similarity: CosmosDBSimilarityType = CosmosDBSimilarityType.COS,
    ) -> dict[str, Any]:
        """Creates an index using the index name specified at
            instance construction
        Setting the numLists parameter correctly is important for achieving
            good accuracy and performance.
            Since the vector store uses IVF as the indexing strategy,
            you should create the index only after you
            have loaded a large enough sample documents to ensure that the
            centroids for the respective buckets are
            faily distributed.
        We recommend that numLists is set to documentCount/1000 for up
            to 1 million documents
            and to sqrt(documentCount) for more than 1 million documents.
            As the number of items in your database grows, you should
            tune numLists to be larger
            in order to achieve good latency performance for vector search.
            If you're experimenting with a new scenario or creating a
            small demo, you can start with numLists
            set to 1 to perform a brute-force search across all vectors.
            This should provide you with the most
            accurate results from the vector search, however be aware that
            the search speed and latency will be slow.
            After your initial setup, you should go ahead and tune
            the numLists parameter using the above guidance.
        Args:
            num_lists: This integer is the number of clusters that the
                inverted file (IVF) index uses to group the vector data.
                We recommend that numLists is set to documentCount/1000
                for up to 1 million documents and to sqrt(documentCount)
                for more than 1 million documents.
                Using a numLists value of 1 is akin to performing
                brute-force search, which has limited performance
            dimensions: Number of dimensions for vector similarity.