algos.yaml

float:
  any:
    sptag:
      docker-tag: ann-benchmarks-sptag
      module: ann_benchmarks.algorithms.sptag
      constructor: Sptag
      base-args: ["@metric"]
      run-groups:
        sptag:
          args: [['BKT', 'KDT']]
          query-args: [[100, 200, 400, 1000, 2000, 4000]]
    DolphinnPy:
      disabled: true
      docker-tag: ann-benchmarks-dolphinn # Docker tag
      module: ann_benchmarks.algorithms.dolphinnpy # Python class
      constructor: DolphinnPy # Python class name
      run-groups:
        base:
          args: [[10, 50, 100, 200, 1000, 2000]]
    faiss-lsh:
      disabled: true
      docker-tag: ann-benchmarks-faiss
      module: ann_benchmarks.algorithms.faiss
      constructor: FaissLSH
      base-args: ["@metric"]
      run-groups:
        base:
          # When @args is a list, the result is the Cartesian product of all of
          # the things it contains; entries that aren't a list will be treated
          # as lists of length one.
          args: [[32, 64, 128, 256, 512, 1024, 2048, 4096]]
          # This run group will produce eight algorithm instances:
          # FaissLSH(32), FaissLSH(64), and so on up to FaissLSH(4096).
    faiss-ivf:
      docker-tag: ann-benchmarks-faiss
      module: ann_benchmarks.algorithms.faiss
      constructor: FaissIVF
      base-args: ["@metric"]
      run-groups:
        base:
          args: [[32,64,128,256,512,1024,2048,4096,8192]]
          query-args: [[1, 5, 10, 50, 100, 200]]
    faiss-ivfpqfs:
      docker-tag: ann-benchmarks-faiss
      module: ann_benchmarks.algorithms.faiss
      constructor: FaissIVFPQfs
      base-args: ["@metric"]
      run-groups:
        base:
          args: [[512, 1204, 2048, 4096]]
          query-args: [[1, 5, 10, 50, 100, 200], [0, 10, 100, 1000]]
    faiss-gpu:
      disabled: true
      docker-tag: ann-benchmarks-faiss
      module: ann_benchmarks.algorithms.faiss_gpu
      constructor: FaissGPU
      run-groups:
        base:
          args: [[400, 1024, 4096, 8192, 16384],
                 [1, 10, 40, 100, 200]]
    luceneknn:
      docker-tag: ann-benchmarks-luceneknn
      module: ann_benchmarks.algorithms.luceneknn
      constructor: PyLuceneKNN
      base-args: [ "@metric", "@dimension" ]
      run-groups:
        M-4:
          arg-groups:
            - { "M": 4,  "efConstruction": 500 }
          query-args: [ [ 10, 20, 40, 80, 120, 200, 400, 600, 800 ] ]
        M-8:
          arg-groups:
            - { "M": 8,  "efConstruction": 500 }
          query-args: [ [ 10, 20, 40, 80, 120, 200, 400, 600, 800 ] ]
        M-12:
          arg-groups:
            - { "M": 12,  "efConstruction": 500 }
          query-args: [ [ 10, 20, 40, 80, 120, 200, 400, 600, 800 ] ]
        M-16:
          arg-groups:
            - { "M": 16,  "efConstruction": 500 }
          query-args: [ [ 10, 20, 40, 80, 120, 200, 400, 600, 800 ] ]
        M-24:
          arg-groups:
            - { "M": 24,  "efConstruction": 500 }
          query-args: [ [ 10, 20, 40, 80, 120, 200, 400, 600, 800 ] ]
        M-36:
          arg-groups:
            - { "M": 36,  "efConstruction": 500 }
          query-args: [ [ 10, 20, 40, 80, 120, 200, 400, 600, 800 ] ]
        M-48:
          arg-groups:
            - { "M": 48,  "efConstruction": 500 }
          query-args: [ [ 10, 20, 40, 80, 120, 200, 400, 600, 800 ] ]
        M-64:
          arg-groups:
            - { "M": 64,  "efConstruction": 500 }
          query-args: [ [ 10, 20, 40, 80, 120, 200, 400, 600, 800 ] ]
        M-96:
          arg-groups:
            - { "M": 96,  "efConstruction": 500 }
          query-args: [ [ 10, 20, 40, 80, 120, 200, 400, 600, 800 ] ]
    hnswlib:
      docker-tag: ann-benchmarks-hnswlib
      module: ann_benchmarks.algorithms.hnswlib
      constructor: HnswLib
      base-args: ["@metric"]
      run-groups:
        M-4:
          arg-groups:
            - {"M": 4,  "efConstruction": 500}
          query-args: [[10, 20, 40, 80, 120, 200, 400, 600, 800]]
        M-8:
          arg-groups:
            - {"M": 8,  "efConstruction": 500}
          query-args: [[10, 20, 40, 80, 120, 200, 400, 600, 800]]
        M-12:
          arg-groups:
            - {"M": 12,  "efConstruction": 500}
          query-args: [[10, 20, 40, 80, 120, 200, 400, 600, 800]]
        M-16:
          arg-groups:
            - {"M": 16,  "efConstruction": 500}
          query-args: [[10, 20, 40, 80, 120, 200, 400, 600, 800]]
        M-24:
          arg-groups:
            - {"M": 24,  "efConstruction": 500}
          query-args: [[10, 20, 40, 80, 120, 200, 400, 600, 800]]
        M-36:
          arg-groups:
            - {"M": 36,  "efConstruction": 500}
          query-args: [[10, 20, 40, 80, 120, 200, 400, 600, 800]]
        M-48:
          arg-groups:
            - {"M": 48,  "efConstruction": 500}
          query-args: [[10, 20, 40, 80, 120, 200, 400, 600, 800]]
        M-64:
          arg-groups:
            - {"M": 64,  "efConstruction": 500}
          query-args: [[10, 20, 40, 80, 120, 200, 400, 600, 800]]
        M-96:
          arg-groups:
            - {"M": 96,  "efConstruction": 500}
          query-args: [[10, 20, 40, 80, 120, 200, 400, 600, 800]]

    hnsw(faiss):
     docker-tag: ann-benchmarks-faiss
     module: ann_benchmarks.algorithms.faiss_hnsw
     constructor: FaissHNSW
     base-args: ["@metric"]
     run-groups:
       M-4:
         arg-groups:
           - {"M": 4,  "efConstruction": 500}
         query-args: [[10, 20, 40, 80, 120, 200, 400, 600, 800]]
       M-8:
         arg-groups:
           - {"M": 8,  "efConstruction": 500}
         query-args: [[10, 20, 40, 80, 120, 200, 400, 600, 800]]
       M-12:
         arg-groups:
           - {"M": 12,  "efConstruction": 500}
         query-args: [[10, 20, 40, 80, 120, 200, 400, 600, 800]]
       M-16:
         arg-groups:
           - {"M": 16,  "efConstruction": 500}
         query-args: [[10, 20, 40, 80, 120, 200, 400, 600, 800]]
       M-24:
         arg-groups:
           - {"M": 24,  "efConstruction": 500}
         query-args: [[10, 20, 40, 80, 120, 200, 400, 600, 800]]
       M-36:
         arg-groups:
           - {"M": 36,  "efConstruction": 500}
         query-args: [[10, 20, 40, 80, 120, 200, 400, 600, 800]]
       M-48:
         arg-groups:
           - {"M": 48,  "efConstruction": 500}
         query-args: [[10, 20, 40, 80, 120, 200, 400, 600, 800]]
       M-64:
         arg-groups:
           - {"M": 64,  "efConstruction": 500}
         query-args: [[10, 20, 40, 80, 120, 200, 400, 600, 800]]
       M-96:
         arg-groups:
           - {"M": 96,  "efConstruction": 500}
         query-args: [[10, 20, 40, 80, 120, 200, 400, 600, 800]]

    flann:
     docker-tag: ann-benchmarks-flann
     module: ann_benchmarks.algorithms.flann
     constructor: FLANN
     base-args: ["@metric"]
     run-groups:
       flann:
         args: [[0.2, 0.5, 0.7, 0.8, 0.9, 0.95, 0.97]]
    annoy:
      docker-tag: ann-benchmarks-annoy
      module: ann_benchmarks.algorithms.annoy
      constructor: Annoy
      base-args: ["@metric"]
      run-groups:
        annoy:
          args: [[100, 200, 400]]
          query-args: [[100, 200, 400, 1000, 2000, 4000, 10000, 20000, 40000,
                      100000, 200000, 400000]]
          # This run group produces 3 algorithm instances -- Annoy("angular",
          # 100), Annoy("angular", 200), and Annoy("angular", 400) -- each of
          # which will be used to run 12 different queries.
    tinyknn:
      docker-tag: ann-benchmarks-tinyknn
      module: ann_benchmarks.algorithms.tinyknn
      constructor: TinyKNN
      base-args: ["@metric"]
      run-groups:
