"""
        embedding = self.embedding.embed_query(query)
        script_query = _default_script_query(embedding, filter)
        response = self.client_search(
            self.client, self.index_name, script_query, size=k
        )
        hits = [hit for hit in response["hits"]["hits"]]
        docs_and_scores = [
            (
                Document(
                    page_content=hit["_source"]["text"],
                    metadata=hit["_source"]["metadata"],
                ),
                hit["_score"],
            )
            for hit in hits
        ]
        return docs_and_scores
[docs]    @classmethod
    def from_texts(
        cls,
        texts: List[str],
        embedding: Embeddings,
        metadatas: Optional[List[dict]] = None,
        elasticsearch_url: Optional[str] = None,
        index_name: Optional[str] = None,
        refresh_indices: bool = True,
        **kwargs: Any,
    ) -> ElasticVectorSearch:
        """Construct ElasticVectorSearch wrapper from raw documents.
        This is a user-friendly interface that:
            1. Embeds documents.
            2. Creates a new index for the embeddings in the Elasticsearch instance.
            3. Adds the documents to the newly created Elasticsearch index.
        This is intended to be a quick way to get started.
        Example:
            .. code-block:: python
                from langchain import ElasticVectorSearch
                from langchain.embeddings import OpenAIEmbeddings
                embeddings = OpenAIEmbeddings()
                elastic_vector_search = ElasticVectorSearch.from_texts(
                    texts,
                    embeddings,
                    elasticsearch_url="http://localhost:9200"
                )
        """
        elasticsearch_url = elasticsearch_url or get_from_env(
rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/vectorstores/elastic_vector_search.html