Update rag_demo/preprocessing/base/embeddings.py

rag_demo/preprocessing/base/embeddings.py

@@ -1,15 +1,9 @@
 from functools import cached_property
 from pathlib import Path
 from typing import Optional, ClassVar
-from threading import Lock
 
-import numpy as np
-from loguru import logger
-from numpy.typing import NDArray
-from sentence_transformers.SentenceTransformer import SentenceTransformer
-from transformers import AutoTokenizer
 
-from rag_demo.settings import settings
+
 
 
 class SingletonMeta(type):
@@ -49,97 +43,3 @@ class SingletonMeta(type):
         return cls._instances[cls]
 
 
-class EmbeddingModelSingleton(metaclass=SingletonMeta):
-    """
-    A singleton class that provides a pre-trained transformer model for generating embeddings of input text.
-    """
-
-    def __init__(
-        self,
-        model_id: str = settings.TEXT_EMBEDDING_MODEL_ID,
-        device: str = settings.RAG_MODEL_DEVICE,
-        cache_dir: Optional[Path] = None,
-    ) -> None:
-        self._model_id = model_id
-        self._device = device
-
-        self._model = SentenceTransformer(
-            self._model_id,
-            device=self._device,
-            cache_folder=str(cache_dir) if cache_dir else None,
-        )
-        self._model.eval()
-
-    @property
-    def model_id(self) -> str:
-        """
-        Returns the identifier of the pre-trained transformer model to use.
-
-        Returns:
-            str: The identifier of the pre-trained transformer model to use.
-        """
-
-        return self._model_id
-
-    @cached_property
-    def embedding_size(self) -> int:
-        """
-        Returns the size of the embeddings generated by the pre-trained transformer model.
-
-        Returns:
-            int: The size of the embeddings generated by the pre-trained transformer model.
-        """
-
-        dummy_embedding = self._model.encode("")
-
-        return dummy_embedding.shape[0]
-
-    @property
-    def max_input_length(self) -> int:
-        """
-        Returns the maximum length of input text to tokenize.
-
-        Returns:
-            int: The maximum length of input text to tokenize.
-        """
-
-        return self._model.max_seq_length
-
-    @property
-    def tokenizer(self) -> AutoTokenizer:
-        """
-        Returns the tokenizer used to tokenize input text.
-
-        Returns:
-            AutoTokenizer: The tokenizer used to tokenize input text.
-        """
-
-        return self._model.tokenizer
-
-    def __call__(
-        self, input_text: str | list[str], to_list: bool = True
-    ) -> NDArray[np.float32] | list[float] | list[list[float]]:
-        """
-        Generates embeddings for the input text using the pre-trained transformer model.
-
-        Args:
-            input_text (str): The input text to generate embeddings for.
-            to_list (bool): Whether to return the embeddings as a list or numpy array. Defaults to True.
-
-        Returns:
-            Union[np.ndarray, list]: The embeddings generated for the input text.
-        """
-
-        try:
-            embeddings = self._model.encode(input_text)
-        except Exception:
-            logger.error(
-                f"Error generating embeddings for {self._model_id=} and {input_text=}"
-            )
-
-            return [] if to_list else np.array([])
-
-        if to_list:
-            embeddings = embeddings.tolist()
-
-        return embeddings