self,
        query: str,
        k: int = 5,
        lambda_val: float = 0.025,
        filter: Optional[str] = None,
        n_sentence_context: int = 0,
        **kwargs: Any,
    ) -> List[Document]:
        """Return Vectara documents most similar to query, along with scores.
        Args:
            query: Text to look up documents similar to.
            k: Number of Documents to return. Defaults to 5.
            filter: Dictionary of argument(s) to filter on metadata. For example a
                filter can be "doc.rating > 3.0 and part.lang = 'deu'"} see
                https://docs.vectara.com/docs/search-apis/sql/filter-overview for more
                details.
            n_sentence_context: number of sentences before/after the matching segment
                to add
        Returns:
            List of Documents most similar to the query
        """
        docs_and_scores = self.similarity_search_with_score(
            query,
            k=k,
            lamnbda_val=lambda_val,
            filter=filter,
            n_sentence_context=n_sentence_context,
            **kwargs,
        )
        return [doc for doc, _ in docs_and_scores]
[docs]    @classmethod
    def from_texts(
        cls: Type[Vectara],
        texts: List[str],
        embedding: Optional[Embeddings] = None,
        metadatas: Optional[List[dict]] = None,
        **kwargs: Any,
    ) -> Vectara:
        """Construct Vectara wrapper from raw documents.
        This is intended to be a quick way to get started.
        Example:
            .. code-block:: python
                from langchain import Vectara
rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/vectorstores/vectara.html