242 lines
11 KiB
Python
242 lines
11 KiB
Python
"""
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配置管理
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统一从项目根目录的 .env 文件加载配置
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"""
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import os
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from dotenv import load_dotenv
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# 加载项目根目录的 .env 文件
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# 路径: MiroFish/.env (相对于 backend/app/config.py)
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project_root_env = os.path.join(os.path.dirname(__file__), '../../.env')
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if os.path.exists(project_root_env):
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load_dotenv(project_root_env, override=True)
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else:
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# 如果根目录没有 .env，尝试加载环境变量（用于生产环境）
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load_dotenv(override=True)
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class Config:
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"""Flask配置类"""
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# Flask配置
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SECRET_KEY = os.environ.get('SECRET_KEY', 'mirofish-secret-key')
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DEBUG = os.environ.get('FLASK_DEBUG', 'True').lower() == 'true'
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# JSON配置 - 禁用ASCII转义，让中文直接显示（而不是 \uXXXX 格式）
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JSON_AS_ASCII = False
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# LLM配置（统一使用OpenAI格式）
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LLM_API_KEY = os.environ.get('LLM_API_KEY')
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LLM_BASE_URL = os.environ.get('LLM_BASE_URL', 'https://api.openai.com/v1')
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LLM_MODEL_NAME = os.environ.get('LLM_MODEL_NAME', 'gpt-4o-mini')
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# Zep配置
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ZEP_MODE = os.environ.get('ZEP_MODE', 'cloud').lower()
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ZEP_API_KEY = os.environ.get('ZEP_API_KEY')
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ZEP_BASE_URL = os.environ.get('ZEP_BASE_URL')
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OPENZEP_EMBEDDER_API_KEY = os.environ.get('OPENZEP_EMBEDDER_API_KEY') or None
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OPENZEP_EMBEDDER_BASE_URL = os.environ.get('OPENZEP_EMBEDDER_BASE_URL') or None
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OPENZEP_EMBEDDER_MODEL = os.environ.get('OPENZEP_EMBEDDER_MODEL') or None
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# 图后端配置
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GRAPH_BACKEND = os.environ.get('GRAPH_BACKEND', 'graphiti').lower()
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GRAPH_SEARCH_RERANKER = os.environ.get('GRAPH_SEARCH_RERANKER', 'rrf').strip() or None
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GRAPH_SEARCH_APP_RERANKER = (os.environ.get('GRAPH_SEARCH_APP_RERANKER', 'embedding_rrf').strip().lower() or 'embedding_rrf')
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GRAPH_SEARCH_APP_RERANK_FUSION_K = max(1, int(os.environ.get('GRAPH_SEARCH_APP_RERANK_FUSION_K', '60')))
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GRAPH_SEARCH_APP_SEMANTIC_WEIGHT = max(0.0, float(os.environ.get('GRAPH_SEARCH_APP_SEMANTIC_WEIGHT', '2.0')))
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GRAPH_SEARCH_APP_EMBEDDER_API_KEY = os.environ.get('GRAPH_SEARCH_APP_EMBEDDER_API_KEY') or None
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GRAPH_SEARCH_APP_EMBEDDER_BASE_URL = os.environ.get('GRAPH_SEARCH_APP_EMBEDDER_BASE_URL') or None
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GRAPH_SEARCH_APP_EMBEDDER_MODEL = os.environ.get('GRAPH_SEARCH_APP_EMBEDDER_MODEL') or None
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GRAPH_SEARCH_APP_EMBED_BATCH_SIZE = max(1, int(os.environ.get('GRAPH_SEARCH_APP_EMBED_BATCH_SIZE', '32')))
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GRAPH_SEARCH_APP_RERANKER_API_KEY = os.environ.get('GRAPH_SEARCH_APP_RERANKER_API_KEY') or None
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GRAPH_SEARCH_APP_RERANKER_BASE_URL = os.environ.get('GRAPH_SEARCH_APP_RERANKER_BASE_URL') or None
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GRAPH_SEARCH_APP_RERANKER_MODEL = os.environ.get('GRAPH_SEARCH_APP_RERANKER_MODEL') or None
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GRAPH_SEARCH_APP_RERANKER_PROVIDER = (os.environ.get('GRAPH_SEARCH_APP_RERANKER_PROVIDER', 'auto').strip().lower() or 'auto')
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GRAPH_SEARCH_APP_RERANKER_TIMEOUT = max(1.0, float(os.environ.get('GRAPH_SEARCH_APP_RERANKER_TIMEOUT', '20')))
