use std::cmp::Ordering; use itertools::Itertools; use roaring::RoaringBitmap; use crate::score_details::{ScoreDetails, ScoreValue, ScoringStrategy}; use crate::{MatchingWords, Result, Search, SearchResult}; struct ScoreWithRatioResult { matching_words: MatchingWords, candidates: RoaringBitmap, document_scores: Vec<(u32, ScoreWithRatio)>, } type ScoreWithRatio = (Vec, f32); fn compare_scores( &(ref left_scores, left_ratio): &ScoreWithRatio, &(ref right_scores, right_ratio): &ScoreWithRatio, ) -> Ordering { let mut left_it = ScoreDetails::score_values(left_scores.iter()); let mut right_it = ScoreDetails::score_values(right_scores.iter()); loop { let left = left_it.next(); let right = right_it.next(); match (left, right) { (None, None) => return Ordering::Equal, (None, Some(_)) => return Ordering::Less, (Some(_), None) => return Ordering::Greater, (Some(ScoreValue::Score(left)), Some(ScoreValue::Score(right))) => { let left = left * left_ratio as f64; let right = right * right_ratio as f64; if (left - right).abs() <= f64::EPSILON { continue; } return left.partial_cmp(&right).unwrap(); } (Some(ScoreValue::Sort(left)), Some(ScoreValue::Sort(right))) => { match left.partial_cmp(right).unwrap() { Ordering::Equal => continue, order => return order, } } (Some(ScoreValue::GeoSort(left)), Some(ScoreValue::GeoSort(right))) => { match left.partial_cmp(right).unwrap() { Ordering::Equal => continue, order => return order, } } (Some(ScoreValue::Score(_)), Some(_)) => return Ordering::Greater, (Some(_), Some(ScoreValue::Score(_))) => return Ordering::Less, // if we have this, we're bad (Some(ScoreValue::GeoSort(_)), Some(ScoreValue::Sort(_))) | (Some(ScoreValue::Sort(_)), Some(ScoreValue::GeoSort(_))) => { unreachable!("Unexpected geo and sort comparison") } } } } impl ScoreWithRatioResult { fn new(results: SearchResult, ratio: f32) -> Self { let document_scores = results .documents_ids .into_iter() .zip(results.document_scores.into_iter().map(|scores| (scores, ratio))) .collect(); Self { matching_words: results.matching_words, candidates: results.candidates, document_scores, } } fn merge(left: Self, right: Self, from: usize, length: usize) -> SearchResult { let mut documents_ids = Vec::with_capacity(left.document_scores.len() + right.document_scores.len()); let mut document_scores = Vec::with_capacity(left.document_scores.len() + right.document_scores.len()); let mut documents_seen = RoaringBitmap::new(); for (docid, (main_score, _sub_score)) in left .document_scores .into_iter() .merge_by(right.document_scores.into_iter(), |(_, left), (_, right)| { // the first value is the one with the greatest score compare_scores(left, right).is_ge() }) // remove documents we already saw .filter(|(docid, _)| documents_seen.insert(*docid)) // start skipping **after** the filter .skip(from) // take **after** skipping .take(length) { documents_ids.push(docid); // TODO: pass both scores to documents_score in some way? document_scores.push(main_score); } SearchResult { matching_words: right.matching_words, candidates: left.candidates | right.candidates, documents_ids, document_scores, degraded: false, } } } impl<'a> Search<'a> { pub fn execute_hybrid(&self, semantic_ratio: f32) -> Result { // TODO: find classier way to achieve that than to reset vector and query params // create separate keyword and semantic searches let mut search = Search { query: self.query.clone(), vector: self.vector.clone(), filter: self.filter.clone(), offset: 0, limit: self.limit + self.offset, sort_criteria: self.sort_criteria.clone(), searchable_attributes: self.searchable_attributes, geo_strategy: self.geo_strategy, terms_matching_strategy: self.terms_matching_strategy, scoring_strategy: ScoringStrategy::Detailed, words_limit: self.words_limit, exhaustive_number_hits: self.exhaustive_number_hits, rtxn: self.rtxn, index: self.index, distribution_shift: self.distribution_shift, embedder_name: self.embedder_name.clone(), time_budget: self.time_budget.clone(), }; let vector_query = search.vector.take(); let keyword_results = search.execute()?; // skip semantic search if we don't have a vector query (placeholder search) let Some(vector_query) = vector_query else { return Ok(keyword_results); }; // completely skip semantic search if the results of the keyword search are good enough if self.results_good_enough(&keyword_results, semantic_ratio) { return Ok(keyword_results); } search.vector = Some(vector_query); search.query = None; // TODO: would be better to have two distinct functions at this point let vector_results = search.execute()?; let keyword_results = ScoreWithRatioResult::new(keyword_results, 1.0 - semantic_ratio); let vector_results = ScoreWithRatioResult::new(vector_results, semantic_ratio); let merge_results = ScoreWithRatioResult::merge(vector_results, keyword_results, self.offset, self.limit); assert!(merge_results.documents_ids.len() <= self.limit); Ok(merge_results) } fn results_good_enough(&self, keyword_results: &SearchResult, semantic_ratio: f32) -> bool { // A result is good enough if its keyword score is > 0.9 with a semantic ratio of 0.5 => 0.9 * 0.5 const GOOD_ENOUGH_SCORE: f64 = 0.45; // 1. we check that we got a sufficient number of results if keyword_results.document_scores.len() < self.limit + self.offset { return false; } // 2. and that all results have a good enough score. // we need to check all results because due to sort like rules, they're not necessarily in relevancy order for score in &keyword_results.document_scores { let score = ScoreDetails::global_score(score.iter()); if score * ((1.0 - semantic_ratio) as f64) < GOOD_ENOUGH_SCORE { return false; } } true } }