class_docstr: Wrapper around bigdl-llm embedding models.
  
  Example:
      .. code-block:: python
  
          from bigdl.llm.langchain.embeddings import BigdlNativeEmbeddings
          llama = BigdlNativeEmbeddings(model_path="/path/to/model.bin")
class_docstr_tok: ["Wrapper","around","bigdl-llm","embedding","models",".","Example",":","..","code-block",":",":","python","from","bigdl.llm.langchain.embeddings","import","BigdlNativeEmbeddings","llama","=","BigdlNativeEmbeddings","(","model_path=","''","\/path\/to\/model.bin","''",")"]
docstr: Embed a query using the Llama model.
  
  Args:
      text: The text to embed.
  
  Returns:
      Embeddings for the text.
docstr_tok: ["Embed","a","query","using","the","Llama","model",".","Args",":","text",":","The","text","to","embed",".","Returns",":","Embeddings","for","the","text","."]
code: def embed_query(self, text: str) -> List[float]:
      """Embed a query using the Llama model.
  
      Args:
          text: The text to embed.
  
      Returns:
          Embeddings for the text.
      """
      embedding = self.client.embed(text)
      return list(map(float, embedding))
  
imports: import logging
  import importlib
  from typing import Any, Dict, List, Optional
  from pydantic import BaseModel, Extra, Field, root_validator
  from langchain.embeddings.base import Embeddings
  from .transformersembeddings import TransformersEmbeddings
label_desc: Use image node_id 3 for calling the BigdlNativeEmbeddings obj's underlying member method code with example usage: obj.embed_query(text) and returns: list