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: Embed a list of Documents. The embedding of each Document is stored in the `embedding` field of the Document.
  
  :param documents: A list of Documents to embed.
code: def run(self, documents: List[Document]):
      """
      Embed a list of Documents. The embedding of each Document is stored in the `embedding` field of the Document.
  
      :param documents: A list of Documents to embed.
      """
      if not (
          isinstance(documents, list)
          and all(isinstance(doc, Document) for doc in documents)
      ):
          raise TypeError(
              "Input must be a list of Document instances. For strings, use AzureOpenAITextEmbedder."
          )
  
      texts_to_embed = self._prepare_texts_to_embed(documents=documents)
      embeddings, meta = self._embed_batch(
          texts_to_embed=texts_to_embed, batch_size=self.batch_size
      )
  
      # Assign the corresponding embeddings to each document
      for doc, emb in zip(documents, embeddings):
          doc.embedding = emb
  
      return {"documents": documents, "meta": meta}
  
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 6 for calling the AzureOpenAIDocumentEmbedder obj's underlying member method code with example usage: obj.run(documents) and returns: dict