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 list of documents using the Llama model.
  
  Args:
      texts: The list of texts to embed.
  
  Returns:
      List of embeddings, one for each text.
docstr_tok: ["Embed","a","list","of","documents","using","the","Llama","model",".","Args",":","texts",":","The","list","of","texts","to","embed",".","Returns",":","List","of","embeddings",",","one","for","each","text","."]
code: def embed_documents(self, texts: List[str]) -> List[List[float]]:
      """Embed a list of documents using the Llama model.
  
      Args:
          texts: The list of texts to embed.
  
      Returns:
          List of embeddings, one for each text.
      """
      embeddings = [self.client.embed(text) for text in texts]
      return [list(map(float, e)) for e in embeddings]
  
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 2 for calling the BigdlNativeEmbeddings obj's underlying member method code with example usage: obj.embed_documents(texts) and returns: unknown