es_user: Optional[str] = None,
        es_password: Optional[str] = None,
        input_field: str = "text_field",
    ) -> ElasticsearchEmbeddings:
        """Instantiate embeddings from Elasticsearch credentials.
        Args:
            model_id (str): The model_id of the model deployed in the Elasticsearch
                cluster.
            input_field (str): The name of the key for the input text field in the
                document. Defaults to 'text_field'.
            es_cloud_id: (str, optional): The Elasticsearch cloud ID to connect to.
            es_user: (str, optional): Elasticsearch username.
            es_password: (str, optional): Elasticsearch password.
        Example:
            .. code-block:: python
                from langchain.embeddings import ElasticsearchEmbeddings
                # Define the model ID and input field name (if different from default)
                model_id = "your_model_id"
                # Optional, only if different from 'text_field'
                input_field = "your_input_field"
                # Credentials can be passed in two ways. Either set the env vars
                # ES_CLOUD_ID, ES_USER, ES_PASSWORD and they will be automatically
                # pulled in, or pass them in directly as kwargs.
                embeddings = ElasticsearchEmbeddings.from_credentials(
                    model_id,
                    input_field=input_field,
                    # es_cloud_id="foo",
                    # es_user="bar",
                    # es_password="baz",
                )
                documents = [
                    "This is an example document.",
                    "Another example document to generate embeddings for.",
                ]
                embeddings_generator.embed_documents(documents)
        """
        try:
            from elasticsearch import Elasticsearch
            from elasticsearch.client import MlClient
        except ImportError:
            raise ImportError(