collection_name: str = "LangChainCollection",
        connection_args: dict[str, Any] = DEFAULT_MILVUS_CONNECTION,
        consistency_level: str = "Session",
        index_params: Optional[dict] = None,
        search_params: Optional[dict] = None,
        drop_old: bool = False,
        **kwargs: Any,
    ) -> Milvus:
        """Create a Milvus collection, indexes it with HNSW, and insert data.
        Args:
            texts (List[str]): Text data.
            embedding (Embeddings): Embedding function.
            metadatas (Optional[List[dict]]): Metadata for each text if it exists.
                Defaults to None.
            collection_name (str, optional): Collection name to use. Defaults to
                "LangChainCollection".
            connection_args (dict[str, Any], optional): Connection args to use. Defaults
                to DEFAULT_MILVUS_CONNECTION.
            consistency_level (str, optional): Which consistency level to use. Defaults
                to "Session".
            index_params (Optional[dict], optional): Which index_params to use. Defaults
                to None.
            search_params (Optional[dict], optional): Which search params to use.
                Defaults to None.
            drop_old (Optional[bool], optional): Whether to drop the collection with
                that name if it exists. Defaults to False.
        Returns:
            Milvus: Milvus Vector Store
        """
        vector_db = cls(
            embedding_function=embedding,
            collection_name=collection_name,
            connection_args=connection_args,
            consistency_level=consistency_level,
            index_params=index_params,
            search_params=search_params,
            drop_old=drop_old,
            **kwargs,
        )