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https://github.com/meilisearch/meilisearch.git
synced 2024-11-23 10:37:41 +08:00
Simplify the search algorithm
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commit
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@ -272,6 +272,7 @@ impl<'a> Search<'a> {
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pub fn execute(&self) -> anyhow::Result<SearchResult> {
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let rtxn = self.rtxn;
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let index = self.index;
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let limit = self.limit;
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let fst = match index.fst(rtxn)? {
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Some(fst) => fst,
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@ -292,6 +293,8 @@ impl<'a> Search<'a> {
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let (derived_words, union_positions) = Self::fetch_words_positions(rtxn, index, &fst, dfas)?;
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let candidates = Self::compute_candidates(rtxn, index, &derived_words)?;
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debug!("candidates: {:?}", candidates);
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let union_cache = HashMap::new();
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let mut non_disjoint_cache = HashMap::new();
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@ -342,66 +345,55 @@ impl<'a> Search<'a> {
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positions.iter().enumerate().for_each(|(word, pos)| {
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union_cache.entry((word, *pos)).or_insert_with(|| {
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let words = &&derived_words[word];
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Self::union_word_position(rtxn, index, words, *pos).unwrap()
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});
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});
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// Retrieve the unions along with the popularity of it.
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let mut to_intersect: Vec<_> = positions.iter()
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.enumerate()
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.map(|(word, pos)| {
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let docids = union_cache.get(&(word, *pos)).unwrap();
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(docids.len(), docids)
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})
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.collect();
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let mut to_intersect = Vec::new();
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for (word, pos) in positions.into_iter().enumerate() {
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let docids = union_cache.get(&(word, pos)).unwrap();
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to_intersect.push((docids.len(), docids));
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}
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// Sort the unions by popularity to help reduce
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// the number of documents as soon as possible.
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to_intersect.sort_unstable_by_key(|(l, _)| *l);
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let intersect_docids: Option<RoaringBitmap> = to_intersect.into_iter()
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.fold(None, |acc, (_, union_docids)| {
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match acc {
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Some(mut left) => {
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left.intersect_with(&union_docids);
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Some(left)
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},
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None => Some(union_docids.clone()),
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}
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});
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if let Some(intersect_docids) = intersect_docids {
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same_proximity_union.union_with(&intersect_docids);
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// Intersect all the unions in the inverse popularity order.
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let mut intersect_docids = RoaringBitmap::new();
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for (i, (_, union_docids)) in to_intersect.into_iter().enumerate() {
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if i == 0 {
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intersect_docids = union_docids.clone();
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} else {
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intersect_docids.intersect_with(union_docids);
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}
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}
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// We found enough documents we can stop here
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if documents.iter().map(RoaringBitmap::len).sum::<u64>() + same_proximity_union.len() >= 20 {
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break;
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}
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same_proximity_union.union_with(&intersect_docids);
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}
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// We achieve to find valid documents ids so we remove them from the candidates list.
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candidates.difference_with(&same_proximity_union);
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documents.push(same_proximity_union);
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// We remove the double occurences of documents.
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for i in 0..documents.len() {
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if let Some((docs, others)) = documents[..=i].split_last_mut() {
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others.iter().for_each(|other| docs.difference_with(other));
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}
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// We remove documents we have already been seen in previous
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// fetches from this set of documents we just fetched.
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for previous_documents in &documents {
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same_proximity_union.difference_with(previous_documents);
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}
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if !same_proximity_union.is_empty() {
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documents.push(same_proximity_union);
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}
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documents.retain(|rb| !rb.is_empty());
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// We found enough documents we can stop here.
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if documents.iter().map(RoaringBitmap::len).sum::<u64>() >= 20 {
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if documents.iter().map(RoaringBitmap::len).sum::<u64>() >= limit as u64 {
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break;
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}
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}
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let found_words = derived_words.into_iter().flatten().map(|(w, _, _)| w).collect();
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let documents_ids = documents.iter().flatten().take(20).collect();
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let documents_ids = documents.iter().flatten().take(limit).collect();
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Ok(SearchResult { found_words, documents_ids })
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}
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