        # Arguments are "build_probes" and "skew_factor"
        # Time to build the index is mostly a factor of the skew_factor
        # 1 | 578.4s
        # 2 | 1033.9s
        # 3 | 1410.6s
        # 4 | 2420.7s (?)
        # 5 | 2206.8s
        # 6 | 2443.1s
        # Choices below are based on the graph in
        # https://github.com/erikbern/ann-benchmarks/pull/391#issuecomment-1518093405
        tinyknn_1:
          args: [ 1, [1, 2, 3] ]
          query-args: [[1, 2, 3, 4, 6, 8, 10, 13, 16, 20, 24, 28, 33, 38, 44, 50, 57, 64, 72, 80,
                        88, 106, 126, 148, 172, 198, 256]]
        tinyknn_2:
          args: [ 2, [3, 4] ]
          query-args: [[1, 2, 3, 4, 6, 8, 10, 13, 16, 20, 24, 28, 33, 38, 44, 50, 57, 64, 72, 80,
                        88, 106, 126, 148, 172, 198, 256]]
        tinyknn_3:
          args: [ 3, [5] ]
          query-args: [[1, 2, 3, 4, 6, 8, 10, 13, 16, 20, 24, 28, 33, 38, 44, 50, 57, 64, 72, 80,
                        88, 106, 126, 148, 172, 198, 256]]
    Milvus(Knowhere):
      docker-tag: ann-benchmarks-milvus
      module: ann_benchmarks.algorithms.milvus
      constructor: Milvus
      base-args: ["@metric", "@dimension"]
      run-groups:
        HNSW:
          args: { "M": [4, 8, 12, 16, 24, 36, 48, 64, 96],  "efConstruction": 500 }  # index
          query-args: [ [ 10, 15, 20, 25, 30, 35, 40, 50, 60, 80, 120, 200, 400, 600, 800 ] ]  # ef
    glass:
      docker-tag: ann-benchmarks-glass
      module: ann_benchmarks.algorithms.glass
      constructor: Glass
      base-args: ["@metric", "@dimension"]
      run-groups:
        HNSW:
          args: { "R": [8, 16, 32, 48, 64, 96],  "level" : [1, 2, 3], "L": 200 }  # index
          query-args: [ [ 10, 20, 30, 40, 60, 80, 120, 200, 400, 600, 800 ] ]  # ef
    nearpy:
      disabled: true
      docker-tag: ann-benchmarks-nearpy
      module: ann_benchmarks.algorithms.nearpy
      constructor: NearPy
      base-args: ["@metric"]
      run-groups:
        nearpy:
          args: [[10, 12, 14, 16], [5, 10, 20, 40]]
        extra:
          args: [16, [5, 10, 15, 20, 25, 30, 40]]
    n2:
      docker-tag: ann-benchmarks-n2
      module: ann_benchmarks.algorithms.n2
      constructor: N2
      base-args: ["@metric"]
      run-groups:
        M-4:
          arg-groups:
            - {"M": 4,  "efConstruction": 500}
          query-args: [[10, 20, 40, 80, 120, 200, 400, 600, 800]]
        M-8:
          arg-groups:
            - {"M": 8,  "efConstruction": 500}
          query-args: [[10, 20, 40, 80, 120, 200, 400, 600, 800]]
        M-12:
          arg-groups:
            - {"M": 12,  "efConstruction": 500}
          query-args: [[10, 20, 40, 80, 120, 200, 400, 600, 800]]
        M-16:
          arg-groups:
            - {"M": 16,  "efConstruction": 500}
          query-args: [[10, 20, 40, 80, 120, 200, 400, 600, 800]]
        M-24:
          arg-groups:
            - {"M": 24,  "efConstruction": 500}
          query-args: [[10, 20, 40, 80, 120, 200, 400, 600, 800]]
        M-36:
          arg-groups:
            - {"M": 36,  "efConstruction": 500}
          query-args: [[10, 20, 40, 80, 120, 200, 400, 600, 800]]
        M-48:
          arg-groups:
            - {"M": 48,  "efConstruction": 500}
          query-args: [[10, 20, 40, 80, 120, 200, 400, 600, 800]]
        M-64:
          arg-groups:
            - {"M": 64,  "efConstruction": 500}
          query-args: [[10, 20, 40, 80, 120, 200, 400, 600, 800]]
        M-96:
          arg-groups:
            - {"M": 96,  "efConstruction": 500}
          query-args: [[10, 20, 40, 80, 120, 200, 400, 600, 800]]

    bruteforce:
      disabled: true
      docker-tag: ann-benchmarks-sklearn
      module: ann_benchmarks.algorithms.bruteforce
      constructor: BruteForce
      base-args: ["@metric"]
      run-groups:
        empty:
          args: []
    bruteforce-blas:
      docker-tag: ann-benchmarks-sklearn
      module: ann_benchmarks.algorithms.bruteforce
      constructor: BruteForceBLAS
      base-args: ["@metric"]
      run-groups:
        empty:
          args: []
    dummy-algo-st:
      disabled: true
      docker-tag: ann-benchmarks-sklearn
      module: ann_benchmarks.algorithms.dummy_algo
      constructor: DummyAlgoSt
      base-args: ["@metric"]
      run-groups:
        empty:
          args: []
    dummy-algo-mt:
      disabled: true
      docker-tag: ann-benchmarks-sklearn
      module: ann_benchmarks.algorithms.dummy_algo
      constructor: DummyAlgoMt
      base-args: ["@metric"]
      run-groups:
        empty:
          args: []
    ball:
      disabled: true
      docker-tag: ann-benchmarks-sklearn
      module: ann_benchmarks.algorithms.balltree
      constructor: BallTree
      base-args: ["@metric"]
      run-groups:
        ball:
          args: &treeargs [[10, 20, 40, 100, 200, 400, 1000]]
    kd:
      docker-tag: ann-benchmarks-sklearn
      module: ann_benchmarks.algorithms.kdtree
      constructor: KDTree
      base-args: ["@metric"]
      run-groups:
        ball:
          args: *treeargs
    ckdtree:
      disable: true
      docker-tag: ann-benchmarks-scipy
      module: ann_benchmarks.algorithms.ckdtree
      constructor: CKDTree
      base-args: ["@metric"]
      run-groups:
        ball:
          args: *treeargs
    BallTree(nmslib):
      docker-tag: ann-benchmarks-nmslib
      module: ann_benchmarks.algorithms.nmslib
      constructor: NmslibReuseIndex
      base-args: ["@metric", "vptree"]
      run-groups:
        base:
          # When @args is a dictionary, algorithm instances will be generated
          # by taking the Cartesian product of all of its values.
          arg-groups:
                - {"tuneK": 10, "desiredRecall": [0.99, 0.97, 0.95, 0.9, 0.85, 0.8,
                    0.7, 0.6, 0.5, 0.4, 0.3, 0.2, 0.1]}
                - False
          # This run group produces thirteen algorithm instances:
          # NmslibNewIndex("angular", "vptree", {"tuneK": 10,
          # "desiredRecall": 0.99}), NmslibNewIndex("angular", "vptree",
          # {"tuneK": 10, "desiredRecall": 0.97}), and so on up to
          # NmslibNewIndex("angular", "vptree", {"tuneK": 10, "desiredRecall":
          # 0.1}).
    pynndescent:
      docker-tag: ann-benchmarks-pynndescent
      module: ann_benchmarks.algorithms.pynndescent
      constructor: PyNNDescent
      base-args: ["@metric"]
      run-groups:
        NN-10-20:
          arg-groups:
            - {"n_neighbors": [10, 20], "diversify_prob": [1.0],
               "pruning_degree_multiplier":[1.5, 2.0], "leaf_size": 32}
          query-args: [[0.0, 0.02, 0.04, 0.06, 0.08, 0.10, 0.12, 0.14, 0.16]]
        NN-40-80:
          arg-groups:
            - {"n_neighbors": [40, 80], "diversify_prob": [0.0, 1.0],
               "pruning_degree_multiplier":[2.0, 2.5], "leaf_size": 64}
          query-args: [[0.0, 0.04, 0.08, 0.12, 0.16, 0.20, 0.24, 0.28, 0.32]]
    NGT-panng:
      docker-tag: ann-benchmarks-ngt
      module: ann_benchmarks.algorithms.panng_ngt
      constructor : PANNG
      base-args : ["@metric", "Float"]
      run-groups :
        panng:
           args : [{'edge': 20, 'pathadj': 40, 'searchedge': 60}]
           query-args : [[0.6, 0.8, 0.9, 1.0, 1.02, 1.05, 1.1, 1.2]]
    NGT-onng:
      docker-tag: ann-benchmarks-ngt
      module: ann_benchmarks.algorithms.onng_ngt
      constructor : ONNG
      base-args : ["@metric", "Float", 0.1]
      run-groups :
        e0.1:
           args : [{'edge': 100, 'outdegree': 10, 'indegree': 120}]
           query-args : [[[0.60, -2], [0.90, -2], [1.00, -2], [1.02, -2], [1.03, -2],
