rally-custom/custom_tracks/elasticsearch/openai_vector/track.py [61:98]:
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    }
    script_result = await client.search(
        body=script_query,
        index=index,
        request_cache=request_cache,
        size=max_size,
    )
    return [hit["_id"] for hit in script_result["hits"]["hits"]]

class KnnParamSource:
    def __init__(self, track, params, **kwargs):
        if len(track.indices) == 1:
            default_index = track.indices[0].name
        else:
            default_index = "_all"

        self._index_name = params.get("index", default_index)
        self._cache = params.get("cache", False)
        self._params = params
        self._queries = []

        cwd = os.path.dirname(__file__)
        with bz2.open(os.path.join(cwd, QUERIES_FILENAME), "r") as queries_file:
            for vector_query in queries_file:
                self._queries.append(json.loads(vector_query))
        self._iters = 0
        self._maxIters = len(self._queries)
        self.infinite = True

    def partition(self, partition_index, total_partitions):
        return self

    def params(self):
        result = {"index": self._index_name, "cache": self._params.get("cache", False), "size": self._params.get("k", 10)}

        result["body"] = {
            "query": {
                "knn": {
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rally-custom/custom_tracks/opensearch/openai_vector/track.py [56:95]:
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        }
    script_result = await client.search(
        body=script_query,
        index=index,
        request_cache=request_cache,
        size=max_size,
    )
    return [hit["_id"] for hit in script_result["hits"]["hits"]]

class KnnParamSource:
    def __init__(self, track, params, **kwargs):
        # choose a suitable index: if there is only one defined for this track
        # choose that one, but let the user always override index
        if len(track.indices) == 1:
            default_index = track.indices[0].name
        else:
            default_index = "_all"

        self._index_name = params.get("index", default_index)
        self._cache = params.get("cache", False)
        self._params = params
        self._queries = []

        cwd = os.path.dirname(__file__)
        with bz2.open(os.path.join(cwd, QUERIES_FILENAME), "r") as queries_file:
            for vector_query in queries_file:
                self._queries.append(json.loads(vector_query))
        self._iters = 0
        self._maxIters = len(self._queries)
        self.infinite = True

    def partition(self, partition_index, total_partitions):
        return self

    def params(self):
        result = {"index": self._index_name, "cache": self._params.get("cache", False), "size": self._params.get("k", 10)}

        result["body"] = {
            "query": {
                "knn": {
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