backend/app/services/embeddings.py

from sentence_transformers import SentenceTransformer
from typing import List
import numpy as np
from backend.app.config import settings


class EmbeddingService:
    def __init__(self):
        self.model = SentenceTransformer(settings.EMBEDDING_MODEL)
        self.dimension = 384

    def embed_text(self, text: str) -> List[float]:
        embedding = self.model.encode(text, convert_to_numpy=True)
        return embedding.tolist()

    def embed_batch(self, texts: List[str]) -> List[List[float]]:
        embeddings = self.model.encode(texts, convert_to_numpy=True, batch_size=32)
        return embeddings.tolist()

    def calculate_similarity(
        self, embedding1: List[float], embedding2: List[float]
    ) -> float:
        vec1 = np.array(embedding1)
        vec2 = np.array(embedding2)

        dot_product = np.dot(vec1, vec2)
        norm1 = np.linalg.norm(vec1)
        norm2 = np.linalg.norm(vec2)

        return float(dot_product / (norm1 * norm2))


embedding_service = EmbeddingService()