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>
336 lines
9.4 KiB
Python
336 lines
9.4 KiB
Python
"""
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OpenAI 兼容 AI 客户端
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支持多种 AI 提供商的统一接口
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"""
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import asyncio
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import time
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from typing import Optional
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from dataclasses import dataclass
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import httpx
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from openai import AsyncOpenAI
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from app.schemas.ai_config import AIProvider, ModelCapability
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@dataclass
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class AIResponse:
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"""AI 响应"""
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content: str
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model: str
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usage: dict
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finish_reason: str
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@dataclass
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class ConnectionTestResult:
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"""连接测试结果"""
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success: bool
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latency_ms: int
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error: Optional[str] = None
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class OpenAICompatibleClient:
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"""
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OpenAI 兼容 API 客户端
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支持：
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- OpenAI
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- Azure OpenAI
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- Anthropic (通过 OpenAI 兼容层)
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- DeepSeek
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- Qwen (通义千问)
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- Doubao (豆包)
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- 各种中转服务 (OneAPI, OpenRouter)
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"""
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def __init__(
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self,
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base_url: str,
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api_key: str,
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provider: str = "openai",
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timeout: float = 60.0,
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):
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self.base_url = base_url.rstrip("/")
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self.api_key = api_key
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self.provider = provider
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self.timeout = timeout
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# 创建 OpenAI 客户端
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self.client = AsyncOpenAI(
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base_url=self.base_url,
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api_key=self.api_key,
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timeout=timeout,
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)
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async def chat_completion(
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self,
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messages: list[dict],
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model: str,
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temperature: float = 0.7,
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max_tokens: int = 2000,
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**kwargs,
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) -> AIResponse:
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"""
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聊天补全
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Args:
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messages: 消息列表 [{"role": "user", "content": "..."}]
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model: 模型名称
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temperature: 温度参数
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max_tokens: 最大 token 数
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Returns:
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AIResponse 包含生成的内容
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"""
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response = await self.client.chat.completions.create(
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model=model,
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messages=messages,
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temperature=temperature,
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max_tokens=max_tokens,
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**kwargs,
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)
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choice = response.choices[0]
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return AIResponse(
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content=choice.message.content or "",
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model=response.model,
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usage={
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"prompt_tokens": response.usage.prompt_tokens if response.usage else 0,
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"completion_tokens": response.usage.completion_tokens if response.usage else 0,
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"total_tokens": response.usage.total_tokens if response.usage else 0,
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},
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finish_reason=choice.finish_reason or "stop",
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)
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async def vision_analysis(
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self,
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image_urls: list[str],
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prompt: str,
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model: str,
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temperature: float = 0.3,
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max_tokens: int = 2000,
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) -> AIResponse:
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"""
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视觉分析（图像理解）
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Args:
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image_urls: 图像 URL 列表
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prompt: 分析提示
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model: 视觉模型名称
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Returns:
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AIResponse 包含分析结果
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"""
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# 构建多模态消息
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content = [{"type": "text", "text": prompt}]
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for url in image_urls:
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content.append({
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"type": "image_url",
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"image_url": {"url": url},
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})
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messages = [{"role": "user", "content": content}]
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return await self.chat_completion(
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messages=messages,
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model=model,
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temperature=temperature,
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max_tokens=max_tokens,
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)
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async def audio_transcription(
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self,
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audio_url: str,
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model: str = "whisper-1",
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language: str = "zh",
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) -> AIResponse:
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"""
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音频转写 (ASR)
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Args:
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audio_url: 音频文件 URL
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model: 转写模型
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language: 语言代码
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Returns:
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AIResponse 包含转写文本
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"""
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# 下载音频文件
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async with httpx.AsyncClient() as http_client:
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response = await http_client.get(audio_url, timeout=30)
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response.raise_for_status()
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audio_data = response.content
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# 调用 Whisper API
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transcription = await self.client.audio.transcriptions.create(
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model=model,
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file=("audio.mp3", audio_data, "audio/mpeg"),
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language=language,
