)
        # Convert results to Document objects
        docs = [
            (
                Document(
                    page_content=result.pop(FIELDS_CONTENT),
                    metadata=json.loads(result[FIELDS_METADATA])
                    if FIELDS_METADATA in result
                    else {
                        k: v for k, v in result.items() if k != FIELDS_CONTENT_VECTOR
                    },
                ),
                float(result["@search.score"]),
            )
            for result in results
        ]
        return docs
[docs]    def semantic_hybrid_search(
        self, query: str, k: int = 4, **kwargs: Any
    ) -> List[Document]:
        """
        Returns the most similar indexed documents to the query text.
        Args:
            query (str): The query text for which to find similar documents.
            k (int): The number of documents to return. Default is 4.
        Returns:
            List[Document]: A list of documents that are most similar to the query text.
        """
        docs_and_scores = self.semantic_hybrid_search_with_score(
            query, k=k, filters=kwargs.get("filters", None)
        )
        return [doc for doc, _ in docs_and_scores]
[docs]    def semantic_hybrid_search_with_score(
        self, query: str, k: int = 4, filters: Optional[str] = None
    ) -> List[Tuple[Document, float]]:
        """Return docs most similar to query with an hybrid query.
        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 and score for each
        """
        from azure.search.documents.models import Vector