snowykami/meilisearch
1
0
Fork 0
mirror of https://github.com/meilisearch/meilisearch.git synced 2024-11-26 03:55:07 +08:00
Code Issues Actions 5 Packages Projects Releases Wiki Activity

Fix same vector sort bug

Browse Source
This commit is contained in:
Louis Dureuil 2023-12-12 12:08:23 +01:00
parent 11e2a2c1aa
commit d4715e0c4d
No known key found for this signature in database
1 changed files with 28 additions and 14 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

42
milli/src/search/new/vector_sort.rs
View File

@ -35,7 +35,11 @@ impl<Q: RankingRuleQueryTrait> VectorSort<Q> {
})
}
fn fill_buffer(&mut self, ctx: &mut SearchContext<'_>) -> Result<()> {
fn fill_buffer(
&mut self,
ctx: &mut SearchContext<'_>,
vector_candidates: &RoaringBitmap,
) -> Result<()> {
let readers: std::result::Result<Vec<_>, _> = (0..=u8::MAX)
.map_while(|k| {
arroy::Reader::open(ctx.txn, k.into(), ctx.index.vector_arroy)
@ -54,13 +58,8 @@ impl<Q: RankingRuleQueryTrait> VectorSort<Q> {
let mut results = Vec::new();
for reader in readers.iter() {
let nns_by_vector = reader.nns_by_vector(
ctx.txn,
target,
self.limit,
None,
Some(&self.vector_candidates),
)?;
let nns_by_vector =
reader.nns_by_vector(ctx.txn, target, self.limit, None, Some(vector_candidates))?;
let vectors: std::result::Result<Vec<_>, _> = nns_by_vector
.iter()
.map(|(docid, _)| reader.item_vector(ctx.txn, *docid).transpose().unwrap())
@ -90,8 +89,8 @@ impl<'ctx, Q: RankingRuleQueryTrait> RankingRule<'ctx, Q> for VectorSort<Q> {
assert!(self.query.is_none());
self.query = Some(query.clone());
self.vector_candidates &= universe;
self.fill_buffer(ctx)?;
let vector_candidates = &self.vector_candidates & universe;
self.fill_buffer(ctx, &vector_candidates)?;
Ok(())
}
@ -103,9 +102,9 @@ impl<'ctx, Q: RankingRuleQueryTrait> RankingRule<'ctx, Q> for VectorSort<Q> {
universe: &RoaringBitmap,
) -> Result<Option<RankingRuleOutput<Q>>> {
let query = self.query.as_ref().unwrap().clone();
self.vector_candidates &= universe;
let vector_candidates = &self.vector_candidates & universe;
if self.vector_candidates.is_empty() {
if vector_candidates.is_empty() {
return Ok(Some(RankingRuleOutput {
query,
candidates: universe.clone(),
@ -117,7 +116,7 @@ impl<'ctx, Q: RankingRuleQueryTrait> RankingRule<'ctx, Q> for VectorSort<Q> {
}
for (docid, distance, vector) in self.cached_sorted_docids.by_ref() {
if self.vector_candidates.contains(docid) {
if vector_candidates.contains(docid) {
let score = 1.0 - distance;
let score = self
.distribution_shift
@ -136,7 +135,22 @@ impl<'ctx, Q: RankingRuleQueryTrait> RankingRule<'ctx, Q> for VectorSort<Q> {
// if we got out of this loop it means we've exhausted our cache.
// we need to refill it and run the function again.
self.fill_buffer(ctx)?;
self.fill_buffer(ctx, &vector_candidates)?;
// we tried filling the buffer, but it remained empty 😢
// it means we don't actually have any document remaining in the universe with a vector.
// => exit
if self.cached_sorted_docids.len() == 0 {
return Ok(Some(RankingRuleOutput {
query,
candidates: universe.clone(),
score: ScoreDetails::Vector(score_details::Vector {
target_vector: self.target.clone(),
value_similarity: None,
}),
}));
}
self.next_bucket(ctx, _logger, universe)
}