mirror of
https://github.com/meilisearch/MeiliSearch
synced 2024-11-22 21:04:27 +01:00
Introduce a basic working version of phrase query for splitting words
This commit is contained in:
Clément Renault
parent
0fbd4cd632
commit
03eb7898e7
@ -2,7 +2,7 @@ mod dfa;
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mod query_enhancer;
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use std::cmp::Reverse;
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use std::vec;
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use std::{cmp, vec};
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use fst::{IntoStreamer, Streamer};
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use levenshtein_automata::DFA;
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@ -18,7 +18,7 @@ use self::query_enhancer::QueryEnhancerBuilder;
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const NGRAMS: usize = 3;
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pub struct AutomatonProducer {
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automatons: Vec<Vec<Automaton>>,
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automatons: Vec<AutomatonGroup>,
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}
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impl AutomatonProducer {
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@ -26,19 +26,26 @@ impl AutomatonProducer {
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reader: &heed::RoTxn,
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query: &str,
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main_store: store::Main,
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postings_list_store: store::PostingsLists,
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synonyms_store: store::Synonyms,
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) -> MResult<(AutomatonProducer, QueryEnhancer)> {
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let (automatons, query_enhancer) =
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generate_automatons(reader, query, main_store, synonyms_store)?;
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generate_automatons(reader, query, main_store, postings_list_store, synonyms_store)?;
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Ok((AutomatonProducer { automatons }, query_enhancer))
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}
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pub fn into_iter(self) -> vec::IntoIter<Vec<Automaton>> {
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pub fn into_iter(self) -> vec::IntoIter<AutomatonGroup> {
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self.automatons.into_iter()
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}
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}
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#[derive(Debug)]
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pub enum AutomatonGroup {
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Normal(Vec<Automaton>),
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PhraseQuery(Vec<Automaton>),
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}
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#[derive(Debug)]
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pub struct Automaton {
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pub index: usize,
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@ -102,12 +109,42 @@ pub fn normalize_str(string: &str) -> String {
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string
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}
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fn split_best_frequency<'a>(
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reader: &heed::RoTxn,
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word: &'a str,
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postings_lists_store: store::PostingsLists,
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) -> MResult<Option<(&'a str, &'a str)>> {
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let chars = word.char_indices().skip(1);
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let mut best = None;
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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 = postings_lists_store
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.postings_list(reader, left.as_ref())?
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.map_or(0, |i| i.len());
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let right_freq = postings_lists_store
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.postings_list(reader, right.as_ref())?
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.map_or(0, |i| i.len());
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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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}
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}
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Ok(best.map(|(_, l, r)| (l, r)))
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}
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fn generate_automatons(
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reader: &heed::RoTxn,
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query: &str,
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main_store: store::Main,
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postings_lists_store: store::PostingsLists,
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synonym_store: store::Synonyms,
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) -> MResult<(Vec<Vec<Automaton>>, QueryEnhancer)> {
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) -> MResult<(Vec<AutomatonGroup>, QueryEnhancer)>
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{
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let has_end_whitespace = query.chars().last().map_or(false, char::is_whitespace);
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let query_words: Vec<_> = split_query_string(query).map(str::to_lowercase).collect();
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let synonyms = match main_store.synonyms_fst(reader)? {
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@ -136,7 +173,7 @@ fn generate_automatons(
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original_automatons.push(automaton);
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}
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automatons.push(original_automatons);
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automatons.push(AutomatonGroup::Normal(original_automatons));
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for n in 1..=NGRAMS {
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let mut ngrams = query_words.windows(n).enumerate().peekable();
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@ -188,13 +225,25 @@ fn generate_automatons(
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Automaton::non_exact(automaton_index, n, synonym)
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};
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automaton_index += 1;
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automatons.push(vec![automaton]);
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automatons.push(AutomatonGroup::Normal(vec![automaton]));
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}
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}
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}
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}
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if n != 1 {
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if n == 1 {
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if let Some((left, right)) = split_best_frequency(reader, &normalized, postings_lists_store)? {
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let a = Automaton::exact(automaton_index, 1, left);
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enhancer_builder.declare(query_range.clone(), automaton_index, &[left]);
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automaton_index += 1;
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let b = Automaton::exact(automaton_index, 1, right);
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enhancer_builder.declare(query_range.clone(), automaton_index, &[left]);
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automaton_index += 1;
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automatons.push(AutomatonGroup::PhraseQuery(vec![a, b]));
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}
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} else {
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// automaton of concatenation of query words
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let concat = ngram_slice.concat();
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let normalized = normalize_str(&concat);
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@ -204,15 +253,18 @@ fn generate_automatons(
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let automaton = Automaton::exact(automaton_index, n, &normalized);
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automaton_index += 1;
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automatons.push(vec![automaton]);
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automatons.push(AutomatonGroup::Normal(vec![automaton]));
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}
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}
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}
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// order automatons, the most important first,
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// we keep the original automatons at the front.