                          [1.04, -2], [1.05, -2], [1.07, -2], [1.10, -2], [1.20, -2]]]
        e0.08:
           args : [{'edge': 100, 'outdegree': 10, 'indegree': 120, 'epsilon': 0.08}]
           query-args : [[[0.60, -2], [0.90, -2], [1.00, -2], [1.02, -2], [1.03, -2],
                          [1.04, -2], [1.05, -2], [1.07, -2], [1.10, -2], [1.20, -2]]]
        e0.06:
           args : [{'edge': 100, 'outdegree': 10, 'indegree': 120, 'epsilon': 0.06}]
           query-args : [[[0.60, -2], [0.90, -2], [1.00, -2], [1.02, -2], [1.03, -2],
                          [1.04, -2], [1.05, -2], [1.07, -2], [1.10, -2], [1.20, -2]]]
        e0.04:
           args : [{'edge': 100, 'outdegree': 10, 'indegree': 120, 'epsilon': 0.04}]
           query-args : [[[0.60, -2], [0.90, -2], [1.00, -2], [1.02, -2], [1.03, -2],
                          [1.04, -2], [1.05, -2], [1.07, -2], [1.10, -2], [1.20, -2]]]
        e0.03:
           args : [{'edge': 100, 'outdegree': 10, 'indegree': 120, 'epsilon': 0.03}]
           query-args : [[[0.60, -2], [0.90, -2], [1.00, -2], [1.02, -2], [1.03, -2],
                          [1.04, -2], [1.05, -2], [1.07, -2], [1.10, -2], [1.20, -2]]]
        e0.02:
           args : [{'edge': 100, 'outdegree': 10, 'indegree': 120, 'epsilon': 0.02}]
           query-args : [[[0.60, -2], [0.90, -2], [1.00, -2], [1.02, -2], [1.03, -2],
                          [1.04, -2], [1.05, -2], [1.07, -2], [1.10, -2], [1.20, -2]]]
        e0.1-with-refinement:
           args : [{'edge': 100, 'outdegree': 10, 'indegree': 120, 'tree': False, 'refine': True}]
           query-args : [[[0.995, 40], [0.998, 40], [1.00, 40], [1.005, 40], [1.010, 40],
                          [1.015, 40], [1.020, 40]]]
        e0.08-with-refinement:
           args : [{'edge': 100, 'outdegree': 10, 'indegree': 120, 'tree': False, 'refine': True, 'epsilon': 0.08}]
           query-args : [[[0.995, 40], [0.998, 40], [1.00, 40], [1.005, 40], [1.010, 40],
                          [1.015, 40], [1.020, 40]]]
    NGT-qg:
      docker-tag: ann-benchmarks-ngt
      module: ann_benchmarks.algorithms.qg_ngt
      constructor : QG
      base-args : ["@metric", "Float", 0.1]
      run-groups :
        s20000-e0.1:
           args : [{'edge': 100, 'outdegree': 64, 'indegree': 120, 'max_edge': 96, 'sample': 20000}]
           query-args : [[[0.0, 0.90], [0.0, 0.95], [0.0, 0.98], [0.0, 1.00],
                          [1.2, 0.90], [1.5, 0.90], [2.0, 0.90], [3.0, 0.90],
                          [1.2, 0.95], [1.5, 0.95], [2.0, 0.95], [3.0, 0.95],
                          [1.2, 0.98], [1.5, 0.98], [2.0, 0.98], [3.0, 0.98],
                          [1.2, 1.00], [1.5, 1.00], [2.0, 1.00], [3.0, 1.00],
                          [  5, 1.00], [ 10, 1.00], [ 20, 1.00],
                          [1.2, 1.02], [1.5, 1.02], [2.0, 1.02], [3.0, 1.02],
                          [  2, 1.04], [  3, 1.04], [  5, 1.04], [  8, 1.04]]]
        s4000-e0.08:
           args : [{'edge': 100, 'outdegree': 64, 'indegree': 120, 'max_edge': 96, 'sample': 4000, 'epsilon': 0.08}]
           query-args : [[[0.0, 0.90], [0.0, 0.95], [0.0, 0.98], [0.0, 1.00],
                          [1.2, 0.90], [1.5, 0.90], [2.0, 0.90], [3.0, 0.90],
                          [1.2, 0.95], [1.5, 0.95], [2.0, 0.95], [3.0, 0.95],
                          [1.2, 0.98], [1.5, 0.98], [2.0, 0.98], [3.0, 0.98],
                          [1.2, 1.00], [1.5, 1.00], [2.0, 1.00], [3.0, 1.00],
                          [  5, 1.00], [ 10, 1.00], [ 20, 1.00],
                          [1.2, 1.02], [1.5, 1.02], [2.0, 1.02], [3.0, 1.02],
                          [  2, 1.04], [  3, 1.04], [  5, 1.04], [  8, 1.04]]]
        s4000-e0.06:
           args : [{'edge': 100, 'outdegree': 64, 'indegree': 120, 'max_edge': 96, 'sample': 4000, 'epsilon': 0.06}]
           query-args : [[[0.0, 0.90], [0.0, 0.95], [0.0, 0.98], [0.0, 1.00],
                          [1.2, 0.90], [1.5, 0.90], [2.0, 0.90], [3.0, 0.90],
                          [1.2, 0.95], [1.5, 0.95], [2.0, 0.95], [3.0, 0.95],
                          [1.2, 0.98], [1.5, 0.98], [2.0, 0.98], [3.0, 0.98],
                          [1.2, 1.00], [1.5, 1.00], [2.0, 1.00], [3.0, 1.00],
                          [  5, 1.00], [ 10, 1.00], [ 20, 1.00],
                          [1.2, 1.02], [1.5, 1.02], [2.0, 1.02], [3.0, 1.02],
                          [  2, 1.04], [  3, 1.04], [  5, 1.04], [  8, 1.04]]]
        s4000-e0.04:
           args : [{'edge': 100, 'outdegree': 64, 'indegree': 120, 'max_edge': 96, 'sample': 4000, 'epsilon': 0.04}]
           query-args : [[[0.0, 0.90], [0.0, 0.95], [0.0, 0.98], [0.0, 1.00],
                          [1.2, 0.90], [1.5, 0.90], [2.0, 0.90], [3.0, 0.90],
                          [1.2, 0.95], [1.5, 0.95], [2.0, 0.95], [3.0, 0.95],
                          [1.2, 0.98], [1.5, 0.98], [2.0, 0.98], [3.0, 0.98],
                          [1.2, 1.00], [1.5, 1.00], [2.0, 1.00], [3.0, 1.00],
                          [  5, 1.00], [ 10, 1.00], [ 20, 1.00],
                          [1.2, 1.02], [1.5, 1.02], [2.0, 1.02], [3.0, 1.02],
                          [  2, 1.04], [  3, 1.04], [  5, 1.04], [  8, 1.04]]]
        s4000-e0.02:
           args : [{'edge': 100, 'outdegree': 64, 'indegree': 120, 'max_edge': 96, 'sample': 4000, 'epsilon': 0.02}]
           query-args : [[[0.0, 0.90], [0.0, 0.95], [0.0, 0.98], [0.0, 1.00],
                          [1.2, 0.90], [1.5, 0.90], [2.0, 0.90], [3.0, 0.90],
                          [1.2, 0.95], [1.5, 0.95], [2.0, 0.95], [3.0, 0.95],
                          [1.2, 0.98], [1.5, 0.98], [2.0, 0.98], [3.0, 0.98],
                          [1.2, 1.00], [1.5, 1.00], [2.0, 1.00], [3.0, 1.00],
                          [  5, 1.00], [ 10, 1.00], [ 20, 1.00],
                          [1.2, 1.02], [1.5, 1.02], [2.0, 1.02], [3.0, 1.02],
                          [  2, 1.04], [  3, 1.04], [  5, 1.04], [  8, 1.04]]]
        s2000-e0.02:
           args : [{'edge': 100, 'outdegree': 64, 'indegree': 120, 'max_edge': 96, 'sample': 2000, 'epsilon': 0.02}]
           query-args : [[[0.0, 0.90], [0.0, 0.95], [0.0, 0.98], [0.0, 1.00],
                          [1.2, 0.90], [1.5, 0.90], [2.0, 0.90], [3.0, 0.90],
                          [1.2, 0.95], [1.5, 0.95], [2.0, 0.95], [3.0, 0.95],
                          [1.2, 0.98], [1.5, 0.98], [2.0, 0.98], [3.0, 0.98],
                          [1.2, 1.00], [1.5, 1.00], [2.0, 1.00], [3.0, 1.00],
                          [  5, 1.00], [ 10, 1.00], [ 20, 1.00],
                          [1.2, 1.02], [1.5, 1.02], [2.0, 1.02], [3.0, 1.02],
                          [  2, 1.04], [  3, 1.04], [  5, 1.04], [  8, 1.04]]]
    mrpt:
      docker-tag: ann-benchmarks-mrpt
      module: ann_benchmarks.algorithms.mrpt
      constructor: MRPT
      base-args: ["@metric"]
      run-groups:
        mrpt:
          args: ["@count"]
          query-args: [[0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.85, 0.9,
                        0.925, 0.95, 0.97, 0.98, 0.99, 0.995]]
    elastiknn-exact:
      # Disabled because they are < 10 q/s, which will timeout on default settings.
      disabled: true
      docker-tag: ann-benchmarks-elastiknn
      module: ann_benchmarks.algorithms.elastiknn
      constructor: Exact
      base-args: [ "@metric", "@dimension" ]
      run-groups:
        exact:
          args: []
    vald(NGT-anng):
      docker-tag: ann-benchmarks-vald
      module: ann_benchmarks.algorithms.vald
      constructor: Vald
      base-args: ["@metric", "Float"]
