生成对话

# coding=utf-8

import os
import json
import requests

split_line = "\\n-------------------------------------生成报告链接---------------------------------------------------\\n"

chat_completions_url = 'https://dashscope.aliyuncs.com/api/v2/apps/deep-search-agent/chat/completions'

headers = {
    'Authorization': f'Bearer {os.getenv("DASHSCOPE_API_KEY", "")}',  # 配置 API KEY
    'Content-Type': 'application/json'
}

if __name__ == "__main__":
    params = {
        "input": {
            "messages": [{"role": "user", "content": "目前国内主流多模态模型分别有哪些，根据性能和效果做下评估"}]  # 传入请求消息
        },
        "parameters": {
            "agent_options": {  # 设置 agent 选项
                "agent_id": "${agent_id}",  # 应用ID，可在应用管理页面获取到，例如：aid-8fd***e00
                "agent_version": "${agent_version}"  # 应用版本，beta 测试版本 / release 发布版本
            }
        },
        "stream": True
    }

    response = requests.post(chat_completions_url, headers=headers, json=params, stream=True)

    resultlist = []
    stage = ''
    action = ''
    content = ''
    reasoning_content = ''
    for chunk in response.iter_lines():
        if chunk:
            chunk_str = chunk.decode('utf-8').strip()
            if chunk_str.startswith('data:'):
                json_str = chunk_str[len('data:'):].strip()
                try:
                    obj = json.loads(json_str)
                    # 检查异常
                    if obj.get('code') != '200':
                        print("服务异常：", obj)
                    # 获取消息体
                    msg = obj.get('output', {}).get('choices', [{}])[0].get('message', {})
                    extra_flags = msg.get('extra', {})  # 获取模型状态标记字段

                    if stage != extra_flags.get('group', ''):  # 获取 模型当前阶段
                        print(f"agent stage: {extra_flags.get('group', '')}")
                    stage = extra_flags.get('group', '')

                    if action != extra_flags.get('step', '') and extra_flags.get('step', ''):  # 获取 模型当前阶段
                        print(f"agent action: {extra_flags.get('step', '')}")
                    action = extra_flags.get('step', '')

                    role = msg.get('role', '')  # 获取模型角色 assistant or role
                    content = msg.get('content')  # 获取生成内容
                    toolcalls = msg.get('tool_calls', [])  # 获取工具调用
                    if toolcalls:
                        print(f'{toolcalls}')

                    if not content:  # 如果 content内没有内容，则尝试获取最后一轮深度思考中的reasoning_content内容
                        content = msg.get('reasoning_content', '')

                    if isinstance(content, str):
                        if role == "tool":
                            print("\\n" + content + "\\n", end='')  # 前后都换行
                        else:
                            print(content, end='')  # 流式输出
                    else:
                        # 注意 content 可能不是字符串
                        print(split_line, content)
                    # 可按需保存
                    resultlist.append(obj)
                except Exception as e:
                    print("异常解析:", e)