mirror of
https://github.com/Zippland/Snap-Solver.git
synced 2026-01-19 01:21:13 +08:00
support qwq-max
This commit is contained in:
Zylan
@@ -2,6 +2,7 @@ from .base import BaseModel
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from .anthropic import AnthropicModel
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from .openai import OpenAIModel
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from .deepseek import DeepSeekModel
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from .alibaba import AlibabaModel
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from .factory import ModelFactory
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__all__ = [
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@@ -9,5 +10,6 @@ __all__ = [
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'AnthropicModel',
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'OpenAIModel',
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'DeepSeekModel',
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'AlibabaModel',
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'ModelFactory'
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]
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253
models/alibaba.py
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253
models/alibaba.py
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@@ -0,0 +1,253 @@
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import os
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from typing import Generator, Dict, Optional, Any
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from openai import OpenAI
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from .base import BaseModel
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class AlibabaModel(BaseModel):
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def get_default_system_prompt(self) -> str:
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return """你是一位专业的问题分析与解答助手。当看到一个问题图片时，请：
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1. 仔细阅读并理解问题
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2. 分析问题的关键组成部分
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3. 提供清晰的、逐步的解决方案
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4. 如果相关，解释涉及的概念或理论
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5. 如果有多种解决方法，先解释最高效的方法"""
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def get_model_identifier(self) -> str:
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return "qvq-max"
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def analyze_text(self, text: str, proxies: dict = None) -> Generator[dict, None, None]:
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"""Stream QVQ-Max's response for text analysis"""
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try:
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# Initial status
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yield {"status": "started", "content": ""}
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# Save original environment state
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original_env = {
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'http_proxy': os.environ.get('http_proxy'),
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'https_proxy': os.environ.get('https_proxy')
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}
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try:
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# Set proxy environment variables if provided
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if proxies:
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if 'http' in proxies:
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os.environ['http_proxy'] = proxies['http']
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if 'https' in proxies:
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os.environ['https_proxy'] = proxies['https']
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# Initialize OpenAI compatible client for DashScope
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client = OpenAI(
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api_key=self.api_key,
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base_url="https://dashscope.aliyuncs.com/compatible-mode/v1"
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)
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# Prepare messages
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messages = [
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{
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"role": "system",
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"content": [{"type": "text", "text": self.system_prompt}]
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},
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{
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"role": "user",
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"content": [{"type": "text", "text": text}]
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}
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]
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# 创建聊天完成请求
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response = client.chat.completions.create(
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model=self.get_model_identifier(),
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messages=messages,
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temperature=self.temperature,
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stream=True,
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max_tokens=self.max_tokens if hasattr(self, 'max_tokens') and self.max_tokens else 4000
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)
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# 记录思考过程和回答
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reasoning_content = ""
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answer_content = ""
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is_answering = False
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for chunk in response:
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if not chunk.choices:
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continue
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delta = chunk.choices[0].delta
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# 处理思考过程
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if hasattr(delta, 'reasoning_content') and delta.reasoning_content is not None:
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reasoning_content += delta.reasoning_content
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# 思考过程作为一个独立的内容发送
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yield {
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"status": "reasoning",
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"content": reasoning_content,
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"is_reasoning": True
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}
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elif delta.content != "":
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# 判断是否开始回答（从思考过程切换到回答）
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if not is_answering:
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is_answering = True
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# 发送完整的思考过程
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if reasoning_content:
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yield {
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"status": "reasoning_complete",
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"content": reasoning_content,
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"is_reasoning": True
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}
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# 累积回答内容
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answer_content += delta.content
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# 发送回答内容
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yield {
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"status": "streaming",
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"content": answer_content
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}
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# 确保发送最终完整内容
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if answer_content:
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yield {
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"status": "completed",
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"content": answer_content
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}
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finally:
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# Restore original environment state
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for key, value in original_env.items():
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if value is None:
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if key in os.environ:
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del os.environ[key]
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else:
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os.environ[key] = value
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except Exception as e:
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yield {
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"status": "error",
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"error": str(e)
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}
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def analyze_image(self, image_data: str, proxies: dict = None) -> Generator[dict, None, None]:
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"""Stream QVQ-Max's response for image analysis"""
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try:
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# Initial status
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yield {"status": "started", "content": ""}
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# Save original environment state
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original_env = {
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'http_proxy': os.environ.get('http_proxy'),
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'https_proxy': os.environ.get('https_proxy')
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}
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try:
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# Set proxy environment variables if provided
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if proxies:
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if 'http' in proxies:
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os.environ['http_proxy'] = proxies['http']
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if 'https' in proxies:
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os.environ['https_proxy'] = proxies['https']
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# Initialize OpenAI compatible client for DashScope
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client = OpenAI(
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api_key=self.api_key,
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base_url="https://dashscope.aliyuncs.com/compatible-mode/v1"
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)
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# 检查系统提示词是否已包含语言设置指令
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system_prompt = self.system_prompt
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language = self.language or '中文'
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if not any(phrase in system_prompt for phrase in ['Please respond in', '请用', '使用', '回答']):
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system_prompt = f"{system_prompt}\n\n请务必使用{language}回答，无论问题是什么语言。即使在分析图像时也请使用{language}回答。"
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# Prepare messages with image
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messages = [
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{
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"role": "system",
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"content": [{"type": "text", "text": system_prompt}]
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},
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{
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"role": "user",
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"content": [
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{
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"type": "image_url",
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"image_url": {
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"url": f"data:image/jpeg;base64,{image_data}"
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}
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},
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{
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"type": "text",
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"text": "请分析这个图片并提供详细的解答。"
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}
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]
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}
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]
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# 创建聊天完成请求
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response = client.chat.completions.create(
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model=self.get_model_identifier(),
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messages=messages,
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temperature=self.temperature,
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stream=True,
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max_tokens=self.max_tokens if hasattr(self, 'max_tokens') and self.max_tokens else 4000
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)
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# 记录思考过程和回答
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reasoning_content = ""
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answer_content = ""
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is_answering = False
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for chunk in response:
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if not chunk.choices:
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continue
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delta = chunk.choices[0].delta
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# 处理思考过程
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if hasattr(delta, 'reasoning_content') and delta.reasoning_content is not None:
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reasoning_content += delta.reasoning_content
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# 思考过程作为一个独立的内容发送
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yield {
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"status": "reasoning",
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"content": reasoning_content,
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"is_reasoning": True
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}
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elif delta.content != "":
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# 判断是否开始回答（从思考过程切换到回答）
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if not is_answering:
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is_answering = True
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# 发送完整的思考过程
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if reasoning_content:
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yield {
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"status": "reasoning_complete",
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"content": reasoning_content,
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"is_reasoning": True
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}
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# 累积回答内容
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answer_content += delta.content
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# 发送回答内容
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yield {
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"status": "streaming",
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"content": answer_content
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}
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# 确保发送最终完整内容
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if answer_content:
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yield {
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"status": "completed",
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"content": answer_content
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}
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finally:
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# Restore original environment state
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for key, value in original_env.items():
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if value is None:
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if key in os.environ:
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del os.environ[key]
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else:
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os.environ[key] = value
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except Exception as e:
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yield {
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"status": "error",
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"error": str(e)
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}
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