index_from_dataset returns indices rather than movie names

Hello

In my code I added another feature to the candidate tower in addition to movie title (for each movie I have a vector representation of that movie which is precalculated using some other algorithms) and just feed it directly to the candidate tower.

interactions_dict = a dictionary

ratings = tf.data.Dataset.from_tensor_slices(interactions_dict)

movies = ratings.map(lambda x: {
    'movie_title' : x['movie_title'], 
    'movie_vector' : x['movie_vector'],

})

index = tfrs.layers.factorized_top_k.BruteForce(model.query_model,k=CANDIDATE_POOL_SIZE)
index.index_from_dataset(movies.batch(100).map(lambda x: model.candidate_model(x)))

    query_dict = {'user_id':tf.constant([user]),
              'user_vector':np.stack([user_vector])}

Any idea why title after running _, titles = index(query_dict) contains indices rather than the actual movie names?

Here is the call method in my candidate tower:

  def call(self, titles):
    return tf.concat([
        self.title_embedding(titles["movie_title"]),
        titles["movie_vector"]
    ], axis=1)

Start with the BruteForce index_from_dataset entry point and the provided movies dataset and candidate_model call. Check how indexed values are represented and returned by index(query_dict); done when the query result returns the expected movie names instead of numeric indices, with a regression test for this dataset shape.