num_layers acts like max_num_layers

[manuoliveira6]

I am trying to build a tree setting num_layers=3. When I access to num_layers with RA.tree.num_layers, the value 1 is given, so the three has only one layer.

It seems this parameter is set as a max_num_layers, so if some condition is met, this num_layers is not reached by the tree.

The code used is the following:

from openai import OpenAI
from tenacity import retry, stop_after_attempt, wait_random_exponential

class OpenAIEmbeddingModel(BaseEmbeddingModel):
    def __init__(self, model='text-embedding-3-small'):
        self.client = OpenAI()
        self.model = model

    @retry(wait=wait_random_exponential(min=1, max=20), stop=stop_after_attempt(6))
    def create_embedding(self, text):
        text = text.replace("\n", " ")
        return (
            self.client.embeddings.create(input=[text], model=self.model)
            .data[0]
            .embedding
        )
    
RAC = RetrievalAugmentationConfig(tb_num_layers=3, 
                                  tb_selection_mode='threshold', 
                                  tb_threshold=0.3,
                                  tb_summarization_length=250,
                                  embedding_model=OpenAIEmbeddingModel())