49 lines
2.0 KiB
Python
49 lines
2.0 KiB
Python
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"""全局配置模块。
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课程要求：不要把 API Key 写死在代码里。
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- API Key 从环境变量读取（推荐用 .env + python-dotenv）
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- 若缺失，则给出清晰错误提示
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"""
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from __future__ import annotations
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import logging
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import os
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from typing import Dict
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logger = logging.getLogger(__name__)
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# --- API 配置 ---
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# API Endpoint 和模型 ID 仍从环境变量读取，或使用默认值
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OPENAI_API_BASE: str = os.getenv("OPENAI_API_BASE", "https://api.deepseek.com/v1")
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MODEL_ID: str = os.getenv("MODEL_ID", "deepseek-chat")
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# --- 统一使用一个 API Key ---
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# 从环境变量读取（建议用 .env + python-dotenv）
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# 兼容多种命名：优先 OPENAI_API_KEY，其次 DEEPSEEK_API_KEY
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API_KEY: str = os.getenv("OPENAI_API_KEY") or os.getenv("DEEPSEEK_API_KEY", "")
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API_BASE: str = OPENAI_API_BASE
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# 兼容旧代码：历史版本可能引用 ROLE_API_KEY_MAP
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ROLE_API_KEY_MAP: Dict[str, str] = {"default": API_KEY}
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# --- Agent 默认参数 ---
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MAX_TOKENS: int = 1000
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TEMPERATURE: float = 0.7
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MAX_RETRIES: int = 3
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RETRY_INITIAL_DELAY: float = 1.5 # 秒
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RETRY_BACKOFF_FACTOR: float = 2.0
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RETRY_MAX_DELAY: float = 10.0
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# --- 角色系统提示 ---
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AGENT_ROLES: Dict[str, str] = {
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"product_manager": "你是一位经验丰富的产品经理，擅长从用户需求和市场角度分析方案，关注产品的价值和可行性。",
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"tech_expert": "你是一位资深技术专家，擅长从技术实现角度分析方案，关注架构设计、技术风险和性能优化。",
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"user_representative": "你是一位典型的终端用户，擅长从实际使用角度分析方案，关注用户体验、易用性和实用性。",
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"business_analyst": "你是一位专业的商业分析师，擅长从商业价值角度分析方案，关注成本效益、投资回报率和市场竞争力。",
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"designer": "你是一位优秀的设计师，擅长从设计角度分析方案，关注视觉效果、交互设计和用户体验。",
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}
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# --- 为兼容旧代码，保持部分常量导出 ---
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API_BASE: str = OPENAI_API_BASE
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