mirror of
https://github.com/Zippland/Snap-Solver.git
synced 2026-02-03 08:27:26 +08:00
253 lines
10 KiB
Python
253 lines
10 KiB
Python
import os
|
|
from typing import Generator, Dict, Optional, Any
|
|
from openai import OpenAI
|
|
from .base import BaseModel
|
|
|
|
class AlibabaModel(BaseModel):
|
|
def get_default_system_prompt(self) -> str:
|
|
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：
|
|
1. 仔细阅读并理解问题
|
|
2. 分析问题的关键组成部分
|
|
3. 提供清晰的、逐步的解决方案
|
|
4. 如果相关，解释涉及的概念或理论
|
|
5. 如果有多种解决方法，先解释最高效的方法"""
|
|
|
|
def get_model_identifier(self) -> str:
|
|
return "qvq-max"
|
|
|
|
def analyze_text(self, text: str, proxies: dict = None) -> Generator[dict, None, None]:
|
|
"""Stream QVQ-Max's response for text analysis"""
|
|
try:
|
|
# Initial status
|
|
yield {"status": "started", "content": ""}
|
|
|
|
# Save original environment state
|
|
original_env = {
|
|
'http_proxy': os.environ.get('http_proxy'),
|
|
'https_proxy': os.environ.get('https_proxy')
|
|
}
|
|
|
|
try:
|
|
# Set proxy environment variables if provided
|
|
if proxies:
|
|
if 'http' in proxies:
|
|
os.environ['http_proxy'] = proxies['http']
|
|
if 'https' in proxies:
|
|
os.environ['https_proxy'] = proxies['https']
|
|
|
|
# Initialize OpenAI compatible client for DashScope
|
|
client = OpenAI(
|
|
api_key=self.api_key,
|
|
base_url="https://dashscope.aliyuncs.com/compatible-mode/v1"
|
|
)
|
|
|
|
# Prepare messages
|
|
messages = [
|
|
{
|
|
"role": "system",
|
|
"content": [{"type": "text", "text": self.system_prompt}]
|
|
},
|
|
{
|
|
"role": "user",
|
|
"content": [{"type": "text", "text": text}]
|
|
}
|
|
]
|
|
|
|
# 创建聊天完成请求
|
|
response = client.chat.completions.create(
|
|
model=self.get_model_identifier(),
|
|
messages=messages,
|
|
temperature=self.temperature,
|
|
stream=True,
|
|
max_tokens=self.max_tokens if hasattr(self, 'max_tokens') and self.max_tokens else 4000
|
|
)
|
|
|
|
# 记录思考过程和回答
|
|
reasoning_content = ""
|
|
answer_content = ""
|
|
is_answering = False
|
|
|
|
for chunk in response:
|
|
if not chunk.choices:
|
|
continue
|
|
|
|
delta = chunk.choices[0].delta
|
|
|
|
# 处理思考过程
|
|
if hasattr(delta, 'reasoning_content') and delta.reasoning_content is not None:
|
|
reasoning_content += delta.reasoning_content
|
|
# 思考过程作为一个独立的内容发送
|
|
yield {
|
|
"status": "reasoning",
|
|
"content": reasoning_content,
|
|
"is_reasoning": True
|
|
}
|
|
elif delta.content != "":
|
|
# 判断是否开始回答（从思考过程切换到回答）
|
|
if not is_answering:
|
|
is_answering = True
|
|
# 发送完整的思考过程
|
|
if reasoning_content:
|
|
yield {
|
|
"status": "reasoning_complete",
|
|
"content": reasoning_content,
|
|
"is_reasoning": True
|
|
}
|
|
|
|
# 累积回答内容
|
|
answer_content += delta.content
|
|
|
|
# 发送回答内容
|
|
yield {
|
|
"status": "streaming",
|
|
"content": answer_content
|
|
}
|
|
|
|
# 确保发送最终完整内容
|
|
if answer_content:
|
|
yield {
|
|
"status": "completed",
|
|
"content": answer_content
|
|
}
|
|
|
|
finally:
|
|
# Restore original environment state
|
|
for key, value in original_env.items():
|
|
if value is None:
|
|
if key in os.environ:
|
|
del os.environ[key]
|
|
else:
|
|
os.environ[key] = value
|
|
|
|
