mirror of
https://github.com/meilisearch/meilisearch.git
synced 2024-11-23 02:27:40 +08:00
Use the Typo criteria in the search module
This commit is contained in:
Clément Renault
Kerollmops
parent
ad20d72a39
commit
f091f370d0
@ -18,10 +18,13 @@ use crate::heed_codec::facet::{FacetLevelValueF64Codec, FacetLevelValueI64Codec}
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use crate::heed_codec::facet::{FieldDocIdFacetF64Codec, FieldDocIdFacetI64Codec};
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use crate::mdfs::Mdfs;
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use crate::query_tokens::{query_tokens, QueryToken};
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use crate::{Index, FieldId, DocumentId, Criterion};
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use crate::search::criteria::Criterion;
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use crate::search::criteria::typo::Typo;
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use crate::{Index, FieldId, DocumentId};
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pub use self::facet::{FacetCondition, FacetDistribution, FacetNumberOperator, FacetStringOperator};
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pub use self::facet::{FacetIter};
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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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@ -30,6 +33,7 @@ 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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@ -258,15 +262,22 @@ impl<'a> Search<'a> {
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}
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pub fn execute(&self) -> anyhow::Result<SearchResult> {
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let limit = self.limit;
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let fst = self.index.words_fst(self.rtxn)?;
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// Construct the DFAs related to the query words.
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let derived_words = match self.query.as_deref().map(Self::generate_query_dfas) {
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Some(dfas) if !dfas.is_empty() => Some(self.fetch_words_docids(&fst, dfas)?),
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_otherwise => None,
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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(false, true, 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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@ -276,100 +287,129 @@ impl<'a> Search<'a> {
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debug!("facet candidates: {:?} took {:.02?}", facet_candidates, before.elapsed());
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let order_by_facet = {
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let criteria = self.index.criteria(self.rtxn)?;
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let result = criteria.into_iter().flat_map(|criterion| {
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match criterion {
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Criterion::Asc(fid) => Some((fid, true)),
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Criterion::Desc(fid) => Some((fid, false)),
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_ => None
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}
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}).next();
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match result {
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Some((attr_name, is_ascending)) => {
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let field_id_map = self.index.fields_ids_map(self.rtxn)?;
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let fid = field_id_map.id(&attr_name).with_context(|| format!("unknown field: {:?}", attr_name))?;
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let faceted_fields = self.index.faceted_fields_ids(self.rtxn)?;
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let ftype = *faceted_fields.get(&fid)
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.with_context(|| format!("{:?} not found in the faceted fields.", attr_name))
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.expect("corrupted data: ");
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Some((fid, ftype, is_ascending))
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},
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None => None,
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}
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};
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// We aretesting the typo criteria but there will be more of them soon.
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let mut criteria = Typo::initial(self.index, self.rtxn, query_tree, facet_candidates)?;
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let before = Instant::now();
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let (candidates, derived_words) = match (facet_candidates, derived_words) {
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(Some(mut facet_candidates), Some(derived_words)) => {
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let words_candidates = Self::compute_candidates(&derived_words);
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facet_candidates.intersect_with(&words_candidates);
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(facet_candidates, derived_words)
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},
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(None, Some(derived_words)) => {
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(Self::compute_candidates(&derived_words), derived_words)
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},
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(Some(facet_candidates), None) => {
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// If the query is not set or results in no DFAs but
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// there is some facet conditions we return a placeholder.
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let documents_ids = match order_by_facet {
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Some((fid, ftype, is_ascending)) => {
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self.facet_ordered(fid, ftype, is_ascending, facet_candidates.clone(), limit)?
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},
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None => facet_candidates.iter().take(limit).collect(),
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};
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return Ok(SearchResult {
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documents_ids,
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candidates: facet_candidates,
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..Default::default()
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})
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},
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(None, None) => {
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// If the query is not set or results in no DFAs we return a placeholder.
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let all_docids = self.index.documents_ids(self.rtxn)?;
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let documents_ids = match order_by_facet {
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Some((fid, ftype, is_ascending)) => {
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self.facet_ordered(fid, ftype, is_ascending, all_docids.clone(), limit)?
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},
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None => all_docids.iter().take(limit).collect(),
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};
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return Ok(SearchResult { documents_ids, candidates: all_docids,..Default::default() })
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},
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};
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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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while let Some((_qt, docids)) = criteria.next()? {
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debug!("candidates: {:?} took {:.02?}", candidates, before.elapsed());
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let mut len = docids.len() as usize;
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let mut docids = docids.into_iter();
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// The mana depth first search is a revised DFS that explore
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// solutions in the order of their proximities.
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let mut mdfs = Mdfs::new(self.index, self.rtxn, &derived_words, candidates.clone());
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let mut documents = Vec::new();
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// We execute the Mdfs iterator until we find enough documents.
