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Implement the attribute ranking rule edge computation

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This commit is contained in:
Kerollmops
2023-04-12 11:40:44 +02:00
committed by Loïc Lecrenier
parent e55efc419e
commit d6a7c28e4d
4 changed files with 98 additions and 26 deletions
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51
milli/src/search/new/ranking_rule_graph/attribute/mod.rs
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@ -1,3 +1,4 @@
use fxhash::FxHashSet;
use roaring::RoaringBitmap;
use super::{ComputedCondition, RankingRuleGraphTrait};
@ -10,7 +11,7 @@ use crate::Result;
#[derive(Clone, PartialEq, Eq, Hash)]
pub struct AttributeCondition {
term: LocatedQueryTermSubset,
nbr_typos: u8,
fid: u16,
}
pub enum AttributeGraph {}
@ -44,39 +45,37 @@ impl RankingRuleGraphTrait for AttributeGraph {
) -> Result<Vec<(u32, Interned<Self::Condition>)>> {
let term = to_term;
let mut edges = vec![];
let mut all_fields = FxHashSet::default();
for word in term.term_subset.all_single_words_except_prefix_db(ctx)? {
// ...
let fields = ctx.get_db_word_fids(word)?;
all_fields.extend(fields);
}
// Ngrams have a base typo cost
// 2-gram -> equivalent to 1 typo
// 3-gram -> equivalent to 2 typos
let base_cost = if term.term_ids.len() == 1 { 0 } else { term.term_ids.len() as u32 };
for phrase in term.term_subset.all_phrases(ctx)? {
for &word in phrase.words(ctx).iter().flatten() {
let fields = ctx.get_db_word_fids(word)?;
all_fields.extend(fields);
}
}
for nbr_typos in 0..=term.term_subset.max_nbr_typos(ctx) {
let mut term = term.clone();
match nbr_typos {
0 => {
term.term_subset.clear_one_typo_subset();
term.term_subset.clear_two_typo_subset();
}
1 => {
term.term_subset.clear_zero_typo_subset();
term.term_subset.clear_two_typo_subset();
}
2 => {
term.term_subset.clear_zero_typo_subset();
term.term_subset.clear_one_typo_subset();
}
_ => panic!(),
};
if let Some(word_prefix) = term.term_subset.use_prefix_db(ctx) {
let fields = ctx.get_db_word_prefix_fids(word_prefix)?;
all_fields.extend(fields);
}
let mut edges = vec![];
for fid in all_fields {
// TODO: We can improve performances and relevancy by storing
// the term subsets associated to each field ids fetched.
edges.push((
nbr_typos as u32 + base_cost,
conditions_interner.insert(AttributeCondition { term, nbr_typos }),
fid as u32 * term.term_ids.len() as u32, // TODO improve the fid score i.e. fid^10.
conditions_interner.insert(AttributeCondition {
term: term.clone(), // TODO remove this ugly clone
fid,
}),
));
}
Ok(edges)
}
}

2
milli/src/search/new/ranking_rule_graph/mod.rs
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@ -16,6 +16,8 @@ mod exactness;
mod proximity;
/// Implementation of the `typo` ranking rule
mod typo;
/// Implementation of the `attribute` ranking rule
mod attribute;
use std::hash::Hash;