class_docstr: A component for computing Document embeddings using OpenAI models.
  The embedding of each Document is stored in the `embedding` field of the Document.
  
  Usage example:
  ```python
  from haystack import Document
  from haystack.components.embedders import AzureOpenAIDocumentEmbedder
  
  doc = Document(content="I love pizza!")
  
  document_embedder = AzureOpenAIDocumentEmbedder()
  
  result = document_embedder.run([doc])
  print(result['documents'][0].embedding)
  
  # [0.017020374536514282, -0.023255806416273117, ...]
  ```
docstr: Prepare the texts to embed by concatenating the Document text with the metadata fields to embed.
code: def _prepare_texts_to_embed(
      self, documents: List[Document]
  ) -> List[str]:
      """
      Prepare the texts to embed by concatenating the Document text with the metadata fields to embed.
      """
      texts_to_embed = []
      for doc in documents:
          meta_values_to_embed = [
              str(doc.meta[key])
              for key in self.meta_fields_to_embed
              if key in doc.meta and doc.meta[key] is not None
          ]
  
          text_to_embed = (
              self.prefix
              + self.embedding_separator.join(
                  meta_values_to_embed + [doc.content or ""]
              )
              + self.suffix
          ).replace("\n", " ")
  
          texts_to_embed.append(text_to_embed)
      return texts_to_embed
  
file_path: haystack/haystack/components/embedders/azure_document_embedder.py
imports: import os
  from typing import List, Optional, Dict, Any, Tuple
  from openai.lib.azure import AzureADTokenProvider, AzureOpenAI
  from tqdm import tqdm
  from haystack import component, Document, default_to_dict
label_desc: Use image node_id 4 for calling the AzureOpenAIDocumentEmbedder obj's underlying member method code with example usage: obj._prepare_texts_to_embed(documents) and returns: texts_to_embed