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6da4f27b12
* ✨ feat(auth): 添加群组和机器人唤醒命令支持，优化权限检查逻辑 ✨ feat(llm): 增加额外请求头配置，改进API适配器请求头处理 * 🚨 auto fix by pre-commit hooks * ``` fix(auth): 优化bot权限验证逻辑并改进错误提示 - 将bot存在性检查与状态检查分离，提供更精确的错误信息 - 修复当bot为None时的状态访问问题 - 移除不必要的注释，保持代码简洁 - 优化日志记录的位置和条件判断 ``` --------- Co-authored-by: ATTomatoo <1126160939@qq.com> Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
452 lines
15 KiB
Python
452 lines
15 KiB
Python
"""
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LLM 提供商配置管理
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负责注册和管理 AI 服务提供商的配置项。
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"""
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from functools import lru_cache
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from typing import Any
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from pydantic import BaseModel, Field
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from zhenxun.configs.config import Config
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from zhenxun.configs.utils import parse_as
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from zhenxun.services.log import logger
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from zhenxun.utils.manager.priority_manager import PriorityLifecycle
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from zhenxun.utils.pydantic_compat import model_dump
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from ..core import key_store
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from ..tools import tool_provider_manager
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from ..types.models import ModelDetail, ProviderConfig
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AI_CONFIG_GROUP = "AI"
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PROVIDERS_CONFIG_KEY = "PROVIDERS"
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class DebugLogOptions(BaseModel):
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"""调试日志细粒度控制"""
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show_tools: bool = Field(
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default=True, description="是否在日志中显示工具定义(JSON Schema)"
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)
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show_schema: bool = Field(
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default=True, description="是否在日志中显示结构化输出Schema(response_format)"
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)
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show_safety: bool = Field(
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default=True, description="是否在日志中显示安全设置(safetySettings)"
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)
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def __bool__(self) -> bool:
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"""支持 bool(debug_options) 的语法，方便兼容旧逻辑。"""
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return self.show_tools or self.show_schema or self.show_safety
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class ClientSettings(BaseModel):
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"""LLM 客户端通用设置"""
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timeout: int = Field(default=300, description="API请求超时时间（秒）")
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max_retries: int = Field(default=3, description="请求失败时的最大重试次数")
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retry_delay: int = Field(default=2, description="请求重试的基础延迟时间（秒）")
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structured_retries: int = Field(
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default=2, description="结构化生成校验失败时的最大重试次数 (IVR)"
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)
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proxy: str | None = Field(
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default=None,
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description="网络代理，例如 http://127.0.0.1:7890",
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)
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class LLMConfig(BaseModel):
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"""LLM 服务配置类"""
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default_model_name: str | None = Field(
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default=None,
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description="LLM服务全局默认使用的模型名称 (格式: ProviderName/ModelName)",
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)
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client_settings: ClientSettings = Field(
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default_factory=ClientSettings, description="客户端连接与重试配置"
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)
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providers: list[ProviderConfig] = Field(
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default_factory=list, description="配置多个 AI 服务提供商及其模型信息"
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)
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debug_log: DebugLogOptions | bool = Field(
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default_factory=DebugLogOptions,
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description="LLM请求日志详情开关。支持 bool (全开/全关) 或 dict (细粒度控制)。",
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)
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def get_provider_by_name(self, name: str) -> ProviderConfig | None:
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"""根据名称获取提供商配置
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参数:
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name: 提供商名称
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返回:
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ProviderConfig | None: 提供商配置，如果未找到则返回 None
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"""
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for provider in self.providers:
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if provider.name == name:
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return provider
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return None
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def get_model_by_provider_and_name(
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self, provider_name: str, model_name: str
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) -> tuple[ProviderConfig, ModelDetail] | None:
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"""根据提供商名称和模型名称获取配置
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参数:
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provider_name: 提供商名称
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model_name: 模型名称
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返回:
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tuple[ProviderConfig, ModelDetail] | None: 提供商配置和模型详情的元组，
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如果未找到则返回 None
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"""
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provider = self.get_provider_by_name(provider_name)
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if not provider:
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return None
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for model in provider.models:
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if model.model_name == model_name:
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return provider, model
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return None
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def list_available_models(self) -> list[dict[str, Any]]:
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"""列出所有可用的模型
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返回:
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list[dict[str, Any]]: 模型信息列表
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"""
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models = []
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for provider in self.providers:
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for model in provider.models:
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models.append(
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{
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"provider_name": provider.name,
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"model_name": model.model_name,
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"full_name": f"{provider.name}/{model.model_name}",
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"is_available": model.is_available,
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"is_embedding_model": model.is_embedding_model,
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"api_type": provider.api_type,
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}
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)
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return models
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def validate_model_name(self, provider_model_name: str) -> bool:
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"""验证模型名称格式是否正确
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参数:
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provider_model_name: 格式为 "ProviderName/ModelName" 的字符串
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返回:
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bool: 是否有效
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"""
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if not provider_model_name or "/" not in provider_model_name:
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return False
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parts = provider_model_name.split("/", 1)
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if len(parts) != 2:
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return False
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provider_name, model_name = parts
