mirror of
https://github.com/meilisearch/meilisearch.git
synced 2024-11-27 20:45:06 +08:00
193 lines
6.6 KiB
Rust
193 lines
6.6 KiB
Rust
use std::borrow::Cow;
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use std::fmt;
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use std::time::Instant;
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use fst::{IntoStreamer, Streamer, Set};
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use levenshtein_automata::{DFA, LevenshteinAutomatonBuilder as LevBuilder};
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use log::debug;
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use meilisearch_tokenizer::{AnalyzerConfig, Analyzer};
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use once_cell::sync::Lazy;
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use roaring::bitmap::RoaringBitmap;
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use crate::search::criteria::{Criterion, CriterionResult};
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use crate::search::criteria::{typo::Typo, words::Words, proximity::Proximity};
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use crate::{Index, DocumentId};
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pub use self::facet::FacetIter;
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pub use self::facet::{FacetCondition, FacetDistribution, FacetNumberOperator, FacetStringOperator};
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pub use self::query_tree::MatchingWords;
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use self::query_tree::QueryTreeBuilder;
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// Building these factories is not free.
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static LEVDIST0: Lazy<LevBuilder> = Lazy::new(|| LevBuilder::new(0, true));
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static LEVDIST1: Lazy<LevBuilder> = Lazy::new(|| LevBuilder::new(1, true));
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static LEVDIST2: Lazy<LevBuilder> = Lazy::new(|| LevBuilder::new(2, true));
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mod facet;
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mod query_tree;
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mod criteria;
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pub struct Search<'a> {
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query: Option<String>,
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facet_condition: Option<FacetCondition>,
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offset: usize,
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limit: usize,
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rtxn: &'a heed::RoTxn<'a>,
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index: &'a Index,
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}
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impl<'a> Search<'a> {
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pub fn new(rtxn: &'a heed::RoTxn, index: &'a Index) -> Search<'a> {
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Search { query: None, facet_condition: None, offset: 0, limit: 20, rtxn, index }
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}
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pub fn query(&mut self, query: impl Into<String>) -> &mut Search<'a> {
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self.query = Some(query.into());
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self
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}
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pub fn offset(&mut self, offset: usize) -> &mut Search<'a> {
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self.offset = offset;
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self
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}
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pub fn limit(&mut self, limit: usize) -> &mut Search<'a> {
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self.limit = limit;
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self
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}
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pub fn facet_condition(&mut self, condition: FacetCondition) -> &mut Search<'a> {
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self.facet_condition = Some(condition);
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self
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}
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pub fn execute(&self) -> anyhow::Result<SearchResult> {
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// We create the query tree by spliting the query into tokens.
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let before = Instant::now();
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let query_tree = match self.query.as_ref() {
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Some(query) => {
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let builder = QueryTreeBuilder::new(self.rtxn, self.index);
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let stop_words = &Set::default();
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let analyzer = Analyzer::new(AnalyzerConfig::default_with_stopwords(stop_words));
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let result = analyzer.analyze(query);
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let tokens = result.tokens();
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builder.build(tokens)?
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},
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None => None,
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};
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debug!("query tree: {:?} took {:.02?}", query_tree, before.elapsed());
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// We create the original candidates with the facet conditions results.
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let before = Instant::now();
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let facet_candidates = match &self.facet_condition {
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Some(condition) => Some(condition.evaluate(self.rtxn, self.index)?),
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None => None,
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};
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debug!("facet candidates: {:?} took {:.02?}", facet_candidates, before.elapsed());
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let matching_words = match query_tree.as_ref() {
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Some(query_tree) => MatchingWords::from_query_tree(&query_tree),
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None => MatchingWords::default(),
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};
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// We are testing the typo criteria but there will be more of them soon.
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let criteria_ctx = criteria::HeedContext::new(self.rtxn, self.index)?;
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let typo_criterion = Typo::initial(&criteria_ctx, query_tree, facet_candidates)?;
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let words_criterion = Words::new(&criteria_ctx, Box::new(typo_criterion))?;
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let proximity_criterion = Proximity::new(&criteria_ctx, Box::new(words_criterion))?;
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// let proximity_criterion = Proximity::initial(&criteria_ctx, query_tree, facet_candidates)?;
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let mut criteria = proximity_criterion;
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// // We sort in descending order on a specific field *by hand*, don't do that at home.
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// let attr_name = "released-timestamp";
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// let fid = self.index.fields_ids_map(self.rtxn)?.id(attr_name).unwrap();
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// let ftype = *self.index.faceted_fields(self.rtxn)?.get(attr_name).unwrap();
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// let desc_criterion = AscDesc::desc(self.index, self.rtxn, Box::new(words_criterion), fid, ftype)?;
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let mut offset = self.offset;
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let mut limit = self.limit;
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let mut documents_ids = Vec::new();
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let mut initial_candidates = RoaringBitmap::new();
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while let Some(CriterionResult { candidates, bucket_candidates, .. }) = criteria.next()? {
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debug!("Number of candidates found {}", candidates.len());
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let mut len = candidates.len() as usize;
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let mut candidates = candidates.into_iter();
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initial_candidates.union_with(&bucket_candidates);
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if offset != 0 {
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candidates.by_ref().skip(offset).for_each(drop);
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offset = offset.saturating_sub(len.min(offset));
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len = len.saturating_sub(len.min(offset));
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}
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if len != 0 {
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documents_ids.extend(candidates.take(limit));
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limit = limit.saturating_sub(len.min(limit));
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}
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if limit == 0 { break }
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}
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Ok(SearchResult { matching_words, candidates: initial_candidates, documents_ids })
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}
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}
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impl fmt::Debug for Search<'_> {
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fn fmt(&self, f: &mut fmt::Formatter) -> fmt::Result {
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let Search { query, facet_condition, offset, limit, rtxn: _, index: _ } = self;
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f.debug_struct("Search")
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.field("query", query)
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.field("facet_condition", facet_condition)
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.field("offset", offset)
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.field("limit", limit)
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.finish()
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}
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}
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#[derive(Default)]
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pub struct SearchResult {
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pub matching_words: MatchingWords,
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pub candidates: RoaringBitmap,
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// TODO those documents ids should be associated with their criteria scores.
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pub documents_ids: Vec<DocumentId>,
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}
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pub fn word_derivations(
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word: &str,
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is_prefix: bool,
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max_typo: u8,
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fst: &fst::Set<Cow<[u8]>>,
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) -> anyhow::Result<Vec<(String, u8)>>
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{
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let mut derived_words = Vec::new();
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let dfa = build_dfa(word, max_typo, is_prefix);
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let mut stream = fst.search_with_state(&dfa).into_stream();
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while let Some((word, state)) = stream.next() {
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let word = std::str::from_utf8(word)?;
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let distance = dfa.distance(state);
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derived_words.push((word.to_string(), distance.to_u8()));
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}
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Ok(derived_words)
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}
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pub fn build_dfa(word: &str, typos: u8, is_prefix: bool) -> DFA {
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let lev = match typos {
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0 => &LEVDIST0,
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1 => &LEVDIST1,
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_ => &LEVDIST2,
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};
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if is_prefix {
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lev.build_prefix_dfa(word)
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} else {
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lev.build_dfa(word)
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}
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}
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