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https://github.com/meilisearch/MeiliSearch
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Merge #662
662: Enhance word splitting strategy r=ManyTheFish a=akki1306
# Pull Request
## Related issue
Fixes #648
## What does this PR do?
- [split_best_frequency](55d889522b/milli/src/search/query_tree.rs (L282-L301)
) to use frequency of word pairs near together with proximity value of 1 instead of considering the frequency of individual words. Word pairs having max frequency are considered.
## PR checklist
Please check if your PR fulfills the following requirements:
- [x] Does this PR fix an existing issue, or have you listed the changes applied in the PR description (and why they are needed)?
- [x] Have you read the contributing guidelines?
- [x] Have you made sure that the title is accurate and descriptive of the changes?
Thank you so much for contributing to Meilisearch!
Co-authored-by: Akshay Kulkarni <akshayk.gj@gmail.com>
This commit is contained in:
commit
f30979d021
65
milli/src/search/query_tree.rs
Normal file → Executable file
65
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::borrow::Cow;
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use std::cmp::max;
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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::classifier::ClassifiedTokenIter;
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use charabia::{SeparatorKind, TokenKind};
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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::matches::matching_words::{MatchingWord, PrimitiveWordId};
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use crate::search::TermsMatchingStrategy;
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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 IsOptionalWord = bool;
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type IsPrefix = bool;
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type IsPrefix = bool;
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@ -156,6 +156,12 @@ trait Context {
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/// Returns the minimum word len for 1 and 2 typos.
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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 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 exact_words(&self) -> Option<&fst::Set<Cow<[u8]>>>;
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fn word_pair_frequency(
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&self,
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left_word: &str,
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right_word: &str,
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proximity: u8,
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) -> heed::Result<Option<u64>>;
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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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/// The query tree builder is the interface to build a query tree.
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@ -190,6 +196,19 @@ impl<'a> Context for QueryTreeBuilder<'a> {
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fn exact_words(&self) -> Option<&fst::Set<Cow<[u8]>>> {
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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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self.exact_words.as_ref()
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}
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}
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fn word_pair_frequency(
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&self,
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left_word: &str,
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right_word: &str,
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proximity: u8,
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) -> heed::Result<Option<u64>> {
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let key = (left_word, right_word, proximity);
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self.index
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.word_pair_proximity_docids
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.remap_data_type::<CboRoaringBitmapLenCodec>()
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.get(&self.rtxn, &key)
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}
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}
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}
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impl<'a> QueryTreeBuilder<'a> {
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impl<'a> QueryTreeBuilder<'a> {
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@ -263,7 +282,7 @@ impl<'a> QueryTreeBuilder<'a> {
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}
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}
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}
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}
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/// Split the word depending on the frequency of subwords in the database documents.
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/// Split the word depending on the frequency of pairs near together in the database documents.
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fn split_best_frequency<'a>(
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fn split_best_frequency<'a>(
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ctx: &impl Context,
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ctx: &impl Context,
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word: &'a str,
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word: &'a str,
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@ -274,12 +293,10 @@ fn split_best_frequency<'a>(
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for (i, _) in chars {
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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, 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 pair_freq = ctx.word_pair_frequency(left, right, 1)?.unwrap_or(0);
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let right_freq = ctx.word_documents_count(right)?.unwrap_or(0);
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let min_freq = cmp::min(left_freq, right_freq);
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if pair_freq != 0 && best.map_or(true, |(old, _, _)| pair_freq > old) {
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if min_freq != 0 && best.map_or(true, |(old, _, _)| min_freq > old) {
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best = Some((pair_freq, left, right));
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best = Some((min_freq, left, right));
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}
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}
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}
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}
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@ -836,6 +853,18 @@ mod test {
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fn exact_words(&self) -> Option<&fst::Set<Cow<[u8]>>> {
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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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self.exact_words.as_ref()
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}
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}
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fn word_pair_frequency(
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&self,
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left_word: &str,
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right_word: &str,
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_proximity: u8,
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) -> heed::Result<Option<u64>> {
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match self.word_docids(&format!("{} {}", left_word, right_word))? {
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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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}
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impl Default for TestContext {
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impl Default for TestContext {
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@ -894,6 +923,9 @@ mod test {
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String::from("this") => 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("good") => random_postings(rng, 1250),
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String::from("morning") => random_postings(rng, 125),
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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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},
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exact_words,
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exact_words,
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}
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}
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@ -1041,6 +1073,23 @@ mod test {
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"###);
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"###);
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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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#[test]
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fn phrase() {
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fn phrase() {
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let query = "\"hey friends\" \" \" \"wooop";
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let query = "\"hey friends\" \" \" \"wooop";
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