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clean warnings

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many 2021-02-18 16:31:10 +01:00 committed by Kerollmops
parent 9e093d5ff3
commit a273c46559
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4 changed files with 25 additions and 326 deletions
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18
milli/src/search/criteria/mod.rs
View File

@ -1,7 +1,7 @@
use std::borrow::Cow;
use crate::Index;
use crate::search::word_typos;
use crate::search::word_derivations;
use roaring::RoaringBitmap;
@ -124,7 +124,7 @@ fn query_docids(ctx: &dyn Context, query: &Query) -> anyhow::Result<RoaringBitma
if query.prefix && ctx.in_prefix_cache(&word) {
Ok(ctx.word_prefix_docids(&word)?.unwrap_or_default())
} else if query.prefix {
let words = word_typos(&word, true, 0, ctx.words_fst())?;
let words = word_derivations(&word, true, 0, ctx.words_fst())?;
let mut docids = RoaringBitmap::new();
for (word, _typo) in words {
let current_docids = ctx.word_docids(&word)?.unwrap_or_default();
@ -136,7 +136,7 @@ fn query_docids(ctx: &dyn Context, query: &Query) -> anyhow::Result<RoaringBitma
}
},
QueryKind::Tolerant { typo, word } => {
let words = word_typos(&word, query.prefix, *typo, ctx.words_fst())?;
let words = word_derivations(&word, query.prefix, *typo, ctx.words_fst())?;
let mut docids = RoaringBitmap::new();
for (word, _typo) in words {
let current_docids = ctx.word_docids(&word)?.unwrap_or_default();
@ -155,14 +155,14 @@ fn query_pair_proximity_docids(ctx: &dyn Context, left: &Query, right: &Query, p
if prefix && ctx.in_prefix_cache(&right) {
Ok(ctx.word_prefix_pair_proximity_docids(left.as_str(), right.as_str(), proximity)?.unwrap_or_default())
} else if prefix {
let r_words = word_typos(&right, true, 0, ctx.words_fst())?;
let r_words = word_derivations(&right, true, 0, ctx.words_fst())?;
all_word_pair_proximity_docids(ctx, &[(left, 0)], &r_words, proximity)
} else {
Ok(ctx.word_pair_proximity_docids(left.as_str(), right.as_str(), proximity)?.unwrap_or_default())
}
},
(QueryKind::Tolerant { typo, word: left }, QueryKind::Exact { word: right, .. }) => {
let l_words = word_typos(&left, false, *typo, ctx.words_fst())?;
let l_words = word_derivations(&left, false, *typo, ctx.words_fst())?;
if prefix && ctx.in_prefix_cache(&right) {
let mut docids = RoaringBitmap::new();
for (left, _) in l_words {
@ -171,19 +171,19 @@ fn query_pair_proximity_docids(ctx: &dyn Context, left: &Query, right: &Query, p
}
Ok(docids)
} else if prefix {
let r_words = word_typos(&right, true, 0, ctx.words_fst())?;
let r_words = word_derivations(&right, true, 0, ctx.words_fst())?;
all_word_pair_proximity_docids(ctx, &l_words, &r_words, proximity)
} else {
all_word_pair_proximity_docids(ctx, &l_words, &[(right, 0)], proximity)
}
},
(QueryKind::Exact { word: left, .. }, QueryKind::Tolerant { typo, word: right }) => {
let r_words = word_typos(&right, prefix, *typo, ctx.words_fst())?;
let r_words = word_derivations(&right, prefix, *typo, ctx.words_fst())?;
all_word_pair_proximity_docids(ctx, &[(left, 0)], &r_words, proximity)
},
(QueryKind::Tolerant { typo: l_typo, word: left }, QueryKind::Tolerant { typo: r_typo, word: right }) => {
let l_words = word_typos(&left, false, *l_typo, ctx.words_fst())?;
let r_words = word_typos(&right, prefix, *r_typo, ctx.words_fst())?;
let l_words = word_derivations(&left, false, *l_typo, ctx.words_fst())?;
let r_words = word_derivations(&right, prefix, *r_typo, ctx.words_fst())?;
all_word_pair_proximity_docids(ctx, &l_words, &r_words, proximity)
},
}

