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import os
from dotenv import load_dotenv
import json
load_dotenv()
def env_float(key: str, default: float) -> float:
try:
return float(os.getenv(key, default))
except (TypeError, ValueError):
return default
def env_int_list(key: str, default: list[int]) -> list[int]:
try:
val = os.getenv(key)
if val is None:
return default
return list(map(int, json.loads(val)))
except Exception:
return default
config = {
"app": {
'port': os.getenv("PORT"),
},
"db": {
"host": os.getenv("DB_HOST"),
"port": os.getenv("DB_PORT"),
"user": os.getenv("DB_USER"),
"password": os.getenv("DB_PASSWORD"),
"database": os.getenv("DB_DATABASE"),
"pool_size": int(os.getenv("POOL_SIZE")) | 8,
"max_overflow": int(os.getenv("MAX_OVERFLOW")) | 16,
"pool_recycle": int(os.getenv("POOL_RECYCLE")),
},
"jwt": {
"expired_in": int(os.getenv("JWT_EXPIRATION_DELTA")) | 24, # hour
"algorithm": os.getenv("JWT_ALGORITHM"),
"secret_key": os.getenv("JWT_SECRET"),
},
"google": {
"api_key": os.getenv("GOOGLE_API_KEY"),
},
"elastic": {
"url": os.getenv("ELASTIC_URL"),
"api_key": os.getenv("ELASTIC_API_KEY")
},
"evaluation": {
"weights": {
"structure": env_float("WEIGHT_STRUCTURE", 0.2),
"popularity": env_float("WEIGHT_POPULARITY", 0.2),
"distractor": env_float("WEIGHT_DISTRACTOR", 0.4),
"ai_adjust_factor": env_float("WEIGHT_AI_ADJUST_FACTOR", 0.8),
},
"penalty_for_error": {
"structure": {
"missing_question_text": env_float("PENALTY_MISSING_QUESTION_TEXT", 0.4),
"missing_choice": env_float("PENALTY_MISSING_CHOICE", 0.2),
"no_correct_answer": env_float("PENALTY_NO_CORRECT_ANSWER", 0.4),
"empty_choice": env_float("PENALTY_EMPTY_CHOICE", 0.1),
"duplicated_choices": env_float("PENALTY_DUPLICATED_CHOICES", 0.1),
"grammar_error": env_float("PENALTY_GRAMMAR_ERROR", 0.05),
}
},
"distractor": {
"empty_choice_deduction": env_float("DISTRACTOR_EMPTY_CHOICE_DEDUCTION", 0.05),
"embedding_similarity_thresholds": {
"too_different": env_float("DISTRACTOR_EMBEDDING_SIMILARITY_TOO_DIFFERENT", 0.35),
"moderate": env_float("DISTRACTOR_EMBEDDING_SIMILARITY_MODERATE", 0.45),
"good": env_float("DISTRACTOR_EMBEDDING_SIMILARITY_GOOD", 0.6),
"strong": env_float("DISTRACTOR_EMBEDDING_SIMILARITY_STRONG", 0.7),
},
"paragraph": {
"length_weight": env_float("DISTRACTOR_PARAGRAPH_LENGTH_WEIGHT", 0.1),
"difficulty_weight": env_float("DISTRACTOR_PARAGRAPH_DIFFICULTY_WEIGHT", 0.9),
"vocab_length_thresholds": env_int_list(
"DISTRACTOR_PARAGRAPH_VOCAB_LENGTH_THRESHOLDS",
[50, 100, 200, 300]
),
"other_length_thresholds": env_int_list(
"DISTRACTOR_PARAGRAPH_OTHER_LENGTH_THRESHOLDS",
[50, 100, 200, 300]
),
"direct_match_sim": env_float("DISTRACTOR_PARAGRAPH_DIRECT_MATCH_SIM", 0.85),
"paraphrase_sim": env_float("DISTRACTOR_PARAGRAPH_PARAPHRASE_SIM", 0.5),
"difficulty_levels": env_int_list(
"DISTRACTOR_PARAGRAPH_DIFFICULTY_LEVELS",
[1, 3, 5]
),
},
"lexical_family": {
"thresholds": {
"high_lemma": env_float("DISTRACTOR_LEXICAL_FAMILY_HIGH_LEMMA", 0.9),
"high_pos": env_float("DISTRACTOR_LEXICAL_FAMILY_HIGH_POS", 0.9),
"medium_high_pos": env_float("DISTRACTOR_LEXICAL_FAMILY_MEDIUM_HIGH_POS", 0.6),
"medium_lemma": env_float("DISTRACTOR_LEXICAL_FAMILY_MEDIUM_LEMMA", 0.7),
"medium_both": env_float("DISTRACTOR_LEXICAL_FAMILY_MEDIUM_BOTH", 0.4),
"low": env_float("DISTRACTOR_LEXICAL_FAMILY_LOW", 0.3),
},
"scores": {
"high_lemma": env_float("DISTRACTOR_LEXICAL_FAMILY_HIGH_LEMMA_SCORE", 0.75),
"high_pos": env_float("DISTRACTOR_LEXICAL_FAMILY_HIGH_POS_SCORE", 0.9),
"medium_high_pos": env_float("DISTRACTOR_LEXICAL_FAMILY_MEDIUM_HIGH_POS_SCORE", 0.7),
"medium_lemma": env_float("DISTRACTOR_LEXICAL_FAMILY_MEDIUM_LEMMA_SCORE", 0.6),
"medium_both": env_float("DISTRACTOR_LEXICAL_FAMILY_MEDIUM_BOTH_SCORE", 0.45),
"low": env_float("DISTRACTOR_LEXICAL_FAMILY_LOW_SCORE", 0.3),
},
},
},
}
} |