content: name =          "sparse_hnsw_search"
  title =         "Search sparse HNSWM on MSMARCO"
  description =   "Search sparse HNSWM on MSMARCO with memory budget 1.5"
  dataset =       "Splade cocondenser on MS-MARCO"
  # compile-command = "cargo build" # Compile in debug mode? default is release mode
  build-command = "./target/release/hnsw_build"  # Unified build binary
  query-command = "./target/release/hnsw_search" # Unified search binary
  vector-type = "sparse"   # Vector type: "dense" or "sparse"
  precision = "f16"        # Precision: "f32" for full-precision, "f16" for half-precision (Note: sparse only supports f16)
  quantizer = "plain"      # Quantizer: "plain" for no compression, "pq" for Product Quantization (dense only)
  graph-type = "standard"  # Graph type: "standard" or "fixed-degree"
  
  [settings]
  k =             10
  n-runs =        1       # Number of runs. PLEASE think carefully how increasing 
                          # this may influence your query time
  NUMA =        "numactl --physcpubind='0-15' --localalloc"    # Is the machine NUMA and need to prepend "numactl --physcpubind='0-15' --localalloc" to query command? Otherwise comment this!
  build =         true   # Need to build the index or the index is already built?
  metric =        "RR@10" # ir_measures metric (e.g. RR)
  
  [folder] 
  data =          "~/knn_datasets/sparse_datasets/msmarco_v1_passage/splade" # data base path
  index =         "~/knn_indexes/sparse_datasets/msmarco_v1_passage/splade" # index input path
  qrels_path =    "~/knn_datasets/sparse_datasets/msmarco_v1_passage/qrels.dev.small.tsv" 
  experiment =    "."     # stdout and stderr here of running the experiment is saved here. in a specific subfolder for the current execution
  
  [filename]
  dataset =       "documents.bin"
  queries =       "queries.bin" # queries input file
  groundtruth =   "groundtruth.tsv" # ground truth input file
  doc_ids =       "doc_ids.npy"
  query_ids =     "queries_ids.npy"
  index =         "ecir_2025_splade" # index input file name
  
  [indexing_parameters]
  m =            32
  efc =          2000
  metric =       "ip" # distance/similarity metric of the index, either "l2" or "ip"
  
  [query]
      [query.efs_10]
      ef-search =      10
      [query.efs_20]
      ef-search =      20
      [query.efs_40]
      ef-search =      40
      [query.efs_80]
      ef-search =      80
      [query.efs_120]
      ef-search =      120
      [query.efs_200]
      ef-search =      200
      [query.efs_400]
      ef-search =      400
      [query.efs_600]
      ef-search =      600
      [query.efs_800]
      ef-search =      800