394 lines
17 KiB
Rust
394 lines
17 KiB
Rust
use std::borrow::Cow;
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use std::collections::{HashMap, HashSet};
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use std::fmt;
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use std::time::Instant;
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use anyhow::{bail, Context};
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use fst::{IntoStreamer, Streamer, Set};
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use levenshtein_automata::DFA;
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use levenshtein_automata::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 ordered_float::OrderedFloat;
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use roaring::bitmap::RoaringBitmap;
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use crate::facet::FacetType;
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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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pub use self::facet::{FacetCondition, FacetDistribution, FacetNumberOperator, FacetStringOperator};
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pub use self::facet::{FacetIter};
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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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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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/// Extracts the query words from the query string and returns the DFAs accordingly.
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/// TODO introduce settings for the number of typos regarding the words lengths.
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fn generate_query_dfas(query: &str) -> Vec<(String, bool, DFA)> {
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let (lev0, lev1, lev2) = (&LEVDIST0, &LEVDIST1, &LEVDIST2);
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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 analyzed = analyzer.analyze(query);
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let tokens = analyzed.tokens();
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let words: Vec<_> = query_tokens(tokens).collect();
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let ends_with_whitespace = query.chars().last().map_or(false, char::is_whitespace);
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let number_of_words = words.len();
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words.into_iter().enumerate().map(|(i, word)| {
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let (word, quoted) = match word {
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QueryToken::Free(token) => (token.text().to_string(), token.text().len() <= 3),
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QueryToken::Quoted(token) => (token.text().to_string(), true),
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};
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let is_last = i + 1 == number_of_words;
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let is_prefix = is_last && !ends_with_whitespace && !quoted;
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let lev = match word.len() {
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0..=4 => if quoted { lev0 } else { lev0 },
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5..=8 => if quoted { lev0 } else { lev1 },
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_ => if quoted { lev0 } else { lev2 },
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};
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let dfa = 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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(word, is_prefix, dfa)
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})
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.collect()
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}
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/// Fetch the words from the given FST related to the given DFAs along with
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/// the associated documents ids.
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fn fetch_words_docids(
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&self,
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fst: &fst::Set<Cow<[u8]>>,
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dfas: Vec<(String, bool, DFA)>,
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) -> anyhow::Result<Vec<(HashMap<String, (u8, RoaringBitmap)>, RoaringBitmap)>>
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{
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// A Vec storing all the derived words from the original query words, associated
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// with the distance from the original word and the docids where the words appears.
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let mut derived_words = Vec::<(HashMap::<String, (u8, RoaringBitmap)>, RoaringBitmap)>::with_capacity(dfas.len());
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for (_word, _is_prefix, dfa) in dfas {
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let mut acc_derived_words = HashMap::new();
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let mut unions_docids = RoaringBitmap::new();
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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 docids = self.index.word_docids.get(self.rtxn, word)?.unwrap();
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let distance = dfa.distance(state);
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unions_docids.union_with(&docids);
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acc_derived_words.insert(word.to_string(), (distance.to_u8(), docids));
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}
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derived_words.push((acc_derived_words, unions_docids));
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}
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Ok(derived_words)
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}
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/// Returns the set of docids that contains all of the query words.
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fn compute_candidates(
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derived_words: &[(HashMap<String, (u8, RoaringBitmap)>, RoaringBitmap)],
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) -> RoaringBitmap
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{
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// We sort the derived words by inverse popularity, this way intersections are faster.
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let mut derived_words: Vec<_> = derived_words.iter().collect();
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derived_words.sort_unstable_by_key(|(_, docids)| docids.len());
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// we do a union between all the docids of each of the derived words,
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// we got N unions (the number of original query words), we then intersect them.
