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| from langchain_huggingface import HuggingFaceEmbeddings | |
| from src.logger import logger | |
| def get_embeddings(model_name: str = "all-MiniLM-L6-v2"): | |
| """ | |
| Initializes and returns the HuggingFace embedding model. | |
| This model translates text chunks into mathematical vectors. | |
| """ | |
| logger.info(f"Initializing embedding model: {model_name}") | |
| try: | |
| # Initialize the HuggingFace embeddings model | |
| embeddings = HuggingFaceEmbeddings(model_name=model_name) | |
| logger.info("Successfully loaded embedding model.") | |
| # Return the embedding object so ChromaDB can use it | |
| return embeddings | |
| except Exception as e: | |
| logger.error(f"Failed to initialize embeddings: {str(e)}", exc_info=True) | |
| raise e |