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重构模型管理和配置加载逻辑，支持多模态和推理模型，优化API密钥管理，改进前端模型选择和版本显示
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Zylan
209
models/openai.py
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209
models/openai.py
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import os
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from typing import Generator, Dict, Optional
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from openai import OpenAI
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from .base import BaseModel
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class OpenAIModel(BaseModel):
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def get_default_system_prompt(self) -> str:
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return """You are an expert at analyzing questions and providing detailed solutions. When presented with an image of a question:
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1. First read and understand the question carefully
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2. Break down the key components of the question
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3. Provide a clear, step-by-step solution
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4. If relevant, explain any concepts or theories involved
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5. If there are multiple approaches, explain the most efficient one first"""
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def get_model_identifier(self) -> str:
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return "gpt-4o-2024-11-20"
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def analyze_text(self, text: str, proxies: dict = None) -> Generator[dict, None, None]:
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"""Stream GPT-4o'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 client
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client = OpenAI(api_key=self.api_key)
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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": self.system_prompt
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},
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{
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"role": "user",
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"content": text
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}
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]
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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=4000
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)
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# 使用累积缓冲区
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response_buffer = ""
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for chunk in response:
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if hasattr(chunk.choices[0].delta, 'content'):
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content = chunk.choices[0].delta.content
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if content:
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# 累积内容
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response_buffer += content
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# 只在累积一定数量的字符或遇到句子结束标记时才发送
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if len(content) >= 10 or content.endswith(('.', '!', '?', '。', '！', '？', '\n')):
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yield {
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"status": "streaming",
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"content": response_buffer
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}
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# 确保发送最终完整内容
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if response_buffer:
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yield {
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"status": "streaming",
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"content": response_buffer
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}
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# Send completion status
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yield {
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"status": "completed",
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"content": response_buffer
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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 GPT-4o'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 client
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client = OpenAI(api_key=self.api_key)
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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": 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": "Please analyze this image and provide a detailed solution."
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}
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]
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}
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]
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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=4000
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)
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# 使用累积缓冲区
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response_buffer = ""
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for chunk in response:
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if hasattr(chunk.choices[0].delta, 'content'):
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content = chunk.choices[0].delta.content
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if content:
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# 累积内容
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response_buffer += content
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# 只在累积一定数量的字符或遇到句子结束标记时才发送
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if len(content) >= 10 or content.endswith(('.', '!', '?', '。', '！', '？', '\n')):
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yield {
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"status": "streaming",
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"content": response_buffer
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}
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# 确保发送最终完整内容
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if response_buffer:
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yield {
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"status": "streaming",
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"content": response_buffer
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}
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# Send completion status
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yield {
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"status": "completed",
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"content": response_buffer
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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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