app/core/embeddings.py

from sentence_transformers import SentenceTransformer
from typing import List

class EmbeddingGenerator:
    def __init__(self, model_name: str):
        self.model = SentenceTransformer(model_name)
        self.dimension = self.model.get_sentence_embedding_dimension()
    
    def generate(self, texts: List[str]) -> List[List[float]]:
        """Generate embeddings for a list of texts"""
        embeddings = self.model.encode(texts, convert_to_numpy=True)
        return embeddings.tolist()
    
    def generate_single(self, text: str) -> List[float]:
        """Generate embedding for a single text"""
        embedding = self.model.encode([text], convert_to_numpy=True)
        return embedding[0].tolist()