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https://github.com/meilisearch/MeiliSearch
synced 2024-12-23 13:10:06 +01:00
Implement the attribute ranking rule edge computation
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e55efc419e
commit
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@ -34,6 +34,9 @@ pub struct DatabaseCache<'ctx> {
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pub words_fst: Option<fst::Set<Cow<'ctx, [u8]>>>,
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pub word_position_docids: FxHashMap<(Interned<String>, u16), Option<&'ctx [u8]>>,
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pub word_fid_docids: FxHashMap<(Interned<String>, u16), Option<&'ctx [u8]>>,
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pub word_prefix_fid_docids: FxHashMap<(Interned<String>, u16), Option<&'ctx [u8]>>,
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pub word_fids: FxHashMap<Interned<String>, Vec<u16>>,
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pub word_prefix_fids: FxHashMap<Interned<String>, Vec<u16>>,
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}
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impl<'ctx> DatabaseCache<'ctx> {
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fn get_value<'v, K1, KC>(
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@ -284,4 +287,68 @@ impl<'ctx> SearchContext<'ctx> {
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.map(|bytes| CboRoaringBitmapCodec::bytes_decode(bytes).ok_or(heed::Error::Decoding.into()))
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.transpose()
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}
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pub fn get_db_word_prefix_fid_docids(
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&mut self,
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word_prefix: Interned<String>,
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fid: u16,
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) -> Result<Option<&'ctx [u8]>> {
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DatabaseCache::get_value(
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self.txn,
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(word_prefix, fid),
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&(self.word_interner.get(word_prefix).as_str(), fid),
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&mut self.db_cache.word_prefix_fid_docids,
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self.index.word_prefix_fid_docids.remap_data_type::<ByteSlice>(),
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)
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}
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pub fn get_db_word_fids(&mut self, word: Interned<String>) -> Result<Vec<u16>> {
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let fids = match self.db_cache.word_fids.entry(word) {
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Entry::Occupied(fids) => fids.get().clone(),
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Entry::Vacant(entry) => {
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let key = self.word_interner.get(word).as_bytes();
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let mut fids = vec![];
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let remap_key_type = self
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.index
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.word_fid_docids
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.remap_types::<ByteSlice, ByteSlice>()
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.prefix_iter(self.txn, key)?
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.remap_key_type::<StrBEU16Codec>();
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for result in remap_key_type {
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let ((_, fid), value) = result?;
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// filling other caches to avoid searching for them again
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self.db_cache.word_fid_docids.insert((word, fid), Some(value));
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fids.push(fid);
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}
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entry.insert(fids.clone());
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fids
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}
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};
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Ok(fids)
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}
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pub fn get_db_word_prefix_fids(&mut self, word_prefix: Interned<String>) -> Result<Vec<u16>> {
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let fids = match self.db_cache.word_prefix_fids.entry(word_prefix) {
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Entry::Occupied(fids) => fids.get().clone(),
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Entry::Vacant(entry) => {
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let key = self.word_interner.get(word_prefix).as_bytes();
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let mut fids = vec![];
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let remap_key_type = self
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.index
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.word_prefix_fid_docids
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.remap_types::<ByteSlice, ByteSlice>()
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.prefix_iter(self.txn, key)?
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.remap_key_type::<StrBEU16Codec>();
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for result in remap_key_type {
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let ((_, fid), value) = result?;
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// filling other caches to avoid searching for them again
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self.db_cache.word_prefix_fid_docids.insert((word_prefix, fid), Some(value));
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fids.push(fid);
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}
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entry.insert(fids.clone());
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fids
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}
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};
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Ok(fids)
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}
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}
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@ -13,4 +13,8 @@ impl Interned<Phrase> {
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let p = ctx.phrase_interner.get(self);
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p.words.iter().flatten().map(|w| ctx.word_interner.get(*w)).join(" ")
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}
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pub fn words(self, ctx: &SearchContext) -> Vec<Option<Interned<String>>> {
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let p = ctx.phrase_interner.get(self);
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p.words.clone()
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}
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}
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@ -1,3 +1,4 @@
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use fxhash::FxHashSet;
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use roaring::RoaringBitmap;
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use super::{ComputedCondition, RankingRuleGraphTrait};
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@ -10,7 +11,7 @@ use crate::Result;
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#[derive(Clone, PartialEq, Eq, Hash)]
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pub struct AttributeCondition {
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term: LocatedQueryTermSubset,
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nbr_typos: u8,
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fid: u16,
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}
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pub enum AttributeGraph {}
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@ -44,39 +45,37 @@ impl RankingRuleGraphTrait for AttributeGraph {
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) -> Result<Vec<(u32, Interned<Self::Condition>)>> {
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let term = to_term;
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let mut edges = vec![];
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let mut all_fields = FxHashSet::default();
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for word in term.term_subset.all_single_words_except_prefix_db(ctx)? {
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// ...
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let fields = ctx.get_db_word_fids(word)?;
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all_fields.extend(fields);
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}
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// Ngrams have a base typo cost
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// 2-gram -> equivalent to 1 typo
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// 3-gram -> equivalent to 2 typos
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let base_cost = if term.term_ids.len() == 1 { 0 } else { term.term_ids.len() as u32 };
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for phrase in term.term_subset.all_phrases(ctx)? {
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for &word in phrase.words(ctx).iter().flatten() {
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let fields = ctx.get_db_word_fids(word)?;
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all_fields.extend(fields);
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}
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}
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for nbr_typos in 0..=term.term_subset.max_nbr_typos(ctx) {
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let mut term = term.clone();
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match nbr_typos {
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0 => {
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term.term_subset.clear_one_typo_subset();
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term.term_subset.clear_two_typo_subset();
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}
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1 => {
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term.term_subset.clear_zero_typo_subset();
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term.term_subset.clear_two_typo_subset();
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}
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2 => {
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term.term_subset.clear_zero_typo_subset();
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term.term_subset.clear_one_typo_subset();
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}
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_ => panic!(),
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};
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if let Some(word_prefix) = term.term_subset.use_prefix_db(ctx) {
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let fields = ctx.get_db_word_prefix_fids(word_prefix)?;
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all_fields.extend(fields);
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}
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let mut edges = vec![];
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for fid in all_fields {
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// TODO: We can improve performances and relevancy by storing
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// the term subsets associated to each field ids fetched.
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edges.push((
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nbr_typos as u32 + base_cost,
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conditions_interner.insert(AttributeCondition { term, nbr_typos }),
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fid as u32 * term.term_ids.len() as u32, // TODO improve the fid score i.e. fid^10.
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conditions_interner.insert(AttributeCondition {
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term: term.clone(), // TODO remove this ugly clone
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fid,
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}),
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));
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}
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Ok(edges)
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}
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}
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@ -16,6 +16,8 @@ mod exactness;
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mod proximity;
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/// Implementation of the `typo` ranking rule
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mod typo;
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/// Implementation of the `attribute` ranking rule
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mod attribute;
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use std::hash::Hash;
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