rally-custom/custom_tracks/elasticsearch/dense_vector/track.py [193:229]:
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    def partition(self, partition_index, total_partitions):
        return self

    def params(self):
        return {
            "index": self._index_name,
            "cache": self._params.get("cache", False),
            "size": self._params.get("k", 10),
            "num_candidates": self._params.get("num-candidates", 100),
            "target_k": self._target_k,
            "knn_vector_store": KnnVectorStore.get_instance(self._queries_file, self._vector_field),
            "invalidate_vector_store": self._params.get("invalidate-vector-store", False),
        }


# Used in tandem with the KnnRecallParamSource. This executes both a knn query
# and an equivalent score script query. Results are then compared to gauge
# the accuracy of the knn query.
class KnnRecallRunner:
    async def __call__(self, es, params):
        k = params["size"]
        num_candidates = params["num_candidates"]
        index = params["index"]
        request_cache = params["cache"]
        target_k = max(params["target_k"], k)
        recall_total = 0
        exact_total = 0
        min_recall = k
        nodes_visited = []

        knn_vector_store: KnnVectorStore = params["knn_vector_store"]
        invalidate_vector_store: bool = params["invalidate_vector_store"]
        if invalidate_vector_store:
            knn_vector_store.invalidate_all()
        for query_id, query_vector in knn_vector_store.get_query_vectors().items():
            knn_result = await es.search(
                body={
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rally-custom/custom_tracks/opensearch/openai_vector/track.py [186:222]:
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    def partition(self, partition_index, total_partitions):
        return self

    def params(self):
        return {
            "index": self._index_name,
            "cache": self._params.get("cache", False),
            "size": self._params.get("k", 10),
            "num_candidates": self._params.get("num-candidates", 100),
            "target_k": self._target_k,
            "knn_vector_store": KnnVectorStore.get_instance(self._queries_file, self._vector_field),
            "invalidate_vector_store": self._params.get("invalidate-vector-store", False),
        }


# Used in tandem with the KnnRecallParamSource. This executes both a knn query
# and an equivalent score script query. Results are then compared to gauge
# the accuracy of the knn query.
class KnnRecallRunner:
    async def __call__(self, es, params):
        k = params["size"]
        num_candidates = params["num_candidates"]
        index = params["index"]
        request_cache = params["cache"]
        target_k = max(params["target_k"], k)
        recall_total = 0
        exact_total = 0
        min_recall = k
        nodes_visited = []

        knn_vector_store: KnnVectorStore = params["knn_vector_store"]
        invalidate_vector_store: bool = params["invalidate_vector_store"]
        if invalidate_vector_store:
            knn_vector_store.invalidate_all()
        for query_id, query_vector in knn_vector_store.get_query_vectors().items():
            knn_result = await es.search(
                body={
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