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https://github.com/meilisearch/MeiliSearch
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Add exactness ranking rules
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175
milli/src/search/new/exact_attribute.rs
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175
milli/src/search/new/exact_attribute.rs
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@ -0,0 +1,175 @@
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use heed::BytesDecode;
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use roaring::MultiOps;
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use super::query_graph::QueryGraph;
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use super::ranking_rules::{RankingRule, RankingRuleOutput};
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use crate::search::new::query_graph::QueryNodeData;
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use crate::search::new::query_term::ExactTerm;
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use crate::{CboRoaringBitmapCodec, Result, SearchContext, SearchLogger};
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/// FIXME:
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///
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/// - A lot of work done in next_bucket that start_iteration could do.
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/// - Consider calling the graph based rule directly from this one.
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/// - currently we did exact term, don't forget about prefix
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/// - some tests
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pub struct ExactAttribute {
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query_graph: Option<QueryGraph>,
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}
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impl ExactAttribute {
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pub fn new() -> Self {
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Self { query_graph: None }
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}
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}
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impl<'ctx> RankingRule<'ctx, QueryGraph> for ExactAttribute {
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fn id(&self) -> String {
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"exact_attribute".to_owned()
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}
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fn start_iteration(
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&mut self,
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_ctx: &mut SearchContext<'ctx>,
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_logger: &mut dyn SearchLogger<QueryGraph>,
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_universe: &roaring::RoaringBitmap,
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query: &QueryGraph,
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) -> Result<()> {
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self.query_graph = Some(query.clone());
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Ok(())
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}
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fn next_bucket(
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&mut self,
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ctx: &mut SearchContext<'ctx>,
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_logger: &mut dyn SearchLogger<QueryGraph>,
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universe: &roaring::RoaringBitmap,
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) -> Result<Option<RankingRuleOutput<QueryGraph>>> {
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// iterate on the nodes of the graph, retain LocatedQueryTermSubset
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let query_graph = self.query_graph.as_ref().unwrap();
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let mut exact_term_position_ids: Vec<(ExactTerm, u16, u8)> =
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Vec::with_capacity(query_graph.nodes.len() as usize);
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for (_, node) in query_graph.nodes.iter() {
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match &node.data {
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QueryNodeData::Term(term) => {
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let exact_term = if let Some(exact_term) = term.term_subset.exact_term(ctx) {
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exact_term
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} else {
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// FIXME: Use `None` or some function indicating that we're passing down the bucket to our child rules
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return Ok(Some(RankingRuleOutput {
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query: query_graph.clone(),
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candidates: universe.clone(),
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}));
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};
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exact_term_position_ids.push((
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exact_term,
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*term.positions.start(),
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*term.term_ids.start(),
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))
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}
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QueryNodeData::Deleted | QueryNodeData::Start | QueryNodeData::End => continue,
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}
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}
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exact_term_position_ids.sort_by_key(|(_, _, id)| *id);
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// bail if there is a "hole" (missing word) in remaining query graph
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let mut previous_id = 0;
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for (_, _, id) in exact_term_position_ids.iter().copied() {
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if id < previous_id || id - previous_id > 1 {
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// FIXME: Use `None` or some function indicating that we're passing down the bucket to our child rules
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return Ok(Some(RankingRuleOutput {
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query: query_graph.clone(),
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candidates: universe.clone(),
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}));
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} else {
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previous_id = id;
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}
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}
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// sample query: "sunflower are pretty"
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// sunflower at pos 0 in attr A
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// are at pos 1 in attr B
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// pretty at pos 2 in attr C
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// We want to eliminate such document
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// first check that for each term, there exists some attribute that has this term at the correct position
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//"word-position-docids";
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let mut candidates = universe.clone();
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let words_positions: Vec<(Vec<_>, _)> = exact_term_position_ids
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.iter()
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.copied()
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.map(|(term, position, _)| (term.interned_words(ctx).collect(), position))
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.collect();
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for (words, position) in &words_positions {
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if candidates.is_empty() {
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// FIXME: Use `None` or some function indicating that we're passing down the bucket to our child rules
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return Ok(Some(RankingRuleOutput {
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query: query_graph.clone(),
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candidates: universe.clone(),
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}));
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}
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'words: for (offset, word) in words.iter().enumerate() {
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let offset = offset as u16;
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let word = if let Some(word) = word {
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word
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} else {
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continue 'words;
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};
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let word_position_docids = CboRoaringBitmapCodec::bytes_decode(
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ctx.get_db_word_position_docids(*word, position + offset)?.unwrap_or_default(),
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)
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.unwrap_or_default();
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candidates &= word_position_docids;
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}
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}
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let candidates = candidates;
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if candidates.is_empty() {
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// FIXME: Use `None` or some function indicating that we're passing down the bucket to our child rules
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return Ok(Some(RankingRuleOutput {
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query: query_graph.clone(),
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candidates: universe.clone(),
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}));
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}
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let searchable_fields_ids = ctx.index.searchable_fields_ids(ctx.txn)?.unwrap_or_default();
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let mut candidates_per_attributes = Vec::with_capacity(searchable_fields_ids.len());
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// then check that there exists at least one attribute that has all of the terms
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for fid in searchable_fields_ids {
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let mut intersection = MultiOps::intersection(
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words_positions
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.iter()
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.flat_map(|(words, ..)| words.iter())
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// ignore stop words words in phrases
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.flatten()
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.map(|word| -> Result<_> {
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Ok(ctx
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.get_db_word_fid_docids(*word, fid)?
