mapping = _default_text_mapping(
            dim, engine, space_type, ef_search, ef_construction, m, vector_field
        )
        return _bulk_ingest_embeddings(
            self.client,
            self.index_name,
            embeddings,
            texts,
            metadatas=metadatas,
            ids=ids,
            vector_field=vector_field,
            text_field=text_field,
            mapping=mapping,
            max_chunk_bytes=max_chunk_bytes,
            is_aoss=self.is_aoss,
        )
[docs]    def similarity_search(
        self, query: str, k: int = 4, **kwargs: Any
    ) -> List[Document]:
        """Return docs most similar to query.
        By default, supports Approximate Search.
        Also supports Script Scoring and Painless Scripting.
        Args:
            query: Text to look up documents similar to.
            k: Number of Documents to return. Defaults to 4.
        Returns:
            List of Documents most similar to the query.
        Optional Args:
            vector_field: Document field embeddings are stored in. Defaults to
            "vector_field".
            text_field: Document field the text of the document is stored in. Defaults
            to "text".
            metadata_field: Document field that metadata is stored in. Defaults to
            "metadata".
            Can be set to a special value "*" to include the entire document.
        Optional Args for Approximate Search:
            search_type: "approximate_search"; default: "approximate_search"
            boolean_filter: A Boolean filter is a post filter consists of a Boolean
            query that contains a k-NN query and a filter.
            subquery_clause: Query clause on the knn vector field; default: "must"