def verify_vector_support(self) -> None:
        """
        Check if the connected Memgraph database supports vector indices.

        Compares the current version with the required version (2.22.0) that
        supports vector indexing.
        """
        response = self.structured_query("SHOW VERSION;")
        current_version = response[0]["version"]
        current_version = tuple(map(int, current_version.split(".")))
        required_version = "2.22"
        required_version = tuple(map(int, required_version.split(".")))

        # Check if the version is equal to or larger than the required version
        if current_version >= required_version:
            # Check if vector index is configured
            try:
                self.structured_query(
                    f"""
                    CREATE VECTOR INDEX {VECTOR_INDEX_NAME} ON :{BASE_ENTITY_LABEL}(embedding) WITH CONFIG {{"dimension": 1536, "capacity": 1000}};
                    """
                )
                self._supports_vector_index = True
                logger.info(
                    "Vector index %s was created with a fixed embedding dimension of 1536. "
                    "If your chosen LLM model uses a different dimension, manually create the vector index with the following query:\n"
                    'CREATE VECTOR INDEX %s ON :%s(embedding) WITH CONFIG {"dimension": <INSERT_DIMENSION>, "capacity": 1000};',
                    VECTOR_INDEX_NAME,
                    VECTOR_INDEX_NAME,
                    BASE_ENTITY_LABEL,
                )
            except neo4j.exceptions.Neo4jError as decode_error:
                self._supports_vector_index = False
                if (
                    decode_error.code
                    == "Memgraph.ClientError.MemgraphError.MemgraphError"
                    and "vector_search.show_index_info" in decode_error.message
                ):
                    logger.info(
                        """Failed to create vector index entity:
                        Given vector index already exists."""
                    )
        else:
            self._supports_vector_index = False
            logger.info(
                """Vector indexing is not supported by your current Memgraph
                version (%s). Please upgrade to version 2.22.0 or newer to use
                vector indices.""",
                ".".join(map(str, current_version)),
            )

        Check if the connected Memgraph database supports vector indices.

        Compares the current version with the required version (2.22.0) that
        supports vector indexing.
        
llama-index-integrations/graph_stores/llama-index-graph-stores-memgraph/llama_index/graph_stores/memgraph/property_graph.py