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automatons[1..].sort_by_key(|a| {
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let a = a.first().unwrap();
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automatons[1..].sort_by_key(|group| {
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let a = match group {
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AutomatonGroup::Normal(group) => group.first().unwrap(),
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AutomatonGroup::PhraseQuery(group) => group.first().unwrap(),
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};
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(Reverse(a.is_exact), a.ngram)
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});
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@ -8,7 +8,7 @@ use fst::{IntoStreamer, Streamer};
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use sdset::SetBuf;
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use slice_group_by::{GroupBy, GroupByMut};
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use crate::automaton::{Automaton, AutomatonProducer, QueryEnhancer};
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use crate::automaton::{Automaton, AutomatonGroup, AutomatonProducer, QueryEnhancer};
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use crate::distinct_map::{BufferedDistinctMap, DistinctMap};
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use crate::raw_document::{raw_documents_from, RawDocument};
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use crate::{criterion::Criteria, Document, DocumentId, Highlight, TmpMatch};
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@ -138,7 +138,7 @@ fn multiword_rewrite_matches(
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fn fetch_raw_documents(
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reader: &heed::RoTxn,
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automatons: &[Automaton],
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automatons_groups: &[AutomatonGroup],
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query_enhancer: &QueryEnhancer,
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searchables: Option<&ReorderedAttrs>,
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main_store: store::Main,
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@ -148,52 +148,127 @@ fn fetch_raw_documents(
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let mut matches = Vec::new();
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let mut highlights = Vec::new();
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for automaton in automatons {
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let Automaton {
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index,
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is_exact,
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query_len,
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..
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} = automaton;
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let dfa = automaton.dfa();
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for group in automatons_groups {
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match group {
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AutomatonGroup::Normal(automatons) => {
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for automaton in automatons {
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let Automaton { index, is_exact, query_len, .. } = automaton;
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let dfa = automaton.dfa();
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let words = match main_store.words_fst(reader)? {
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Some(words) => words,
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None => return Ok(Vec::new()),
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};
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let mut stream = words.search(&dfa).into_stream();
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while let Some(input) = stream.next() {
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let distance = dfa.eval(input).to_u8();
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let is_exact = *is_exact && distance == 0 && input.len() == *query_len;
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let doc_indexes = match postings_lists_store.postings_list(reader, input)? {
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Some(doc_indexes) => doc_indexes,
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None => continue,
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};
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matches.reserve(doc_indexes.len());
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highlights.reserve(doc_indexes.len());
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for di in doc_indexes.as_ref() {
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let attribute = searchables.map_or(Some(di.attribute), |r| r.get(di.attribute));
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if let Some(attribute) = attribute {
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let match_ = TmpMatch {
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query_index: *index as u32,
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distance,
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attribute,
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word_index: di.word_index,
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is_exact,
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let words = match main_store.words_fst(reader)? {
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Some(words) => words,
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None => return Ok(Vec::new()),
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};
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let highlight = Highlight {
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attribute: di.attribute,
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char_index: di.char_index,
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char_length: di.char_length,
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let mut stream = words.search(&dfa).into_stream();
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while let Some(input) = stream.next() {
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let distance = dfa.eval(input).to_u8();
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let is_exact = *is_exact && distance == 0 && input.len() == *query_len;
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let doc_indexes = match postings_lists_store.postings_list(reader, input)? {
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Some(doc_indexes) => doc_indexes,
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None => continue,
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};
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matches.reserve(doc_indexes.len());
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highlights.reserve(doc_indexes.len());
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for di in doc_indexes.as_ref() {
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let attribute = searchables.map_or(Some(di.attribute), |r| r.get(di.attribute));
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if let Some(attribute) = attribute {
