Embeddings returned in payload from retriever

When searching with MMR and a langchain retriever, the full embeddings are returned. Recommend a pop of embedding in the convert firestore document function: Working code: def convert_firestore_document( document: DocumentSnapshot, page_content_fields: Optional[List[str]] = None, metadata_fields: Optional[List[str]] = None, ) -> Document: data_doc = document.to_dict() # Remove the 'embedding' field if it exists in data_doc data_doc.pop('embedding', None) metadata = { "reference": { "path": document.reference.path, FIRESTORE_TYPE: DOC_REF, } } # Check for vector fields and move them from the data_doc to the metadata vector_keys = [k for k in data_doc if isinstance(data_doc[k], Vector)] for k in vector_keys: metadata[k] = _convert_from_firestore(data_doc.pop(k)) set_page_fields = set( page_content_fields or (data_doc.keys() - set(metadata_fields or [])) ) set_metadata_fields = set(metadata_fields or (data_doc.keys() - set_page_fields)) page_content = {} for k in sorted(set_metadata_fields): if k in data_doc: metadata[k] = _convert_from_firestore(data_doc[k]) for k in sorted(set_page_fields): if k in data_doc: page_content[k] = _convert_from_firestore(data_doc[k]) if len(page_content) == 1: page_content = str(page_content.popitem()[1]) # type: ignore else: page_content = json.dumps(page_content) # type: ignore return Document(page_content=page_content, metadata=metadata) # type: ignore