e4959d584f
主要更新： - 更新代理商端文档，明确项目由品牌方分配流程 - 新增Brief配置详情页(已配置)设计稿 - 完善工作台紧急待办中品牌新任务功能 - 整理Pencil设计文件中代理商端页面顺序 - 新增后端FastAPI框架及核心API - 新增前端Next.js页面和组件库 - 添加.gitignore排除构建和缓存文件 Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
315 lines
9.3 KiB
Python
315 lines
9.3 KiB
Python
"""
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AI 服务配置 API
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品牌方管理 AI 提供商配置、模型选择、连通性测试
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"""
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import asyncio
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from datetime import datetime, timezone
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from typing import Optional
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from fastapi import APIRouter, Depends, HTTPException, Header, status
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from sqlalchemy import select
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from sqlalchemy.ext.asyncio import AsyncSession
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from app.database import get_db
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from app.models.ai_config import AIConfig
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from app.models.tenant import Tenant
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from app.schemas.ai_config import (
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AIProvider,
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AIConfigUpdate,
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AIConfigResponse,
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AIModelsConfig,
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AIParametersConfig,
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GetModelsRequest,
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TestConnectionRequest,
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ModelsListResponse,
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ConnectionTestResponse,
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ModelTestResult,
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ModelInfo,
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ModelCapability,
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mask_api_key,
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)
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from app.services.ai_client import OpenAICompatibleClient
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from app.services.ai_service import AIServiceFactory
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from app.utils.crypto import encrypt_api_key, decrypt_api_key
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router = APIRouter(prefix="/ai-config", tags=["ai-config"])
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async def _ensure_tenant_exists(tenant_id: str, db: AsyncSession) -> Tenant:
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"""确保租户存在，不存在则自动创建"""
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result = await db.execute(
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select(Tenant).where(Tenant.id == tenant_id)
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)
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tenant = result.scalar_one_or_none()
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if not tenant:
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tenant = Tenant(id=tenant_id, name=f"租户-{tenant_id}")
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db.add(tenant)
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await db.flush()
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return tenant
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@router.get("", response_model=AIConfigResponse)
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async def get_ai_config(
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x_tenant_id: str = Header(..., alias="X-Tenant-ID"),
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db: AsyncSession = Depends(get_db),
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) -> AIConfigResponse:
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"""
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获取当前 AI 配置
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- 未配置返回 404
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- 已配置返回配置信息（API Key 脱敏）
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"""
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result = await db.execute(
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select(AIConfig).where(
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AIConfig.tenant_id == x_tenant_id,
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AIConfig.is_configured == True,
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)
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)
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config = result.scalar_one_or_none()
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if not config:
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raise HTTPException(
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status_code=status.HTTP_404_NOT_FOUND,
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detail="AI 服务未配置，请先完成配置",
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)
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# 解密 API Key 用于脱敏显示
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api_key = decrypt_api_key(config.api_key_encrypted)
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return AIConfigResponse(
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provider=config.provider,
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base_url=config.base_url,
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api_key_masked=mask_api_key(api_key),
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models=AIModelsConfig(**config.models),
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parameters=AIParametersConfig(
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temperature=config.temperature,
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max_tokens=config.max_tokens,
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),
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available_models=config.available_models or {},
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is_configured=config.is_configured,
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last_test_at=config.last_test_at.isoformat() if config.last_test_at else None,
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last_test_result=config.last_test_result,
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)
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@router.put("", response_model=AIConfigResponse)
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async def update_ai_config(
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request: AIConfigUpdate,
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x_tenant_id: str = Header(..., alias="X-Tenant-ID"),
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db: AsyncSession = Depends(get_db),
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) -> AIConfigResponse:
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"""
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更新 AI 配置
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- 保存提供商、连接信息、模型配置
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- API Key 加密存储
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"""
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# 确保租户存在
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await _ensure_tenant_exists(x_tenant_id, db)
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# 加密 API Key
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api_key_encrypted = encrypt_api_key(request.api_key)
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# 创建或更新配置
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config = await AIServiceFactory.create_or_update_config(
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tenant_id=x_tenant_id,
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provider=request.provider.value,
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base_url=request.base_url,
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api_key_encrypted=api_key_encrypted,
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models=request.models.model_dump(),
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temperature=request.parameters.temperature,
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max_tokens=request.parameters.max_tokens,
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db=db,
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)
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return AIConfigResponse(
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provider=config.provider,
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base_url=config.base_url,
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api_key_masked=mask_api_key(request.api_key),
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models=AIModelsConfig(**config.models),
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parameters=AIParametersConfig(
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temperature=config.temperature,
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max_tokens=config.max_tokens,
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),
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available_models=config.available_models or {},
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is_configured=True,
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last_test_at=config.last_test_at.isoformat() if config.last_test_at else None,
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last_test_result=config.last_test_result,
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)
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@router.post("/models", response_model=ModelsListResponse)
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async def get_available_models(
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request: GetModelsRequest,
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x_tenant_id: str = Header(..., alias="X-Tenant-ID"),
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db: AsyncSession = Depends(get_db),
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) -> ModelsListResponse:
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"""
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获取可用模型列表
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- 调用提供商 API 获取模型列表
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- 按能力分类（text/vision/audio）
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"""
