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import os
from dataclasses import dataclass, asdict
from typing import Dict, Union
from api.logger import logger
def get_env(env_name: str, default = None) -> str:
env = os.getenv(env_name)
if not env:
if default:
logger.warning(
f'Environment variable {env_name} not found.' \
f'Using the default value: {default}.'
)
return default
else:
raise ValueError(f'Cannot parse: {env_name}')
else:
return env
@dataclass
class Config:
huggingface_token: str = get_env('HUGGINGFACEHUB_API_TOKEN')
question_answering_model_id: str = get_env('QUESTION_ANSWERING_MODEL_ID')
embedding_model_id: str = get_env('EMBEDDING_MODEL_ID')
index_repo_id: str = get_env('INDEX_REPO_ID')
use_docs_for_context: bool = eval(get_env('USE_DOCS_FOR_CONTEXT', 'True'))
add_sources_to_response: bool = eval(get_env('ADD_SOURCES_TO_RESPONSE', 'True'))
use_messages_in_context: bool = eval(get_env('USE_MESSAGES_IN_CONTEXT', 'True'))
num_relevant_docs: bool = eval(get_env('NUM_RELEVANT_DOCS', 3))
debug: bool = eval(get_env('DEBUG', 'True'))
def __post_init__(self):
# validate config
if not self.use_docs_for_context and self.add_sources_to_response:
raise ValueError('Cannot add sources to response if not using docs in context')
if self.num_relevant_docs < 1:
raise ValueError('num_relevant_docs must be greater than 0')
self.log()
def asdict(self) -> Dict:
return asdict(self)
def log(self) -> None:
logger.info('Config:')
for key, value in self.asdict().items():
logger.info(f'{key}: {value}')
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