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https://github.com/meilisearch/meilisearch.git
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Enhance word splitting strategy
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parent
358aa337ea
commit
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79
milli/src/search/query_tree.rs
Normal file → Executable file
79
milli/src/search/query_tree.rs
Normal file → Executable file
@ -1,6 +1,6 @@
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use std::borrow::Cow;
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use std::cmp::max;
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use std::{cmp, fmt, mem};
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use std::{fmt, mem};
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use charabia::classifier::ClassifiedTokenIter;
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use charabia::{SeparatorKind, TokenKind};
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@ -10,7 +10,7 @@ use slice_group_by::GroupBy;
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use crate::search::matches::matching_words::{MatchingWord, PrimitiveWordId};
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use crate::search::TermsMatchingStrategy;
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use crate::{Index, MatchingWords, Result};
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use crate::{CboRoaringBitmapLenCodec, Index, MatchingWords, Result};
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type IsOptionalWord = bool;
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type IsPrefix = bool;
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@ -146,6 +146,7 @@ impl fmt::Debug for Query {
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trait Context {
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fn word_docids(&self, word: &str) -> heed::Result<Option<RoaringBitmap>>;
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fn word_pair_proximity_docids(&self, right_word: &str, left_word: &str, proximity: u8) -> heed::Result<Option<RoaringBitmap>>;
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fn synonyms<S: AsRef<str>>(&self, words: &[S]) -> heed::Result<Option<Vec<Vec<String>>>>;
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fn word_documents_count(&self, word: &str) -> heed::Result<Option<u64>> {
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match self.word_docids(word)? {
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@ -156,6 +157,12 @@ trait Context {
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/// Returns the minimum word len for 1 and 2 typos.
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fn min_word_len_for_typo(&self) -> heed::Result<(u8, u8)>;
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fn exact_words(&self) -> Option<&fst::Set<Cow<[u8]>>>;
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fn word_pair_frequency(&self, left_word: &str, right_word: &str, proximity: u8) -> heed::Result<Option<u64>> {
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match self.word_pair_proximity_docids(right_word, left_word, proximity)? {
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Some(rb) => Ok(Some(rb.len())),
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None => Ok(None),
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}
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}
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}
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/// The query tree builder is the interface to build a query tree.
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@ -173,6 +180,10 @@ impl<'a> Context for QueryTreeBuilder<'a> {
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self.index.word_docids.get(self.rtxn, word)
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}
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fn word_pair_proximity_docids(&self, right_word: &str, left_word: &str, proximity: u8) -> heed::Result<Option<RoaringBitmap>> {
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self.index.word_pair_proximity_docids.get(self.rtxn, &(left_word, right_word, proximity))
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}
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fn synonyms<S: AsRef<str>>(&self, words: &[S]) -> heed::Result<Option<Vec<Vec<String>>>> {
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self.index.words_synonyms(self.rtxn, words)
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}
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@ -190,6 +201,11 @@ impl<'a> Context for QueryTreeBuilder<'a> {
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fn exact_words(&self) -> Option<&fst::Set<Cow<[u8]>>> {
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self.exact_words.as_ref()
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}
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fn word_pair_frequency(&self, left_word: &str, right_word: &str, proximity: u8) -> heed::Result<Option<u64>> {
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let key = (left_word, right_word, proximity);
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self.index.word_pair_proximity_docids.remap_data_type::<CboRoaringBitmapLenCodec>().get(&self.rtxn, &key)
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}
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}
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impl<'a> QueryTreeBuilder<'a> {
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@ -274,12 +290,10 @@ fn split_best_frequency<'a>(
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for (i, _) in chars {
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let (left, right) = word.split_at(i);
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let left_freq = ctx.word_documents_count(left)?.unwrap_or(0);
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let right_freq = ctx.word_documents_count(right)?.unwrap_or(0);
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let pair_freq = ctx.word_pair_frequency(left, right, 1)?.unwrap_or(0);
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let min_freq = cmp::min(left_freq, right_freq);
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if min_freq != 0 && best.map_or(true, |(old, _, _)| min_freq > old) {
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best = Some((min_freq, left, right));
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if pair_freq != 0 && best.map_or(true, |(old, _, _)| pair_freq > old) {
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best = Some((pair_freq, left, right));
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}
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}
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@ -824,6 +838,11 @@ mod test {
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Ok(self.postings.get(word).cloned())
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}
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fn word_pair_proximity_docids(&self, right_word: &str, left_word: &str, _: u8) -> heed::Result<Option<RoaringBitmap>> {
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let bitmap = self.postings.get(&format!("{} {}", left_word, right_word));
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Ok(bitmap.cloned())
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}
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fn synonyms<S: AsRef<str>>(&self, words: &[S]) -> heed::Result<Option<Vec<Vec<String>>>> {
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let words: Vec<_> = words.iter().map(|s| s.as_ref().to_owned()).collect();
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Ok(self.synonyms.get(&words).cloned())
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@ -881,19 +900,22 @@ mod test {
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],
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},
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postings: hashmap! {
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String::from("hello") => random_postings(rng, 1500),
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String::from("hi") => random_postings(rng, 4000),
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String::from("word") => random_postings(rng, 2500),
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String::from("split") => random_postings(rng, 400),
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String::from("ngrams") => random_postings(rng, 1400),
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String::from("world") => random_postings(rng, 15_000),
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String::from("earth") => random_postings(rng, 8000),
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String::from("2021") => random_postings(rng, 100),
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String::from("2020") => random_postings(rng, 500),
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String::from("is") => random_postings(rng, 50_000),
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String::from("this") => random_postings(rng, 50_000),
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String::from("good") => random_postings(rng, 1250),
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String::from("morning") => random_postings(rng, 125),
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String::from("hello") => random_postings(rng, 1500),
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String::from("hi") => random_postings(rng, 4000),
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String::from("word") => random_postings(rng, 2500),
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String::from("split") => random_postings(rng, 400),
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String::from("ngrams") => random_postings(rng, 1400),
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String::from("world") => random_postings(rng, 15_000),
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String::from("earth") => random_postings(rng, 8000),
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String::from("2021") => random_postings(rng, 100),
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String::from("2020") => random_postings(rng, 500),
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String::from("is") => random_postings(rng, 50_000),
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String::from("this") => random_postings(rng, 50_000),
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String::from("good") => random_postings(rng, 1250),
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String::from("morning") => random_postings(rng, 125),
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String::from("word split") => random_postings(rng, 5000),
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String::from("quick brownfox") => random_postings(rng, 7000),
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String::from("quickbrown fox") => random_postings(rng, 8000),
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},
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exact_words,
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}
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@ -1041,6 +1063,23 @@ mod test {
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"###);
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}
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#[test]
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fn word_split_choose_pair_with_max_freq() {
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let query = "quickbrownfox";
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let tokens = query.tokenize();
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let (query_tree, _) = TestContext::default()
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.build(TermsMatchingStrategy::All, true, None, tokens)
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.unwrap()
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.unwrap();
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insta::assert_debug_snapshot!(query_tree, @r###"
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OR
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PHRASE ["quickbrown", "fox"]
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PrefixTolerant { word: "quickbrownfox", max typo: 2 }
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"###);
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}
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#[test]
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fn phrase() {
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let query = "\"hey friends\" \" \" \"wooop";
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