10
milli/src/search/criteria/typo.rs
View File

@ -4,7 +4,7 @@ use anyhow::bail;
use roaring::RoaringBitmap;
use crate::search::query_tree::{Operation, Query, QueryKind};
use crate::search::word_typos;
use crate::search::word_derivations;
use super::{Candidates, Criterion, CriterionResult, Context, query_docids, query_pair_proximity_docids};
// FIXME we must stop when the number of typos is equal to
@ -177,7 +177,7 @@ fn alterate_query_tree(
},
Operation::Query(q) => {
if let QueryKind::Tolerant { typo, word } = &q.kind {
// if no typo is allowed we don't call word_typos(..),
// if no typo is allowed we don't call word_derivations function,
// and directly create an Exact query
if number_typos == 0 {
*operation = Operation::Query(Query {
@ -190,7 +190,7 @@ fn alterate_query_tree(
let words = if let Some(derivations) = typo_cache.get(&cache_key) {
derivations.clone()
} else {
let derivations = word_typos(word, q.prefix, typo, words_fst)?;
let derivations = word_derivations(word, q.prefix, typo, words_fst)?;
typo_cache.insert(cache_key, derivations.clone());
derivations
};
@ -222,10 +222,6 @@ fn resolve_candidates<'t>(
cache: &mut HashMap<(Operation, u8), RoaringBitmap>,
) -> anyhow::Result<RoaringBitmap>
{
// FIXME add a cache
// FIXME keep the cache between typos iterations
// cache: HashMap<(&Operation, u8), RoaringBitmap>,
fn resolve_operation<'t>(
ctx: &'t dyn Context,
query_tree: &Operation,