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GRAPH_SEARCH_INCLUDE_NODES = os.environ.get('GRAPH_SEARCH_INCLUDE_NODES', 'true').lower() == 'true'
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GRAPH_SEARCH_EDGE_LIMIT_MULTIPLIER = max(1, int(os.environ.get('GRAPH_SEARCH_EDGE_LIMIT_MULTIPLIER', '2')))
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GRAPH_SEARCH_NODE_LIMIT_MULTIPLIER = max(1, int(os.environ.get('GRAPH_SEARCH_NODE_LIMIT_MULTIPLIER', '1')))
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GRAPH_SEARCH_NODE_SUMMARY_LIMIT = max(1, int(os.environ.get('GRAPH_SEARCH_NODE_SUMMARY_LIMIT', '5')))
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GRAPH_SEARCH_EXPAND_EDGES_FROM_NODES = os.environ.get('GRAPH_SEARCH_EXPAND_EDGES_FROM_NODES', 'true').lower() == 'true'
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GRAPH_SEARCH_NODE_EDGE_EXPANSION_LIMIT = max(0, int(os.environ.get('GRAPH_SEARCH_NODE_EDGE_EXPANSION_LIMIT', '2')))
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GRAPH_SEARCH_NODE_EDGE_PER_NODE_LIMIT = max(1, int(os.environ.get('GRAPH_SEARCH_NODE_EDGE_PER_NODE_LIMIT', '8')))
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GRAPHITI_URI = os.environ.get('GRAPHITI_URI')
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GRAPHITI_USER = os.environ.get('GRAPHITI_USER', 'neo4j')
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GRAPHITI_PASSWORD = os.environ.get('GRAPHITI_PASSWORD')
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GRAPHITI_DATABASE = os.environ.get('GRAPHITI_DATABASE', 'neo4j')
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GRAPHITI_LLM_API_KEY = os.environ.get('GRAPHITI_LLM_API_KEY') or LLM_API_KEY
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GRAPHITI_LLM_BASE_URL = os.environ.get('GRAPHITI_LLM_BASE_URL') or LLM_BASE_URL
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GRAPHITI_LLM_MODEL = os.environ.get('GRAPHITI_LLM_MODEL') or LLM_MODEL_NAME
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GRAPHITI_LLM_SMALL_MODEL = os.environ.get('GRAPHITI_LLM_SMALL_MODEL') or GRAPHITI_LLM_MODEL
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GRAPHITI_LLM_CLIENT_MODE = os.environ.get('GRAPHITI_LLM_CLIENT_MODE', 'openai').lower()
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GRAPHITI_LLM_MAX_TOKENS = max(1024, int(os.environ.get('GRAPHITI_LLM_MAX_TOKENS', '16384')))
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GRAPHITI_EMBEDDER_API_KEY = os.environ.get('GRAPHITI_EMBEDDER_API_KEY') or GRAPHITI_LLM_API_KEY
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GRAPHITI_EMBEDDER_BASE_URL = os.environ.get('GRAPHITI_EMBEDDER_BASE_URL') or GRAPHITI_LLM_BASE_URL
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GRAPHITI_EMBEDDER_MODEL = os.environ.get('GRAPHITI_EMBEDDER_MODEL', 'qwen3-embedding:8b')
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GRAPHITI_EMBEDDER_DIM = max(128, int(os.environ.get('GRAPHITI_EMBEDDER_DIM', '1024')))
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GRAPHITI_RERANKER_API_KEY = os.environ.get('GRAPHITI_RERANKER_API_KEY') or GRAPHITI_LLM_API_KEY
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GRAPHITI_RERANKER_BASE_URL = os.environ.get('GRAPHITI_RERANKER_BASE_URL') or GRAPHITI_LLM_BASE_URL
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GRAPHITI_RERANKER_MODEL = os.environ.get('GRAPHITI_RERANKER_MODEL') or GRAPHITI_LLM_MODEL
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GRAPHITI_ENABLE_CROSS_ENCODER = os.environ.get('GRAPHITI_ENABLE_CROSS_ENCODER', 'false').lower() == 'true'
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GRAPHITI_MAX_COROUTINES = max(1, int(os.environ.get('GRAPHITI_MAX_COROUTINES', '20')))
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# 文件上传配置
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MAX_CONTENT_LENGTH = 50 * 1024 * 1024 # 50MB
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UPLOAD_FOLDER = os.path.join(os.path.dirname(__file__), '../uploads')
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ALLOWED_EXTENSIONS = {'pdf', 'md', 'txt', 'markdown'}
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# 文本处理配置
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DEFAULT_CHUNK_SIZE = 500 # 默认切块大小
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DEFAULT_CHUNK_OVERLAP = 50 # 默认重叠大小
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# OASIS模拟配置
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OASIS_DEFAULT_MAX_ROUNDS = int(os.environ.get('OASIS_DEFAULT_MAX_ROUNDS', '10'))
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OASIS_SIMULATION_DATA_DIR = os.path.join(os.path.dirname(__file__), '../uploads/simulations')
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# OASIS平台可用动作配置
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OASIS_TWITTER_ACTIONS = [
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'CREATE_POST', 'LIKE_POST', 'REPOST', 'FOLLOW', 'DO_NOTHING', 'QUOTE_POST'
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]
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OASIS_REDDIT_ACTIONS = [
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'LIKE_POST', 'DISLIKE_POST', 'CREATE_POST', 'CREATE_COMMENT',
