snowykami/meilisearch
1
0
Fork 0
mirror of https://github.com/meilisearch/meilisearch.git synced 2025-07-29 01:31:00 +00:00
Code Issues Packages Projects Releases Wiki Activity

Replace the hnsw crate by the instant-distance one

Browse Source
This commit is contained in:
Kerollmops
2023-07-25 12:36:01 +02:00
parent 86d8bb3a3e
commit 29ab54b259
7 changed files with 127 additions and 120 deletions
Download Patch File Download Diff File Expand all files Collapse all files

49
milli/src/search/new/mod.rs
View File

@ -28,7 +28,7 @@ use db_cache::DatabaseCache;
use exact_attribute::ExactAttribute;
use graph_based_ranking_rule::{Exactness, Fid, Position, Proximity, Typo};
use heed::RoTxn;
use hnsw::Searcher;
use instant_distance::Search;
use interner::{DedupInterner, Interner};
pub use logger::visual::VisualSearchLogger;
pub use logger::{DefaultSearchLogger, SearchLogger};
@ -40,19 +40,18 @@ use ranking_rules::{
use resolve_query_graph::{compute_query_graph_docids, PhraseDocIdsCache};
use roaring::RoaringBitmap;
use sort::Sort;
use space::Neighbor;
use self::distinct::facet_string_values;
use self::geo_sort::GeoSort;
pub use self::geo_sort::Strategy as GeoSortStrategy;
use self::graph_based_ranking_rule::Words;
use self::interner::Interned;
use crate::distance::NDotProductPoint;
use crate::error::FieldIdMapMissingEntry;
use crate::score_details::{ScoreDetails, ScoringStrategy};
use crate::search::new::distinct::apply_distinct_rule;
use crate::{
normalize_vector, AscDesc, DocumentId, Filter, Index, Member, Result, TermsMatchingStrategy,
UserError, BEU32,
AscDesc, DocumentId, Filter, Index, Member, Result, TermsMatchingStrategy, UserError, BEU32,
};
/// A structure used throughout the execution of a search query.
@ -445,29 +444,31 @@ pub fn execute_search(
check_sort_criteria(ctx, sort_criteria.as_ref())?;
if let Some(vector) = vector {
let mut searcher = Searcher::new();
let hnsw = ctx.index.vector_hnsw(ctx.txn)?.unwrap_or_default();
let ef = hnsw.len().min(100);
let mut dest = vec![Neighbor { index: 0, distance: 0 }; ef];
let vector = normalize_vector(vector.clone());
let neighbors = hnsw.nearest(&vector, ef, &mut searcher, &mut dest[..]);
let mut search = Search::default();
let docids = match ctx.index.vector_hnsw(ctx.txn)? {
Some(hnsw) => {
let vector = NDotProductPoint::new(vector.clone());
let neighbors = hnsw.search(&vector, &mut search);
let mut docids = Vec::new();
let mut uniq_docids = RoaringBitmap::new();
for Neighbor { index, distance: _ } in neighbors.iter() {
let index = BEU32::new(*index as u32);
let docid = ctx.index.vector_id_docid.get(ctx.txn, &index)?.unwrap().get();
if universe.contains(docid) && uniq_docids.insert(docid) {
docids.push(docid);
if docids.len() == (from + length) {
break;
let mut docids = Vec::new();
let mut uniq_docids = RoaringBitmap::new();
for instant_distance::Item { distance: _, pid, point: _ } in neighbors {
let index = BEU32::new(pid.into_inner());
let docid = ctx.index.vector_id_docid.get(ctx.txn, &index)?.unwrap().get();
if universe.contains(docid) && uniq_docids.insert(docid) {
docids.push(docid);
if docids.len() == (from + length) {
break;
}
}
}
}
}
// return the nearest documents that are also part of the candidates
// along with a dummy list of scores that are useless in this context.
let docids: Vec<_> = docids.into_iter().skip(from).take(length).collect();
// return the nearest documents that are also part of the candidates
// along with a dummy list of scores that are useless in this context.
docids.into_iter().skip(from).take(length).collect()
}
None => Vec::new(),
};
return Ok(PartialSearchResult {
candidates: universe,