207 lines
6.9 KiB
Python
207 lines
6.9 KiB
Python
"""
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阿里云百炼 DashScope 适配器
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使用 OpenAI SDK 调用阿里云 OpenAI 兼容 API
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"""
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import json
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import os
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from typing import Any, Dict, List, Optional
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from .base import ChatCompletionRequest, ModelInfo
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from .unified_adapter import UnifiedOpenAIAdapter
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from .plugins import get_web_search_mode
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from core import get_logger
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logger = get_logger()
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# 百炼模型配置
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DASHSCOPE_MODELS = [
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ModelInfo(
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id="qwen3-max",
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name="Qwen3-Max",
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description="千问系列效果最好的模型，适合复杂、多步骤的任务。",
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max_tokens=8192,
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provider="Aliyun",
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supports_thinking=True,
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supports_web_search=True,
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supports_vision=False,
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supports_files=False,
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),
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ModelInfo(
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id="qwen3.5-plus",
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name="Qwen3.5-Plus",
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description="能力均衡，推理效果、成本和速度介于千问Max和千问Flash之间，适合中等复杂任务。",
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max_tokens=8192,
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provider="Aliyun",
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supports_thinking=True,
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supports_web_search=True,
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supports_vision=True,
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supports_files=False,
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),
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ModelInfo(
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id="qwen3.5-flash",
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name="Qwen3.5-Flash",
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description="千问系列速度最快、成本极低的模型，适合简单任务。",
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max_tokens=8192,
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provider="Aliyun",
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supports_thinking=False,
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supports_web_search=True,
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supports_vision=False,
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supports_files=False,
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),
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ModelInfo(
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id="qwen-turbo",
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name="Qwen-Turbo",
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description="快速响应的通用模型",
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max_tokens=8192,
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provider="Aliyun",
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supports_thinking=False,
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supports_web_search=True,
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supports_vision=False,
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supports_files=False,
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),
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ModelInfo(
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id="qwen-vl-max",
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name="通义万相 VL-Max",
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description="支持视觉理解的多模态模型",
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max_tokens=8192,
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provider="Aliyun",
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supports_thinking=False,
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supports_web_search=False,
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supports_vision=True,
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supports_files=False,
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),
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ModelInfo(
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id="qwen-vl-plus",
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name="通义万相 VL-Plus",
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description="支持视觉理解的多模态模型",
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max_tokens=8192,
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provider="Aliyun",
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supports_thinking=False,
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supports_web_search=False,
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supports_vision=True,
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supports_files=False,
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),
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]
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# 从 DASHSCOPE_MODELS 自动计算
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THINKING_MODELS = {m.id.lower() for m in DASHSCOPE_MODELS if m.supports_thinking}
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VISION_MODELS = {m.id.lower() for m in DASHSCOPE_MODELS if m.supports_vision}
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class DashScopeAdapter(UnifiedOpenAIAdapter):
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"""阿里云百炼 DashScope 平台适配器"""
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_provider_type = "dashscope"
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@property
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def provider_name(self) -> str:
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return "dashscope"
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def list_models(self) -> List[ModelInfo]:
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return DASHSCOPE_MODELS
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def _supports_thinking(self, model: str) -> bool:
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"""检查模型是否支持深度思考"""
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return model.lower() in THINKING_MODELS
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def _is_vision_model(self, model: str) -> bool:
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"""检查是否为多模态模型"""
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return model.lower() in VISION_MODELS
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def _build_messages(self, request: ChatCompletionRequest) -> List[Dict]:
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"""
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构建 DashScope 格式的消息
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处理多模态内容
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"""
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messages = []
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for msg in request.messages:
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role = msg.get("role", "user")
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content = msg.get("content", "")
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if isinstance(content, str):
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if content.strip():
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messages.append({"role": role, "content": content})
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elif isinstance(content, list):
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# 多模态内容 - OpenAI 格式兼容
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ds_content = []
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for item in content:
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if isinstance(item, dict):
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if item.get("type") == "text":
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ds_content.append({"type": "text", "text": item.get("text", "")})
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elif item.get("type") == "image_url":
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img_url = self._extract_image_url(item)
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if img_url:
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ds_content.append({
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"type": "image_url",
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"image_url": {"url": img_url}
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})
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if ds_content:
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messages.append({"role": role, "content": ds_content})
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return messages
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def _extract_image_url(self, item: Dict) -> Optional[str]:
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"""提取图片 URL"""
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img_val = item.get("image_url", "")
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if isinstance(img_val, str):
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img_url = img_val
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elif isinstance(img_val, dict):
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img_url = img_val.get("url", "")
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else:
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return None
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# 记录图片 URL 转换
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logger.info(f"[DashScope] 图片URL: {img_url}")
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# 处理本地文件 URL
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if img_url.startswith(("http://", "https://")):
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from urllib.parse import urlparse
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parsed = urlparse(img_url)
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if "localhost" in parsed.netloc or "127.0.0.1" in parsed.netloc:
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path_parts = parsed.path.split("/")
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try:
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uploads_idx = path_parts.index("uploads")
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img_url = f"file://{'/'.join(path_parts[uploads_idx:])}"
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except ValueError:
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pass
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elif not img_url.startswith("file://") and not img_url.startswith(("http://", "https://")):
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img_url = f"file://{img_url}"
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return img_url
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def _get_extra_params(self, request: ChatCompletionRequest) -> Dict[str, Any]:
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"""
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获取 DashScope 特殊参数
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- 深度思考: extra_body={"enable_thinking": True/False}
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- 联网搜索: extra_body={"enable_search": True}
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"""
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extra_params = {}
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extra_body = {}
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model = request.model
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# 深度思考 - 始终传递，明确启用或禁用
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logger.info(f"[DashScope] 深度思考请求: deep_thinking={request.deep_thinking}, model={model}")
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supports_thinking = self._supports_thinking(model)
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logger.info(f"[DashScope] 模型 {model} 支持深度思考: {supports_thinking}")
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thinking_enabled = request.deep_thinking and supports_thinking
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extra_body["enable_thinking"] = thinking_enabled
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logger.info(f"[DashScope] 深度思考最终状态: {thinking_enabled}")
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# 联网搜索
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web_search_mode = get_web_search_mode(request)
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if web_search_mode:
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extra_body["enable_search"] = True
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if web_search_mode == "deep":
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extra_body["search_options"] = {"enable_search_extension": True}
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logger.info(f"[DashScope] 联网搜索已启用: mode={web_search_mode}")
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# 添加 extra_body 到参数
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extra_params["extra_body"] = extra_body
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return extra_params |