      run-groups:
        vald:
          args: [{'edge': 20, 'searchedge': 60, 'bulk': 100}]
          query-args: [[0.6, 0.8, 0.9, 1.0, 1.02, 1.05, 1.1, 1.2]]
    qdrant:
      docker-tag: ann-benchmarks-qdrant
      module: ann_benchmarks.algorithms.qdrant
      constructor: Qdrant
      base-args: ["@metric"]
      run-groups:
        default:
          args: [
            [True, False], #quantization
            [ 8, 16, 24, 32, 40, 48, 64, 72 ], #m
            [ 64, 128, 256, 512 ], #ef_construct
          ]
          query-args: [
            [null, 8, 16, 32, 64, 128, 256, 512, 768], #hnsw_ef
            [True, False], # re-score
          ]
    qsgngt:
      docker-tag: ann-benchmarks-qsgngt
      module: ann_benchmarks.algorithms.qsg_ngt
      constructor: QSG
      base-args: ["@metric", "Float", 0.1]
      run-groups:
        cos-60-1:
          args : [{'edge': 100, 'outdegree': 64, 'indegree': 120, 'max_edge': 96, 'sample': 20000, 'epsilon': 0.1, 'range': 175, 'threshold': 60, 'rangeMax': 175, 'searchA': 400, 'ifES': 0, 'paramE': [400, 400, 12, 10, 250], 'paramS': [100, 100, 60]}]
          query-args : [[[0.0, 0.90], [0.0, 0.95], [0.0, 0.98], [0.0, 1.00], [1.2, 0.90], [1.5, 0.90], [2.0, 0.90], [3.0, 0.90], [1.2, 0.95], [1.5, 0.95], [2.0, 0.95], [3.0, 0.95], [1.2, 0.98], [1.5, 0.98], [2.0, 0.98], [3.0, 0.98],
                         [1.2, 1.00], [1.5, 1.00], [2.0, 1.00], [3.0, 1.00], [  5, 1.00], [ 10, 1.00], [ 20, 1.00], [1.2, 1.02], [1.5, 1.02], [2.0, 1.02], [3.0, 1.02], [  2, 1.04], [  3, 1.04], [  5, 1.04], [  8, 1.04]]]
        cos-60-2:
          args : [{'edge': 100, 'outdegree': 64, 'indegree': 120, 'max_edge': 96, 'sample': 4000, 'epsilon': 0.04, 'range': 175, 'threshold': 60, 'rangeMax': 175, 'searchA': 400, 'ifES': 0, 'paramE': [400, 400, 12, 10, 250], 'paramS': [100, 100, 60]}]
          query-args : [[[0.0, 0.90], [0.0, 0.95], [0.0, 0.98], [0.0, 1.00], [1.2, 0.90], [1.5, 0.90], [2.0, 0.90], [3.0, 0.90], [1.2, 0.95], [1.5, 0.95], [2.0, 0.95], [3.0, 0.95], [1.2, 0.98], [1.5, 0.98], [2.0, 0.98], [3.0, 0.98],
                         [1.2, 1.00], [1.5, 1.00], [2.0, 1.00], [3.0, 1.00], [  5, 1.00], [ 10, 1.00], [ 20, 1.00], [1.2, 1.02], [1.5, 1.02], [2.0, 1.02], [3.0, 1.02], [  2, 1.04], [  3, 1.04], [  5, 1.04], [  8, 1.04]]]
        cos-60-3:
          args : [{'edge': 100, 'outdegree': 64, 'indegree': 120, 'max_edge': 96, 'sample': 2000, 'epsilon': 0.02, 'range': 175, 'threshold': 60, 'rangeMax': 175, 'searchA': 400, 'ifES': 0, 'paramE': [400, 400, 12, 10, 250], 'paramS': [100, 100, 60]}]
          query-args : [[[0.0, 0.90], [0.0, 0.95], [0.0, 0.98], [0.0, 1.00], [1.2, 0.90], [1.5, 0.90], [2.0, 0.90], [3.0, 0.90], [1.2, 0.95], [1.5, 0.95], [2.0, 0.95], [3.0, 0.95], [1.2, 0.98], [1.5, 0.98], [2.0, 0.98], [3.0, 0.98],
                         [1.2, 1.00], [1.5, 1.00], [2.0, 1.00], [3.0, 1.00], [  5, 1.00], [ 10, 1.00], [ 20, 1.00], [1.2, 1.02], [1.5, 1.02], [2.0, 1.02], [3.0, 1.02], [  2, 1.04], [  3, 1.04], [  5, 1.04], [  8, 1.04]]]
        cos-60-4:
          args : [{'edge': 100, 'outdegree': 64, 'indegree': 120, 'max_edge': 96, 'sample': 4000, 'epsilon': 0.04, 'range': 100, 'threshold': 60, 'rangeMax': 300, 'searchA': 400, 'ifES': 0, 'paramE': [400, 400, 12, 10, 250], 'paramS': [100, 100, 60]}]
          query-args : [[[0.0, 0.90], [0.0, 0.95], [0.0, 0.98], [0.0, 1.00], [1.2, 0.90], [1.5, 0.90], [2.0, 0.90], [3.0, 0.90], [1.2, 0.95], [1.5, 0.95], [2.0, 0.95], [3.0, 0.95], [1.2, 0.98], [1.5, 0.98], [2.0, 0.98], [3.0, 0.98],
                         [1.2, 1.00], [1.5, 1.00], [2.0, 1.00], [3.0, 1.00], [  5, 1.00], [ 10, 1.00], [ 20, 1.00], [1.2, 1.02], [1.5, 1.02], [2.0, 1.02], [3.0, 1.02], [  2, 1.04], [  3, 1.04], [  5, 1.04], [  8, 1.04]]]
        cos-60-5:
          args : [{'edge': 100, 'outdegree': 64, 'indegree': 120, 'max_edge': 96, 'sample': 4000, 'epsilon': 0.02, 'range': 100, 'threshold': 60, 'rangeMax': 300, 'searchA': 400, 'ifES': 0, 'paramE': [400, 400, 12, 10, 250], 'paramS': [100, 100, 60]}]
          query-args : [[[0.0, 0.90], [0.0, 0.95], [0.0, 0.98], [0.0, 1.00], [1.2, 0.90], [1.5, 0.90], [2.0, 0.90], [3.0, 0.90], [1.2, 0.95], [1.5, 0.95], [2.0, 0.95], [3.0, 0.95], [1.2, 0.98], [1.5, 0.98], [2.0, 0.98], [3.0, 0.98],
                         [1.2, 1.00], [1.5, 1.00], [2.0, 1.00], [3.0, 1.00], [  5, 1.00], [ 10, 1.00], [ 20, 1.00], [1.2, 1.02], [1.5, 1.02], [2.0, 1.02], [3.0, 1.02], [  2, 1.04], [  3, 1.04], [  5, 1.04], [  8, 1.04]]]
        cos-60-6:
          args : [{'edge': 100, 'outdegree': 64, 'indegree': 120, 'max_edge': 96, 'sample': 4000, 'epsilon': 0.02, 'range': 175, 'threshold': 60, 'rangeMax': 175, 'searchA': 400, 'ifES': 0, 'paramE': [400, 400, 12, 10, 250], 'paramS': [100, 100, 60]}]
          query-args : [[[0.0, 0.90], [0.0, 0.95], [0.0, 0.98], [0.0, 1.00], [1.2, 0.90], [1.5, 0.90], [2.0, 0.90], [3.0, 0.90], [1.2, 0.95], [1.5, 0.95], [2.0, 0.95], [3.0, 0.95], [1.2, 0.98], [1.5, 0.98], [2.0, 0.98], [3.0, 0.98],
                         [1.2, 1.00], [1.5, 1.00], [2.0, 1.00], [3.0, 1.00], [  5, 1.00], [ 10, 1.00], [ 20, 1.00], [1.2, 1.02], [1.5, 1.02], [2.0, 1.02], [3.0, 1.02], [  2, 1.04], [  3, 1.04], [  5, 1.04], [  8, 1.04]]]
        cos-60-7:
          args : [{'edge': 100, 'outdegree': 64, 'indegree': 120, 'max_edge': 96, 'sample': 4000, 'epsilon': 0.08, 'range': 200, 'threshold': 60, 'rangeMax': 200, 'searchA': 400, 'ifES': 0, 'paramE': [400, 400, 12, 10, 250], 'paramS': [100, 100, 60]}]
          query-args : [[[0.0, 0.90], [0.0, 0.95], [0.0, 0.98], [0.0, 1.00], [1.2, 0.90], [1.5, 0.90], [2.0, 0.90], [3.0, 0.90], [1.2, 0.95], [1.5, 0.95], [2.0, 0.95], [3.0, 0.95], [1.2, 0.98], [1.5, 0.98], [2.0, 0.98], [3.0, 0.98],
                         [1.2, 1.00], [1.5, 1.00], [2.0, 1.00], [3.0, 1.00], [  5, 1.00], [ 10, 1.00], [ 20, 1.00], [1.2, 1.02], [1.5, 1.02], [2.0, 1.02], [3.0, 1.02], [  2, 1.04], [  3, 1.04], [  5, 1.04], [  8, 1.04]]]
        cos-60-8:
          args : [{'edge': 100, 'outdegree': 64, 'indegree': 120, 'max_edge': 96, 'sample': 4000, 'epsilon': 0.04, 'range': 200, 'threshold': 60, 'rangeMax': 200, 'searchA': 400, 'ifES': 0, 'paramE': [400, 400, 12, 10, 250], 'paramS': [100, 100, 60]}]
          query-args : [[[0.0, 0.90], [0.0, 0.95], [0.0, 0.98], [0.0, 1.00], [1.2, 0.90], [1.5, 0.90], [2.0, 0.90], [3.0, 0.90], [1.2, 0.95], [1.5, 0.95], [2.0, 0.95], [3.0, 0.95], [1.2, 0.98], [1.5, 0.98], [2.0, 0.98], [3.0, 0.98],
                         [1.2, 1.00], [1.5, 1.00], [2.0, 1.00], [3.0, 1.00], [  5, 1.00], [ 10, 1.00], [ 20, 1.00], [1.2, 1.02], [1.5, 1.02], [2.0, 1.02], [3.0, 1.02], [  2, 1.04], [  3, 1.04], [  5, 1.04], [  8, 1.04]]]
        cos-60-9:
          args : [{'edge': 100, 'outdegree': 64, 'indegree': 120, 'max_edge': 96, 'sample': 4000, 'epsilon': 0.02, 'range': 200, 'threshold': 60, 'rangeMax': 200, 'searchA': 400, 'ifES': 0, 'paramE': [400, 400, 12, 10, 250], 'paramS': [100, 100, 60]}]
          query-args : [[[0.0, 0.90], [0.0, 0.95], [0.0, 0.98], [0.0, 1.00], [1.2, 0.90], [1.5, 0.90], [2.0, 0.90], [3.0, 0.90], [1.2, 0.95], [1.5, 0.95], [2.0, 0.95], [3.0, 0.95], [1.2, 0.98], [1.5, 0.98], [2.0, 0.98], [3.0, 0.98],
                         [1.2, 1.00], [1.5, 1.00], [2.0, 1.00], [3.0, 1.00], [  5, 1.00], [ 10, 1.00], [ 20, 1.00], [1.2, 1.02], [1.5, 1.02], [2.0, 1.02], [3.0, 1.02], [  2, 1.04], [  3, 1.04], [  5, 1.04], [  8, 1.04]]]
        cos-89:
          args : [{'edge': 100, 'outdegree': 64, 'indegree': 120, 'max_edge': 96, 'sample': 4000, 'epsilon': 0.08, 'range': 200, 'threshold': 89, 'rangeMax': 200, 'searchA': 400, 'ifES': 0, 'paramE': [400, 400, 12, 10, 250], 'paramS': [100, 100, 60]}]