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let match_ = TmpMatch {
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query_index: *index as u32,
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distance,
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attribute,
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word_index: di.word_index,
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is_exact,
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};
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let highlight = Highlight {
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attribute: di.attribute,
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char_index: di.char_index,
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char_length: di.char_length,
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};
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matches.push((di.document_id, match_));
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highlights.push((di.document_id, highlight));
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}
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}
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}
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}
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},
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AutomatonGroup::PhraseQuery(automatons) => {
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let mut tmp_matches = Vec::new();
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let phrase_query_len = automatons.len();
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for (id, automaton) in automatons.into_iter().enumerate() {
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let Automaton { index, is_exact, query_len, .. } = automaton;
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let dfa = automaton.dfa();
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let words = match main_store.words_fst(reader)? {
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Some(words) => words,
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None => return Ok(Vec::new()),
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};
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matches.push((di.document_id, match_));
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highlights.push((di.document_id, highlight));
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let mut stream = words.search(&dfa).into_stream();
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while let Some(input) = stream.next() {
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let distance = dfa.eval(input).to_u8();
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let is_exact = *is_exact && distance == 0 && input.len() == *query_len;
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let doc_indexes = match postings_lists_store.postings_list(reader, input)? {
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Some(doc_indexes) => doc_indexes,
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None => continue,
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};
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tmp_matches.reserve(doc_indexes.len());
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for di in doc_indexes.as_ref() {
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let attribute = searchables.map_or(Some(di.attribute), |r| r.get(di.attribute));
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if let Some(attribute) = attribute {
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let match_ = TmpMatch {
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query_index: *index as u32,
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distance,
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attribute,
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word_index: di.word_index,
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is_exact,
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};
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let highlight = Highlight {
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attribute: di.attribute,
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char_index: di.char_index,
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char_length: di.char_length,
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};
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tmp_matches.push((di.document_id, id, match_, highlight));
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}
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}
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}
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}
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tmp_matches.sort_unstable_by_key(|(id, _, m, _)| (*id, m.attribute, m.word_index));
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for group in tmp_matches.linear_group_by_key(|(id, _, m, _)| (*id, m.attribute)) {
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for window in group.windows(2) {
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let (ida, ia, ma, ha) = window[0];
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let (idb, ib, mb, hb) = window[1];
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debug_assert_eq!(ida, idb);
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// if matches must follow and actually follows themselves
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if ia + 1 == ib && ma.word_index + 1 == mb.word_index {
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// TODO we must make it work for phrase query longer than 2
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// if the second match is the last phrase query word
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if ib + 1 == phrase_query_len {
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// insert first match
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matches.push((ida, ma));
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highlights.push((ida, ha));
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// insert second match
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matches.push((idb, mb));
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highlights.push((idb, hb));
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}
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}
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}
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}
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}
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}
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@ -368,14 +443,14 @@ where
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let mut raw_documents_processed = Vec::with_capacity(range.len());
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let (automaton_producer, query_enhancer) =
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AutomatonProducer::new(reader, query, main_store, synonyms_store)?;
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AutomatonProducer::new(reader, query, main_store, postings_lists_store, synonyms_store)?;
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let automaton_producer = automaton_producer.into_iter();
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let mut automatons = Vec::new();