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)
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return AIResponse(
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content=transcription.text,
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model=model,
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usage={"prompt_tokens": 0, "completion_tokens": 0, "total_tokens": 0},
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finish_reason="stop",
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)
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async def test_connection(
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self,
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model: str,
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capability: ModelCapability = ModelCapability.TEXT,
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) -> ConnectionTestResult:
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"""
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测试模型连接
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Args:
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model: 模型名称
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capability: 模型能力类型
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Returns:
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ConnectionTestResult 包含测试结果
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"""
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start_time = time.time()
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try:
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if capability == ModelCapability.AUDIO:
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# 音频模型无法简单测试，只验证 API 可达
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async with httpx.AsyncClient() as http_client:
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response = await http_client.get(
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f"{self.base_url}/models",
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headers={"Authorization": f"Bearer {self.api_key}"},
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timeout=10,
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)
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response.raise_for_status()
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latency_ms = int((time.time() - start_time) * 1000)
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return ConnectionTestResult(success=True, latency_ms=latency_ms)
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elif capability == ModelCapability.VISION:
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# 视觉模型测试：发送简单的文本请求
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response = await self.chat_completion(
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messages=[{"role": "user", "content": "Hi"}],
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model=model,
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max_tokens=5,
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)
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else:
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# 文本模型测试
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response = await self.chat_completion(
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messages=[{"role": "user", "content": "Hi"}],
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model=model,
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max_tokens=5,
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)
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latency_ms = int((time.time() - start_time) * 1000)
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return ConnectionTestResult(success=True, latency_ms=latency_ms)
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except Exception as e:
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latency_ms = int((time.time() - start_time) * 1000)
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return ConnectionTestResult(
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success=False,
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latency_ms=latency_ms,
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error=str(e),
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)
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async def list_models(self) -> dict[str, list[dict]]:
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"""
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获取可用模型列表
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Returns:
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按能力分类的模型列表
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{"text": [...], "vision": [...], "audio": [...]}
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"""
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try:
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models = await self.client.models.list()
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# 已知模型能力映射
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known_capabilities = {
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# OpenAI
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"gpt-4o": ["text", "vision"],
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"gpt-4o-mini": ["text", "vision"],
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"gpt-4-turbo": ["text", "vision"],
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"gpt-4": ["text"],
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"gpt-3.5-turbo": ["text"],
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"whisper-1": ["audio"],
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# Claude (通过兼容层)
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"claude-3-opus": ["text", "vision"],
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"claude-3-sonnet": ["text", "vision"],
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"claude-3-haiku": ["text", "vision"],
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# DeepSeek
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"deepseek-chat": ["text"],
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"deepseek-coder": ["text"],
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# Qwen
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"qwen-turbo": ["text"],
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"qwen-plus": ["text"],
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"qwen-max": ["text"],
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"qwen-vl-plus": ["vision"],
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"qwen-vl-max": ["vision"],
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# Doubao
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"doubao-pro": ["text"],
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"doubao-lite": ["text"],
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}
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result: dict[str, list[dict]] = {
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"text": [],
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"vision": [],
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"audio": [],
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}
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for model in models.data:
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model_id = model.id
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capabilities = known_capabilities.get(model_id, ["text"])
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for cap in capabilities:
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if cap in result:
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result[cap].append({
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"id": model_id,
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"name": model_id.replace("-", " ").title(),
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})
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return result
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except Exception:
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# 如果无法获取模型列表，返回预设列表
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return {
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"text": [
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{"id": "gpt-4o", "name": "GPT-4o"},
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{"id": "gpt-4o-mini", "name": "GPT-4o Mini"},
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{"id": "deepseek-chat", "name": "DeepSeek Chat"},
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],
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"vision": [
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{"id": "gpt-4o", "name": "GPT-4o"},
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{"id": "qwen-vl-max", "name": "Qwen VL Max"},
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],
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"audio": [
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{"id": "whisper-1", "name": "Whisper"},
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],
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}
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async def close(self):
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"""关闭客户端"""
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try:
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await self.client.close()
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except Exception:
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# 关闭失败不应影响主流程
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pass
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# 便捷函数
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async def create_ai_client(
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base_url: str,
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api_key: str,
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provider: str = "openai",
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) -> OpenAICompatibleClient:
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"""创建 AI 客户端"""
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return OpenAICompatibleClient(
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base_url=base_url,
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api_key=api_key,
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provider=provider,
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)
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