          query-args : [[[0.0, 0.90], [0.0, 0.95], [0.0, 0.98], [0.0, 1.00], [1.2, 0.90], [1.5, 0.90], [2.0, 0.90], [3.0, 0.90], [1.2, 0.95], [1.5, 0.95], [2.0, 0.95], [3.0, 0.95], [1.2, 0.98], [1.5, 0.98], [2.0, 0.98], [3.0, 0.98],
                         [1.2, 1.00], [1.5, 1.00], [2.0, 1.00], [3.0, 1.00], [  5, 1.00], [ 10, 1.00], [ 20, 1.00], [1.2, 1.02], [1.5, 1.02], [2.0, 1.02], [3.0, 1.02], [  2, 1.04], [  3, 1.04], [  5, 1.04], [  8, 1.04]]]
        K-200-100:
          args: [ { 'edge': 100, 'outdegree': 64, 'indegree': 120, 'max_edge': 96, 'sample': 20000, 'epsilon': 0.1, 'range': 200, 'threshold': 60, 'rangeMax': 200, 'searchA': 400, 'ifES': 1, 'paramE': [200, 200, 15, 10, 100], 'paramS': [100, 100, 60]}]
          query-args : [[[0.0, 0.90], [0.0, 0.95], [0.0, 0.98], [0.0, 1.00], [1.2, 0.90], [1.5, 0.90], [2.0, 0.90], [3.0, 0.90], [1.2, 0.95], [1.5, 0.95], [2.0, 0.95], [3.0, 0.95], [1.2, 0.98], [1.5, 0.98], [2.0, 0.98], [3.0, 0.98],
                         [1.2, 1.00], [1.5, 1.00], [2.0, 1.00], [3.0, 1.00], [  5, 1.00], [ 10, 1.00], [ 20, 1.00], [1.2, 1.02], [1.5, 1.02], [2.0, 1.02], [3.0, 1.02], [  2, 1.04], [  3, 1.04], [  5, 1.04], [  8, 1.04]]]
    pgvector:
      docker-tag: ann-benchmarks-pgvector
      module: ann_benchmarks.algorithms.pgvector
      constructor: PGVector
      base-args: ["@metric"]
      run-groups:
        pgvector:
          args: [[100, 200, 400, 1000, 2000, 4000]]
          query-args: [[1, 2, 4, 10, 20, 40, 100]]

    redisearch:
      docker-tag: ann-benchmarks-redisearch
      module: ann_benchmarks.algorithms.redisearch
      constructor: Redisearch
      base-args: ["@metric"]
      run-groups:
        redisearch:
          arg-groups: [[4, 8, 12, 16, 24, 36, 48, 64, 96]]
          query-args: [[10, 20, 40, 80, 120, 200, 400, 600, 800]]

    weaviate:
      docker-tag: ann-benchmarks-weaviate
      module: ann_benchmarks.algorithms.weaviate
      constructor: Weaviate
      base-args: ["@metric"]
      run-groups:
        weaviate:
          args: [
            [8, 16, 24, 32, 40, 48, 64, 72], #maxConnections
            [64, 128, 256, 512], #efConstruction
          ]
          query-args: [[16, 32, 48, 64, 96, 128, 256, 512, 768]]

  euclidean:
    vamana(diskann):
      docker-tag: ann-benchmarks-diskann
      module: ann_benchmarks.algorithms.diskann
      constructor: Vamana
      base-args : ["@metric"]
      run-groups :
        vamana_100_64_1-2:
           args : [{'l_build': 100, 'max_outdegree': 64, 'alpha': 1.2}]
           query-args : [[10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120]]
        vamana_100_64_1-1:
           args : [{'l_build': 100, 'max_outdegree': 64, 'alpha': 1.1}]
           query-args : [[10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120]]
        vamana_100_64_1:
           args : [{'l_build': 100, 'max_outdegree': 64, 'alpha': 1}]
           query-args : [[10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120]]
        vamana_125_32_1-2:
           args : [{'l_build': 125, 'max_outdegree': 32, 'alpha': 1.2}]
           query-args : [[10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120]]
        vamana_125_32_1-1:
           args : [{'l_build': 125, 'max_outdegree': 32, 'alpha': 1.1}]
           query-args : [[10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120]]
        vamana_125_32_1:
           args : [{'l_build': 125, 'max_outdegree': 32, 'alpha': 1}]
           query-args : [[10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120]]
    vamana-pq(diskann):
      docker-tag: ann-benchmarks-diskann_pq
      module: ann_benchmarks.algorithms.diskann
      constructor: VamanaPQ
      base-args : ["@metric"]
      run-groups :
        vamana_pq_100_64_1-2_32:
           args : [{'l_build': 100, 'max_outdegree': 64, 'alpha': 1.2, 'chunks': 32}]
           query-args : [[10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120]]
        vamana_pq_100_64_1_32:
           args : [{'l_build': 100, 'max_outdegree': 64, 'alpha': 1, 'chunks': 32}]
           query-args : [[10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120]]
        vamana_pq_80_64_1-2_96:
           args : [{'l_build': 80, 'max_outdegree': 64, 'alpha': 1.2, 'chunks': 96}]
           query-args : [[10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120]]
        vamana_pq_80_64_1_96:
           args : [{'l_build': 80, 'max_outdegree': 64, 'alpha': 1, 'chunks': 96}]
           query-args : [[10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120]]
        vamana_pq_80_64_1-2_112:
           args : [{'l_build': 80, 'max_outdegree': 64, 'alpha': 1.2, 'chunks': 112}]
           query-args : [[10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120]]
        vamana_pq_80_64_1_112:
           args : [{'l_build': 80, 'max_outdegree': 64, 'alpha': 1, 'chunks': 112}]
           query-args : [[10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120]]
        vamana_pq_125_32_1-2_32:
           args : [{'l_build': 125, 'max_outdegree': 32, 'alpha': 1.2, 'chunks': 32}]
           query-args : [[10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120]]
        vamana_pq_125_32_1_32:
           args : [{'l_build': 125, 'max_outdegree': 32, 'alpha': 1, 'chunks': 32}]
           query-args : [[10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120]]
        vamana_pq_125_32_1-2_96:
           args : [{'l_build': 125, 'max_outdegree': 32, 'alpha': 1.2, 'chunks': 96}]
           query-args : [[10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120]]
        vamana_pq_125_32_1_96:
           args : [{'l_build': 125, 'max_outdegree': 32, 'alpha': 1, 'chunks': 96}]
           query-args : [[10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120]]
        vamana_pq_125_32_1-2_112:
           args : [{'l_build': 125, 'max_outdegree': 32, 'alpha': 1.2, 'chunks': 112}]
           query-args : [[10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120]]
        vamana_pq_125_32_1_112:
           args : [{'l_build': 125, 'max_outdegree': 32, 'alpha': 1, 'chunks': 112}]
           query-args : [[10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120]]
    scann:
      docker-tag: ann-benchmarks-scann
      module: ann_benchmarks.algorithms.scann
      constructor: Scann
      run-groups:
        scann1:
          args: [[600], [.nan], [2], ["squared_l2"]]
          query-args: [[[4, 40], [3, 30], [6, 60], [8, 74], [9, 78], [10, 82], [11, 85], [13, 100], [16, 120], [20, 140], [30, 180], [35, 240], [50, 360]]]
        scann2:
          args: [[2000], [.nan], [4], ["squared_l2"]]
          query-args: [[[10, 100], [15, 140], [25, 160], [35, 190], [40, 200], [45, 220], [50, 240], [60, 250], [70, 300], [80, 400], [100, 500], [120, 600], [150, 800], [200, 900]]]
        scann3:
          args: [[100], [.nan], [4], ["squared_l2"]]
          query-args: [[[2, 20], [3, 20], [3, 30], [4, 30], [5, 40], [8, 80]]]
    kgraph:
      disable: true
      docker-tag: ann-benchmarks-kgraph
      module: ann_benchmarks.algorithms.kgraph
      constructor: KGraph
      base-args: ["@metric"]
      run-groups:
        kgraph:
          args: [ {'reverse': -1}, True] # XXX: hard-codes save_index as True
          query-args: [[1, 2, 3, 4, 5, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100]]
    hnsw(nmslib):
      docker-tag: ann-benchmarks-nmslib
      module: ann_benchmarks.algorithms.nmslib
      constructor: NmslibReuseIndex
      base-args: ["@metric", "hnsw"]
      run-groups:
        M-32:
