mirror of
https://github.com/meilisearch/MeiliSearch
synced 2024-11-27 07:14:26 +01:00
Move the facets related system into the new search module
This commit is contained in:
Clément Renault
parent
531bd6ddc7
commit
278391d961
@ -1,33 +1,21 @@
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use std::borrow::Cow;
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use std::collections::{HashMap, HashSet};
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use std::error::Error as StdError;
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use std::error::Error as StdError;
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use std::fmt::{self, Debug};
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use std::fmt::Debug;
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use std::ops::Bound::{self, Unbounded, Included, Excluded};
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use std::ops::Bound::{self, Unbounded, Included, Excluded};
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use std::str::FromStr;
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use std::str::FromStr;
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use anyhow::{bail, ensure, Context};
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use anyhow::{bail, ensure, Context};
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use fst::{IntoStreamer, Streamer};
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use heed::types::{ByteSlice, DecodeIgnore};
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use heed::types::{ByteSlice, DecodeIgnore};
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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 log::debug;
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use num_traits::Bounded;
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use num_traits::Bounded;
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use once_cell::sync::Lazy;
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use roaring::RoaringBitmap;
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use roaring::bitmap::RoaringBitmap;
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use crate::facet::FacetType;
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use crate::facet::FacetType;
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use crate::heed_codec::facet::{FacetLevelValueI64Codec, FacetLevelValueF64Codec};
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use crate::heed_codec::facet::{FacetLevelValueI64Codec, FacetLevelValueF64Codec};
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use crate::heed_codec::CboRoaringBitmapCodec;
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use crate::{Index, CboRoaringBitmapCodec};
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use crate::mdfs::Mdfs;
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use crate::query_tokens::{QueryTokens, QueryToken};
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use crate::{Index, DocumentId};
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// Building these factories is not free.
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use self::FacetCondition::*;
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static LEVDIST0: Lazy<LevBuilder> = Lazy::new(|| LevBuilder::new(0, true));
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use self::FacetOperator::*;
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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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// TODO support also floats
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#[derive(Debug, Copy, Clone, PartialEq)]
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#[derive(Debug, Copy, Clone, PartialEq)]
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pub enum FacetOperator<T> {
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pub enum FacetOperator<T> {
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GreaterThan(T),
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GreaterThan(T),
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@ -52,8 +40,6 @@ impl FacetCondition {
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string: &str,
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string: &str,
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) -> anyhow::Result<Option<FacetCondition>>
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) -> anyhow::Result<Option<FacetCondition>>
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{
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{
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use FacetCondition::*;
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let fields_ids_map = index.fields_ids_map(rtxn)?;
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let fields_ids_map = index.fields_ids_map(rtxn)?;
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let faceted_fields = index.faceted_fields(rtxn)?;
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let faceted_fields = index.faceted_fields(rtxn)?;
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@ -80,8 +66,6 @@ impl FacetCondition {
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) -> anyhow::Result<FacetOperator<T>>
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) -> anyhow::Result<FacetOperator<T>>
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where T::Err: Send + Sync + StdError + 'static,
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where T::Err: Send + Sync + StdError + 'static,
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{
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{
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use FacetOperator::*;
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match iter.next() {
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match iter.next() {
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Some(">") => {
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Some(">") => {
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let param = iter.next().context("missing parameter")?;
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let param = iter.next().context("missing parameter")?;
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@ -228,8 +212,6 @@ impl FacetCondition {
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KC: heed::BytesDecode<'t, DItem = (u8, u8, T, T)>,
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KC: heed::BytesDecode<'t, DItem = (u8, u8, T, T)>,
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KC: for<'x> heed::BytesEncode<'x, EItem = (u8, u8, T, T)>,
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KC: for<'x> heed::BytesEncode<'x, EItem = (u8, u8, T, T)>,
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{
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{
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use FacetOperator::*;
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// Make sure we always bound the ranges with the field id and the level,
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// Make sure we always bound the ranges with the field id and the level,
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// as the facets values are all in the same database and prefixed by the
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// as the facets values are all in the same database and prefixed by the
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// field id and the level.
