| import os | |
| from pathlib import Path | |
| from dotenv import load_dotenv | |
| load_dotenv() | |
| class Config: | |
| # Project root directory resolved relative to this file (rag_project/) | |
| BASE_DIR = Path(__file__).parent.parent | |
| DATA_DIR = BASE_DIR / "data" | |
| CHUNK_FILES = [ | |
| str(DATA_DIR / "BoYTe200_v3.json"), | |
| str(DATA_DIR / "NHIKHOA2.json"), | |
| str(DATA_DIR / "PHACDODIEUTRI_2016.json"), | |
| ] | |
| # EMBEDDING_MODEL = "sentence-transformers/all-MiniLM-L6-v2" | |
| # EMBEDDING_MODEL = "bkai-foundation-models/vietnamese-bi-encoder" | |
| EMBEDDING_MODEL = "VoVanPhuc/sup-SimCSE-VietNamese-phobert-base" | |
| # Groq API keys (set in rag_project/.env) | |
| GROQ_API_KEY_1 = os.getenv('GROQ_API_KEY_1', '') | |
| GROQ_API_KEY_2 = os.getenv('GROQ_API_KEY_2', '') | |
| GROQ_API_KEY_3 = os.getenv('GROQ_API_KEY_3', '') | |
| # Keep Google key in case frontend/other services need it | |
| GOOGLE_API_KEY = os.getenv('GOOGLE_API_KEY', '') | |
| GROQ_MODEL = os.getenv('GROQ_MODEL', 'llama-3.3-70b-versatile') | |
| LLM_MODEL = GROQ_MODEL # alias used in older code | |
| K_RETRIEVE = 3 | |
| TEMPERATURE = 0 | |