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| from sentence_transformers import SentenceTransformer | |
| from app.config import settings | |
| import numpy as np | |
| from typing import List | |
| class EmbeddingManager: | |
| """Manage local embeddings (no API cost)""" | |
| def __init__(self): | |
| self.model = SentenceTransformer(settings.EMBEDDING_MODEL) | |
| self.dimension = self.model.get_sentence_embedding_dimension() | |
| print(f"✅ Loaded embedding model: {settings.EMBEDDING_MODEL} (dim: {self.dimension})") | |
| def embed_texts(self, texts: List[str]) -> List[List[float]]: | |
| """Generate embeddings for list of texts""" | |
| embeddings = self.model.encode(texts, convert_to_numpy=True) | |
| return embeddings.tolist() | |
| def embed_query(self, query: str) -> List[float]: | |
| """Generate embedding for a single query""" | |
| embedding = self.model.encode([query], convert_to_numpy=True) | |
| return embedding[0].tolist() |