| from sentence_transformers import SentenceTransformer | |
| class EmbeddingService: | |
| def __init__(self): | |
| self.model = SentenceTransformer("all-MiniLM-L6-v2") | |
| def embed(self, texts: list[str]) -> list[list[float]]: | |
| return self.model.encode(texts, normalize_embeddings=True).tolist() | |