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sparse: disable refinement by default #1033

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Jan 16, 2025
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sparse: disable refinement by default #1033

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5 changes: 3 additions & 2 deletions src/index/sparse/sparse_index_node.cc
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Original file line number Diff line number Diff line change
Expand Up @@ -104,7 +104,7 @@ class SparseInvertedIndexNode : public IndexNode {
auto nq = dataset->GetRows();
auto queries = static_cast<const sparse::SparseRow<T>*>(dataset->GetTensor());
auto k = cfg.k.value();
auto refine_factor = cfg.refine_factor.value_or(10);
auto refine_factor = cfg.refine_factor.value_or(1);
auto drop_ratio_search = cfg.drop_ratio_search.value_or(0.0f);

auto p_id = std::make_unique<sparse::label_t[]>(nq * k);
Expand Down Expand Up @@ -185,7 +185,8 @@ class SparseInvertedIndexNode : public IndexNode {
auto computer = computer_or.value();
auto drop_ratio_search = cfg.drop_ratio_search.value_or(0.0f);

const bool approximated = drop_ratio_search > 0;
// TODO: set approximated to false for now since the refinement is too slow after forward index is removed.
const bool approximated = false;

auto vec = std::vector<std::shared_ptr<IndexNode::iterator>>(nq, nullptr);
try {
Expand Down
16 changes: 12 additions & 4 deletions src/index/sparse/sparse_inverted_index_config.h
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Expand Up @@ -38,11 +38,19 @@ class SparseInvertedIndexConfig : public BaseConfig {
.for_search()
.for_range_search()
.for_iterator();
/**
* refine_factor is used for approximate search.
* refine_factor == 1 means no refinement, and is the default value.
* refine_factor > 1 means refinement. The larger the value, the more
* accurate the approximate result will be, but the slower the
* performance.
* Be aware that if you opt to use a large drop_ratio_search, it is
* necessary for you to manually modify this value.
*/
KNOWHERE_CONFIG_DECLARE_FIELD(refine_factor)
.description("refine factor")
.set_default(10)
.for_search()
.for_range_search();
.description("refine factor for approximate search")
.set_default(1)
.for_search();
/**
* The term frequency part of score of BM25 is:
* tf * (k1 + 1) / (tf + k1 * (1 - b + b * (doc_len / avgdl)))
Expand Down