114 lines
3.5 KiB
Python
114 lines
3.5 KiB
Python
"""持久化配置管理
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使用 data/settings.json 存储可通过管理面板修改的设置：
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- proxy_target_url / proxy_api_key: 可覆盖环境变量的全局配置
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- model_mappings: Cursor 模型名 → {upstream_model, backend, target_url, api_key, custom_instructions}
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"""
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import json
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import os
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import threading
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from config import Config
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# 数据目录放在项目根目录下，便于 Docker 卷挂载
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_ROOT_DIR = os.path.dirname(os.path.abspath(__file__))
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DATA_DIR = os.path.join(_ROOT_DIR, 'data')
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SETTINGS_FILE = os.path.join(DATA_DIR, 'settings.json')
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_lock = threading.Lock()
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_cache = None
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_DEFAULTS = {
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'proxy_target_url': '',
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'proxy_api_key': '',
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'model_mappings': {},
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}
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def load():
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"""从持久化文件读取配置并刷新内存缓存。
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如果配置文件不存在或内容损坏，会回退到默认值，保证服务仍然可以正常启动。
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"""
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global _cache
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with _lock:
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if os.path.exists(SETTINGS_FILE):
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try:
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with open(SETTINGS_FILE, 'r', encoding='utf-8') as f:
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_cache = {**_DEFAULTS, **json.load(f)}
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except (json.JSONDecodeError, OSError):
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_cache = dict(_DEFAULTS)
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else:
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_cache = dict(_DEFAULTS)
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return dict(_cache)
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def save(data):
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"""将当前配置写回到持久化文件并同步缓存。
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保存前会确保数据目录存在，并始终以默认配置为基底合并缺失字段。
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"""
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global _cache
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with _lock:
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os.makedirs(DATA_DIR, exist_ok=True)
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_cache = {**_DEFAULTS, **data}
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with open(SETTINGS_FILE, 'w', encoding='utf-8') as f:
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json.dump(_cache, f, ensure_ascii=False, indent=2)
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def get():
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"""获取当前配置快照，优先返回内存缓存中的结果。"""
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if _cache is None:
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return load()
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return dict(_cache)
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def get_url():
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"""获取当前生效的上游 URL，优先使用持久化配置。"""
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return get().get('proxy_target_url') or Config.PROXY_TARGET_URL
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def get_key():
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"""获取当前生效的 API 密钥，优先使用持久化配置。"""
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return get().get('proxy_api_key') or Config.PROXY_API_KEY
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def resolve_model(model_name):
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"""解析模型映射并返回完整的上游路由信息。"""
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settings = get()
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mappings = settings.get('model_mappings', {})
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base_url, base_key = get_url(), get_key()
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if model_name in mappings:
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m = mappings[model_name]
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backend = m.get('backend')
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if backend in ('', None, 'auto'):
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backend = _auto_detect(model_name)
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return {
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'upstream_model': m.get('upstream_model') or model_name,
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'backend': backend,
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'target_url': m.get('target_url') or base_url,
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'api_key': m.get('api_key') or base_key,
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'custom_instructions': m.get('custom_instructions') or '',
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'instructions_position': m.get('instructions_position') or 'prepend',
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}
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return {
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'upstream_model': model_name,
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'backend': _auto_detect(model_name),
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'target_url': base_url,
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'api_key': base_key,
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'custom_instructions': '',
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'instructions_position': 'prepend',
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}
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def _auto_detect(name):
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"""根据模型名关键字推断默认后端协议类型。
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当前规则较为保守：命中 `claude` 或 `anthropic` 走 Anthropic，
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其余模型默认视为 OpenAI 兼容后端。
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"""
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lower = (name or '').lower()
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return 'anthropic' if ('claude' in lower or 'anthropic' in lower) else 'openai'
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