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return (
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self.get_model_by_provider_and_name(provider_name, model_name) is not None
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)
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def get_ai_config():
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"""获取 AI 配置组"""
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return Config.get(AI_CONFIG_GROUP)
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def get_default_providers() -> list[dict[str, Any]]:
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"""获取默认的提供商配置
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返回:
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list[dict[str, Any]]: 默认提供商配置列表
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"""
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return [
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{
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"name": "DeepSeek",
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"api_key": "YOUR_ARK_API_KEY",
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"api_base": "https://api.deepseek.com",
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"api_type": "openai",
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"extra_headers": {},
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"models": [
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{
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"model_name": "deepseek-chat",
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"max_tokens": 4096,
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"temperature": 0.7,
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},
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{
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"model_name": "deepseek-reasoner",
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},
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],
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},
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{
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"name": "ARK",
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"api_key": "YOUR_ARK_API_KEY",
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"api_base": "https://ark.cn-beijing.volces.com",
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"api_type": "ark",
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"models": [
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{"model_name": "deepseek-r1-250528"},
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{"model_name": "doubao-seed-1-6-250615"},
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{"model_name": "doubao-seed-1-6-flash-250615"},
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{"model_name": "doubao-seed-1-6-thinking-250615"},
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],
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},
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{
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"name": "siliconflow",
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"api_key": "YOUR_ARK_API_KEY",
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"api_base": "https://api.siliconflow.cn",
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"api_type": "openai",
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"models": [
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{"model_name": "deepseek-ai/DeepSeek-V3"},
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],
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},
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{
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"name": "GLM",
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"api_key": "YOUR_ARK_API_KEY",
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"api_base": "https://open.bigmodel.cn",
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"api_type": "zhipu",
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"models": [
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{"model_name": "glm-4-flash"},
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{"model_name": "glm-4-plus"},
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],
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},
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{
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"name": "Gemini",
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"api_key": [
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"AIzaSy*****************************",
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"AIzaSy*****************************",
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"AIzaSy*****************************",
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],
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"api_base": "https://generativelanguage.googleapis.com",
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"api_type": "gemini",
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"models": [
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{"model_name": "gemini-2.5-flash"},
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{"model_name": "gemini-2.5-pro"},
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{"model_name": "gemini-2.5-flash-lite"},
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],
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},
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{
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"name": "OpenRouter",
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"api_key": "YOUR_OPENROUTER_API_KEY",
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"api_base": "https://openrouter.ai/api",
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"api_type": "openrouter",
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"models": [
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{"model_name": "google/gemini-2.5-pro"},
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{"model_name": "google/gemini-2.5-flash"},
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{"model_name": "x-ai/grok-4"},
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],
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},
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]
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def register_llm_configs():
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"""注册 LLM 服务的配置项"""
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logger.info("注册 LLM 服务的配置项")
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llm_config = LLMConfig()
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Config.add_plugin_config(
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AI_CONFIG_GROUP,
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"default_model_name",
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llm_config.default_model_name,
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help="LLM服务全局默认使用的模型名称 (格式: ProviderName/ModelName)",
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type=str,
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)
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Config.add_plugin_config(
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AI_CONFIG_GROUP,
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"client_settings",
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model_dump(llm_config.client_settings),
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help=(
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"LLM客户端高级设置。\n"
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"包含: timeout(超时秒数), max_retries(重试次数), "
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"retry_delay(重试延迟), structured_retries(结构化生成重试), proxy(代理)"
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),
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type=dict,
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)
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Config.add_plugin_config(
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AI_CONFIG_GROUP,
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"debug_log",
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{"show_tools": True, "show_schema": True, "show_safety": True},
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help=(
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"LLM日志详情开关。示例: {'show_tools': True, 'show_schema': False, "
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"'show_safety': False}"
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),
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type=dict,
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)
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Config.add_plugin_config(
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AI_CONFIG_GROUP,
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"gemini_safety_threshold",
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"BLOCK_NONE",
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help=(
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"Gemini 安全过滤阈值 "
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"(BLOCK_LOW_AND_ABOVE: 阻止低级别及以上, "
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"BLOCK_MEDIUM_AND_ABOVE: 阻止中等级别及以上, "
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"BLOCK_ONLY_HIGH: 只阻止高级别, "
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"BLOCK_NONE: 不阻止)"
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),
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type=str,
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)
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Config.add_plugin_config(
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AI_CONFIG_GROUP,
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PROVIDERS_CONFIG_KEY,
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get_default_providers(),
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help=(