          # If a run group has an array called @arg-groups instead of one
          # called @args, then every element in that array will be separately
          # expanded before then taking the Cartesian product of all of those
          # expansions.
          #
          # Yes, this is a bit of a hack, but some constructors are weird.
          # (This one used to require that dictionaries be encoded as lists
          # of environment variable-style strings -- ["M=32", "post=2",
          # "efConstruction=400"] -- which didn't work with this at all...)
          arg-groups:
            - {"M": 32, "post": 2, "efConstruction": 400}
            - False
          query-args: [[20, 30, 40, 50, 60, 70, 80, 90, 100, 120, 140, 160, 200,
              300, 400]]
        M-20:
          arg-groups:
            - {"M": 20, "post": 2, "efConstruction": 400}
            - False
          query-args: [[2, 5, 10, 15, 20, 30, 40, 50, 70, 80, 120, 200, 400]]
        M-12:
          arg-groups:
            - {"M": 12, "post": 0, "efConstruction": 400}
            - False
          query-args: [[1, 2, 5, 10, 15, 20, 30, 40, 50, 70, 80, 120]]
        M-4:
          arg-groups:
            - {"M": 4, "post": 0, "efConstruction": 400}
            - False
          query-args: [[1, 2, 5, 10, 20, 30, 50, 70, 90, 120]]
        M-8:
          arg-groups:
            - {"M": 8, "post": 0, "efConstruction": 400}
            - False
          query-args: [[1, 2, 5, 10, 20, 30, 50, 70, 90, 120, 160]]
    SW-graph(nmslib):
      docker-tag: ann-benchmarks-nmslib
      module: ann_benchmarks.algorithms.nmslib
      constructor: NmslibReuseIndex
      base-args: ["@metric", "sw-graph"]
      run-groups:
        NN-24:
          arg-groups:
            - {"NN": 24}
            - False
          query-args: [[800, 400, 200, 100, 50, 30, 20, 15, 10, 5, 1]]
        NN-16:
          arg-groups:
            - {"NN": 16}
            - False
          query-args: [[800, 400, 200, 100, 50, 30, 20, 15, 10, 5, 1]]
        NN-10:
          arg-groups:
            - {"NN": 10}
            - False
          query-args: [[800, 400, 200, 100, 50, 30, 20, 15, 10, 5, 1]]
        NN-5:
          arg-groups:
            - {"NN": 5}
            - False
          query-args: [[30, 25, 20, 15, 10, 5, 4, 3, 2, 1]]
    pynndescent:
      docker-tag: ann-benchmarks-pynndescent
      module: ann_benchmarks.algorithms.pynndescent
      constructor: PyNNDescent
      base-args: ["@metric"]
      run-groups:
        NN-10:
          arg-groups:
            - {"n_neighbors": 10, "diversify_prob": 1.0,
               "pruning_degree_multiplier":[0.5, 1.0], "leaf_size": 24}
          query-args: [[0.0, 0.01, 0.02, 0.03, 0.04, 0.06, 0.08, 0.10, 0.12]]
        NN-20:
          arg-groups:
            - {"n_neighbors": 20, "diversify_prob": 1.0,
               "pruning_degree_multiplier":[0.75, 1.5], "leaf_size": 24}
          query-args: [[0.0, 0.02, 0.04, 0.06, 0.08, 0.10, 0.12, 0.14, 0.16, 0.20]]
        NN-40:
          arg-groups:
            - {"n_neighbors": 40, "diversify_prob": [0.0, 1.0],
               "pruning_degree_multiplier":[1.0, 2.0], "leaf_size": 36}
          query-args: [[0.0, 0.02, 0.04, 0.08, 0.12, 0.16, 0.20, 0.24, 0.28, 0.32]]
        NN-60:
          arg-groups:
            - {"n_neighbors": 60, "diversify_prob": 0.0,
               "pruning_degree_multiplier":[2.0, 3.0], "leaf_size": 48}
          query-args: [[0.0, 0.04, 0.08, 0.12, 0.16, 0.20, 0.24, 0.28, 0.32, 0.36]]
    elasticsearch:
      docker-tag: ann-benchmarks-elasticsearch
      module: ann_benchmarks.algorithms.elasticsearch
      constructor: ElasticsearchKNN
      base-args: ["@metric", "@dimension"]
      run-groups:
        m-16-ef-100:
          arg-groups:
            - {"m": 16,  "ef_construction": 100} # index_options
          query-args: [[10, 20, 40, 80, 160]] # num_candidates
    elastiknn-l2lsh:
      docker-tag: ann-benchmarks-elastiknn
      module: ann_benchmarks.algorithms.elastiknn
      constructor: L2Lsh
      base-args: []
      run-groups:
        elastiknn-l2lsh:
          args:
          - [100]         # L
          - [4]           # k
          - [1024, 2048]  # w
          query-args:
          - [500, 1000]   # candidates
          - [0, 3]        # probes
    opensearchknn:
      docker-tag: ann-benchmarks-opensearchknn
      module: ann_benchmarks.algorithms.opensearchknn
      constructor: OpenSearchKNN
      base-args: ["@metric", "@dimension"]
      run-groups:
        M-4:
          arg-groups:
            - {"M": 4,  "efConstruction": 500}
          query-args: [[10, 20, 40, 80, 120, 200, 400, 600, 800]]
        M-8:
          arg-groups:
            - {"M": 8,  "efConstruction": 500}
          query-args: [[10, 20, 40, 80, 120, 200, 400, 600, 800]]
        M-12:
          arg-groups:
            - {"M": 12,  "efConstruction": 500}
          query-args: [[10, 20, 40, 80, 120, 200, 400, 600, 800]]
        M-16:
          arg-groups:
            - {"M": 16,  "efConstruction": 500}
          query-args: [[10, 20, 40, 80, 120, 200, 400, 600, 800]]
        M-24:
          arg-groups:
            - {"M": 24,  "efConstruction": 500}
          query-args: [[10, 20, 40, 80, 120, 200, 400, 600, 800]]
        M-36:
          arg-groups:
            - {"M": 36,  "efConstruction": 500}
          query-args: [[10, 20, 40, 80, 120, 200, 400, 600, 800]]
        M-48:
          arg-groups:
            - {"M": 48,  "efConstruction": 500}
          query-args: [[10, 20, 40, 80, 120, 200, 400, 600, 800]]
    hnsw(vespa):
      docker-tag: ann-benchmarks-vespa
      module: ann_benchmarks.algorithms.vespa
      constructor: VespaHnsw
      base-args: ["@metric", "@dimension"]
      run-groups:
        M-4:
          arg-groups:
            - {"M": 4, "efConstruction": 500}
          query-args: [[10, 20, 40, 80, 120, 200, 400, 600, 800]]
        M-8:
          arg-groups:
            - {"M": 8, "efConstruction": 500}
          query-args: [[10, 20, 40, 80, 120, 200, 400, 600, 800]]
        M-12:
          arg-groups:
            - {"M": 12, "efConstruction": 500}
          query-args: [[10, 20, 40, 80, 120, 200, 400, 600, 800]]
        M-16:
          arg-groups:
            - {"M": 16, "efConstruction": 500}
          query-args: [[10, 20, 40, 80, 120, 200, 400, 600, 800]]
        M-24:
          arg-groups:
            - {"M": 24, "efConstruction": 500}
          query-args: [[10, 20, 40, 80, 120, 200, 400, 600, 800]]
        M-36:
          arg-groups:
            - {"M": 36, "efConstruction": 500}
          query-args: [[10, 20, 40, 80, 120, 200, 400, 600, 800]]
        M-48:
          arg-groups:
            - {"M": 48, "efConstruction": 500}
          query-args: [[10, 20, 40, 80, 120, 200, 400, 600, 800]]
        M-64:
          arg-groups:
            - {"M": 64, "efConstruction": 500}
          query-args: [[10, 20, 40, 80, 120, 200, 400, 600, 800]]
        M-96:
          arg-groups:
            - {"M": 96, "efConstruction": 500}
          query-args: [[10, 20, 40, 80, 120, 200, 400, 600, 800]]
    vearch:
      docker-tag: ann-benchmarks-vearch
      module: ann_benchmarks.algorithms.vearch
      constructor: VearchIndex
      base-args: ["@metric"]
      run-groups:
        vearch1:
          args: [[256], [-1], [2, 4]]
          query-args: [[4, 6, 8, 10, 13, 16, 19, 22], [8, 10, 12]]
        vearch2:
          args: [[512], [-1], [2, 4]]
          query-args: [[4, 5, 6, 7, 8, 10, 12, 14, 16, 20, 25, 35, 40, 50], [4, 5, 10]]
        vearch3:
          args: [[1024], [-1], [2, 4]]
          query-args: [[6, 8, 10, 13, 16, 20, 25, 30, 35, 40, 50, 65, 80, 100, 130], [5, 10]]
        vearch4:
          args: [[2048], [-1], [2, 4]]
          query-args: [[10, 15, 25, 35, 40, 45, 50, 60, 70, 85, 100, 120, 150, 200, 300], [5, 10]]

  angular:
    vamana(diskann):
      docker-tag: ann-benchmarks-diskann
      module: ann_benchmarks.algorithms.diskann
      constructor: Vamana
      base-args : ["@metric"]
      run-groups :
        vamana_125_64_1-2:
           args : [{'l_build': 125, 'max_outdegree': 64, 'alpha': 1.2}]
           query-args : [[10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120]]
        vamana_125_64_1-1:
           args : [{'l_build': 125, 'max_outdegree': 64, 'alpha': 1.1}]
           query-args : [[10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120]]
        vamana_125_64_1:
           args : [{'l_build': 125, 'max_outdegree': 64, 'alpha': 1}]
           query-args : [[10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120]]
        vamana_125_32_1-2:
           args : [{'l_build': 125, 'max_outdegree': 32, 'alpha': 1.2}]
           query-args : [[10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120]]
        vamana_125_32_1-1:
           args : [{'l_build': 125, 'max_outdegree': 32, 'alpha': 1.1}]
           query-args : [[10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120]]
        vamana_125_32_1:
           args : [{'l_build': 125, 'max_outdegree': 32, 'alpha': 1}]
           query-args : [[10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120]]
    vamana-pq(diskann):
      docker-tag: ann-benchmarks-diskann_pq
      module: ann_benchmarks.algorithms.diskann
      constructor: VamanaPQ
      base-args : ["@metric"]
      run-groups :
        vamana_pq_125_64_1-2_14:
           args : [{'l_build': 125, 'max_outdegree': 64, 'alpha': 1.2, 'chunks': 14}]
           query-args : [[10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120]]
        vamana_pq_125_64_1_14:
           args : [{'l_build': 125, 'max_outdegree': 64, 'alpha': 1, 'chunks': 14}]
           query-args : [[10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120]]
        vamana_pq_125_64_1-2_28:
           args : [{'l_build': 125, 'max_outdegree': 64, 'alpha': 1.2, 'chunks': 28}]
           query-args : [[10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120]]
        vamana_pq_125_64_1_28:
           args : [{'l_build': 125, 'max_outdegree': 64, 'alpha': 1, 'chunks': 28}]
           query-args : [[10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120]]
        vamana_pq_125_64_1-2_42:
           args : [{'l_build': 125, 'max_outdegree': 64, 'alpha': 1.2, 'chunks': 42}]
           query-args : [[10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120]]
        vamana_pq_125_64_1_42:
           args : [{'l_build': 125, 'max_outdegree': 64, 'alpha': 1, 'chunks': 42}]
           query-args : [[10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120]]
        vamana_pq_125_32_1-2_14:
           args : [{'l_build': 125, 'max_outdegree': 32, 'alpha': 1.2, 'chunks': 14}]
