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Move the facets related system into the new search module

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This commit is contained in:
Clément Renault 2020-11-20 10:54:41 +01:00
parent 531bd6ddc7
commit 278391d961
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GPG Key ID: 92ADA4E935E71FA4
2 changed files with 234 additions and 229 deletions
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235
src/search.rs → src/search/facet.rs
View File

@ -1,33 +1,21 @@
use std::borrow::Cow;
use std::collections::{HashMap, HashSet};
use std::error::Error as StdError;
use std::fmt::{self, Debug};
use std::fmt::Debug;
use std::ops::Bound::{self, Unbounded, Included, Excluded};
use std::str::FromStr;
use anyhow::{bail, ensure, Context};
use fst::{IntoStreamer, Streamer};
use heed::types::{ByteSlice, DecodeIgnore};
use levenshtein_automata::DFA;
use levenshtein_automata::LevenshteinAutomatonBuilder as LevBuilder;
use log::debug;
use num_traits::Bounded;
use once_cell::sync::Lazy;
use roaring::bitmap::RoaringBitmap;
use roaring::RoaringBitmap;
use crate::facet::FacetType;
use crate::heed_codec::facet::{FacetLevelValueI64Codec, FacetLevelValueF64Codec};
use crate::heed_codec::CboRoaringBitmapCodec;
use crate::mdfs::Mdfs;
use crate::query_tokens::{QueryTokens, QueryToken};
use crate::{Index, DocumentId};
use crate::{Index, CboRoaringBitmapCodec};
// Building these factories is not free.
static LEVDIST0: Lazy<LevBuilder> = Lazy::new(|| LevBuilder::new(0, true));
static LEVDIST1: Lazy<LevBuilder> = Lazy::new(|| LevBuilder::new(1, true));
static LEVDIST2: Lazy<LevBuilder> = Lazy::new(|| LevBuilder::new(2, true));
use self::FacetCondition::*;
use self::FacetOperator::*;
// TODO support also floats
#[derive(Debug, Copy, Clone, PartialEq)]
pub enum FacetOperator<T> {
GreaterThan(T),
@ -52,8 +40,6 @@ impl FacetCondition {
string: &str,
) -> anyhow::Result<Option<FacetCondition>>
{
use FacetCondition::*;
let fields_ids_map = index.fields_ids_map(rtxn)?;
let faceted_fields = index.faceted_fields(rtxn)?;
@ -80,8 +66,6 @@ impl FacetCondition {
) -> anyhow::Result<FacetOperator<T>>
where T::Err: Send + Sync + StdError + 'static,
{
use FacetOperator::*;
match iter.next() {
Some(">") => {
let param = iter.next().context("missing parameter")?;
@ -228,8 +212,6 @@ impl FacetCondition {
KC: heed::BytesDecode<'t, DItem = (u8, u8, T, T)>,
KC: for<'x> heed::BytesEncode<'x, EItem = (u8, u8, T, T)>,
{
use FacetOperator::*;
// Make sure we always bound the ranges with the field id and the level,
// as the facets values are all in the same database and prefixed by the
// field id and the level.
@ -259,7 +241,7 @@ impl FacetCondition {
}
}
fn evaluate(
pub fn evaluate(
&self,
rtxn: &heed::RoTxn,
db: heed::Database<ByteSlice, CboRoaringBitmapCodec>,
@ -275,208 +257,3 @@ impl FacetCondition {
}
}
}
pub struct Search<'a> {
query: Option<String>,
facet_condition: Option<FacetCondition>,
offset: usize,
limit: usize,
rtxn: &'a heed::RoTxn<'a>,
index: &'a Index,
}
impl<'a> Search<'a> {
pub fn new(rtxn: &'a heed::RoTxn, index: &'a Index) -> Search<'a> {
Search { query: None, facet_condition: None, offset: 0, limit: 20, rtxn, index }
}
pub fn query(&mut self, query: impl Into<String>) -> &mut Search<'a> {
self.query = Some(query.into());
self
}
pub fn offset(&mut self, offset: usize) -> &mut Search<'a> {
self.offset = offset;
self
}
pub fn limit(&mut self, limit: usize) -> &mut Search<'a> {
self.limit = limit;
self
}
pub fn facet_condition(&mut self, condition: FacetCondition) -> &mut Search<'a> {
self.facet_condition = Some(condition);
self
}
/// 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>,
}

228
src/search/mod.rs Normal file
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@ -0,0 +1,228 @@
use std::borrow::Cow;
use std::collections::{HashMap, HashSet};
use std::fmt;
use fst::{IntoStreamer, Streamer};
use levenshtein_automata::DFA;
use levenshtein_automata::LevenshteinAutomatonBuilder as LevBuilder;
use log::debug;
use once_cell::sync::Lazy;
use roaring::bitmap::RoaringBitmap;
use crate::mdfs::Mdfs;
use crate::query_tokens::{QueryTokens, QueryToken};
use crate::{Index, DocumentId};
pub use self::facet::FacetCondition;
// Building these factories is not free.
static LEVDIST0: Lazy<LevBuilder> = Lazy::new(|| LevBuilder::new(0, true));
static LEVDIST1: Lazy<LevBuilder> = Lazy::new(|| LevBuilder::new(1, true));
static LEVDIST2: Lazy<LevBuilder> = Lazy::new(|| LevBuilder::new(2, true));
mod facet;
pub struct Search<'a> {
query: Option<String>,
facet_condition: Option<FacetCondition>,
offset: usize,
limit: usize,
rtxn: &'a heed::RoTxn<'a>,
index: &'a Index,
}
impl<'a> Search<'a> {
pub fn new(rtxn: &'a heed::RoTxn, index: &'a Index) -> Search<'a> {
Search { query: None, facet_condition: None, offset: 0, limit: 20, rtxn, index }
}
pub fn query(&mut self, query: impl Into<String>) -> &mut Search<'a> {
self.query = Some(query.into());
self
}
pub fn offset(&mut self, offset: usize) -> &mut Search<'a> {
self.offset = offset;
self
}
pub fn limit(&mut self, limit: usize) -> &mut Search<'a> {
self.limit = limit;
self
}
pub fn facet_condition(&mut self, condition: FacetCondition) -> &mut Search<'a> {
self.facet_condition = Some(condition);
self
}
/// 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>,
}