128 lines
4.6 KiB
Python
128 lines
4.6 KiB
Python
"""Configuration for cognee - cognitive architecture framework."""
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import os
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import configparser
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import uuid
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from typing import Optional, Dict, Any
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from dataclasses import dataclass, field
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from pathlib import Path
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from dotenv import load_dotenv
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base_dir = Path(__file__).resolve().parent.parent
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# Load the .env file from the base directory
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dotenv_path = base_dir / ".env"
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load_dotenv(dotenv_path=dotenv_path)
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@dataclass
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class Config:
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""" Configuration for cognee - cognitive architecture framework. """
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cognee_dir: str = field(
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default_factory=lambda: os.getenv("COG_ARCH_DIR", "cognitive_achitecture")
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)
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config_path: str = field(
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default_factory=lambda: os.path.join(
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os.getenv("COG_ARCH_DIR", "cognitive_achitecture"), "config"
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)
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)
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db_path = Path(__file__).resolve().parent / "database/data"
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vectordb: str = os.getenv("VECTORDB", "weaviate")
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db_type: str = os.getenv("DB_TYPE", "sqlite")
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db_name: str = os.getenv("DB_NAME", "cognee.db")
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db_host: str = os.getenv("DB_HOST", "localhost")
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db_port: str = os.getenv("DB_PORT", "5432")
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db_user: str = os.getenv("DB_USER", "cognee")
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db_password: str = os.getenv("DB_PASSWORD", "cognee")
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sqlalchemy_logging: bool = os.getenv("SQLALCHEMY_LOGGING", True)
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# Model parameters
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model: str = "gpt-4-1106-preview"
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model_endpoint: str = "openai"
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openai_key: Optional[str] = os.getenv("OPENAI_API_KEY")
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openai_temperature: float = float(os.getenv("OPENAI_TEMPERATURE", 0.0))
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# Embedding parameters
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embedding_model: str = "openai"
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embedding_dim: int = 1536
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embedding_chunk_size: int = 300
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# Database parameters
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if (
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os.getenv("ENV") == "prod"
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or os.getenv("ENV") == "dev"
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or os.getenv("AWS_ENV") == "dev"
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or os.getenv("AWS_ENV") == "prd"
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):
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graph_database_url: str = os.getenv("GRAPH_DB_URL_PROD")
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graph_database_username: str = os.getenv("GRAPH_DB_USER")
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graph_database_password: str = os.getenv("GRAPH_DB_PW")
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else:
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graph_database_url: str = os.getenv("GRAPH_DB_URL")
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graph_database_username: str = os.getenv("GRAPH_DB_USER")
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graph_database_password: str = os.getenv("GRAPH_DB_PW")
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weaviate_url: str = os.getenv("WEAVIATE_URL")
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weaviate_api_key: str = os.getenv("WEAVIATE_API_KEY")
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postgres_user: str = os.getenv("POSTGRES_USER")
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postgres_password: str = os.getenv("POSTGRES_PASSWORD")
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postgres_db: str = os.getenv("POSTGRES_DB")
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if (
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os.getenv("ENV") == "prod"
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or os.getenv("ENV") == "dev"
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or os.getenv("AWS_ENV") == "dev"
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or os.getenv("AWS_ENV") == "prd"
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):
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postgres_host: str = os.getenv("POSTGRES_PROD_HOST")
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elif os.getenv("ENV") == "docker":
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postgres_host: str = os.getenv("POSTGRES_HOST_DOCKER")
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elif os.getenv("ENV") == "local":
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postgres_host: str = os.getenv("POSTGRES_HOST_LOCAL")
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# Client ID
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anon_clientid: Optional[str] = field(default_factory=lambda: uuid.uuid4().hex)
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def load(self):
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"""Loads the configuration from a file or environment variables."""
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config = configparser.ConfigParser()
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config.read(self.config_path)
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# Override with environment variables if they exist
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for attr in self.__annotations__:
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env_value = os.getenv(attr.upper())
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if env_value is not None:
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setattr(self, attr, env_value)
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# Load from config file
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if config.sections():
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for section in config.sections():
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for key, value in config.items(section):
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if hasattr(self, key):
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setattr(self, key, value)
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def save(self):
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"""Saves the current configuration to a file."""
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config = configparser.ConfigParser()
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# Save the current settings to the config file
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for attr, value in self.__dict__.items():
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section, option = attr.split("_", 1)
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if not config.has_section(section):
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config.add_section(section)
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config.set(section, option, str(value))
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with open(self.config_path, "w") as configfile:
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config.write(configfile)
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def to_dict(self) -> Dict[str, Any]:
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"""Returns a dictionary representation of the configuration."""
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return {attr: getattr(self, attr) for attr in self.__annotations__}
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@classmethod
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def from_dict(cls, config_dict: Dict[str, Any]) -> "Config":
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"""Creates a Config instance from a dictionary."""
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config = cls()
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for attr, value in config_dict.items():
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if hasattr(config, attr):
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setattr(config, attr, value)
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return config
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