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'LIKE_COMMENT', 'DISLIKE_COMMENT', 'SEARCH_POSTS', 'SEARCH_USER',
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'TREND', 'REFRESH', 'DO_NOTHING', 'FOLLOW', 'MUTE'
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]
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# Report Agent配置
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REPORT_AGENT_MAX_TOOL_CALLS = int(os.environ.get('REPORT_AGENT_MAX_TOOL_CALLS', '5'))
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REPORT_AGENT_MAX_REFLECTION_ROUNDS = int(os.environ.get('REPORT_AGENT_MAX_REFLECTION_ROUNDS', '2'))
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REPORT_AGENT_TEMPERATURE = float(os.environ.get('REPORT_AGENT_TEMPERATURE', '0.5'))
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# Experimental Memory (Spike S1)
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USE_EXPERIMENTAL_MEMORY = os.environ.get('USE_EXPERIMENTAL_MEMORY', 'False').lower() == 'true'
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DATA_DIR = os.path.join(os.path.dirname(__file__), '../data')
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@classmethod
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def use_openzep(cls):
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"""是否启用 OpenZep / 自定义 Zep endpoint。"""
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return cls.ZEP_MODE == 'openzep' or bool(cls.ZEP_BASE_URL)
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@classmethod
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def is_zep_configured(cls, api_key=None):
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"""检查 Zep/OpenZep 是否已完成最小配置。"""
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resolved_api_key = cls.ZEP_API_KEY if api_key is None else api_key
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if cls.use_openzep():
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return bool(cls.ZEP_BASE_URL)
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return bool(resolved_api_key)
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@classmethod
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def get_zep_client_kwargs(cls, api_key=None):
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"""生成 Zep 客户端初始化参数。"""
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resolved_api_key = cls.ZEP_API_KEY if api_key is None else api_key
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kwargs = {}
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# OpenZep 场景允许关闭鉴权；此时不要传空字符串 api_key，
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# 否则 zep sdk 会构造出非法的 `Api-Key:` 请求头，httpx 会直接拒绝。
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if resolved_api_key:
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kwargs['api_key'] = resolved_api_key
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if cls.ZEP_BASE_URL:
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kwargs['base_url'] = cls.ZEP_BASE_URL
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return kwargs
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@classmethod
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def get_zep_config_errors(cls, api_key=None):
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"""返回 Zep/OpenZep 的配置错误。"""
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if cls.is_zep_configured(api_key=api_key):
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return []
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if cls.use_openzep():
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return ["ZEP_BASE_URL 未配置"]
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return ["ZEP_API_KEY 未配置"]
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@classmethod
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def get_graph_search_embedder_config(cls):
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"""返回 app-side 图搜索语义重排使用的 embedding 配置。"""
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api_key = cls.GRAPH_SEARCH_APP_EMBEDDER_API_KEY
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base_url = cls.GRAPH_SEARCH_APP_EMBEDDER_BASE_URL
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model = cls.GRAPH_SEARCH_APP_EMBEDDER_MODEL
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backend = (cls.GRAPH_BACKEND or 'graphiti').lower()
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if backend == 'graphiti':
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api_key = api_key or cls.GRAPHITI_EMBEDDER_API_KEY
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base_url = base_url or cls.GRAPHITI_EMBEDDER_BASE_URL
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model = model or cls.GRAPHITI_EMBEDDER_MODEL
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elif cls.use_openzep():
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api_key = api_key or cls.OPENZEP_EMBEDDER_API_KEY
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base_url = base_url or cls.OPENZEP_EMBEDDER_BASE_URL
