pipeline/hf_embedding.py

import logging
from typing import List, Any, Optional, Tuple
import numpy as np
from sentence_transformers import SentenceTransformer

logger = logging.getLogger(__name__)

# Cache for loaded models
_model_cache = {}

def get_model(model_id: str) -> Tuple[Optional[SentenceTransformer], Optional[str]]:
    """
    Loads a SentenceTransformer model from the Hugging Face Hub.

    Args:
        model_id (str): The identifier for the model to load (e.g., 'sentence-transformers/LaBSE').

    Returns:
        Tuple[Optional[SentenceTransformer], Optional[str]]: A tuple containing the loaded model and its type ('sentence-transformer'),
                                                              or (None, None) if loading fails.
    """
    if model_id in _model_cache:
        logger.info(f"Returning cached model: {model_id}")
        return _model_cache[model_id], "sentence-transformer"

    logger.info(f"Loading SentenceTransformer model: {model_id}")
    try:
        model = SentenceTransformer(model_id)
        _model_cache[model_id] = model
        logger.info(f"Model '{model_id}' loaded successfully.")
        return model, "sentence-transformer"
    except Exception as e:
        logger.error(f"Failed to load SentenceTransformer model '{model_id}': {e}", exc_info=True)
        return None, None

def generate_embeddings(texts: List[str], model: SentenceTransformer) -> Optional[np.ndarray]:
    """
    Generates embeddings for a list of texts using a SentenceTransformer model.

    Args:
        texts (list[str]): A list of texts to embed.
        model (SentenceTransformer): The loaded SentenceTransformer model.

    Returns:
        Optional[np.ndarray]: A numpy array containing the embeddings. Returns None if generation fails.
    """
    if not texts or not isinstance(model, SentenceTransformer):
        logger.warning("Invalid input for generating embeddings. Texts list is empty or model is not a SentenceTransformer.")
        return None

    logger.info(f"Generating embeddings for {len(texts)} texts with {type(model).__name__}...")
    try:
        embeddings = model.encode(texts, convert_to_numpy=True, show_progress_bar=False)
        logger.info(f"Embeddings generated with shape: {embeddings.shape}")
        return embeddings
    except Exception as e:
        logger.error(f"An unexpected error occurred during embedding generation: {e}", exc_info=True)
        return None