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try:
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client = OpenAICompatibleClient(
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base_url=request.base_url,
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api_key=request.api_key,
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provider=request.provider.value,
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)
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models_dict = await client.list_models()
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await client.close()
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# 转换为 ModelInfo 对象
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models = {
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k: [ModelInfo(**m) for m in v]
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for k, v in models_dict.items()
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}
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# 更新配置中的可用模型缓存
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result = await db.execute(
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select(AIConfig).where(AIConfig.tenant_id == x_tenant_id)
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)
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config = result.scalar_one_or_none()
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if config:
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config.available_models = models_dict
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await db.flush()
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return ModelsListResponse(
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success=True,
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models=models,
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)
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except Exception as e:
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raise HTTPException(
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status_code=status.HTTP_502_BAD_GATEWAY,
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detail=f"获取模型列表失败: {str(e)}",
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)
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@router.post("/test", response_model=ConnectionTestResponse)
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async def test_connection(
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request: TestConnectionRequest,
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x_tenant_id: str = Header(..., alias="X-Tenant-ID"),
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db: AsyncSession = Depends(get_db),
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) -> ConnectionTestResponse:
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"""
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测试 AI 服务连接
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- 并行测试三个模型
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- 返回每个模型的测试结果
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"""
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client = None
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models = request.models.model_dump()
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try:
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client = OpenAICompatibleClient(
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base_url=request.base_url,
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api_key=request.api_key,
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provider=request.provider.value,
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)
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# 定义模型能力映射
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capability_map = {
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"text": ModelCapability.TEXT,
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"vision": ModelCapability.VISION,
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"audio": ModelCapability.AUDIO,
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}
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async def test_single(model_type: str, model_id: str) -> tuple[str, ModelTestResult]:
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capability = capability_map.get(model_type, ModelCapability.TEXT)
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result = await client.test_connection(model_id, capability)
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return model_type, ModelTestResult(
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success=result.success,
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latency_ms=result.latency_ms,
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error=result.error,
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model=model_id,
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)
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# 并行测试所有模型
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tasks = [
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test_single(model_type, model_id)
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for model_type, model_id in models.items()
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]
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results_list = await asyncio.gather(*tasks)
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results = {model_type: result for model_type, result in results_list}
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# 计算测试结果
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all_success = all(r.success for r in results.values())
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failed_count = sum(1 for r in results.values() if not r.success)
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if all_success:
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message = "所有模型连接成功"
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else:
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message = f"{failed_count} 个模型连接失败，请检查模型名称或 API 权限"
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response = ConnectionTestResponse(
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success=all_success,
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results=results,
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message=message,
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)
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except Exception as exc:
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# 确保接口返回 200，并返回失败详情
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results = {
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model_type: ModelTestResult(
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success=False,
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latency_ms=0,
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error=str(exc),
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model=model_id,
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)
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for model_type, model_id in models.items()
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}
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response = ConnectionTestResponse(
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success=False,
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results=results,
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message=f"连接测试失败: {str(exc)}",
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)
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finally:
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if client is not None:
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try:
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await client.close()
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except Exception:
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pass
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# 保存测试结果到数据库
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db_result = await db.execute(
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select(AIConfig).where(AIConfig.tenant_id == x_tenant_id)
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)
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config = db_result.scalar_one_or_none()
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if config:
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config.last_test_at = datetime.now(timezone.utc)
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config.last_test_result = {
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k: v.model_dump() for k, v in response.results.items()
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}
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await db.flush()
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return response
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# ==================== 供其他模块调用 ====================
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async def get_ai_config_for_tenant(
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tenant_id: str,
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db: AsyncSession,
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) -> Optional[dict]:
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"""获取租户的 AI 配置（供审核服务调用）"""
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result = await db.execute(
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select(AIConfig).where(
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AIConfig.tenant_id == tenant_id,
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AIConfig.is_configured == True,
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)
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)
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config = result.scalar_one_or_none()
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if not config:
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return None
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return {
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"tenant_id": config.tenant_id,
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"provider": config.provider,
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"base_url": config.base_url,
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"api_key": decrypt_api_key(config.api_key_encrypted),
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"models": config.models,
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"temperature": config.temperature,
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"max_tokens": config.max_tokens,
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}
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