
110 lines
3.6 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 FilesystemBackend, LocalShellBackend, CompositeBackend
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# -------------------------------------------------
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# LLM configuration (OpenRouter)
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# -------------------------------------------------
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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=0.0,
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)
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# -------------------------------------------------
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# Backend for tool execution
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# -------------------------------------------------
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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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# -------------------------------------------------
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# Example tool (can be replaced with any real tool)
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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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Return a mock price table for the given product in the specified city.
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"""
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# In a real scenario this could call an external API or run a script.
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return f"""| Продукт | Цена (руб.) | Город |
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| {product} | 89 | {city} |
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"""
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# -------------------------------------------------
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# Create the deep agent
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# -------------------------------------------------
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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="You are a helpful assistant that can call tools when needed.",
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)
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# -------------------------------------------------
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# Helper functions for streaming output
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# -------------------------------------------------
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def format_message(message) -> str:
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"""
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Convert a LangChain message to a printable string.
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If the message contains tool calls, format them as a function call.
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"""
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if getattr(message, "content", None):
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return message.content
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# Tool call case
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if getattr(message, "tool_calls", None):
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tool_call = message.tool_calls[0]
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name = tool_call["name"]
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args = tool_call["args"]
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return f"{name}({args})"
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return ""
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def format_chunk_message(chunk):
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"""
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Print token fragments from 'messages' chunks.
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Insert a separator when the LangGraph step changes.
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"""
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message, meta = chunk
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global current_step
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step = meta.get("langgraph_step", 0)
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if step != current_step:
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current_step = step
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print("\n--- --- ---\n")
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if getattr(message, "content", None):
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print(message.content, end="", flush=True)
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# -------------------------------------------------
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# Main async entry point
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# -------------------------------------------------
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async def main():
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user_query = "Сколько стоит молоко в Казани?"
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stream = agent.stream(
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{"messages": [HumanMessage(content=user_query)]},
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stream_mode=["messages", "updates"],
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)
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global current_step
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current_step = -1 # initialize step counter
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for chunk_type, chunk_data in stream:
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if chunk_type == "messages":
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format_chunk_message(chunk_data)
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elif chunk_type == "updates":
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# When a model update contains a finished message, print it nicely
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model_info = chunk_data.get("model")
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if model_info and "messages" in model_info:
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last_msg = model_info["messages"][-1]
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print("\n" + format_message(last_msg))
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# Ensure final newline
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print()
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if __name__ == "__main__":
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asyncio.run(main()) |