embedding_vector,
            k,
            fetch_k,
            lambda_mult=lambda_mult,
            filter=filter,
        )
[docs]    @classmethod
    def from_texts(
        cls: Type[CVST],
        texts: List[str],
        embedding: Embeddings,
        metadatas: Optional[List[dict]] = None,
        batch_size: int = 16,
        **kwargs: Any,
    ) -> CVST:
        """Create a Cassandra vectorstore from raw texts.
        No support for specifying text IDs
        Returns:
            a Cassandra vectorstore.
        """
        session: Session = kwargs["session"]
        keyspace: str = kwargs["keyspace"]
        table_name: str = kwargs["table_name"]
        cassandraStore = cls(
            embedding=embedding,
            session=session,
            keyspace=keyspace,
            table_name=table_name,
        )
        cassandraStore.add_texts(texts=texts, metadatas=metadatas)
        return cassandraStore
[docs]    @classmethod
    def from_documents(
        cls: Type[CVST],
        documents: List[Document],
        embedding: Embeddings,
        batch_size: int = 16,
        **kwargs: Any,
    ) -> CVST:
        """Create a Cassandra vectorstore from a document list.
        No support for specifying text IDs
        Returns:
            a Cassandra vectorstore.
        """
        texts = [doc.page_content for doc in documents]
        metadatas = [doc.metadata for doc in documents]
        session: Session = kwargs["session"]
        keyspace: str = kwargs["keyspace"]
        table_name: str = kwargs["table_name"]
        return cls.from_texts(