AgentCoreService/pqai_agent/agents/simple_chat_agent.py

import json
from typing import List, Optional

import pqai_agent.utils
from pqai_agent.agent import DEFAULT_MAX_RUN_STEPS
from pqai_agent.chat_service import OpenAICompatible
from pqai_agent.data_models.agent_task_detail import AgentTaskDetail
from pqai_agent.logging import logger
from pqai_agent.toolkit.function_tool import FunctionTool
from pqai_agent_server.const.status_enum import AgentTaskDetailStatus


class SimpleOpenAICompatibleChatAgent:
    """ 最简单的多步Agent实现 """
    def __init__(self, model: str, system_prompt: str, tools: Optional[List[FunctionTool]] = None,
                 generate_cfg: Optional[dict] = None, max_run_step: Optional[int] = None):
        self.model = model
        self.llm_client = OpenAICompatible.create_client(model)
        self.system_prompt = system_prompt
        if tools:
            self.tools = [*tools]
        else:
            self.tools = []
        self.tool_map = {tool.name: tool for tool in self.tools}
        self.generate_cfg = generate_cfg or {}
        self.max_run_step = max_run_step or DEFAULT_MAX_RUN_STEPS
        self.tool_call_records = []
        self.agent_task_details: list[AgentTaskDetail] = []
        self.total_input_tokens = 0
        self.total_output_tokens = 0
        logger.debug(self.tool_map)

    def add_tool(self, tool: FunctionTool):
        """添加一个工具到Agent中"""
        if tool.name in self.tool_map:
            logger.warning(f"Tool {tool.name} already exists, replacing it.")
        self.tools.append(tool)
        self.tool_map[tool.name] = tool

    def run(self, user_input: str) -> str:
        run_id = pqai_agent.utils.random_str()[:12]
        messages = [{"role": "system", "content": self.system_prompt}]
        tools = [tool.get_openai_tool_schema() for tool in self.tools]
        messages.append({"role": "user", "content": user_input})

        n_steps = 0
        logger.debug(f"run_id[{run_id}] start agent loop. messages: {messages}")
        while n_steps < self.max_run_step:
            response = self.llm_client.chat.completions.create(model=self.model, messages=messages, tools=tools, **self.generate_cfg)
            message = response.choices[0].message
            self.total_input_tokens += response.usage.prompt_tokens
            self.total_output_tokens += response.usage.completion_tokens
            messages.append(message)
            logger.debug(f"run_id[{run_id}] current step content: {message.content}")

            if message.tool_calls:
                for tool_call in message.tool_calls:
                    function_name = tool_call.function.name
                    arguments = json.loads(tool_call.function.arguments)
                    logger.debug(f"run_id[{run_id}] call function[{function_name}], parameter: {arguments}")

                    agent_task_detail = AgentTaskDetail()
                    agent_task_detail.executor_type = 'tool'
                    agent_task_detail.executor_name = function_name
                    agent_task_detail.input_data = tool_call.function.arguments
                    self.agent_task_details.append(agent_task_detail)

                    if function_name in self.tool_map:
                        result = self.tool_map[function_name](**arguments)
                        messages.append({
                            "role": "tool",
                            "tool_call_id": tool_call.id,
                            "content": json.dumps(result, ensure_ascii=False)
                        })
                        self.tool_call_records.append({
                            "name": function_name,
                            "arguments": arguments,
                            "result": result
                        })
                        agent_task_detail.output_data = json.dumps(result, ensure_ascii=False)
                        agent_task_detail.status = AgentTaskDetailStatus.SUCCESS.value
                    else:
                        agent_task_detail.error_message = f"Function {function_name} not found in tool map."
                        agent_task_detail.status = AgentTaskDetailStatus.FAILED.value
                        logger.error(f"run_id[{run_id}] Function {function_name} not found in tool map.")
                        raise Exception(f"Function {function_name} not found in tool map.")
            else:
                agent_task_detail = AgentTaskDetail()
                agent_task_detail.executor_type = 'llm'
                agent_task_detail.executor_name = self.model
                agent_task_detail.output_data = message.content
                agent_task_detail.status = AgentTaskDetailStatus.SUCCESS.value
                self.agent_task_details.append(agent_task_detail)
                return message.content
            n_steps += 1

        raise Exception("Max run steps exceeded")

    # 新增方法：获取步骤记录
    def get_agent_task_details(self) -> list:
        """返回代理运行过程中的详细步骤记录"""
        return self.agent_task_details

    def get_total_input_tokens(self) -> int:
        """获取总输入token数"""
        return self.total_input_tokens

    def get_total_output_tokens(self) -> int:
        """获取总输出token数"""
        return self.total_output_tokens

    def get_total_cost(self) -> float:
        return OpenAICompatible.calculate_cost(self.model, self.total_input_tokens, self.total_output_tokens)