import numpy as np
import faiss
from sentence_transformers import SentenceTransformer
from app.core.config import settings
import logging
import time

logging.basicConfig(level=logging.INFO)

def log_latency(func):
    def wrapper(*args, **kwargs):
        start_time = time.time()
        result = func(*args, **kwargs)
        end_time = time.time()
        latency_ms = (end_time - start_time) * 1000
        logging.info(f"Latency for {func.__name__}: {latency_ms:.2f} ms")
        return result
    return wrapper

class EmbeddingService:
    
    def __init__(self):
        self.model = SentenceTransformer(settings.MODEL_NAME)

    @log_latency
    def get_embeddings(self, texts: list[str]) -> np.ndarray:
        """Generate embeddings for a list of texts."""
        embeddings = self.model.encode(texts, convert_to_tensor=False)
        return np.array(embeddings, dtype="float32")

    @log_latency
    def build_faiss_index(self, vectors: np.ndarray) -> faiss.Index:
        """Build a FAISS index from vectors."""
        dim = vectors.shape[1]
        index = faiss.IndexFlatL2(dim)
        index.add(vectors)
        return index
    
embedding_service = EmbeddingService()