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http://hblu.top:3000/st2411020106/111.git
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55 lines
1.5 KiB
Python
55 lines
1.5 KiB
Python
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import os
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from dotenv import load_dotenv
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load_dotenv()
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class Config:
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# 硅基流动 API 配置
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SILICONFLOW_API_KEY = os.getenv("SILICONFLOW_API_KEY")
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SILICONFLOW_BASE_URL = os.getenv("SILICONFLOW_BASE_URL", "https://api.siliconflow.cn/v1")
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SILICONFLOW_MODEL = os.getenv("SILICONFLOW_MODEL", "deepseek-ai/DeepSeek-V2.5")
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# OpenAI 兼容配置（保留但默认使用 SiliconFlow）
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OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")
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OPENAI_MODEL = os.getenv("OPENAI_MODEL", "gpt-4")
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# 应用配置
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APP_HOST = os.getenv("APP_HOST", "0.0.0.0")
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APP_PORT = int(os.getenv("APP_PORT", "8000"))
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# 支持的编程语言
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SUPPORTED_LANGUAGES = [
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"python",
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"javascript",
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"typescript",
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"java",
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"cpp",
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"c",
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"go",
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"rust",
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"ruby",
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"php"
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]
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# 解释详细程度
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EXPLANATION_DEPTH = {
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"basic": "简单解释每行代码的作用",
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"detailed": "详细解释，包括语法、原理和最佳实践",
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"comprehensive": "全面深入的分析，包括复杂度、性能考虑等"
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}
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# 硅基流动支持的模型列表
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SILICONFLOW_MODELS = [
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"deepseek-ai/DeepSeek-V2.5",
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"deepseek-ai/DeepSeek-V2",
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"Qwen/Qwen2.5-72B-Instruct",
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"Qwen/Qwen2.5-32B-Instruct",
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"Qwen/Qwen2.5-7B-Instruct",
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"01-ai/Yi-1.5-34B-Chat",
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"01-ai/Yi-1.5-9B-Chat",
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"meta-llama/Llama-3.1-405B-Instruct",
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"meta-llama/Llama-3.1-70B-Instruct"
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]
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config = Config()
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