fiatrete
98 lines
3.0 KiB
Python
98 lines
3.0 KiB
Python
import logging
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import os
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import dotenv
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dotenv.load_dotenv()
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# ==== Utils
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def _string_to_bool(s: str | None):
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if s is None:
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return None
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s = s.lower()
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if s in ['y', 'yes', 't', 'true']:
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return True
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if s in ['n', 'no', 'f', 'false']:
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return False
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raise Exception(f"Invalid argument '{s}', should be a bool value: y/yes/n/no/t/true/f/false.")
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def _string_to_log_level(s: str | None):
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if s is None:
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return None
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s = s.lower()
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if s in ['debug', 'd']:
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return logging.DEBUG
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if s in ['info', 'i']:
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return logging.INFO
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if s in ['w', 'warn', 'warning']:
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return logging.WARNING
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if s in ['error', 'e', 'err']:
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return logging.ERROR
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if s in ['fatal', 'critical']:
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return logging.FATAL
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raise Exception(f"Invalid argument '{s}', should be a log level: debug, info, warn, error, fatal")
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def _get_env_str(name: str, must_not_empty: bool = False):
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v = os.getenv(name)
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if must_not_empty and (v is None or v == ''):
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raise Exception(f"Environment variable '{name}' is required!")
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return v
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def _get_env_bool(name: str): return _string_to_bool(os.getenv(name))
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def _get_env_int(name: str): return int(os.getenv(name))
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def _get_env_float(name: str): return float(os.getenv(name))
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def _get_env_log_level(name: str): return _string_to_log_level(os.getenv(name))
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# The config
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# DO NOT use it, it's still not mature yet
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use_private_ai = _get_env_bool("JARVIS_USE_PRIVATE_AI") or False
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private_ai_address = _get_env_str("JARVIS_PRIVATE_AI_URL", use_private_ai)
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is_server_mode = _get_env_bool("JARVIS_SERVER_MODE") or False
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# The port used in server mode
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server_mode_port = _get_env_int("JARVIS_SERVER_MODE_PORT") or 1000
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# Jarvis can also connect to a server as a client.
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# This is the server's address
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bot_server_url = _get_env_str("JARVIS_BOT_SERVER_URL") or "http://localhost:8081"
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# The directory where the chat history should be stored,
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# By storing the chat history, each time Jarvis starts up, the chat context is restored
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chat_history_dir = _get_env_str("JARVIS_CHAT_HISTORY_DIR") or None
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# ChatGPT temperature
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temperature = _get_env_float("JARVIS_AI_TEMPERATURE") or 0
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debug_mode = _get_env_bool("JARVIS_DEBUG_MODE") or False
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log_level = _get_env_log_level("JARVIS_LOG_LEVEL") or logging.INFO
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# The main llm model
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llm_model = _get_env_str("JARVIS_LLM_MODEL") or "gpt-3.5-turbo-0301"
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# The model used to handle some simple tasks
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small_llm_model = _get_env_str("JARVIS_SMALL_LLM_MODEL") or "gpt-3.5-turbo-0301"
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token_limit = _get_env_int("JARVIS_TOKEN_LIMIT") or 4000
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openai_api_key = _get_env_str("JARVIS_OPENAI_API_KEY", True)
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# If your service is not provided directly by openai,
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# or you just deployed you own AI model with a same API as opeai.
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# Or this configuration is useless
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openai_url_base = _get_env_str("JARVIS_OPENAI_URL_BASE") or None
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# Tell Jarvis where to load function modules
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external_function_module_dirs = _get_env_str("JARVIS_EXTERNAL_FUNCTION_MODULE_DIR")
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use_azure = False
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def get_azure_deployment_id_for_model(model):
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assert False
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# TODO
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