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// field id and the level.
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@ -259,7 +241,7 @@ impl FacetCondition {
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}
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}
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}
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}
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fn evaluate(
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pub fn evaluate(
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&self,
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&self,
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rtxn: &heed::RoTxn,
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rtxn: &heed::RoTxn,
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db: heed::Database<ByteSlice, CboRoaringBitmapCodec>,
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db: heed::Database<ByteSlice, CboRoaringBitmapCodec>,
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@ -275,208 +257,3 @@ impl FacetCondition {
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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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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 words: Vec<_> = QueryTokens::new(query).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(word) => (word.to_lowercase(), word.len() <= 3),
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QueryToken::Quoted(word) => (word.to_lowercase(), 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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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 facet_db = self.index.facet_field_id_value_docids;
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let facet_candidates = match self.facet_condition {
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Some(condition) => Some(condition.evaluate(self.rtxn, facet_db)?),
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None => None,
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};
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debug!("facet candidates: {:?}", facet_candidates);
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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 = facet_candidates.iter().take(limit).collect();
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return Ok(SearchResult { documents_ids, ..Default::default() })
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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 documents_ids = self.index.documents_ids(self.rtxn)?.iter().take(limit).collect();
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return Ok(SearchResult { documents_ids, ..Default::default() })
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},
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};
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debug!("candidates: {:?}", candidates);
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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);
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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 = documents.into_iter().flatten().take(limit).collect();
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Ok(SearchResult { found_words, 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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f.debug_struct("Search")
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.field("query", &self.query)
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.field("facet_condition", &self.facet_condition)
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.field("offset", &self.offset)
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.field("limit", &self.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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// 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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228
src/search/mod.rs
Normal file
228
src/search/mod.rs
Normal file
@ -0,0 +1,228 @@
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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 fst::{IntoStreamer, Streamer};
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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 once_cell::sync::Lazy;
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use roaring::bitmap::RoaringBitmap;
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|
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use crate::mdfs::Mdfs;
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use crate::query_tokens::{QueryTokens, QueryToken};
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use crate::{Index, DocumentId};
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|
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pub use self::facet::FacetCondition;
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|
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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.
|
||||||
|
/// TODO introduce settings for the number of typos regarding the words lengths.
|
||||||
|
fn generate_query_dfas(query: &str) -> Vec<(String, bool, DFA)> {
|
||||||
|
let (lev0, lev1, lev2) = (&LEVDIST0, &LEVDIST1, &LEVDIST2);
|
||||||
|
|
||||||
|
let words: Vec<_> = QueryTokens::new(query).collect();
|
||||||
|
let ends_with_whitespace = query.chars().last().map_or(false, char::is_whitespace);
|
||||||
|
let number_of_words = words.len();
|
||||||
|
|
||||||
|
words.into_iter().enumerate().map(|(i, word)| {
|
||||||
|
let (word, quoted) = match word {
|
||||||
|
QueryToken::Free(word) => (word.to_lowercase(), word.len() <= 3),
|
||||||
|
QueryToken::Quoted(word) => (word.to_lowercase(), true),
|
||||||
|
};
|
||||||
|
let is_last = i + 1 == number_of_words;
|
||||||
|
let is_prefix = is_last && !ends_with_whitespace && !quoted;
|
||||||
|
let lev = match word.len() {
|
||||||
|
0..=4 => if quoted { lev0 } else { lev0 },
|
||||||
|
5..=8 => if quoted { lev0 } else { lev1 },
|
||||||
|
_ => if quoted { lev0 } else { lev2 },
|
||||||
|
};
|
||||||
|
|
||||||
|
let dfa = if is_prefix {
|
||||||
|
lev.build_prefix_dfa(&word)
|
||||||
|
} else {
|
||||||
|
lev.build_dfa(&word)
|
||||||
|
};
|
||||||
|
|
||||||
|
(word, is_prefix, dfa)
|
||||||
|
})
|
||||||
|
.collect()
|
||||||
|
}
|
||||||
|
|
||||||
|
/// Fetch the words from the given FST related to the given DFAs along with
|
||||||
|
/// the associated documents ids.
