# Copyright 2023 # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """文件预加载助手管理器 - 管理基于unique_id的助手实例缓存""" import hashlib import time import json from typing import Dict, List, Optional from qwen_agent.agents import Assistant from qwen_agent.log import logger from modified_assistant import init_modified_agent_service_with_files, update_agent_llm from .prompt_loader import load_system_prompt, load_mcp_settings class FileLoadedAgentManager: """文件预加载助手管理器 基于 unique_id 缓存助手实例，避免重复创建和文件解析 """ def __init__(self, max_cached_agents: int = 20): self.agents: Dict[str, Assistant] = {} # {cache_key: assistant_instance} self.unique_ids: Dict[str, str] = {} # {cache_key: unique_id} self.access_times: Dict[str, float] = {} # LRU 访问时间管理 self.creation_times: Dict[str, float] = {} # 创建时间记录 self.max_cached_agents = max_cached_agents def _get_cache_key(self, bot_id: str, model_name: str = None, api_key: str = None, model_server: str = None, generate_cfg: Dict = None, system_prompt: str = None, mcp_settings: List[Dict] = None) -> str: """获取包含所有相关参数的哈希值作为缓存键 Args: bot_id: 机器人项目ID model_name: 模型名称 api_key: API密钥 model_server: 模型服务器地址 generate_cfg: 生成配置 system_prompt: 系统提示词 mcp_settings: MCP设置列表 Returns: str: 缓存键的哈希值 """ # 构建包含所有相关参数的字符串 cache_data = { 'bot_id': bot_id, 'model_name': model_name or '', 'api_key': api_key or '', 'model_server': model_server or '', 'generate_cfg': json.dumps(generate_cfg or {}, sort_keys=True), 'system_prompt': system_prompt or '', 'mcp_settings': json.dumps(mcp_settings or [], sort_keys=True) } # 将字典转换为JSON字符串并计算哈希值 cache_str = json.dumps(cache_data, sort_keys=True) return hashlib.md5(cache_str.encode('utf-8')).hexdigest()[:16] def _update_access_time(self, cache_key: str): """更新访问时间（LRU 管理）""" self.access_times[cache_key] = time.time() def _cleanup_old_agents(self): """清理旧的助手实例，基于 LRU 策略""" if len(self.agents) <= self.max_cached_agents: return # 按 LRU 顺序排序，删除最久未访问的实例 sorted_keys = sorted(self.access_times.keys(), key=lambda k: self.access_times[k]) keys_to_remove = sorted_keys[:-self.max_cached_agents] removed_count = 0 for cache_key in keys_to_remove: try: del self.agents[cache_key] del self.unique_ids[cache_key] del self.access_times[cache_key] del self.creation_times[cache_key] removed_count += 1 logger.info(f"清理过期的助手实例缓存: {cache_key}") except KeyError: continue if removed_count > 0: logger.info(f"已清理 {removed_count} 个过期的助手实例缓存") async def get_or_create_agent(self, bot_id: str, project_dir: Optional[str], model_name: str = "qwen3-next", api_key: Optional[str] = None, model_server: Optional[str] = None, generate_cfg: Optional[Dict] = None, language: Optional[str] = None, system_prompt: Optional[str] = None, mcp_settings: Optional[List[Dict]] = None, robot_type: Optional[str] = "agent") -> Assistant: """获取或创建文件预加载的助手实例 Args: bot_id: 项目的唯一标识符 project_dir: 项目目录路径，用于读取system_prompt.md和mcp_settings.json，可以为None model_name: 模型名称 api_key: API 密钥 model_server: 模型服务器地址 generate_cfg: 生成配置 language: 语言代码，用于选择对应的系统提示词 system_prompt: 可选的系统提示词，优先级高于项目配置 mcp_settings: 可选的MCP设置，优先级高于项目配置 robot_type: 机器人类型，取值 agent/catalog_agent Returns: Assistant: 配置好的助手实例 """ import os # 实现参数优先级逻辑：传入参数 > 项目配置 > 默认配置 final_system_prompt = load_system_prompt(project_dir, language, system_prompt, robot_type, bot_id) final_mcp_settings = load_mcp_settings(project_dir, mcp_settings, bot_id, robot_type) cache_key = self._get_cache_key(bot_id, model_name, api_key, model_server, generate_cfg, final_system_prompt, final_mcp_settings) # 检查是否已存在该助手实例 if cache_key in self.agents: self._update_access_time(cache_key) agent = self.agents[cache_key] # 动态更新 LLM 配置和系统设置（如果参数有变化） update_agent_llm(agent, model_name, api_key, model_server, generate_cfg) logger.info(f"复用现有的助手实例缓存: {cache_key} (bot_id: {bot_id}") return agent # 清理过期实例 self._cleanup_old_agents() # 创建新的助手实例，预加载文件 logger.info(f"创建新的助手实例缓存: {cache_key}, bot_id: {bot_id}") current_time = time.time() agent = init_modified_agent_service_with_files( model_name=model_name, api_key=api_key, model_server=model_server, generate_cfg=generate_cfg, system_prompt=final_system_prompt, mcp=final_mcp_settings ) # 缓存实例 self.agents[cache_key] = agent self.unique_ids[cache_key] = bot_id self.access_times[cache_key] = current_time self.creation_times[cache_key] = current_time logger.info(f"助手实例缓存创建完成: {cache_key}") return agent def get_cache_stats(self) -> Dict: """获取缓存统计信息""" current_time = time.time() stats = { "total_cached_agents": len(self.agents), "max_cached_agents": self.max_cached_agents, "agents": {} } for cache_key, agent in self.agents.items(): stats["agents"][cache_key] = { "unique_id": self.unique_ids.get(cache_key, "unknown"), "created_at": self.creation_times.get(cache_key, 0), "last_accessed": self.access_times.get(cache_key, 0), "age_seconds": int(current_time - self.creation_times.get(cache_key, current_time)), "idle_seconds": int(current_time - self.access_times.get(cache_key, current_time)) } return stats def clear_cache(self) -> int: """清空所有缓存 Returns: int: 清理的实例数量 """ cache_count = len(self.agents) self.agents.clear() self.unique_ids.clear() self.access_times.clear() self.creation_times.clear() logger.info(f"已清空所有助手实例缓存，共清理 {cache_count} 个实例") return cache_count def remove_cache_by_unique_id(self, unique_id: str) -> int: """根据 unique_id 移除所有相关的缓存 由于缓存key现在包含 system_prompt 和 mcp_settings， 一个 unique_id 可能对应多个缓存实例。 Args: unique_id: 项目的唯一标识符 Returns: int: 成功移除的实例数量 """ keys_to_remove = [] # 找到所有匹配的 unique_id 的缓存键 for cache_key, stored_unique_id in self.unique_ids.items(): if stored_unique_id == unique_id: keys_to_remove.append(cache_key) # 移除找到的缓存 removed_count = 0 for cache_key in keys_to_remove: try: del self.agents[cache_key] del self.unique_ids[cache_key] del self.access_times[cache_key] del self.creation_times[cache_key] removed_count += 1 logger.info(f"已移除助手实例缓存: {cache_key} (unique_id: {unique_id})") except KeyError: continue if removed_count > 0: logger.info(f"已移除 unique_id={unique_id} 的 {removed_count} 个助手实例缓存") else: logger.warning(f"未找到 unique_id={unique_id} 的缓存实例") return removed_count # 全局文件预加载助手管理器实例 _global_agent_manager: Optional[FileLoadedAgentManager] = None def get_global_agent_manager() -> FileLoadedAgentManager: """获取全局文件预加载助手管理器实例""" global _global_agent_manager if _global_agent_manager is None: _global_agent_manager = FileLoadedAgentManager() return _global_agent_manager def init_global_agent_manager(max_cached_agents: int = 20): """初始化全局文件预加载助手管理器""" global _global_agent_manager _global_agent_manager = FileLoadedAgentManager(max_cached_agents) return _global_agent_manager