| """ | |
| Embedding Service — Manages dense and sparse embedding models. | |
| """ | |
| import logging | |
| from backend.src.config import settings | |
| logger = logging.getLogger("math_assistant.embeddings") | |
| _DENSE_EMBEDDINGS_CACHE = None | |
| def get_dense_embeddings(): | |
| """Get or create the singleton dense embedding model (text-embedding-004).""" | |
| global _DENSE_EMBEDDINGS_CACHE | |
| if _DENSE_EMBEDDINGS_CACHE is None: | |
| try: | |
| # First try new gemini integration | |
| from langchain_google_genai import GoogleGenerativeAIEmbeddings | |
| logger.info("Loading dense embedding model: text-embedding-004") | |
| _DENSE_EMBEDDINGS_CACHE = GoogleGenerativeAIEmbeddings( | |
| model="models/text-embedding-004", | |
| task_type="retrieval_document" | |
| ) | |
| except Exception as e: | |
| logger.warning(f"Failed to load Google embeddings ({e}), falling back to HuggingFace") | |
| from langchain_huggingface import HuggingFaceEmbeddings | |
| _DENSE_EMBEDDINGS_CACHE = HuggingFaceEmbeddings( | |
| model_name=settings.embedding_model, | |
| model_kwargs={"device": "cpu"}, | |
| encode_kwargs={"normalize_embeddings": True}, | |
| ) | |
| return _DENSE_EMBEDDINGS_CACHE | |