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"配置多个 AI 服务提供商及其模型信息。\n"
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"注意：可以在特定模型配置下添加 'api_type' 以覆盖提供商的全局设置。\n"
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"可选：在 provider 下添加 'extra_headers' 传递额外请求头，"
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"用于 AI 网关鉴权（例如 cf-aig-authorization）。\n"
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"支持的 api_type 包括:\n"
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"- 'openai': 标准 OpenAI 格式 (DeepSeek, SiliconFlow, Moonshot 等)\n"
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"- 'gemini': Google Gemini API\n"
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"- 'zhipu': 智谱 AI (GLM)\n"
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"- 'ark': 字节跳动火山引擎 (Doubao)\n"
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"- 'openrouter': OpenRouter 聚合平台\n"
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"- 'openai_image': OpenAI 兼容的图像生成接口 (DALL-E)\n"
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"- 'openai_responses': 支持新版 responses 格式的 OpenAI 兼容接口\n"
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"- 'smart': 智能路由模式 (主要用于第三方中转场景，自动根据模型名"
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"分发请求到 openai 或 gemini)"
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),
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default_value=[],
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type=list[ProviderConfig],
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)
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@lru_cache(maxsize=1)
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def get_llm_config() -> LLMConfig:
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"""获取 LLM 配置实例"""
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ai_config = get_ai_config()
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raw_debug = ai_config.get("debug_log", False)
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if isinstance(raw_debug, bool):
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debug_log_val = DebugLogOptions(
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show_tools=raw_debug, show_schema=raw_debug, show_safety=raw_debug
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)
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else:
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debug_log_val = raw_debug
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config_data = {
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"default_model_name": ai_config.get("default_model_name"),
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"client_settings": ai_config.get("client_settings", {}),
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"debug_log": debug_log_val,
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PROVIDERS_CONFIG_KEY: ai_config.get(PROVIDERS_CONFIG_KEY, []),
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}
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return parse_as(LLMConfig, config_data)
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def get_gemini_safety_threshold() -> str:
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"""获取 Gemini 安全过滤阈值配置
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返回:
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str: 安全过滤阈值
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"""
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ai_config = get_ai_config()
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return ai_config.get("gemini_safety_threshold", "BLOCK_MEDIUM_AND_ABOVE")
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def validate_llm_config() -> tuple[bool, list[str]]:
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"""验证 LLM 配置的有效性
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返回:
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tuple[bool, list[str]]: (是否有效, 错误信息列表)
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"""
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errors = []
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try:
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llm_config = get_llm_config()
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if llm_config.client_settings.timeout <= 0:
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errors.append("timeout 必须大于 0")
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if llm_config.client_settings.max_retries < 0:
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errors.append("max_retries 不能小于 0")
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if llm_config.client_settings.retry_delay <= 0:
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errors.append("retry_delay 必须大于 0")
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if not llm_config.providers:
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errors.append("至少需要配置一个 AI 服务提供商")
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else:
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provider_names = set()
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for provider in llm_config.providers:
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if provider.name in provider_names:
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errors.append(f"提供商名称重复: {provider.name}")
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provider_names.add(provider.name)
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if not provider.api_key:
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errors.append(f"提供商 {provider.name} 缺少 API Key")
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if not provider.models:
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errors.append(f"提供商 {provider.name} 没有配置任何模型")
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else:
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model_names = set()
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for model in provider.models:
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if model.model_name in model_names:
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errors.append(
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f"提供商 {provider.name} 中模型名称重复: "
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f"{model.model_name}"
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)
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model_names.add(model.model_name)
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if llm_config.default_model_name:
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if not llm_config.validate_model_name(llm_config.default_model_name):
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errors.append(
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f"默认模型 {llm_config.default_model_name} 在配置中不存在"
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)
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except Exception as e:
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errors.append(f"配置解析失败: {e!s}")
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return len(errors) == 0, errors
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def set_default_model(provider_model_name: str | None) -> bool:
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"""设置默认模型
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参数:
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provider_model_name: 模型名称，格式为 "ProviderName/ModelName"，None 表示清除
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返回:
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bool: 是否设置成功
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"""
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if provider_model_name:
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llm_config = get_llm_config()
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if not llm_config.validate_model_name(provider_model_name):
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logger.error(f"模型 {provider_model_name} 在配置中不存在")
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return False
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Config.set_config(
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AI_CONFIG_GROUP, "default_model_name", provider_model_name, auto_save=True
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)
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if provider_model_name:
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logger.info(f"默认模型已设置为: {provider_model_name}")
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else:
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logger.info("默认模型已清除")
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return True
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@PriorityLifecycle.on_startup(priority=10)
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async def _init_llm_config_on_startup():
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"""
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在服务启动时主动调用一次 get_llm_config 和 key_store.initialize，
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并预热工具提供者管理器。
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"""
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logger.info("正在初始化 LLM 配置并加载密钥状态...")
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try:
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get_llm_config()
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await key_store.initialize()
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logger.debug("LLM 配置和密钥状态初始化完成。")
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logger.debug("正在预热 LLM 工具提供者管理器...")
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await tool_provider_manager.initialize()
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logger.debug("LLM 工具提供者管理器预热完成。")
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except Exception as e:
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logger.error(f"LLM 配置或密钥状态初始化时发生错误: {e}", e=e)
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