305
milli/src/search/mod.rs
View File

@ -1,26 +1,18 @@
use std::borrow::Cow;
use std::collections::{HashMap, HashSet};
use std::collections::HashSet;
use std::fmt;
use std::time::Instant;
use anyhow::{bail, Context};
use fst::{IntoStreamer, Streamer, Set};
use levenshtein_automata::DFA;
use levenshtein_automata::LevenshteinAutomatonBuilder as LevBuilder;
use log::debug;
use meilisearch_tokenizer::{AnalyzerConfig, Analyzer};
use once_cell::sync::Lazy;
use ordered_float::OrderedFloat;
use roaring::bitmap::RoaringBitmap;
use crate::facet::FacetType;
use crate::heed_codec::facet::{FacetLevelValueF64Codec, FacetLevelValueI64Codec};
use crate::heed_codec::facet::{FieldDocIdFacetF64Codec, FieldDocIdFacetI64Codec};
use crate::mdfs::Mdfs;
use crate::query_tokens::{query_tokens, QueryToken};
use crate::search::criteria::{Criterion, CriterionResult};
use crate::search::criteria::typo::Typo;
use crate::{Index, FieldId, DocumentId};
use crate::{Index, DocumentId};
pub use self::facet::{FacetCondition, FacetDistribution, FacetNumberOperator, FacetStringOperator};
pub use self::facet::{FacetIter};
@ -69,198 +61,6 @@ impl<'a> Search<'a> {
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 stop_words = Set::default();
let analyzer = Analyzer::new(AnalyzerConfig::default_with_stopwords(&stop_words));
let analyzed = analyzer.analyze(query);
let tokens = analyzed.tokens();
let words: Vec<_> = query_tokens(tokens).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(token) => (token.text().to_string(), token.text().len() <= 3),
QueryToken::Quoted(token) => (token.text().to_string(), 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
}
fn facet_ordered(
&self,
field_id: FieldId,
facet_type: FacetType,
ascending: bool,
mut documents_ids: RoaringBitmap,
limit: usize,
) -> anyhow::Result<Vec<DocumentId>>
{
let mut output: Vec<_> = match facet_type {
FacetType::Float => {
if documents_ids.len() <= 1000 {
let db = self.index.field_id_docid_facet_values.remap_key_type::<FieldDocIdFacetF64Codec>();
let mut docids_values = Vec::with_capacity(documents_ids.len() as usize);
for docid in documents_ids.iter() {
let left = (field_id, docid, f64::MIN);
let right = (field_id, docid, f64::MAX);
let mut iter = db.range(self.rtxn, &(left..=right))?;
let entry = if ascending { iter.next() } else { iter.last() };
if let Some(((_, _, value), ())) = entry.transpose()? {
docids_values.push((docid, OrderedFloat(value)));
}
}
docids_values.sort_unstable_by_key(|(_, value)| *value);
let iter = docids_values.into_iter().map(|(id, _)| id);
if ascending {
iter.take(limit).collect()
} else {
iter.rev().take(limit).collect()
}
} else {
let facet_fn = if ascending {
FacetIter::<f64, FacetLevelValueF64Codec>::new_reducing
} else {
FacetIter::<f64, FacetLevelValueF64Codec>::new_reverse_reducing
};
let mut limit_tmp = limit;
let mut output = Vec::new();
for result in facet_fn(self.rtxn, self.index, field_id, documents_ids.clone())? {
let (_val, docids) = result?;
limit_tmp = limit_tmp.saturating_sub(docids.len() as usize);
output.push(docids);
if limit_tmp == 0 { break }
}
output.into_iter().flatten().take(limit).collect()
}
},
FacetType::Integer => {
if documents_ids.len() <= 1000 {
let db = self.index.field_id_docid_facet_values.remap_key_type::<FieldDocIdFacetI64Codec>();
let mut docids_values = Vec::with_capacity(documents_ids.len() as usize);
for docid in documents_ids.iter() {
let left = (field_id, docid, i64::MIN);
let right = (field_id, docid, i64::MAX);
let mut iter = db.range(self.rtxn, &(left..=right))?;
let entry = if ascending { iter.next() } else { iter.last() };
if let Some(((_, _, value), ())) = entry.transpose()? {
docids_values.push((docid, value));
}
}
docids_values.sort_unstable_by_key(|(_, value)| *value);
let iter = docids_values.into_iter().map(|(id, _)| id);
if ascending {
iter.take(limit).collect()
} else {
iter.rev().take(limit).collect()
}
} else {
let facet_fn = if ascending {
FacetIter::<i64, FacetLevelValueI64Codec>::new_reducing
} else {
FacetIter::<i64, FacetLevelValueI64Codec>::new_reverse_reducing
};
let mut limit_tmp = limit;
let mut output = Vec::new();
for result in facet_fn(self.rtxn, self.index, field_id, documents_ids.clone())? {
let (_val, docids) = result?;
limit_tmp = limit_tmp.saturating_sub(docids.len() as usize);
output.push(docids);
if limit_tmp == 0 { break }
}
