def __init__(
        self,
        docdb_client: Optional[Any] = None,
        db_name: str = "default_db",
        index_name: str = "default_index",
        collection_name: str = "default_collection",
        id_key: str = "id",
        embedding_key: str = "embedding",
        text_key: str = "text",
        metadata_key: str = "metadata",
        insert_kwargs: Optional[Dict] = None,
        similarity_score="cosine",
        **kwargs: Any,
    ) -> None:
        """
        Initialize the vector store.

        Args:
            docdb_client: A DocumentDB client.
            db_name: A DocumentDB database name.
            collection_name: A DocumentDB collection name.
            id_key: The data field to use as the id.
            embedding_key: A DocumentDB field that will contain
            the embedding for each document.
            text_key: A DocumentDB field that will contain the text for each document.
            metadata_key: A DocumentDB field that will contain
            the metadata for each document.
            insert_kwargs: The kwargs used during `insert`.

        """
        super().__init__()

        if docdb_client is not None:
            self._docdb_client = cast(MongoClient, docdb_client)
        else:
            raise ValueError("Must specify connection string to DocumentDB instance ")
        self._similarity_score = similarity_score
        self._collection = self._docdb_client[db_name][collection_name]
        self._embedding_key = embedding_key
        self._id_key = id_key
        self._text_key = text_key
        self._metadata_key = metadata_key
        self._insert_kwargs = insert_kwargs or {}
        self._index_crud = DocDbIndex(index_name, self._embedding_key, self._collection)

        Initialize the vector store.

        Args:
            docdb_client: A DocumentDB client.
            db_name: A DocumentDB database name.
            collection_name: A DocumentDB collection name.
            id_key: The data field to use as the id.
            embedding_key: A DocumentDB field that will contain
            the embedding for each document.
            text_key: A DocumentDB field that will contain the text for each document.
            metadata_key: A DocumentDB field that will contain
            the metadata for each document.
            insert_kwargs: The kwargs used during `insert`.

        
llama-index-integrations/vector_stores/llama-index-vector-stores-awsdocdb/llama_index/vector_stores/awsdocdb/base.py