           query-args : [[10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120]]
        vamana_pq_125_32_1_14:
           args : [{'l_build': 125, 'max_outdegree': 32, 'alpha': 1, 'chunks': 14}]
           query-args : [[10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120]]
        vamana_pq_125_32_1-2_28:
           args : [{'l_build': 125, 'max_outdegree': 32, 'alpha': 1.2, 'chunks': 28}]
           query-args : [[10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120]]
        vamana_pq_125_32_1_28:
           args : [{'l_build': 125, 'max_outdegree': 32, 'alpha': 1, 'chunks': 28}]
           query-args : [[10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120]]
        vamana_pq_125_32_1-2_42:
           args : [{'l_build': 125, 'max_outdegree': 32, 'alpha': 1.2, 'chunks': 42}]
           query-args : [[10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120]]
        vamana_pq_125_32_1_42:
           args : [{'l_build': 125, 'max_outdegree': 32, 'alpha': 1, 'chunks': 42}]
           query-args : [[10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120]]
    puffinn:
      disable: true
      docker-tag: ann-benchmarks-puffinn
      module: ann_benchmarks.algorithms.puffinn
      constructor: Puffinn
      base-args: ["@metric"]
      run-groups:
        base:
            args: [
              [ 268435456, 536870912, 1073741824, 2147483648, 4294967296],
              ['fht_crosspolytope'],
            ]
            query-args: [[0.1, 0.2, 0.5, 0.7, 0.9, 0.95, 0.99]]
    kgraph:
      disable: true
      docker-tag: ann-benchmarks-kgraph
      module: ann_benchmarks.algorithms.kgraph
      constructor: KGraph
      base-args: ["@metric"]
      run-groups:
        kgraph:
          args: [{'reverse': -1, "K": 200, "L": 300, "S": 20}, False]
          query-args: [[1, 2, 3, 4, 5, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100]]
    hnsw(nmslib):
      docker-tag: ann-benchmarks-nmslib
      module: ann_benchmarks.algorithms.nmslib
      constructor: NmslibReuseIndex
      base-args: ["@metric", "hnsw"]
      run-groups:
        M-48:
          arg-groups:
            - {"M": 48, "post": 2, "efConstruction": 800}
            - False
          query-args: [[50, 70, 90, 120, 160, 200, 400, 600, 700, 800, 1000,
              1400, 1600, 2000]]
        M-32:
          arg-groups:
            - {"M": 32, "post": 2, "efConstruction": 800}
            - False
          query-args: [[10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 120, 140, 160,
              200, 300, 400, 600, 700, 800, 1000, 1200, 1400, 1600, 2000]]
        M-20:
          arg-groups:
            - {"M": 20, "post": 0, "efConstruction": 800}
            - False
          query-args: [[2, 5, 10, 15, 20, 30, 40, 50, 70, 80]]
        M-12:
          arg-groups:
            - {"M": 12, "post": 0, "efConstruction": 800}
            - False
          query-args: [[1, 2, 5, 10, 15, 20, 30, 40, 50, 70, 80]]
    SW-graph(nmslib):
      docker-tag: ann-benchmarks-nmslib
      module: ann_benchmarks.algorithms.nmslib
      constructor: NmslibReuseIndex
      base-args: ["@metric", "sw-graph"]
      run-groups:
        NN-30:
          arg-groups:
            - {"NN": 30}
            - False
          query-args: [[700, 650, 550, 450, 350, 275, 200, 150, 120, 80,
              50, 30]]
        NN-15:
          arg-groups:
            - {"NN": 15}
            - False
          query-args: [[80, 50, 30, 20]]
        NN-3:
          arg-groups:
            - {"NN": 3}
            - False
          query-args: [[120, 80, 60, 40, 20, 10, 8, 4, 2]]
    rpforest:
      disable: true
      docker-tag: ann-benchmarks-rpforest
      module: ann_benchmarks.algorithms.rpforest
      constructor: RPForest
      run-groups:
        base:
          args: [[3, 10, 40, 100, 400],
                [3, 10, 40, 100, 400]]
    pynndescent:
      docker-tag: ann-benchmarks-pynndescent
      module: ann_benchmarks.algorithms.pynndescent
      constructor: PyNNDescent
      base-args: ["@metric"]
      run-groups:
        NN-20:
          arg-groups:
            - {"n_neighbors": [20], "diversify_prob": [1.0],
               "pruning_degree_multiplier":[0.5, 1.0], leaf_size: 20}
          query-args: [[0.0, 0.02, 0.04, 0.06, 0.08, 0.10, 0.12]]
        NN-40:
          arg-groups:
            - {"n_neighbors": [40], "diversify_prob": [0.5, 1.0],
               "pruning_degree_multiplier":[1.5], leaf_size: 25}
          query-args: [[0.0, 0.02, 0.04, 0.06, 0.08, 0.10, 0.12, 0.14, 0.16]]
        NN-80-fast:
          arg-groups:
            - {"n_neighbors": 80, diversify_prob: 1.0,
               "pruning_degree_multiplier": 2.0, leaf_size: 20}
          query-args: [[0.0, 0.02, 0.04, 0.08, 0.12, 0.16, 0.20, 0.24]]
        NN-80-accurate:
          arg-groups:
            - {"n_neighbors": 80, diversify_prob: 0.25,
                "pruning_degree_multiplier": 2.0, leaf_size: 30}
          query-args: [[0.08, 0.12, 0.16, 0.20, 0.24, 0.28, 0.32, 0.36]]
        NN-120-fast:
          arg-groups:
            - {"n_neighbors": 120, "diversify_prob": 1.0,
               "pruning_degree_multiplier":2.5, leaf_size: 20}
          query-args: [[0.0, 0.04, 0.08, 0.16, 0.20, 0.24, 0.28, 0.32]]
        NN-120-accurate:
          arg-groups:
            - {"n_neighbors": 120, "diversify_prob": 0.125,
               "pruning_degree_multiplier":2.5, leaf_size: 35}
          query-args: [[0.16, 0.20, 0.24, 0.28, 0.32, 0.36]]
    scann:
      docker-tag: ann-benchmarks-scann
      module: ann_benchmarks.algorithms.scann
      constructor: Scann
      run-groups:
        scann1:
          args: [[2000], [0.2], [2], ["dot_product"]]
          query-args: [[[1, 30], [2, 30], [4, 30], [8, 30], [30, 120], [35, 100], [40, 80], [45, 80], [50, 80], [55, 95], [60, 110], [65, 110], [75, 110], [90, 110], [110, 120], [130, 150], [150, 200], [170, 200], [200, 300], [220, 500], [250, 500], [310, 300], [400, 300], [500, 500], [800, 1000]]]
        scann2:
          args: [[1500], [0.55], [1], ["dot_product"]]
          query-args: [[[1, 30], [2, 30], [4, 30], [8, 30], [8, 25], [10, 25], [12, 25], [13, 25], [14, 27], [15, 30], [17, 30], [18, 40], [20, 40], [22, 40], [25, 50], [30, 50], [35, 55], [50, 60], [60, 60], [80, 80], [100, 100]]]
        scann3:
          args: [[1000], [.2], [1], ["dot_product"]]
          query-args: [[[1, 30], [2, 30], [4, 30], [8, 30], [9, 25], [11, 35], [12, 35], [13, 35], [14, 40], [15, 40], [16, 40], [17, 45], [20, 45], [20, 55], [25, 55], [25, 70], [30, 70], [40, 90], [50, 100], [60, 120], [70, 140]]]
        scann4:
          args: [[1400], [0.15], [3], ["dot_product"]]
          query-args: [[[1, 30], [4, 30], [9, 30], [16, 32], [25, 50], [36, 72], [49, 98], [70, 150], [90, 200], [120, 210], [180, 270], [210, 330], [260, 400], [320, 500], [400, 600], [500, 700], [800, 900]]]
    elasticsearch:
      docker-tag: ann-benchmarks-elasticsearch
      module: ann_benchmarks.algorithms.elasticsearch
      constructor: ElasticsearchKNN
      base-args: ["@metric", "@dimension"]
      run-groups:
        m-16-ef-100:
          arg-groups:
            - {"m": 16,  "ef_construction": 100} # index_options
          query-args: [[10, 20, 40, 80, 160]] # num_candidates
    opensearchknn:
      docker-tag: ann-benchmarks-opensearchknn
      module: ann_benchmarks.algorithms.opensearchknn
      constructor: OpenSearchKNN
      base-args: ["@metric", "@dimension"]
      run-groups:
        M-4:
          arg-groups:
            - {"M": 4,  "efConstruction": 500}
          query-args: [[10, 20, 40, 80, 120, 200, 400, 600, 800]]
        M-8:
          arg-groups:
            - {"M": 8,  "efConstruction": 500}
          query-args: [[10, 20, 40, 80, 120, 200, 400, 600, 800]]
        M-12:
          arg-groups:
            - {"M": 12,  "efConstruction": 500}
          query-args: [[10, 20, 40, 80, 120, 200, 400, 600, 800]]
        M-16:
          arg-groups:
            - {"M": 16,  "efConstruction": 500}
          query-args: [[10, 20, 40, 80, 120, 200, 400, 600, 800]]
        M-24:
          arg-groups:
            - {"M": 24,  "efConstruction": 500}
          query-args: [[10, 20, 40, 80, 120, 200, 400, 600, 800]]
        M-36:
          arg-groups:
            - {"M": 36,  "efConstruction": 500}
          query-args: [[10, 20, 40, 80, 120, 200, 400, 600, 800]]
        M-48:
          arg-groups:
            - {"M": 48,  "efConstruction": 500}
          query-args: [[10, 20, 40, 80, 120, 200, 400, 600, 800]]
    hnsw(vespa):
      docker-tag: ann-benchmarks-vespa
      module: ann_benchmarks.algorithms.vespa
      constructor: VespaHnsw
      base-args: ["@metric", "@dimension"]
      run-groups:
        M-4:
          arg-groups:
            - {"M": 4, "efConstruction": 500}
          query-args: [[10, 20, 40, 80, 120, 200, 400, 600, 800]]
        M-8:
          arg-groups:
            - {"M": 8, "efConstruction": 500}
          query-args: [[10, 20, 40, 80, 120, 200, 400, 600, 800]]
        M-12:
          arg-groups:
            - {"M": 12, "efConstruction": 500}
          query-args: [[10, 20, 40, 80, 120, 200, 400, 600, 800]]
        M-16:
          arg-groups:
            - {"M": 16, "efConstruction": 500}
          query-args: [[10, 20, 40, 80, 120, 200, 400, 600, 800]]
        M-24:
          arg-groups:
            - {"M": 24, "efConstruction": 500}
          query-args: [[10, 20, 40, 80, 120, 200, 400, 600, 800]]
        M-36:
          arg-groups:
            - {"M": 36, "efConstruction": 500}
          query-args: [[10, 20, 40, 80, 120, 200, 400, 600, 800]]
        M-48:
          arg-groups:
            - {"M": 48, "efConstruction": 500}
          query-args: [[10, 20, 40, 80, 120, 200, 400, 600, 800]]
        M-64:
          arg-groups:
            - {"M": 64, "efConstruction": 500}
          query-args: [[10, 20, 40, 80, 120, 200, 400, 600, 800]]
        M-96:
          arg-groups:
            - {"M": 96, "efConstruction": 500}
          query-args: [[10, 20, 40, 80, 120, 200, 400, 600, 800]]
    vearch:
      docker-tag: ann-benchmarks-vearch
      module: ann_benchmarks.algorithms.vearch
      constructor: VearchIndex
      base-args: ["@metric"]
      run-groups:
        vearch1:
          args: [[2048], [0.2], [1, 2]]
          query-args: [[1, 2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 25, 28, 31, 34, 37, 40, 45, 50, 55, 60, 65, 75, 90, 115, 130, 150, 170, 200, 230, 250, 300, 400, 500, 800], [3, 5, 10]]

bit:
  hamming:
    mih:
      docker-tag: ann-benchmarks-mih
      module: ann_benchmarks.algorithms.subprocess
      constructor: BitSubprocessPrepared
      base-args: [["mih/bin/mihwrapper"]]
      disabled: True
      run-groups:
        base:
