1446bf5f39
变更项: 1) 新增 t_llm_config 数据访问层与建表逻辑。 2) Robot 启动时自动初始化并在空库时从 YAML 导入。 3) 后台 system LLM API 改为读写 MySQL。 4) LLMRegistry 改为优先 MySQL 读取并回退 YAML。 5) DashboardServer 挂载 llm_config_db 提供后台访问。
207 lines
7.5 KiB
Python
207 lines
7.5 KiB
Python
from __future__ import annotations
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import json
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import time
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from pathlib import Path
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from typing import Any, Dict, Optional
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import yaml
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from db.connection import DBConnectionManager
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class LLMRegistry:
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"""集中式 LLM 配置注册器。
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读取优先级：
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1. 优先读取 MySQL（t_llm_config）；
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2. MySQL 不可用或无数据时，回退读取 config.yaml 的 llm 节点。
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"""
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_cache: Dict[str, Any] = {
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# cache_until: 缓存过期时间戳，避免每次调用都打数据库；
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# data: 最近一次成功读取并归一化后的 llm 配置对象。
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"cache_until": 0.0,
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"data": {},
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}
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@classmethod
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def get_root_config_path(cls) -> Path:
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return Path(__file__).resolve().parents[2] / "config.yaml"
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@classmethod
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def _load_llm_from_yaml(cls) -> Dict[str, Any]:
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"""从 YAML 读取 llm 配置（兜底来源）。"""
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path = cls.get_root_config_path()
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if not path.exists():
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return {}
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with open(path, "r", encoding="utf-8") as fp:
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root = yaml.safe_load(fp) or {}
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llm_config = root.get("llm", {}) or {}
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if not isinstance(llm_config, dict):
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return {}
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return llm_config
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@staticmethod
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def _loads_json(value: Any) -> Dict[str, Any]:
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"""把数据库 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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@classmethod
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def _load_llm_from_mysql(cls) -> Dict[str, Any]:
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"""从 MySQL 读取 llm 配置。
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注意：
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1. 该函数必须“无副作用失败”，即任何异常都返回空 dict，交由上层做 YAML 回退；
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2. 不依赖 Robot 实例，直接走 DBConnectionManager 单例，便于在插件调用链路中复用。
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"""
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try:
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db_manager = DBConnectionManager.get_instance()
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if not db_manager or not db_manager.mysql_pool:
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return {}
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conn = db_manager.get_mysql_connection()
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try:
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with conn.cursor(dictionary=True) as cursor:
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cursor.execute(
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"""
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SELECT default_backend, backends_json, scenes_json
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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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)
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row = cursor.fetchone() or {}
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finally:
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conn.close()
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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": cls._loads_json(row.get("backends_json")),
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"scenes": cls._loads_json(row.get("scenes_json")),
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}
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except Exception:
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return {}
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@classmethod
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def _normalize_llm_config(cls, llm_config: Dict[str, Any]) -> Dict[str, Any]:
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"""统一规整 llm 配置结构，避免下游出现类型分支。"""
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data = llm_config if isinstance(llm_config, dict) else {}
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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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return {
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"default_backend": default_backend,
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"backends": backends,
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"scenes": scenes,
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}
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@classmethod
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def get_llm_config(cls) -> Dict[str, Any]:
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"""获取运行时 LLM 配置（优先 MySQL，失败回退 YAML）。"""
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now = time.time()
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if cls._cache.get("cache_until", 0.0) > now and cls._cache.get("data"):
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return cls._cache["data"]
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llm_config = cls._load_llm_from_mysql()
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if not llm_config:
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llm_config = cls._load_llm_from_yaml()
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normalized = cls._normalize_llm_config(llm_config)
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# 轻量缓存 3 秒：兼顾“后台编辑后较快生效”和“降低高频调用的 DB 压力”。
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cls._cache = {
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"cache_until": now + 3.0,
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"data": normalized,
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}
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return normalized
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@classmethod
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def get_default_backend(cls) -> str:
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"""读取全局默认后端名称。"""
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llm_config = cls.get_llm_config()
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return str(llm_config.get("default_backend", "") or "").strip()
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@classmethod
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def get_backend(cls, backend_name: str) -> Dict[str, Any]:
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if not backend_name:
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return {}
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llm_config = cls.get_llm_config()
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backends = llm_config.get("backends", {}) or {}
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backend = backends.get(backend_name, {}) or {}
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return dict(backend) if isinstance(backend, dict) else {}
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@classmethod
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def get_scenes(cls) -> Dict[str, str]:
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"""读取 llm.scenes 场景路由配置，返回 scene->backend 的映射。"""
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llm_config = cls.get_llm_config()
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raw_scenes = llm_config.get("scenes", {}) or {}
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if not isinstance(raw_scenes, dict):
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return {}
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normalized: Dict[str, str] = {}
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for raw_scene, raw_backend in raw_scenes.items():
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scene_name = str(raw_scene or "").strip()
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backend_name = str(raw_backend or "").strip()
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if not scene_name or not backend_name:
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continue
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normalized[scene_name] = backend_name
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return normalized
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@classmethod
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def get_scene_backend_name(cls, scene_name: str) -> str:
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"""根据场景名解析后端名；若场景不存在则自动回退 default_backend。"""
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name = str(scene_name or "").strip()
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if not name:
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return cls.get_default_backend()
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scenes = cls.get_scenes()
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backend_name = str(scenes.get(name, "") or "").strip()
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if backend_name:
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return backend_name
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return cls.get_default_backend()
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@classmethod
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def resolve_by_scene(cls, scene_name: str) -> Dict[str, Any]:
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"""按场景解析最终后端配置，并附带 scene 字段用于链路追踪。"""
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backend_name = cls.get_scene_backend_name(scene_name)
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backend = cls.get_backend(backend_name)
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if backend_name:
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backend["backend"] = backend_name
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if scene_name:
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backend["scene"] = str(scene_name).strip()
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return backend
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@classmethod
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def resolve(cls, local_config: Optional[Dict[str, Any]] = None) -> Dict[str, Any]:
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local = dict(local_config or {})
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# 严格模式说明：
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# 1. 统一只认 scene 作为路由入口，避免 backend/backend_ref 等多入口并存；
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# 2. 若未声明 scene，则视为“调用方直接给出完整连接参数”，原样返回 local。
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scene_name = local.get("scene") or ""
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scene_name = str(scene_name).strip()
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if scene_name:
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merged = cls.resolve_by_scene(scene_name)
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merged.update(local)
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# 约定：只要声明了 scene，就以 scene 路由结果为准。
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# 这样后台切换 scene 绑定时，无需改插件配置即可全局生效。
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merged["backend"] = cls.get_scene_backend_name(scene_name)
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merged["scene"] = scene_name
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return merged
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return local
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