import os
from dotenv import load_dotenv

load_dotenv()

BASE_DIR = os.path.abspath(os.path.dirname(__file__))


class Config:
    SECRET_KEY = os.environ.get("SECRET_KEY", "change-me-in-production")
    _db_uri = os.environ.get("DATABASE_URI", "")
    _default_uri = f"sqlite:///{os.path.join(BASE_DIR, 'data', 'app.db')}"
    # Fall back to SQLite if DATABASE_URI is empty or not a valid SQLAlchemy URL
    SQLALCHEMY_DATABASE_URI = (
        _db_uri if _db_uri and "://" in _db_uri else _default_uri
    )
    SQLALCHEMY_TRACK_MODIFICATIONS = False

    UPLOAD_FOLDER = os.environ.get("UPLOAD_FOLDER", os.path.join(BASE_DIR, "uploads"))
    VECTORDB_PATH = os.environ.get("VECTORDB_PATH", os.path.join(BASE_DIR, "vectordb"))

    ALLOWED_EXTENSIONS = {"pdf", "txt", "docx", "md"}
    MAX_CONTENT_LENGTH = 50 * 1024 * 1024  # 50 MB

    # LLM Provider: "lmstudio" or "openai"
    AI_PROVIDER = os.environ.get("AI_PROVIDER", "lmstudio")
    LM_STUDIO_URL = os.environ.get("LM_STUDIO_URL", "http://localhost:1234")
    LM_STUDIO_MODEL = os.environ.get("LM_STUDIO_MODEL", "local-model")
    LM_STUDIO_EMBEDDING_MODEL = os.environ.get("LM_STUDIO_EMBEDDING_MODEL", "local-model")
    OPENAI_API_KEY = os.environ.get("OPENAI_API_KEY", "")
    OPENAI_MODEL = os.environ.get("OPENAI_MODEL", "gpt-4o")

    RAG_TOP_K = int(os.environ.get("RAG_TOP_K", "6"))
    RAG_CHUNK_SIZE = int(os.environ.get("RAG_CHUNK_SIZE", "300"))
    RAG_CHUNK_OVERLAP = int(os.environ.get("RAG_CHUNK_OVERLAP", "75"))