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Runtime error
| import os | |
| os.environ["PYTORCH_CUDA_ALLOC_CONF"] = "expandable_segments:True" | |
| import sys | |
| from pathlib import Path | |
| from dotenv import load_dotenv | |
| # Project paths | |
| CODE_ROOT = Path(__file__).parent | |
| if getattr(sys, "frozen", False) or "Program Files" in str(CODE_ROOT) or "ProgramFiles" in str(CODE_ROOT): | |
| PROJECT_ROOT = Path(os.getenv("LOCALAPPDATA", Path.home() / "AppData" / "Local")) / "NotebookPRO" | |
| else: | |
| PROJECT_ROOT = CODE_ROOT | |
| # Load environment variables from backend/.env and project-root/.env. | |
| for env_file in [PROJECT_ROOT / ".env", PROJECT_ROOT.parent / ".env"]: | |
| if env_file.exists(): | |
| load_dotenv(env_file, override=False) | |
| DATA_DIR = PROJECT_ROOT / "data" | |
| MODELS_DIR = PROJECT_ROOT / "models" | |
| UPLOADS_DIR = DATA_DIR / "uploads" | |
| VECTOR_DB_DIR = DATA_DIR / "vector_db" | |
| CHATS_DIR = DATA_DIR / "chats" | |
| # Create directories if they don't exist | |
| for dir_path in [DATA_DIR, MODELS_DIR, UPLOADS_DIR, VECTOR_DB_DIR, CHATS_DIR]: | |
| dir_path.mkdir(parents=True, exist_ok=True) | |
| # Model configuration | |
| # RAG uses pre-trained models directly - no training required! | |
| MODEL_NAME = os.getenv("MODEL_NAME", "microsoft/phi-2") # Pre-trained model | |
| FOLLOWUP_MODEL = os.getenv("FOLLOWUP_MODEL", "meta/llama-3.3-70b-instruct") # Fast model for follow-ups | |
| USE_PRETRAINED = os.getenv("USE_PRETRAINED", "true").lower() == "true" # Use pre-trained by default | |
| MODEL_PATH = os.getenv("MODEL_PATH", str(MODELS_DIR / "trained_model")) # Only if fine-tuned | |
| EMBEDDING_MODEL = os.getenv("EMBEDDING_MODEL", "BAAI/bge-small-en-v1.5") # For document embeddings | |
| RERANKER_MODEL = os.getenv("RERANKER_MODEL", "cross-encoder/ms-marco-MiniLM-L-6-v2") | |
| EMBEDDING_BATCH_SIZE = int(os.getenv("EMBEDDING_BATCH_SIZE", "16")) | |
| RERANKER_BATCH_SIZE = int(os.getenv("RERANKER_BATCH_SIZE", "8")) | |
| # API Keys | |
| OPENAI_API_KEY = os.getenv("OPENAI_API_KEY", "") | |
| HUGGINGFACE_API_KEY = os.getenv("HUGGINGFACE_API_KEY", "") | |
| # Application settings | |
| MAX_UPLOAD_SIZE = int(os.getenv("MAX_UPLOAD_SIZE", "200")) # MB | |
| TEMPERATURE = float(os.getenv("TEMPERATURE", "0.7")) | |
| MAX_TOKENS = int(os.getenv("MAX_TOKENS", "2048")) | |
| # Memory behavior when switching spaces | |
| SPACE_SWITCH_CLEAR_CUDA_CACHE = os.getenv("SPACE_SWITCH_CLEAR_CUDA_CACHE", "true").lower() == "true" | |
| SPACE_SWITCH_UNLOAD_EMBEDDING_MODEL = os.getenv("SPACE_SWITCH_UNLOAD_EMBEDDING_MODEL", "false").lower() == "true" | |
| CHUNK_SIZE = int(os.getenv("CHUNK_SIZE", "700")) | |
| CHUNK_OVERLAP = int(os.getenv("CHUNK_OVERLAP", "120")) | |
| # Use cases | |
| USE_CASES = { | |
| "explanation": "Provide detailed explanation of concepts", | |
| "summary": "Generate concise summary of content", | |
| "qa": "Answer questions based on content", | |
| "notes": "Create structured study notes" | |
| } | |