106 lines
4 KiB
Python
106 lines
4 KiB
Python
import os
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import json
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import configparser
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import uuid
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from typing import Optional, List, 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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# Paths and Directories
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memgpt_dir: str = field(default_factory=lambda: os.getenv('COG_ARCH_DIR', 'cognitive_achitecture'))
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config_path: str = field(default_factory=lambda: os.path.join(os.getenv('COG_ARCH_DIR', 'cognitive_achitecture'), 'config'))
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vectordb:str = 'lancedb'
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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 os.getenv('ENV') == 'prod' or os.getenv('ENV') == 'dev' or os.getenv('AWS_ENV') == 'dev' or os.getenv('AWS_ENV') == 'prd':
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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 os.getenv('ENV') == 'prod' or os.getenv('ENV') == 'dev' or os.getenv('AWS_ENV') == 'dev' or os.getenv('AWS_ENV') == 'prd':
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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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