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.map(CboRoaringBitmapCodec::bytes_decode)
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.unwrap_or_default()
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.unwrap_or_default())
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}),
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)?;
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intersection &= &candidates;
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if !intersection.is_empty() {
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candidates_per_attributes.push(intersection);
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}
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}
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// note we could have "false positives" where there both exist different attributes that collectively
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// have the terms in the correct order and a single attribute that have all the terms, but in the incorrect order.
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let candidates = MultiOps::union(candidates_per_attributes.into_iter());
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Ok(Some(RankingRuleOutput { query: query_graph.clone(), candidates }))
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}
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fn end_iteration(
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&mut self,
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_ctx: &mut SearchContext<'ctx>,
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_logger: &mut dyn SearchLogger<QueryGraph>,
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) {
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}
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}
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@ -44,8 +44,8 @@ use super::interner::{Interned, MappedInterner};
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use super::logger::SearchLogger;
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use super::query_graph::QueryNode;
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use super::ranking_rule_graph::{
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ConditionDocIdsCache, DeadEndsCache, ProximityGraph, RankingRuleGraph, RankingRuleGraphTrait,
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TypoGraph,
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ConditionDocIdsCache, DeadEndsCache, ExactnessGraph, ProximityGraph, RankingRuleGraph,
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RankingRuleGraphTrait, TypoGraph,
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};
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use super::small_bitmap::SmallBitmap;
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use super::{QueryGraph, RankingRule, RankingRuleOutput, SearchContext};
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@ -65,6 +65,12 @@ impl GraphBasedRankingRule<TypoGraph> {
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Self::new_with_id("typo".to_owned(), terms_matching_strategy)
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}
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}
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pub type Exactness = GraphBasedRankingRule<ExactnessGraph>;
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impl GraphBasedRankingRule<ExactnessGraph> {
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pub fn new() -> Self {
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Self::new_with_id("exactness".to_owned(), None)
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}
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}
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/// A generic graph-based ranking rule
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pub struct GraphBasedRankingRule<G: RankingRuleGraphTrait> {
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@ -9,8 +9,9 @@ mod query_term;
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mod ranking_rule_graph;
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mod ranking_rules;
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mod resolve_query_graph;
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// TODO: documentation + comments
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mod small_bitmap;
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mod exact_attribute;
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// TODO: documentation + comments
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// implementation is currently an adaptation of the previous implementation to fit with the new model
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mod sort;
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@ -33,6 +34,8 @@ use resolve_query_graph::PhraseDocIdsCache;
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use roaring::RoaringBitmap;
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use words::Words;
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use self::exact_attribute::ExactAttribute;
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use self::graph_based_ranking_rule::Exactness;
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use self::interner::Interner;
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use self::ranking_rules::{BoxRankingRule, RankingRule};
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use self::resolve_query_graph::compute_query_graph_docids;
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@ -150,7 +153,7 @@ fn get_ranking_rules_for_query_graph_search<'ctx>(
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let mut proximity = false;
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let mut sort = false;
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let attribute = false;
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let exactness = false;
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let mut exactness = false;
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let mut asc = HashSet::new();
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let mut desc = HashSet::new();
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@ -211,8 +214,9 @@ fn get_ranking_rules_for_query_graph_search<'ctx>(
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if exactness {
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continue;
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}
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// todo!();
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// exactness = false;
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ranking_rules.push(Box::new(ExactAttribute::new()));
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ranking_rules.push(Box::new(Exactness::new()));
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exactness = true;
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}
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crate::Criterion::Asc(field_name) => {
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if asc.contains(&field_name) {
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107
milli/src/search/new/ranking_rule_graph/exactness/mod.rs
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107
milli/src/search/new/ranking_rule_graph/exactness/mod.rs
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@ -0,0 +1,107 @@
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use roaring::RoaringBitmap;
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use super::{ComputedCondition, DeadEndsCache, RankingRuleGraph, RankingRuleGraphTrait};
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use crate::search::new::interner::{DedupInterner, Interned, MappedInterner};
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use crate::search::new::query_graph::{QueryGraph, QueryNode};
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use crate::search::new::query_term::{ExactTerm, LocatedQueryTermSubset};
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use crate::{CboRoaringBitmapCodec, Result, SearchContext, SearchLogger};
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/// - Exactness as first ranking rule: TermsMatchingStrategy? prefer a document that matches 1 word exactly and no other
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/// word than a doc that matches 9 words non exactly but none exactly
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/// - `TermsMatchingStrategy` as a word + exactness optimization: we could consider
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///
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/// "naive vision"
