react_agent_example.py

import asyncio
import logging
import os
import sys

from typing_extensions import Annotated, Doc

from dbgpt.agent import AgentContext, AgentMemory, LLMConfig, UserProxyAgent
from dbgpt.agent.expand.actions.react_action import ReActAction, Terminate
from dbgpt.agent.expand.react_agent import ReActAgent
from dbgpt.agent.resource import ToolPack, tool

logging.basicConfig(
    stream=sys.stdout,
    level=logging.INFO,
    format="%(asctime)s | %(levelname)s | %(name)s | %(message)s",
)


@tool
def simple_calculator(first_number: int, second_number: int, operator: str) -> float:
    """Simple calculator tool. Just support +, -, *, /.
    When users need to do numerical calculations, you must use this tool to calculate, \
    and you are not allowed to directly infer calculation results from user input or \
    external observations.
    """
    if isinstance(first_number, str):
        first_number = int(first_number)
    if isinstance(second_number, str):
        second_number = int(second_number)
    if operator == "+":
        return first_number + second_number
    elif operator == "-":
        return first_number - second_number
    elif operator == "*":
        return first_number * second_number
    elif operator == "/":
        return first_number / second_number
    else:
        raise ValueError(f"Invalid operator: {operator}")


@tool
def count_directory_files(path: Annotated[str, Doc("The directory path")]) -> int:
    """Count the number of files in a directory."""
    if not os.path.isdir(path):
        raise ValueError(f"Invalid directory path: {path}")
    return len(os.listdir(path))


async def main():
    from dbgpt.model import AutoLLMClient

    llm_client = AutoLLMClient(
        # provider=os.getenv("LLM_PROVIDER", "proxy/deepseek"),
        # name=os.getenv("LLM_MODEL_NAME", "deepseek-chat"),
        provider=os.getenv("LLM_PROVIDER", "proxy/siliconflow"),
        name=os.getenv("LLM_MODEL_NAME", "Qwen/Qwen2.5-Coder-32B-Instruct"),
    )
    agent_memory = AgentMemory()
    agent_memory.gpts_memory.init(conv_id="test456")

    # It is important to set the temperature to a low value to get a better result
    context: AgentContext = AgentContext(
        conv_id="test456", gpts_app_name="ReAct", temperature=0.01
    )

    tools = ToolPack([simple_calculator, count_directory_files, Terminate()])

    user_proxy = await UserProxyAgent().bind(agent_memory).bind(context).build()

    tool_engineer = (
        await ReActAgent(max_retry_count=10)
        .bind(context)
        .bind(LLMConfig(llm_client=llm_client))
        .bind(agent_memory)
        .bind(tools)
        .build()
    )

    await user_proxy.initiate_chat(
        recipient=tool_engineer,
        reviewer=user_proxy,
        message="Calculate the product of 10 and 99, then count the number of files in /tmp",
        # message="Calculate the product of 10 and 99",
        # message="Count the number of files in /tmp",
    )

    # dbgpt-vis message infos
    print(await agent_memory.gpts_memory.app_link_chat_message("test456"))


if __name__ == "__main__":
    asyncio.run(main())