|
||||||
|
fn fetch_words_docids(
|
||||||
|
&self,
|
||||||
|
fst: &fst::Set<Cow<[u8]>>,
|
||||||
|
dfas: Vec<(String, bool, DFA)>,
|
||||||
|
) -> anyhow::Result<Vec<(HashMap<String, (u8, RoaringBitmap)>, RoaringBitmap)>>
|
||||||
|
{
|
||||||
|
// A Vec storing all the derived words from the original query words, associated
|
||||||
|
// with the distance from the original word and the docids where the words appears.
|
||||||
|
let mut derived_words = Vec::<(HashMap::<String, (u8, RoaringBitmap)>, RoaringBitmap)>::with_capacity(dfas.len());
|
||||||
|
|
||||||
|
for (_word, _is_prefix, dfa) in dfas {
|
||||||
|
|
||||||
|
let mut acc_derived_words = HashMap::new();
|
||||||
|
let mut unions_docids = RoaringBitmap::new();
|
||||||
|
let mut stream = fst.search_with_state(&dfa).into_stream();
|
||||||
|
while let Some((word, state)) = stream.next() {
|
||||||
|
|
||||||
|
let word = std::str::from_utf8(word)?;
|
||||||
|
let docids = self.index.word_docids.get(self.rtxn, word)?.unwrap();
|
||||||
|
let distance = dfa.distance(state);
|
||||||
|
unions_docids.union_with(&docids);
|
||||||
|
acc_derived_words.insert(word.to_string(), (distance.to_u8(), docids));
|
||||||
|
}
|
||||||
|
derived_words.push((acc_derived_words, unions_docids));
|
||||||
|
}
|
||||||
|
|
||||||
|
Ok(derived_words)
|
||||||
|
}
|
||||||
|
|
||||||
|
/// Returns the set of docids that contains all of the query words.
|
||||||
|
fn compute_candidates(
|
||||||
|
derived_words: &[(HashMap<String, (u8, RoaringBitmap)>, RoaringBitmap)],
|
||||||
|
) -> RoaringBitmap
|
||||||
|
{
|
||||||
|
// We sort the derived words by inverse popularity, this way intersections are faster.
|
||||||
|
let mut derived_words: Vec<_> = derived_words.iter().collect();
|
||||||
|
derived_words.sort_unstable_by_key(|(_, docids)| docids.len());
|
||||||
|
|
||||||
|
// we do a union between all the docids of each of the derived words,
|
||||||
|
// we got N unions (the number of original query words), we then intersect them.
|
||||||
|
let mut candidates = RoaringBitmap::new();
|
||||||
|
|
||||||
|
for (i, (_, union_docids)) in derived_words.iter().enumerate() {
|
||||||
|
if i == 0 {
|
||||||
|
candidates = union_docids.clone();
|
||||||
|
} else {
|
||||||
|
candidates.intersect_with(&union_docids);
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
candidates
|
||||||
|
}
|
||||||
|
|
||||||
|
pub fn execute(&self) -> anyhow::Result<SearchResult> {
|
||||||
|
let limit = self.limit;
|
||||||
|
let fst = self.index.words_fst(self.rtxn)?;
|
||||||
|
|
||||||
|
// Construct the DFAs related to the query words.
|
||||||
|
let derived_words = match self.query.as_deref().map(Self::generate_query_dfas) {
|
||||||
|
Some(dfas) if !dfas.is_empty() => Some(self.fetch_words_docids(&fst, dfas)?),
|
||||||
|
_otherwise => None,
|
||||||
|
};
|
||||||
|
|
||||||
|
// We create the original candidates with the facet conditions results.
