Update docstring for embedding_field and embedding_dim (#208)

'embedding_field' and 'embedding_dim' are useful not only for EmbeddingRetriever but also for DensePassageRetriever. Hence, could be good if you generalise it as "dense retriever".

haystack/database/elasticsearch.py

@@ -49,8 +49,8 @@ def __init__(
                            If no Reader is used (e.g. in FAQ-Style QA) the plain content of this field will just be returned.
         :param name_field: Name of field that contains the title of the the doc
         :param external_source_id_field: If you have an external id (= non-elasticsearch) that identifies your documents, you can specify it here.
-        :param embedding_field: Name of field containing an embedding vector (Only needed when using the EmbeddingRetriever on top)
-        :param embedding_dim: Dimensionality of embedding vector (Only needed when using the EmbeddingRetriever on top)
+        :param embedding_field: Name of field containing an embedding vector (Only needed when using a dense retriever (e.g. DensePassageRetriever, EmbeddingRetriever) on top)
+        :param embedding_dim: Dimensionality of embedding vector (Only needed when using a dense retriever (e.g. DensePassageRetriever, EmbeddingRetriever) on top)
         :param custom_mapping: If you want to use your own custom mapping for creating a new index in Elasticsearch, you can supply it here as a dictionary.
         :param excluded_meta_data: Name of fields in Elasticsearch that should not be returned (e.g. [field_one, field_two]).
                                    Helpful if you have fields with long, irrelevant content that you don't want to display in results (e.g. embedding vectors).
@@ -353,4 +353,4 @@ def get_all_documents_in_index(self, index: str, filters: Optional[dict] = None)
            body["query"]["bool"]["filter"] = {"term": filters}
         result = scan(self.client, query=body, index=index)
 
-        return result
+        return result