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while documents.iter().map(RoaringBitmap::len).sum::<u64>() < limit as u64 {
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match mdfs.next().transpose()? {
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Some((proximity, answer)) => {
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debug!("answer with a proximity of {}: {:?}", proximity, answer);
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documents.push(answer);
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},
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None => break,
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}
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if offset != 0 {
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docids.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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let found_words = derived_words.into_iter().flat_map(|(w, _)| w).map(|(w, _)| w).collect();
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let documents_ids = match order_by_facet {
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Some((fid, ftype, order)) => {
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let mut ordered_documents = Vec::new();
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for documents_ids in documents {
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let docids = self.facet_ordered(fid, ftype, order, documents_ids, limit)?;
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ordered_documents.push(docids);
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if ordered_documents.iter().map(Vec::len).sum::<usize>() >= limit { break }
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if len != 0 {
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documents_ids.extend(docids.take(limit));
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limit = limit.saturating_sub(len.min(limit));
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}
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ordered_documents.into_iter().flatten().take(limit).collect()
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},
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None => documents.into_iter().flatten().take(limit).collect(),
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};
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if limit == 0 { break }
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}
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let found_words = HashSet::new();
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let candidates = RoaringBitmap::new();
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Ok(SearchResult { found_words, candidates, documents_ids })
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// let order_by_facet = {
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// let criteria = self.index.criteria(self.rtxn)?;
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// let result = criteria.into_iter().flat_map(|criterion| {
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// match criterion {
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// Criterion::Asc(fid) => Some((fid, true)),
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// Criterion::Desc(fid) => Some((fid, false)),
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// _ => None
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// }
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// }).next();
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// match result {
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// Some((attr_name, is_ascending)) => {
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// let field_id_map = self.index.fields_ids_map(self.rtxn)?;
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// let fid = field_id_map.id(&attr_name).with_context(|| format!("unknown field: {:?}", attr_name))?;
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// let faceted_fields = self.index.faceted_fields_ids(self.rtxn)?;
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// let ftype = *faceted_fields.get(&fid)
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// .with_context(|| format!("{:?} not found in the faceted fields.", attr_name))
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// .expect("corrupted data: ");
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// Some((fid, ftype, is_ascending))
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// },
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// None => None,
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// }
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// };
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// let before = Instant::now();
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// let (candidates, derived_words) = match (facet_candidates, derived_words) {
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// (Some(mut facet_candidates), Some(derived_words)) => {
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// let words_candidates = Self::compute_candidates(&derived_words);
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// facet_candidates.intersect_with(&words_candidates);
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// (facet_candidates, derived_words)
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// },
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// (None, Some(derived_words)) => {
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// (Self::compute_candidates(&derived_words), derived_words)
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// },
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// (Some(facet_candidates), None) => {
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// // If the query is not set or results in no DFAs but
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// // there is some facet conditions we return a placeholder.
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// let documents_ids = match order_by_facet {
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// Some((fid, ftype, is_ascending)) => {
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// self.facet_ordered(fid, ftype, is_ascending, facet_candidates.clone(), limit)?
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// },
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// None => facet_candidates.iter().take(limit).collect(),
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// };
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// return Ok(SearchResult {
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// documents_ids,
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// candidates: facet_candidates,
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// ..Default::default()
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// })
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// },
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// (None, None) => {
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// // If the query is not set or results in no DFAs we return a placeholder.
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// let all_docids = self.index.documents_ids(self.rtxn)?;
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// let documents_ids = match order_by_facet {
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// Some((fid, ftype, is_ascending)) => {
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// self.facet_ordered(fid, ftype, is_ascending, all_docids.clone(), limit)?
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// },
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// None => all_docids.iter().take(limit).collect(),
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// };
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// return Ok(SearchResult { documents_ids, candidates: all_docids,..Default::default() })
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// },
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// };
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// debug!("candidates: {:?} took {:.02?}", candidates, before.elapsed());
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// // The mana depth first search is a revised DFS that explore
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// // solutions in the order of their proximities.
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// let mut mdfs = Mdfs::new(self.index, self.rtxn, &derived_words, candidates.clone());
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// let mut documents = Vec::new();
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// // We execute the Mdfs iterator until we find enough documents.
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// while documents.iter().map(RoaringBitmap::len).sum::<u64>() < limit as u64 {
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// match mdfs.next().transpose()? {
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// Some((proximity, answer)) => {
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// debug!("answer with a proximity of {}: {:?}", proximity, answer);
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// documents.push(answer);
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// },
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// None => break,
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// }
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// }
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// let found_words = derived_words.into_iter().flat_map(|(w, _)| w).map(|(w, _)| w).collect();
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// let documents_ids = match order_by_facet {
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// Some((fid, ftype, order)) => {
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// let mut ordered_documents = Vec::new();
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// for documents_ids in documents {
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// let docids = self.facet_ordered(fid, ftype, order, documents_ids, limit)?;
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// ordered_documents.push(docids);
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// if ordered_documents.iter().map(Vec::len).sum::<usize>() >= limit { break }
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// }
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// ordered_documents.into_iter().flatten().take(limit).collect()
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// },
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// None => documents.into_iter().flatten().take(limit).collect(),
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// };
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// Ok(SearchResult { found_words, candidates, documents_ids })
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}
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}
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