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// aggregate automatons groups by groups after time
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for auts in automaton_producer {
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automatons.extend(auts);
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automatons.push(auts);
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// we must retrieve the documents associated
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// with the current automatons
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@ -481,14 +556,14 @@ where
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let mut raw_documents_processed = Vec::new();
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let (automaton_producer, query_enhancer) =
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AutomatonProducer::new(reader, query, main_store, synonyms_store)?;
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AutomatonProducer::new(reader, query, main_store, postings_lists_store, synonyms_store)?;
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let automaton_producer = automaton_producer.into_iter();
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let mut automatons = Vec::new();
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// aggregate automatons groups by groups after time
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for auts in automaton_producer {
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automatons.extend(auts);
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automatons.push(auts);
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// we must retrieve the documents associated
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// with the current automatons
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@ -1697,4 +1772,71 @@ mod tests {
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});
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assert_matches!(iter.next(), None);
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}
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#[test]
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fn simple_phrase_query_splitting() {
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let store = TempDatabase::from_iter(vec![
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("search", &[doc_index(0, 0)][..]),
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("engine", &[doc_index(0, 1)][..]),
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("search", &[doc_index(1, 0)][..]),
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("slow", &[doc_index(1, 1)][..]),
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("engine", &[doc_index(1, 2)][..]),
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]);
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let env = &store.database.env;
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let reader = env.read_txn().unwrap();
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let builder = store.query_builder();
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let results = builder.query(&reader, "searchengine", 0..20).unwrap();
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let mut iter = results.into_iter();
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assert_matches!(iter.next(), Some(Document { id: DocumentId(0), matches, .. }) => {
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let mut iter = matches.into_iter();
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assert_matches!(iter.next(), Some(TmpMatch { query_index: 0, word_index: 0, distance: 0, .. })); // search
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assert_matches!(iter.next(), Some(TmpMatch { query_index: 0, word_index: 1, distance: 0, .. })); // engine
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assert_matches!(iter.next(), None);
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});
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assert_matches!(iter.next(), None);
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}
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#[test]
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fn harder_phrase_query_splitting() {
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let store = TempDatabase::from_iter(vec![
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("search", &[doc_index(0, 0)][..]),
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("search", &[doc_index(0, 1)][..]),
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("engine", &[doc_index(0, 2)][..]),
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("search", &[doc_index(1, 0)][..]),
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("slow", &[doc_index(1, 1)][..]),
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("search", &[doc_index(1, 2)][..]),
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("engine", &[doc_index(1, 3)][..]),
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("search", &[doc_index(1, 0)][..]),
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("search", &[doc_index(1, 1)][..]),
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("slow", &[doc_index(1, 2)][..]),
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("engine", &[doc_index(1, 3)][..]),
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]);
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let env = &store.database.env;
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let reader = env.read_txn().unwrap();
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let builder = store.query_builder();
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let results = builder.query(&reader, "searchengine", 0..20).unwrap();
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let mut iter = results.into_iter();
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assert_matches!(iter.next(), Some(Document { id: DocumentId(0), matches, .. }) => {
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let mut iter = matches.into_iter();
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assert_matches!(iter.next(), Some(TmpMatch { query_index: 0, word_index: 1, distance: 0, .. })); // search
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assert_matches!(iter.next(), Some(TmpMatch { query_index: 0, word_index: 2, distance: 0, .. })); // engine
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assert_matches!(iter.next(), None);
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});
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assert_matches!(iter.next(), Some(Document { id: DocumentId(1), matches, .. }) => {
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let mut iter = matches.into_iter();
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assert_matches!(iter.next(), Some(TmpMatch { query_index: 0, word_index: 2, distance: 0, .. })); // search
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assert_matches!(iter.next(), Some(TmpMatch { query_index: 0, word_index: 3, distance: 0, .. })); // engine
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assert_matches!(iter.next(), None);
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});
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assert_matches!(iter.next(), None);
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}
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}
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