class_docstr: Wrapper around bigdl-llm embedding models.
  
  param model_path: If running with ``native int4``, the path should be converted BigDL-LLM
        optimized ggml binary checkpoint, which should be converted by ``bigdl.llm.llm_convert``.
        If running with ``transformers int4``, the path should be the huggingface repo id
        to be downloaded or the huggingface checkpoint folder.
  
  Example:
      .. code-block:: python
  
          from bigdl.llm.langchain.embeddings import LlamaEmbeddings
          llama = LlamaEmbeddings(model_path="/path/to/model.bin")
class_docstr_tok: ["Wrapper","around","bigdl-llm","embedding","models",".","param","model_path",":","If","running","with","``","native","int4","``",",","the","path","should","be","converted","BigDL-LLM","optimized","ggml","binary","checkpoint",",","which","should","be","converted","by","``","bigdl.llm.llm_convert","``",".","If","running","with","``","transformers","int4","``",",","the","path","should","be","the","huggingface","repo","id","to","be","downloaded","or","the","huggingface","checkpoint","folder",".","Example",":","..","code-block",":",":","python","from","bigdl.llm.langchain.embeddings","import","LlamaEmbeddings","llama","=","LlamaEmbeddings","(","model_path=","''","\/path\/to\/model.bin","''",")"]
docstr: Embed a query using the optimized int4 model.
  
  Args:
      text: The text to embed.
  
  Returns:
      Embeddings for the text.
docstr_tok: ["Embed","a","query","using","the","optimized","int4","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 optimized int4 model.
  
      Args:
          text: The text to embed.
  
      Returns:
          Embeddings for the text.
      """
      if self.native:
          embedding = self.client.embed(text)
          return list(map(float, embedding))
      else:
          return self.client.embed_query(text)
  
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 _BaseEmbeddings obj's underlying member method code with example usage: obj.embed_query(text) and returns: list, self