mirror of
https://github.com/meilisearch/MeiliSearch
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Make the highlight system much better
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parent
02af4ff113
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134
meilidb-core/src/levenshtein.rs
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134
meilidb-core/src/levenshtein.rs
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use std::cmp::min;
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use std::collections::BTreeMap;
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use std::ops::{Index, IndexMut};
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// A simple wrapper around vec so we can get contiguous but index it like it's 2D array.
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struct N2Array<T> {
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y_size: usize,
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buf: Vec<T>,
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}
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impl<T: Clone> N2Array<T> {
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fn new(x: usize, y: usize, value: T) -> N2Array<T> {
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N2Array {
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y_size: y,
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buf: vec![value; x * y],
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}
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}
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}
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impl<T> Index<(usize, usize)> for N2Array<T> {
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type Output = T;
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#[inline]
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fn index(&self, (x, y): (usize, usize)) -> &T {
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&self.buf[(x * self.y_size) + y]
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}
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}
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impl<T> IndexMut<(usize, usize)> for N2Array<T> {
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#[inline]
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fn index_mut(&mut self, (x, y): (usize, usize)) -> &mut T {
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&mut self.buf[(x * self.y_size) + y]
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}
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}
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pub fn prefix_damerau_levenshtein(source: &[u8], target: &[u8]) -> (u32, usize) {
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let (n, m) = (source.len(), target.len());
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assert!(
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n <= m,
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"the source string must be shorter than the target one"
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);
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if n == 0 {
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return (m as u32, 0);
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}
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if m == 0 {
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return (n as u32, 0);
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}
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if n == m && source == target {
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return (0, m);
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}
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let inf = n + m;
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let mut matrix = N2Array::new(n + 2, m + 2, 0);
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matrix[(0, 0)] = inf;
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for i in 0..n + 1 {
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matrix[(i + 1, 0)] = inf;
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matrix[(i + 1, 1)] = i;
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}
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for j in 0..m + 1 {
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matrix[(0, j + 1)] = inf;
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matrix[(1, j + 1)] = j;
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}
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let mut last_row = BTreeMap::new();
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for (row, char_s) in source.iter().enumerate() {
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let mut last_match_col = 0;
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let row = row + 1;
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for (col, char_t) in target.iter().enumerate() {
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let col = col + 1;
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let last_match_row = *last_row.get(&char_t).unwrap_or(&0);
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let cost = if char_s == char_t { 0 } else { 1 };
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let dist_add = matrix[(row, col + 1)] + 1;
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let dist_del = matrix[(row + 1, col)] + 1;
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let dist_sub = matrix[(row, col)] + cost;
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let dist_trans = matrix[(last_match_row, last_match_col)]
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+ (row - last_match_row - 1)
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+ 1
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+ (col - last_match_col - 1);
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let dist = min(min(dist_add, dist_del), min(dist_sub, dist_trans));
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matrix[(row + 1, col + 1)] = dist;
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if cost == 0 {
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last_match_col = col;
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}
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}
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last_row.insert(char_s, row);
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}
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let mut minimum = (u32::max_value(), 0);
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for x in n..=m {
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let dist = matrix[(n + 1, x + 1)] as u32;
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if dist < minimum.0 {
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minimum = (dist, x)
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}
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}
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minimum
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}
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#[cfg(test)]
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mod tests {
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use super::*;
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#[test]
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fn matched_length() {
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let query = "Levenste";
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let text = "Levenshtein";
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let (dist, length) = prefix_damerau_levenshtein(query.as_bytes(), text.as_bytes());
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assert_eq!(dist, 1);
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assert_eq!(&text[..length], "Levenshte");
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}
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#[test]
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#[should_panic]
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fn matched_length_panic() {
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let query = "Levenshtein";
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let text = "Levenste";
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// this function will panic if source if longer than target
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prefix_damerau_levenshtein(query.as_bytes(), text.as_bytes());
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}
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}
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@ -7,6 +7,7 @@ pub mod criterion;
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mod database;
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mod database;
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mod distinct_map;
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mod distinct_map;
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mod error;
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mod error;
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mod levenshtein;
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mod number;
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mod number;
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mod query_builder;
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mod query_builder;
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mod ranked_map;
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mod ranked_map;
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@ -11,6 +11,7 @@ use slice_group_by::{GroupBy, GroupByMut};
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use crate::automaton::{Automaton, AutomatonGroup, 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::distinct_map::{BufferedDistinctMap, DistinctMap};
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use crate::levenshtein::prefix_damerau_levenshtein;
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use crate::raw_document::{raw_documents_from, RawDocument};
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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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use crate::{criterion::Criteria, Document, DocumentId, Highlight, TmpMatch};
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use crate::{reordered_attrs::ReorderedAttrs, store, MResult};
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use crate::{reordered_attrs::ReorderedAttrs, store, MResult};
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@ -162,6 +163,7 @@ fn fetch_raw_documents(
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index,
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index,
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is_exact,
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is_exact,
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query_len,
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query_len,
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query,
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..
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..
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} = automaton;
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} = automaton;
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let dfa = automaton.dfa();
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let dfa = automaton.dfa();
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@ -176,6 +178,12 @@ fn fetch_raw_documents(
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let distance = dfa.eval(input).to_u8();
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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 is_exact = *is_exact && distance == 0 && input.len() == *query_len;
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let covered_area = if query.len() > input.len() {
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query.len()
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} else {
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prefix_damerau_levenshtein(query.as_bytes(), input).1
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};
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let doc_indexes = match postings_lists_store.postings_list(reader, input)? {
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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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Some(doc_indexes) => doc_indexes,
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None => continue,
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None => continue,
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@ -197,7 +205,7 @@ fn fetch_raw_documents(
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let highlight = Highlight {
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let highlight = Highlight {
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attribute: di.attribute,
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attribute: di.attribute,
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char_index: di.char_index,
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char_index: di.char_index,
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char_length: u16::try_from(*query_len).unwrap_or(u16::max_value()),
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char_length: u16::try_from(covered_area).unwrap_or(u16::max_value()),
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};
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};
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tmp_matches.push((di.document_id, id, match_, highlight));
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tmp_matches.push((di.document_id, id, match_, highlight));
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