except Exception as e:
|
|
yield {
|
|
"status": "error",
|
|
"error": str(e)
|
|
}
|
|
|
|
def analyze_image(self, image_data: str, proxies: dict = None) -> Generator[dict, None, None]:
|
|
"""Stream QVQ-Max's response for image analysis"""
|
|
try:
|
|
# Initial status
|
|
yield {"status": "started", "content": ""}
|
|
|
|
# Save original environment state
|
|
original_env = {
|
|
'http_proxy': os.environ.get('http_proxy'),
|
|
'https_proxy': os.environ.get('https_proxy')
|
|
}
|
|
|
|
try:
|
|
# Set proxy environment variables if provided
|
|
if proxies:
|
|
if 'http' in proxies:
|
|
os.environ['http_proxy'] = proxies['http']
|
|
if 'https' in proxies:
|
|
os.environ['https_proxy'] = proxies['https']
|
|
|
|
# Initialize OpenAI compatible client for DashScope
|
|
client = OpenAI(
|
|
api_key=self.api_key,
|
|
base_url="https://dashscope.aliyuncs.com/compatible-mode/v1"
|
|
)
|
|
|
|
# 检查系统提示词是否已包含语言设置指令
|
|
system_prompt = self.system_prompt
|
|
language = self.language or '中文'
|
|
if not any(phrase in system_prompt for phrase in ['Please respond in', '请用', '使用', '回答']):
|
|
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。"
|
|
|
|
# Prepare messages with image
|
|
messages = [
|
|
{
|
|
"role": "system",
|
|
"content": [{"type": "text", "text": system_prompt}]
|
|
},
|
|
{
|
|
"role": "user",
|
|
"content": [
|
|
{
|
|
"type": "image_url",
|
|
"image_url": {
|
|
"url": f"data:image/jpeg;base64,{image_data}"
|
|
}
|
|
},
|
|
{
|
|
"type": "text",
|
|
"text": "请分析这个图片并提供详细的解答。"
|
|
}
|
|
]
|
|
}
|
|
]
|
|
|
|
# 创建聊天完成请求
|
|
response = client.chat.completions.create(
|
|
model=self.get_model_identifier(),
|
|
messages=messages,
|
|
temperature=self.temperature,
|
|
stream=True,
|
|
max_tokens=self.max_tokens if hasattr(self, 'max_tokens') and self.max_tokens else 4000
|
|
)
|
|
|
|
# 记录思考过程和回答
|
|
reasoning_content = ""
|
|
answer_content = ""
|
|
is_answering = False
|
|
|
|
for chunk in response:
|
|
if not chunk.choices:
|
|
continue
|
|
|
|
delta = chunk.choices[0].delta
|
|
|
|
# 处理思考过程
|
|
if hasattr(delta, 'reasoning_content') and delta.reasoning_content is not None:
|
|
reasoning_content += delta.reasoning_content
|
|
# 思考过程作为一个独立的内容发送
|
|
yield {
|
|
"status": "reasoning",
|
|
"content": reasoning_content,
|
|
"is_reasoning": True
|
|
}
|
|
elif delta.content != "":
|
|
# 判断是否开始回答（从思考过程切换到回答）
|
|
if not is_answering:
|
|
is_answering = True
|
|
# 发送完整的思考过程
|
|
if reasoning_content:
|
|
yield {
|
|
"status": "reasoning_complete",
|
|
"content": reasoning_content,
|
|
"is_reasoning": True
|
|
}
|
|
|
|
# 累积回答内容
|
|
answer_content += delta.content
|
|
|
|
# 发送回答内容
|
|
yield {
|
|
"status": "streaming",
|
|
"content": answer_content
|
|
}
|
|
|
|
# 确保发送最终完整内容
|
|
if answer_content:
|
|
yield {
|
|
"status": "completed",
|
|
"content": answer_content
|
|
}
|
|
|
|
finally:
|
|
# Restore original environment state
|
|
for key, value in original_env.items():
|
|
if value is None:
|
|
if key in os.environ:
|
|
del os.environ[key]
|
|
else:
|
|
os.environ[key] = value
|
|
|
|
except Exception as e:
|
|
yield {
|
|
"status": "error",
|
|
"error": str(e)
|
|
} |