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model = model or cls.OPENZEP_EMBEDDER_MODEL
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if model and not api_key:
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api_key = 'ollama'
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return {
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'api_key': api_key,
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'base_url': base_url,
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'model': model,
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}
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@classmethod
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def get_graph_search_reranker_config(cls):
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"""返回 app-side 图搜索交叉编码重排使用的 reranker 配置。"""
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api_key = cls.GRAPH_SEARCH_APP_RERANKER_API_KEY
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base_url = cls.GRAPH_SEARCH_APP_RERANKER_BASE_URL
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model = cls.GRAPH_SEARCH_APP_RERANKER_MODEL
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provider = cls.GRAPH_SEARCH_APP_RERANKER_PROVIDER
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graphiti_reranker_base_url = os.environ.get('GRAPHITI_RERANKER_BASE_URL') or None
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if graphiti_reranker_base_url:
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api_key = api_key or (os.environ.get('GRAPHITI_RERANKER_API_KEY') or None)
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base_url = base_url or graphiti_reranker_base_url
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model = model or (os.environ.get('GRAPHITI_RERANKER_MODEL') or None)
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return {
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'api_key': api_key,
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'base_url': base_url,
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'model': model,
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'provider': provider,
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'timeout': cls.GRAPH_SEARCH_APP_RERANKER_TIMEOUT,
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}
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@classmethod
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def get_graphiti_config_errors(cls):
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"""返回 Graphiti + Neo4j 的配置错误。"""
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errors = []
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if not cls.GRAPHITI_URI:
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errors.append("GRAPHITI_URI 未配置")
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if not cls.GRAPHITI_DATABASE:
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errors.append("GRAPHITI_DATABASE 未配置")
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if not cls.GRAPHITI_LLM_MODEL:
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errors.append("GRAPHITI_LLM_MODEL 未配置")
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if not cls.GRAPHITI_EMBEDDER_MODEL:
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errors.append("GRAPHITI_EMBEDDER_MODEL 未配置")
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return errors
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@classmethod
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def get_graph_backend_config_errors(cls, api_key=None):
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"""根据当前 GRAPH_BACKEND 返回对应的配置错误。"""
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backend = (cls.GRAPH_BACKEND or 'graphiti').lower()
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if backend in {'zep', 'openzep'}:
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return cls.get_zep_config_errors(api_key=api_key)
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if backend == 'graphiti':
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return cls.get_graphiti_config_errors()
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return [f"不支持的 GRAPH_BACKEND: {backend}"]
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@classmethod
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def is_graph_backend_configured(cls, api_key=None):
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return len(cls.get_graph_backend_config_errors(api_key=api_key)) == 0
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@classmethod
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def validate(cls):
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"""验证必要配置"""
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errors = []
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if not cls.LLM_API_KEY:
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errors.append("LLM_API_KEY 未配置")
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errors.extend(cls.get_graph_backend_config_errors())
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return errors
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