def _embed(
        self, inputs: List[Union[str, BytesIO]], embed_type: str = "text"
    ) -> List[List[float]]:
        """
        Generates Embeddings with input validation and retry mechanism.

        Args:
            sentences: Texts or Sentences to embed
            prompt_name: The name of the prompt to use for encoding

        Returns:
            List of embedding vectors

        Raises:
            ValueError: If any input text is invalid
            Exception: If embedding fails after retries

        """
        if embed_type not in SUPPORT_EMBED_TYPES:
            raise (ValueError("Not Implemented"))
        return self._embed_with_retry(inputs, embed_type)

        Generates Embeddings with input validation and retry mechanism.

        Args:
            sentences: Texts or Sentences to embed
            prompt_name: The name of the prompt to use for encoding

        Returns:
            List of embedding vectors

        Raises:
            ValueError: If any input text is invalid
            Exception: If embedding fails after retries

        
llama-index-integrations/embeddings/llama-index-embeddings-vllm/llama_index/embeddings/vllm/base.py