refactor: 移除t_llm_config相关代码依赖
变更项: 1. 删除废弃数据层文件 db/llm_config_db.py。 2. Robot 启动流程移除 llm_config_db 初始化与导入逻辑。 3. DashboardServer 移除 llm_config_db 挂载引用。 4. 复查确认代码中已无 t_llm_config/llm_config_db/LLMConfigDBOperator 运行时依赖。
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@@ -52,7 +52,6 @@ class DashboardServer:
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self.plugin_schedule_manager = robot_instance.plugin_schedule_manager
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self.group_plugin_config_db = robot_instance.group_plugin_config_db
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self.llm_catalog_db = robot_instance.llm_catalog_db
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self.llm_config_db = robot_instance.llm_config_db
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self.group_plugin_config_service = robot_instance.group_plugin_config_service
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# 获取联系人管理器实例
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self.contact_manager = robot_instance.contact_manager
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@@ -1,125 +0,0 @@
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# -*- coding: utf-8 -*-
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import json
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from typing import Any, Dict
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from loguru import logger
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from db.base import BaseDBOperator
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from db.connection import DBConnectionManager
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class LLMConfigDBOperator(BaseDBOperator):
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"""LLM 配置数据库操作器。
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设计目标:
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1. 把原先存放在 config.yaml 的 llm 配置迁移到 MySQL,便于后台实时维护;
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2. 采用“单行配置”模型,降低维护复杂度:一行记录保存 default_backend/backends/scenes;
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3. 支持“首次启动自动导入 YAML 配置”,保证迁移过程对线上透明。
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"""
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def __init__(self, db_manager: DBConnectionManager):
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super().__init__(db_manager)
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def init_tables(self) -> bool:
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"""初始化 LLM 配置表。
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字段说明:
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- id: 固定主键,当前仅使用 id=1 作为全局配置;
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- default_backend: 全局默认后端;
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- backends_json: 后端配置大对象(JSON 字符串);
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- scenes_json: 场景路由对象(JSON 字符串);
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- source: 记录当前配置来源,便于后续排障。
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"""
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try:
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return self.execute_update(
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"""
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CREATE TABLE IF NOT EXISTS t_llm_config (
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id TINYINT PRIMARY KEY,
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default_backend VARCHAR(128) NOT NULL DEFAULT '',
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backends_json JSON NOT NULL,
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scenes_json JSON NOT NULL,
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source VARCHAR(32) NOT NULL DEFAULT 'mysql',
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created_at DATETIME DEFAULT CURRENT_TIMESTAMP,
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updated_at DATETIME DEFAULT CURRENT_TIMESTAMP ON UPDATE CURRENT_TIMESTAMP
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)
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"""
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)
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except Exception as e:
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logger.error(f"初始化 t_llm_config 失败: {e}")
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return False
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@staticmethod
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def _loads_json(value: Any) -> Dict[str, Any]:
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"""将数据库 JSON 字段统一解析为 dict。"""
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if isinstance(value, dict):
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return value
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if isinstance(value, str):
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try:
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obj = json.loads(value)
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return obj if isinstance(obj, dict) else {}
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except json.JSONDecodeError:
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return {}
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return {}
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def get_config(self) -> Dict[str, Any]:
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"""读取数据库中的 LLM 配置。"""
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row = self.execute_query(
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"""
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SELECT id, default_backend, backends_json, scenes_json, source, updated_at
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FROM t_llm_config
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WHERE id = 1
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LIMIT 1
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""",
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fetch_one=True,
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) or {}
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if not row:
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return {}
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return {
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"default_backend": str(row.get("default_backend") or "").strip(),
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"backends": self._loads_json(row.get("backends_json")),
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"scenes": self._loads_json(row.get("scenes_json")),
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"source": str(row.get("source") or "mysql").strip(),
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"updated_at": row.get("updated_at"),
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}
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def save_config(self, llm_config: Dict[str, Any], source: str = "mysql") -> bool:
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"""保存(覆盖)LLM 配置到数据库。"""
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data = llm_config or {}
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default_backend = str(data.get("default_backend") or "").strip()
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backends = data.get("backends", {}) or {}
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scenes = data.get("scenes", {}) or {}
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if not isinstance(backends, dict):
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backends = {}
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if not isinstance(scenes, dict):
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scenes = {}
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sql = """
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INSERT INTO t_llm_config (id, default_backend, backends_json, scenes_json, source)
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VALUES (1, %s, %s, %s, %s)
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ON DUPLICATE KEY UPDATE
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default_backend = VALUES(default_backend),
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backends_json = VALUES(backends_json),
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scenes_json = VALUES(scenes_json),
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source = VALUES(source)
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"""
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params = (
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default_backend,
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json.dumps(backends, ensure_ascii=False),
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json.dumps(scenes, ensure_ascii=False),
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str(source or "mysql"),
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)
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return self.execute_update(sql, params)
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def bootstrap_from_yaml_if_empty(self, yaml_llm_config: Dict[str, Any]) -> bool:
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"""当数据库为空时,把 YAML 里的 llm 配置导入到数据库。
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迁移策略:
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1. 只在“表中无 id=1 配置”时执行,避免覆盖后台已维护的数据;
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2. 导入后标记 source=yaml_bootstrap,便于识别初始数据来源;
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3. 返回 True 表示“已有配置或导入成功”,False 表示导入失败。
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"""
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existed = self.get_config()
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if existed:
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return True
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return self.save_config(yaml_llm_config or {}, source="yaml_bootstrap")
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8
robot.py
8
robot.py
@@ -19,7 +19,6 @@ from db.connection import DBConnectionManager
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from db.contacts_db import ContactsDBOperator
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from db.group_plugin_config_db import GroupPluginConfigDBOperator
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from db.llm_catalog_db import LLMCatalogDBOperator
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from db.llm_config_db import LLMConfigDBOperator
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from db.plugin_schedule_db import PluginScheduleDBOperator
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from db.system_job_db import SystemJobDBOperator
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from utils.system_jobs import SystemJobLoader
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@@ -74,19 +73,12 @@ class Robot:
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self.contacts_db = ContactsDBOperator(self.db_manager)
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self.group_plugin_config_db = GroupPluginConfigDBOperator(self.db_manager)
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self.llm_catalog_db = LLMCatalogDBOperator(self.db_manager)
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self.llm_config_db = LLMConfigDBOperator(self.db_manager)
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self.plugin_schedule_db = PluginScheduleDBOperator(self.db_manager)
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self.system_job_db = SystemJobDBOperator(self.db_manager)
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self.group_plugin_config_db.init_tables()
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# 新版 LLM 目录模型(Provider 模板 / Dify 应用 / Scene)初始化。
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self.llm_catalog_db.init_tables()
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self.llm_catalog_db.bootstrap_from_legacy_llm(self.config.llm)
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# LLM 配置迁移到 MySQL:
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# 1. 先确保表存在;
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# 2. 若库里没有配置,则从 config.yaml 的 llm 节点导入一次;
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# 3. 后续运行时以数据库为准,YAML 仅作为初始导入来源与兜底。
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self.llm_config_db.init_tables()
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self.llm_config_db.bootstrap_from_yaml_if_empty(self.config.llm)
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self.group_plugin_config_service = GroupPluginConfigService(
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db_operator=self.group_plugin_config_db,
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redis_client=self.db_manager.get_redis_connection(),
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