117 lines
4.9 KiB
Python
117 lines
4.9 KiB
Python
"""
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Configuration management
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Loads config uniformly from the .env file at the project root
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"""
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import os
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from dotenv import load_dotenv
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# Load the .env file from the project root
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# Path: MiroFish/.env (relative to 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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# If no root-level .env file found, load from environment variables (production)
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load_dotenv(override=True)
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class Config:
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"""Flask configuration class"""
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# Flask settings
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SECRET_KEY = os.environ.get('SECRET_KEY', 'mirofish-secret-key')
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DEMO_PASSWORD = os.environ.get('DEMO_PASSWORD', '')
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DEBUG = os.environ.get('FLASK_DEBUG', 'True').lower() == 'true'
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# JSON settings - disable ASCII escaping so non-ASCII chars are output directly (not as \uXXXX)
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JSON_AS_ASCII = False
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# LLM settings (unified OpenAI-compatible format)
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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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# Embedding LLM (used by Graphiti for vector indexing)
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# Falls back to LLM_* values if not set
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LLM_EMBED_API_KEY = os.environ.get('LLM_EMBED_API_KEY') or os.environ.get('LLM_API_KEY')
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LLM_EMBED_BASE_URL = os.environ.get('LLM_EMBED_BASE_URL') or os.environ.get('LLM_BASE_URL', 'https://api.openai.com/v1')
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LLM_EMBED_MODEL_NAME = os.environ.get('LLM_EMBED_MODEL_NAME', 'text-embedding-3-small')
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# Small/fast LLM (used by Graphiti for lightweight tasks like reranking)
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# Falls back to LLM_* values if not set
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LLM_SMALL_API_KEY = os.environ.get('LLM_SMALL_API_KEY') or os.environ.get('LLM_API_KEY')
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LLM_SMALL_BASE_URL = os.environ.get('LLM_SMALL_BASE_URL') or os.environ.get('LLM_BASE_URL', 'https://api.openai.com/v1')
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LLM_SMALL_MODEL_NAME = os.environ.get('LLM_SMALL_MODEL_NAME') or os.environ.get('LLM_MODEL_NAME', 'gpt-4o-mini')
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# Graph backend: "zep" (default, cloud) o "graphiti" (Neo4j local)
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GRAPH_BACKEND = os.environ.get('GRAPH_BACKEND', 'zep')
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# Zep Cloud
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ZEP_API_KEY = os.environ.get('ZEP_API_KEY')
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# Graphiti + Neo4j
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NEO4J_URI = os.environ.get('NEO4J_URI', 'bolt://localhost:7687')
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NEO4J_USER = os.environ.get('NEO4J_USER', 'neo4j')
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NEO4J_PASSWORD = os.environ.get('NEO4J_PASSWORD')
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GRAPHITI_BATCH_SIZE = int(os.environ.get('GRAPHITI_BATCH_SIZE', '10'))
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# LLM provider ("" = OpenAI-compatible per defecte, "gemini" = Google AI Studio)
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LLM_PROVIDER = os.environ.get('LLM_PROVIDER', '')
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# File upload settings
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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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# Text processing settings
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DEFAULT_CHUNK_SIZE = 500 # default chunk size
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DEFAULT_CHUNK_OVERLAP = 50 # default overlap size
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# Ontology generation limits
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ONTOLOGY_MAX_ENTITY_TYPES = int(os.environ.get('ONTOLOGY_MAX_ENTITY_TYPES', '12'))
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ONTOLOGY_MAX_EDGE_TYPES = int(os.environ.get('ONTOLOGY_MAX_EDGE_TYPES', '10'))
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# OASIS simulation settings
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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 platform available actions
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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 settings
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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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@classmethod
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def get_graph_config_errors(cls) -> list:
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errors = []
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if cls.GRAPH_BACKEND == 'zep':
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if not cls.ZEP_API_KEY:
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errors.append("ZEP_API_KEY is not configured (required when GRAPH_BACKEND=zep)")
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elif cls.GRAPH_BACKEND == 'graphiti':
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if not cls.NEO4J_PASSWORD:
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errors.append("NEO4J_PASSWORD is not configured (required when GRAPH_BACKEND=graphiti)")
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else:
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errors.append(f"Unknown GRAPH_BACKEND value: '{cls.GRAPH_BACKEND}'. Use 'zep' or 'graphiti'.")
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return errors
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@classmethod
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def validate(cls):
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"""Validate required configuration"""
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errors = []
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if not cls.LLM_API_KEY:
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errors.append("LLM_API_KEY is not configured")
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errors.extend(cls.get_graph_config_errors())
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return errors
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