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let mut candidates = RoaringBitmap::new();
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for (i, (_, union_docids)) in derived_words.iter().enumerate() {
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if i == 0 {
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candidates = union_docids.clone();
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} else {
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candidates.intersect_with(&union_docids);
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}
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}
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candidates
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}
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fn facet_ordered(
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&self,
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field_id: FieldId,
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facet_type: FacetType,
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ascending: bool,
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mut documents_ids: RoaringBitmap,
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limit: usize,
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) -> anyhow::Result<Vec<DocumentId>>
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{
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let mut output: Vec<_> = match facet_type {
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FacetType::Float => {
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if documents_ids.len() <= 1000 {
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let db = self.index.field_id_docid_facet_values.remap_key_type::<FieldDocIdFacetF64Codec>();
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let mut docids_values = Vec::with_capacity(documents_ids.len() as usize);
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for docid in documents_ids.iter() {
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let left = (field_id, docid, f64::MIN);
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let right = (field_id, docid, f64::MAX);
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let mut iter = db.range(self.rtxn, &(left..=right))?;
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let entry = if ascending { iter.next() } else { iter.last() };
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if let Some(((_, _, value), ())) = entry.transpose()? {
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docids_values.push((docid, OrderedFloat(value)));
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}
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}
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docids_values.sort_unstable_by_key(|(_, value)| *value);
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let iter = docids_values.into_iter().map(|(id, _)| id);
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if ascending {
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iter.take(limit).collect()
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} else {
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iter.rev().take(limit).collect()
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}
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} else {
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let facet_fn = if ascending {
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FacetIter::<f64, FacetLevelValueF64Codec>::new_reducing
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} else {
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FacetIter::<f64, FacetLevelValueF64Codec>::new_reverse_reducing
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};
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let mut limit_tmp = limit;
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let mut output = Vec::new();
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for result in facet_fn(self.rtxn, self.index, field_id, documents_ids.clone())? {
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let (_val, docids) = result?;
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limit_tmp = limit_tmp.saturating_sub(docids.len() as usize);
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output.push(docids);
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if limit_tmp == 0 { break }
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}
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output.into_iter().flatten().take(limit).collect()
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}
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},
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FacetType::Integer => {
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if documents_ids.len() <= 1000 {
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let db = self.index.field_id_docid_facet_values.remap_key_type::<FieldDocIdFacetI64Codec>();
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let mut docids_values = Vec::with_capacity(documents_ids.len() as usize);
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for docid in documents_ids.iter() {
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let left = (field_id, docid, i64::MIN);
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let right = (field_id, docid, i64::MAX);
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let mut iter = db.range(self.rtxn, &(left..=right))?;
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let entry = if ascending { iter.next() } else { iter.last() };
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if let Some(((_, _, value), ())) = entry.transpose()? {
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docids_values.push((docid, value));
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}
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}
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docids_values.sort_unstable_by_key(|(_, value)| *value);
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let iter = docids_values.into_iter().map(|(id, _)| id);
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if ascending {
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iter.take(limit).collect()
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} else {
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iter.rev().take(limit).collect()
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}
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} else {
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let facet_fn = if ascending {
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FacetIter::<i64, FacetLevelValueI64Codec>::new_reducing
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} else {
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FacetIter::<i64, FacetLevelValueI64Codec>::new_reverse_reducing
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};
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let mut limit_tmp = limit;
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let mut output = Vec::new();
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for result in facet_fn(self.rtxn, self.index, field_id, documents_ids.clone())? {
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let (_val, docids) = result?;
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limit_tmp = limit_tmp.saturating_sub(docids.len() as usize);
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output.push(docids);
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if limit_tmp == 0 { break }
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}
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output.into_iter().flatten().take(limit).collect()
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}
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},
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FacetType::String => bail!("criteria facet type must be a number"),
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};
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// if there isn't enough documents to return we try to complete that list
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// with documents that are maybe not faceted under this field and therefore
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// not returned by the previous facet iteration.
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if output.len() < limit {
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output.iter().for_each(|n| { documents_ids.remove(*n); });
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let remaining = documents_ids.iter().take(limit - output.len());
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output.extend(remaining);
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}
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Ok(output)
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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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};
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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 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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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 found_words: HashSet<String>,
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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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