msmarco-v2-vector/track.py [89:114]:
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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):
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openai_vector/track.py [22:47]:
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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):
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