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import os
from pathlib import Path
from typing import Dict, List
import torch
from pydantic_settings import BaseSettings
class Settings(BaseSettings):
# Model Configuration
MODEL_NAME: str = "sentence-transformers/embeddinggemma-300m-medical"
CLASSIFIER_NAME: str = "davidgray/health-query-triage"
CATEGORIES: List[str] = ["medical", "insurance"]
# Paths
CHECKPOINT_PATH: str = "classifier/checkpoints"
CACHE_DIR: str = ".cache/embeddings"
# Device
DEVICE: str = "cuda" if torch.cuda.is_available() else ("mps" if torch.backends.mps.is_available() else "cpu")
# Corpora Configuration
CORPORA_CONFIG: Dict[str, dict] = {
"medical_qa": {"path": "data/corpora/medical_qa.jsonl",
"text_fields": ["question", "answer", "title"]},
"miriad": {"path": "data/corpora/miriad_text.jsonl",
"text_fields": ["question", "answer", "title"]},
"pubmed": {"path": "data/corpora/pubmed.json",
"text_fields": ["contents","title"]},
"unidoc": {"path": "data/corpora/unidoc_qa.jsonl",
"text_fields": ["question", "answer", "title"]},
}
class Config:
env_file = ".env"
settings = Settings()
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