from sentence_transformers import SentenceTransformer
import numpy as np
import time

class Embedder:
    def __init__(self):
        print("Loading embedding model...")
        self.model = SentenceTransformer('sentence-transformers/paraphrase-MiniLM-L6-v2')
        print("Embedder loaded: BAAI/bge-large-en-v1.5")

    def embed(self, chunks, batch_size=32): 
        try:
            vectors = self.model.encode(
                chunks, 
                batch_size=batch_size, 
                show_progress_bar=False,
                convert_to_numpy=True
            )
            return vectors.tolist()
        except Exception as e:
            print(f"Embedding failed: {e}")
            raise e

    def embed_q(self, query):
        
        try:
            v = self.model.encode(query, convert_to_numpy=True)
            return v.tolist()
        except Exception as e:
            print(f"Query embedding failed: {e}")
            raise e