update pay function
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@@ -55,19 +55,40 @@ HTTP_CLIENT = httpx.AsyncClient(timeout=60.0)
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# ==================== Agent系统配置 ====================
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# Kimi 配置 - 用于计划制定和深度推理
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KIMI_CONFIG = {
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"api_key": "sk-TzB4VYJfCoXGcGrGMiewukVRzjuDsbVCkaZXi2LvkS8s60E5",
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"base_url": "https://api.moonshot.cn/v1",
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"model": "kimi-k2-thinking", # 思考模型
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# ==================== 多模型配置 ====================
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# 模型配置字典（支持动态切换）
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MODEL_CONFIGS = {
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"kimi-k2": {
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"api_key": "sk-TzB4VYJfCoXGcGrGMiewukVRzjuDsbVCkaZXi2LvkS8s60E5",
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"base_url": "https://api.moonshot.cn/v1",
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"model": "moonshot-v1-8k", # 快速模型
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},
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"kimi-k2-thinking": {
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"api_key": "sk-TzB4VYJfCoXGcGrGMiewukVRzjuDsbVCkaZXi2LvkS8s60E5",
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"base_url": "https://api.moonshot.cn/v1",
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"model": "kimi-k2-thinking", # 深度思考模型
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},
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"glm-4.6": {
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"api_key": "", # 需要配置智谱AI密钥
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"base_url": "https://open.bigmodel.cn/api/paas/v4",
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"model": "glm-4",
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},
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"deepmoney": {
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"api_key": "", # 空值
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"base_url": "http://111.62.35.50:8000/v1",
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"model": "deepmoney",
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},
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"gemini-3": {
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"api_key": "", # 需要配置Google API密钥
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"base_url": "https://generativelanguage.googleapis.com/v1",
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"model": "gemini-pro",
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},
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}
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# DeepMoney 配置 - 用于新闻总结
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DEEPMONEY_CONFIG = {
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"api_key": "", # 空值
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"base_url": "http://111.62.35.50:8000/v1",
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"model": "deepmoney",
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}
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# 保持向后兼容的配置（默认使用 kimi-k2-thinking）
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KIMI_CONFIG = MODEL_CONFIGS["kimi-k2-thinking"]
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DEEPMONEY_CONFIG = MODEL_CONFIGS["deepmoney"]
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# ==================== MCP协议数据模型 ====================
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@@ -143,6 +164,8 @@ class AgentChatRequest(BaseModel):
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user_avatar: Optional[str] = None # 用户头像URL
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subscription_type: Optional[str] = None # 用户订阅类型（free/pro/max）
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session_id: Optional[str] = None # 会话ID（如果为空则创建新会话）
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model: Optional[str] = "kimi-k2-thinking" # 选择的模型（kimi-k2, kimi-k2-thinking, glm-4.6, deepmoney, gemini-3）
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tools: Optional[List[str]] = None # 选择的工具列表（工具名称数组，如果为None则使用全部工具）
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# ==================== MCP工具定义 ====================
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@@ -1579,6 +1602,7 @@ class MCPAgentIntegrated:
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user_nickname: str = None,
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user_avatar: str = None,
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cookies: dict = None,
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model_config: dict = None, # 新增：动态模型配置
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) -> AsyncGenerator[str, None]:
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"""主流程（流式输出）- 逐步返回执行结果"""
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logger.info(f"[Agent Stream] 处理查询: {user_query}")
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@@ -1586,11 +1610,24 @@ class MCPAgentIntegrated:
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# 将 cookies 存储为实例属性，供工具调用时使用
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self.cookies = cookies or {}
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# 如果传入了自定义模型配置，使用自定义配置，否则使用默认的 Kimi
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if model_config:
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planning_client = OpenAI(
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api_key=model_config["api_key"],
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base_url=model_config["base_url"],
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)
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planning_model = model_config["model"]
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logger.info(f"[Agent Stream] 使用自定义模型: {planning_model}")
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else:
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planning_client = self.kimi_client
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planning_model = self.kimi_model
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logger.info(f"[Agent Stream] 使用默认模型: {planning_model}")
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try:
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# 发送开始事件
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yield self._format_sse("status", {"stage": "start", "message": "开始处理查询"})
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# 阶段1: Kimi 制定计划（流式，带 DeepMoney 备选）
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# 阶段1: 使用选中的模型制定计划（流式，带 DeepMoney 备选）
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yield self._format_sse("status", {"stage": "planning", "message": "正在制定执行计划..."})
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messages = [
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@@ -1603,9 +1640,9 @@ class MCPAgentIntegrated:
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use_fallback = False
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try:
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# 尝试使用 Kimi 流式 API
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stream = self.kimi_client.chat.completions.create(
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model=self.kimi_model,
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# 尝试使用选中的模型流式 API
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stream = planning_client.chat.completions.create(
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model=planning_model,
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messages=messages,
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temperature=1.0,
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max_tokens=16000,
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@@ -2165,11 +2202,12 @@ async def agent_chat_stream(chat_request: AgentChatRequest, request: Request):
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except Exception as e:
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logger.error(f"[ES] 保存用户消息失败: {e}")
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# 获取工具列表
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tools = [tool.dict() for tool in TOOLS]
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# ==================== 动态工具过滤 ====================
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# 获取所有可用工具
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all_tools = [tool.dict() for tool in TOOLS]
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# 添加特殊工具：summarize_news
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tools.append({
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all_tools.append({
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"name": "summarize_news",
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"description": "使用 DeepMoney 模型总结新闻数据，提取关键信息",
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"parameters": {
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@@ -2188,6 +2226,21 @@ async def agent_chat_stream(chat_request: AgentChatRequest, request: Request):
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}
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})
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# 如果用户指定了工具列表，则进行过滤
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if chat_request.tools is not None and len(chat_request.tools) > 0:
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selected_tool_names = set(chat_request.tools)
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tools = [tool for tool in all_tools if tool["name"] in selected_tool_names]
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logger.info(f"[工具过滤] 用户选择了 {len(tools)}/{len(all_tools)} 个工具: {selected_tool_names}")
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else:
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# 默认使用全部工具
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tools = all_tools
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logger.info(f"[工具过滤] 使用全部 {len(tools)} 个工具")
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# ==================== 动态模型选择 ====================
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selected_model = chat_request.model or "kimi-k2-thinking"
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model_config = MODEL_CONFIGS.get(selected_model, MODEL_CONFIGS["kimi-k2-thinking"])
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logger.info(f"[模型选择] 使用模型: {selected_model} ({model_config['model']})")
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# 返回流式响应
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return StreamingResponse(
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agent.process_query_stream(
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@@ -2199,6 +2252,7 @@ async def agent_chat_stream(chat_request: AgentChatRequest, request: Request):
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user_nickname=chat_request.user_nickname,
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user_avatar=chat_request.user_avatar,
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cookies=cookies, # 传递 cookies 用于认证 API 调用
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model_config=model_config, # 传递选中的模型配置
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),
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media_type="text/event-stream",
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headers={
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