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/// condition from one node to another:
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/// - word exactly present: cost 0
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/// - word typo/ngram/prefix/missing: cost 1, not remove from query graph, edge btwn the two nodes, return the universe without condition when resolving, destination query term is inside
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///
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/// Three strategies:
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/// 1. ExactAttribute: word position / word_fid_docid
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/// 2. AttributeStart:
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/// 3. AttributeContainsExact => implementable via `RankingRuleGraphTrait`
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#[derive(Clone, PartialEq, Eq, Hash)]
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pub enum ExactnessCondition {
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ExactInAttribute(LocatedQueryTermSubset),
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Skip(LocatedQueryTermSubset),
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}
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pub enum ExactnessGraph {}
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fn compute_docids(
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ctx: &mut SearchContext,
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dest_node: &LocatedQueryTermSubset,
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universe: &RoaringBitmap,
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) -> Result<RoaringBitmap> {
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let exact_term = if let Some(exact_term) = dest_node.term_subset.exact_term(ctx) {
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exact_term
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} else {
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return Ok(Default::default());
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};
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let mut candidates = match exact_term {
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ExactTerm::Phrase(phrase) => ctx.get_phrase_docids(phrase)?.clone(),
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ExactTerm::Word(word) => {
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if let Some(word_candidates) = ctx.get_db_word_docids(word)? {
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CboRoaringBitmapCodec::deserialize_from(word_candidates)?
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} else {
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return Ok(Default::default());
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}
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}
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};
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// TODO: synonyms?
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candidates &= universe;
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Ok(candidates)
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}
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impl RankingRuleGraphTrait for ExactnessGraph {
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type Condition = ExactnessCondition;
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fn resolve_condition(
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ctx: &mut SearchContext,
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condition: &Self::Condition,
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universe: &RoaringBitmap,
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) -> Result<ComputedCondition> {
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let (docids, dest_node) = match condition {
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ExactnessCondition::ExactInAttribute(dest_node) => {
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(compute_docids(ctx, dest_node, universe)?, dest_node)
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}
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ExactnessCondition::Skip(dest_node) => (universe.clone(), dest_node),
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};
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Ok(ComputedCondition {
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docids,
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universe_len: universe.len(),
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start_term_subset: None,
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end_term_subset: dest_node.clone(),
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})
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}
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fn build_edges(
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_ctx: &mut SearchContext,
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conditions_interner: &mut DedupInterner<Self::Condition>,
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_source_node: Option<&LocatedQueryTermSubset>,
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dest_node: &LocatedQueryTermSubset,
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) -> Result<Vec<(u32, Interned<Self::Condition>)>> {
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let exact_condition = ExactnessCondition::ExactInAttribute(dest_node.clone());
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let exact_condition = conditions_interner.insert(exact_condition);
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let skip_condition = ExactnessCondition::Skip(dest_node.clone());
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let skip_condition = conditions_interner.insert(skip_condition);
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Ok(vec![(0, exact_condition), (1, skip_condition)])
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}
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fn log_state(
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graph: &RankingRuleGraph<Self>,
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paths: &[Vec<Interned<Self::Condition>>],
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dead_ends_cache: &DeadEndsCache<Self::Condition>,
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universe: &RoaringBitmap,
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costs: &MappedInterner<QueryNode, Vec<u64>>,
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cost: u64,
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logger: &mut dyn SearchLogger<QueryGraph>,
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) {
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todo!()
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}
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fn label_for_condition(ctx: &mut SearchContext, condition: &Self::Condition) -> Result<String> {
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todo!()
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}
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}
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@ -10,6 +10,8 @@ mod cheapest_paths;
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mod condition_docids_cache;
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mod dead_ends_cache;
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/// Implementation of the `exactness` ranking rule
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mod exactness;
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/// Implementation of the `proximity` ranking rule
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mod proximity;
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/// Implementation of the `typo` ranking rule
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@ -20,6 +22,7 @@ use std::hash::Hash;
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pub use cheapest_paths::PathVisitor;
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pub use condition_docids_cache::ConditionDocIdsCache;
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pub use dead_ends_cache::DeadEndsCache;
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pub use exactness::{ExactnessCondition, ExactnessGraph};
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pub use proximity::{ProximityCondition, ProximityGraph};
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use roaring::RoaringBitmap;
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pub use typo::{TypoCondition, TypoGraph};
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