          # Note that MIH assumes that 5 <= dimension / chunks <= 37 and segfaults otherwise.
          # This parameter space is explored below using the `chunk-factor`.
          # A factor of 0 means that dimension / chunks = 5 which proceeds linearly to
          # dimension / chunks = 37 for a factor of 1.
          # The r parameter denotes a reordering with n / r steps. If r is 0, no reordering
          # takes places. Otherwise, n / r points from the dataset are used for a
          # re-ording process. MIH requires either r = 0 or r >= 2.
          args: { "d" : "@dimension", "r" : [0, 2, 3],
            "chunk-factor" : [0.2, 0.5, 0.8] }
    kgraph:
      disable: true
      docker-tag: ann-benchmarks-kgraph
      module: ann_benchmarks.algorithms.kgraph
      constructor: KGraph
      base-args: ["euclidean"]
      run-groups:
        kgraph:
          args: [[1, 2, 3, 4, 5, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100],
                 {'reverse': -1, "K": 200, "L": 300, "S": 20}, False]
    hnsw(nmslib):
      docker-tag: ann-benchmarks-nmslib
      module: ann_benchmarks.algorithms.nmslib
      constructor: NmslibReuseIndex
      base-args: ["euclidean", "hnsw"]
      run-groups:
        M-48:
          arg-groups:
            - {"M": 48, "post": 2, "efConstruction": 800}
            - False
          query-args: [[50, 70, 90, 120, 160, 200, 400, 600, 700, 800, 1000,
              1400, 1600, 2000]]
        M-32:
          arg-groups:
            - {"M": 32, "post": 2, "efConstruction": 800}
            - False
          query-args: [[10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 120, 140, 160,
              200, 300, 400, 600, 700, 800, 1000, 1200, 1400, 1600, 2000]]
        M-20:
          arg-groups:
            - {"M": 20, "post": 0, "efConstruction": 800}
            - False
          query-args: [[2, 5, 10, 15, 20, 30, 40, 50, 70, 80]]
        M-12:
          arg-groups:
            - {"M": 12, "post": 0, "efConstruction": 800}
            - False
          query-args: [[1, 2, 5, 10, 15, 20, 30, 40, 50, 70, 80]]
    pynndescent:
      docker-tag: ann-benchmarks-pynndescent
      module: ann_benchmarks.algorithms.pynndescent
      constructor: PyNNDescent
      base-args: ["@metric"]
      run-groups:
        NN-20:
          arg-groups:
            - {"n_neighbors": [20], "diversify_prob": [0.75, 1.0],
               "pruning_degree_multiplier":[1.0, 1.5], leaf_size: 32}
          query-args: [[0.0, 0.01, 0.02, 0.04, 0.08, 0.12, 0.16]]
        NN-40:
          arg-groups:
            - {"n_neighbors": [40], "diversify_prob": [0.5, 1.0],
               "pruning_degree_multiplier":[1.5, 2.0], leaf_size: 48}
          query-args: [[0.0, 0.04, 0.08, 0.12, 0.16, 0.20, 0.24]]
        NN-80:
          arg-groups:
            - {"n_neighbors": [80], "diversify_prob": [0.25, 1.0],
               "pruning_degree_multiplier":[1.75, 2.25], leaf_size: 64}
          query-args: [[0.0, 0.08, 0.12, 0.16, 0.20, 0.24, 0.28, 0.32]]
        NN-120:
          arg-groups:
            - {"n_neighbors": [120], "diversify_prob": [0.0, 1.0],
               "pruning_degree_multiplier":[2.0, 2.5], leaf_size: 80}
          query-args: [[0.08, 0.16, 0.20, 0.24, 0.28, 0.32, 0.36]]
    annoy:
      docker-tag: ann-benchmarks-annoy
      module: ann_benchmarks.algorithms.annoy
      constructor: Annoy
      base-args: ["@metric"]
      run-groups:
        annoy:
          args: [[100, 200, 400]]
          query-args: [[100, 200, 400, 1000, 2000, 4000, 10000, 20000, 40000,
                      100000, 200000, 400000]]
          # This run group produces 3 algorithm instances -- Annoy("angular",
          # 100), Annoy("angular", 200), and Annoy("angular", 400) -- each of
          # which will be used to run 12 different queries.
    faiss-ivf:
      docker-tag: ann-benchmarks-faiss
      module: ann_benchmarks.algorithms.faiss
      constructor: FaissIVF
      base-args: ["euclidean"]
      run-groups:
        base:
          args: [[32,64,128,256,512,1024,2048,4096,8192]]
          query-args: [[1, 5, 10, 50, 100, 200]]
  jaccard:
    bf:
      docker-tag: ann-benchmarks-sklearn
      module: ann_benchmarks.algorithms.bruteforce
      constructor: BruteForceBLAS
      base-args: ["@metric"]
      run-groups:
        base:
          args: {}
    datasketch:
      docker-tag: ann-benchmarks-datasketch
      module: ann_benchmarks.algorithms.datasketch
      constructor: DataSketch
      base-args: ["@metric"]
      run-groups:
        base:
          args:  [[128, 256, 512],[8, 16, 32, 64, 128]]
    puffinn:
      docker-tag: ann-benchmarks-puffinn
      module: ann_benchmarks.algorithms.puffinn
      constructor: Puffinn
      base-args: ["@metric"]
      run-groups:
        base:
            args: [
              [ 16000000, 512000000],
              ['1bit_minhash'],
            ]
            query-args: [[0.1, 0.2, 0.5, 0.7, 0.9, 0.95, 0.99]]
    pynndescent:
      docker-tag: ann-benchmarks-pynndescent
      module: ann_benchmarks.algorithms.pynndescent
      constructor: PyNNDescent
      base-args: ["@metric"]
      run-groups:
        NN-20:
          arg-groups:
            - {"n_neighbors": 20, "diversify_prob": [0.75, 1.0],
               "pruning_degree_multiplier": 1.0, leaf_size: 30}
          query-args: [[0.0, 0.01, 0.02, 0.03, 0.04, 0.06, 0.08, 0.12, 0.16, 0.20]]
        NN-40:
          arg-groups:
            - {"n_neighbors": 40, "diversify_prob": [0.5, 1.0],
               "pruning_degree_multiplier": 1.0, leaf_size: 30}
          query-args: [[0.0, 0.01, 0.02, 0.03, 0.04, 0.06, 0.08, 0.12, 0.16, 0.20]]
        NN-80:
          arg-groups:
            - {"n_neighbors": 80, "diversify_prob": [1.0, 0.25],
               "pruning_degree_multiplier": 1.0, "leaf_size": 60}
          query-args: [[0.0, 0.02, 0.04, 0.06, 0.08, 0.12, 0.14, 0.16, 0.18, 0.20, 0.22]]
        NN-120:
          arg-groups:
            - {"n_neighbors": 120, "diversify_prob": [1.0, 0.125],
               "pruning_degree_multiplier": 1.0, "leaf_size": 80}
          query-args: [[0.0, 0.02, 0.04, 0.06, 0.08, 0.12, 0.14, 0.16, 0.18, 0.20, 0.22]]
    hnsw(nmslib):
      docker-tag: ann-benchmarks-nmslib
      module: ann_benchmarks.algorithms.nmslib
      constructor: NmslibReuseIndex
      base-args: ["@metric", "hnsw"]
      run-groups:
        M-48:
          arg-groups:
            - {"M": 48, "post": 2, "efConstruction": 800}
            - False
          query-args: [[50, 70, 90, 120, 160, 200, 400, 600, 700, 800, 1000,
              1400, 1600, 2000]]
        M-32:
          arg-groups:
            - {"M": 32, "post": 2, "efConstruction": 800}
            - False
          query-args: [[10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 120, 140, 160,
              200, 300, 400, 600, 700, 800, 1000, 1200, 1400, 1600, 2000]]
        M-20:
          arg-groups:
            - {"M": 20, "post": 0, "efConstruction": 800}
            - False
          query-args: [[2, 5, 10, 15, 20, 30, 40, 50, 70, 80]]
        M-12:
          arg-groups:
            - {"M": 12, "post": 0, "efConstruction": 800}
            - False
          query-args: [[1, 2, 5, 10, 15, 20, 30, 40, 50, 70, 80]]
    SW-graph(nmslib):
      docker-tag: ann-benchmarks-nmslib
      module: ann_benchmarks.algorithms.nmslib
      constructor: NmslibReuseIndex
      base-args: ["@metric", "sw-graph"]
      run-groups:
        NN-30:
          arg-groups:
            - {"NN": 30}
            - False
          query-args: [[700, 650, 550, 450, 350, 275, 200, 150, 120, 80,
              50, 30]]
        NN-15:
          arg-groups:
            - {"NN": 15}
            - False
          query-args: [[80, 50, 30, 20]]
        NN-3:
          arg-groups:
            - {"NN": 3}
            - False
          query-args: [[120, 80, 60, 40, 20, 10, 8, 4, 2]]
    BallTree(nmslib):
      disable: true
      docker-tag: ann-benchmarks-nmslib
      module: ann_benchmarks.algorithms.nmslib
      constructor: NmslibReuseIndex
      base-args: ["@metric", "vptree"]
      run-groups:
        base:
          # When @args is a dictionary, algorithm instances will be generated
          # by taking the Cartesian product of all of its values.
          arg-groups:
                - {"tuneK": 10, "desiredRecall": [0.99, 0.97, 0.95, 0.9, 0.85, 0.8,
                    0.7, 0.6, 0.5, 0.4, 0.3, 0.2, 0.1]}
                - False