output.into_iter().flatten().take(limit).collect()
}
},
FacetType::String => bail!("criteria facet type must be a number"),
};
// if there isn't enough documents to return we try to complete that list
// with documents that are maybe not faceted under this field and therefore
// not returned by the previous facet iteration.
if output.len() < limit {
output.iter().for_each(|n| { documents_ids.remove(*n); });
let remaining = documents_ids.iter().take(limit - output.len());
output.extend(remaining);
}
Ok(output)
}
pub fn execute(&self) -> anyhow::Result<SearchResult> {
// We create the query tree by spliting the query into tokens.
let before = Instant::now();
@ -320,101 +120,6 @@ impl<'a> Search<'a> {
let found_words = HashSet::new();
Ok(SearchResult { found_words, candidates: initial_candidates, documents_ids })
// let order_by_facet = {
// let criteria = self.index.criteria(self.rtxn)?;
// let result = criteria.into_iter().flat_map(|criterion| {
// match criterion {
// Criterion::Asc(fid) => Some((fid, true)),
// Criterion::Desc(fid) => Some((fid, false)),
// _ => None
// }
// }).next();
// match result {
// Some((attr_name, is_ascending)) => {
// let field_id_map = self.index.fields_ids_map(self.rtxn)?;
// let fid = field_id_map.id(&attr_name).with_context(|| format!("unknown field: {:?}", attr_name))?;
// let faceted_fields = self.index.faceted_fields_ids(self.rtxn)?;
// let ftype = *faceted_fields.get(&fid)
// .with_context(|| format!("{:?} not found in the faceted fields.", attr_name))
// .expect("corrupted data: ");
// Some((fid, ftype, is_ascending))
// },
// None => None,
// }
// };
// let before = Instant::now();
// 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 = match order_by_facet {
// Some((fid, ftype, is_ascending)) => {
// self.facet_ordered(fid, ftype, is_ascending, facet_candidates.clone(), limit)?
// },
// None => facet_candidates.iter().take(limit).collect(),
// };
// return Ok(SearchResult {
// documents_ids,
// candidates: facet_candidates,
// ..Default::default()
// })
// },
// (None, None) => {
// // If the query is not set or results in no DFAs we return a placeholder.
// let all_docids = self.index.documents_ids(self.rtxn)?;
// let documents_ids = match order_by_facet {
// Some((fid, ftype, is_ascending)) => {
// self.facet_ordered(fid, ftype, is_ascending, all_docids.clone(), limit)?
// },
// None => all_docids.iter().take(limit).collect(),
// };
// return Ok(SearchResult { documents_ids, candidates: all_docids,..Default::default() })
// },
// };
// debug!("candidates: {:?} took {:.02?}", candidates, before.elapsed());
// // 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.clone());
// 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 = match order_by_facet {
// Some((fid, ftype, order)) => {
// let mut ordered_documents = Vec::new();
// for documents_ids in documents {
// let docids = self.facet_ordered(fid, ftype, order, documents_ids, limit)?;
// ordered_documents.push(docids);
// if ordered_documents.iter().map(Vec::len).sum::<usize>() >= limit { break }
// }
// ordered_documents.into_iter().flatten().take(limit).collect()
// },
// None => documents.into_iter().flatten().take(limit).collect(),
// };
// Ok(SearchResult { found_words, candidates, documents_ids })
}
}
@ -438,19 +143,17 @@ pub struct SearchResult {
pub documents_ids: Vec<DocumentId>,
}
pub fn word_typos(word: &str, is_prefix: bool, max_typo: u8, fst: &fst::Set<Cow<[u8]>>) -> anyhow::Result<Vec<(String, u8)>> {
let dfa = {
pub fn word_derivations(word: &str, is_prefix: bool, max_typo: u8, fst: &fst::Set<Cow<[u8]>>) -> anyhow::Result<Vec<(String, u8)>> {
let lev = match max_typo {
0 => &LEVDIST0,
1 => &LEVDIST1,
_ => &LEVDIST2,
};
if is_prefix {
let dfa = if is_prefix {
lev.build_prefix_dfa(&word)
} else {
lev.build_dfa(&word)
}
};
let mut derived_words = Vec::new();

2
milli/src/search/query_tree.rs
View File

@ -303,7 +303,7 @@ fn fetch_words(tree: &Operation, fst: &fst::Set<Cow<[u8]>>) -> FetchedWords {
match query.kind.clone() {
QueryKind::Exact { word, .. } => vec![(word, query.prefix)],
QueryKind::Tolerant { typo, word } => {
if let Ok(words) = super::word_typos(&word, query.prefix, typo, fst) {
if let Ok(words) = super::word_derivations(&word, query.prefix, typo, fst) {
words.into_iter().map(|(w, _)| (w, query.prefix)).collect()
} else {
vec![(word, query.prefix)]