query_answer="extractive",
            top=k,
        )
        # Get Semantic Answers
        semantic_answers = results.get_answers()
        semantic_answers_dict = {}
        for semantic_answer in semantic_answers:
            semantic_answers_dict[semantic_answer.key] = {
                "text": semantic_answer.text,
                "highlights": semantic_answer.highlights,
            }
        # Convert results to Document objects
        docs = [
            (
                Document(
                    page_content=result["content"],
                    metadata={
                        **json.loads(result["metadata"]),
                        **{
                            "captions": {
                                "text": result.get("@search.captions", [{}])[0].text,
                                "highlights": result.get("@search.captions", [{}])[
                                    0
                                ].highlights,
                            }
                            if result.get("@search.captions")
                            else {},
                            "answers": semantic_answers_dict.get(
                                json.loads(result["metadata"]).get("key"), ""
                            ),
                        },
                    },
                ),
                float(result["@search.score"]),
            )
            for result in results
        ]
        return docs
[docs]    @classmethod
    def from_texts(
        cls: Type[AzureSearch],
        texts: List[str],
        embedding: Embeddings,
        metadatas: Optional[List[dict]] = None,
        azure_search_endpoint: str = "",
        azure_search_key: str = "",
        index_name: str = "langchain-index",
        **kwargs: Any,
    ) -> AzureSearch:
        # Creating a new Azure Search instance
        azure_search = cls(
            azure_search_endpoint,
            azure_search_key,
            index_name,
            embedding.embed_query,
        )
rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/vectorstores/azuresearch.html