|
||||||
|
let facet_db = self.index.facet_field_id_value_docids;
|
||||||
|
let facet_candidates = match self.facet_condition {
|
||||||
|
Some(condition) => Some(condition.evaluate(self.rtxn, facet_db)?),
|
||||||
|
None => None,
|
||||||
|
};
|
||||||
|
|
||||||
|
debug!("facet candidates: {:?}", facet_candidates);
|
||||||
|
|
||||||
|
let (candidates, derived_words) = match (facet_candidates, derived_words) {
|
||||||
|
(Some(mut facet_candidates), Some(derived_words)) => {
|
||||||
|
let words_candidates = Self::compute_candidates(&derived_words);
|
||||||
|
facet_candidates.intersect_with(&words_candidates);
|
||||||
|
(facet_candidates, derived_words)
|
||||||
|
},
|
||||||
|
(None, Some(derived_words)) => {
|
||||||
|
(Self::compute_candidates(&derived_words), derived_words)
|
||||||
|
},
|
||||||
|
(Some(facet_candidates), None) => {
|
||||||
|
// If the query is not set or results in no DFAs but
|
||||||
|
// there is some facet conditions we return a placeholder.
|
||||||
|
let documents_ids = facet_candidates.iter().take(limit).collect();
|
||||||
|
return Ok(SearchResult { documents_ids, ..Default::default() })
|
||||||
|
},
|
||||||
|
(None, None) => {
|
||||||
|
// If the query is not set or results in no DFAs we return a placeholder.
|
||||||
|
let documents_ids = self.index.documents_ids(self.rtxn)?.iter().take(limit).collect();
|
||||||
|
return Ok(SearchResult { documents_ids, ..Default::default() })
|
||||||
|
},
|
||||||
|
};
|
||||||
|
|
||||||
|
debug!("candidates: {:?}", candidates);
|
||||||
|
|
||||||
|
// The mana depth first search is a revised DFS that explore
|
||||||
|
// solutions in the order of their proximities.
|
||||||
|
let mut mdfs = Mdfs::new(self.index, self.rtxn, &derived_words, candidates);
|
||||||
|
let mut documents = Vec::new();
|
||||||
|
|
||||||
|
// We execute the Mdfs iterator until we find enough documents.
|
||||||
|
while documents.iter().map(RoaringBitmap::len).sum::<u64>() < limit as u64 {
|
||||||
|
match mdfs.next().transpose()? {
|
||||||
|
Some((proximity, answer)) => {
|
||||||
|
debug!("answer with a proximity of {}: {:?}", proximity, answer);
|
||||||
|
documents.push(answer);
|
||||||
|
},
|
||||||
|
None => break,
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
let found_words = derived_words.into_iter().flat_map(|(w, _)| w).map(|(w, _)| w).collect();
|
||||||
|
let documents_ids = documents.into_iter().flatten().take(limit).collect();
|
||||||
|
Ok(SearchResult { found_words, documents_ids })
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
impl fmt::Debug for Search<'_> {
|
||||||
|
fn fmt(&self, f: &mut fmt::Formatter) -> fmt::Result {
|
||||||
|
f.debug_struct("Search")
|
||||||
|
.field("query", &self.query)
|
||||||
|
.field("facet_condition", &self.facet_condition)
|
||||||
|
.field("offset", &self.offset)
|
||||||
|
.field("limit", &self.limit)
|
||||||
|
.finish()
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
#[derive(Default)]
|
||||||
|
pub struct SearchResult {
|
||||||
|
pub found_words: HashSet<String>,
|
||||||
|
// TODO those documents ids should be associated with their criteria scores.
|
||||||
|
pub documents_ids: Vec<DocumentId>,
|
||||||
|
}
|
||||||
Loading…
Reference in New Issue
Block a user