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retrieval-pipeline: v0
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183
retrieval-pipeline/src/scoring.rs
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183
retrieval-pipeline/src/scoring.rs
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use std::cmp::Ordering;
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use common::storage::types::StoredObject;
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/// Holds optional subscores gathered from different retrieval signals.
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#[derive(Debug, Clone, Copy, Default)]
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pub struct Scores {
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pub fts: Option<f32>,
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pub vector: Option<f32>,
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pub graph: Option<f32>,
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}
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/// Generic wrapper combining an item with its accumulated retrieval scores.
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#[derive(Debug, Clone)]
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pub struct Scored<T> {
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pub item: T,
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pub scores: Scores,
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pub fused: f32,
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}
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impl<T> Scored<T> {
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pub fn new(item: T) -> Self {
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Self {
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item,
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scores: Scores::default(),
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fused: 0.0,
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}
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}
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pub const fn with_vector_score(mut self, score: f32) -> Self {
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self.scores.vector = Some(score);
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self
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}
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pub const fn with_fts_score(mut self, score: f32) -> Self {
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self.scores.fts = Some(score);
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self
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}
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pub const fn with_graph_score(mut self, score: f32) -> Self {
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self.scores.graph = Some(score);
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self
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}
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pub const fn update_fused(&mut self, fused: f32) {
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self.fused = fused;
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}
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}
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/// Weights used for linear score fusion.
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#[derive(Debug, Clone, Copy)]
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pub struct FusionWeights {
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pub vector: f32,
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pub fts: f32,
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pub graph: f32,
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pub multi_bonus: f32,
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}
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impl Default for FusionWeights {
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fn default() -> Self {
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Self {
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vector: 0.5,
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fts: 0.3,
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graph: 0.2,
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multi_bonus: 0.02,
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}
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}
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}
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pub const fn clamp_unit(value: f32) -> f32 {
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value.clamp(0.0, 1.0)
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}
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pub fn distance_to_similarity(distance: f32) -> f32 {
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if !distance.is_finite() {
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return 0.0;
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}
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clamp_unit(1.0 / (1.0 + distance.max(0.0)))
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}
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pub fn min_max_normalize(scores: &[f32]) -> Vec<f32> {
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if scores.is_empty() {
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return Vec::new();
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}
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let mut min = f32::MAX;
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let mut max = f32::MIN;
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for s in scores {
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if !s.is_finite() {
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continue;
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}
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if *s < min {
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min = *s;
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}
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if *s > max {
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max = *s;
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}
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}
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if !min.is_finite() || !max.is_finite() {
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return scores.iter().map(|_| 0.0).collect();
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}
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if (max - min).abs() < f32::EPSILON {
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return vec![1.0; scores.len()];
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}
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scores
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.iter()
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.map(|score| {
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if score.is_finite() {
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clamp_unit((score - min) / (max - min))
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} else {
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0.0
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}
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})
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.collect()
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}
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pub fn fuse_scores(scores: &Scores, weights: FusionWeights) -> f32 {
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let vector = scores.vector.unwrap_or(0.0);
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let fts = scores.fts.unwrap_or(0.0);
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let graph = scores.graph.unwrap_or(0.0);
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let mut fused = graph.mul_add(
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weights.graph,
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vector.mul_add(weights.vector, fts * weights.fts),
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);
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let signals_present = scores
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.vector
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.iter()
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.chain(scores.fts.iter())
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.chain(scores.graph.iter())
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.count();
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if signals_present >= 2 {
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fused += weights.multi_bonus;
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}
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clamp_unit(fused)
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}
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pub fn merge_scored_by_id<T>(
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target: &mut std::collections::HashMap<String, Scored<T>>,
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incoming: Vec<Scored<T>>,
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) where
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T: StoredObject + Clone,
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{
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for scored in incoming {
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let id = scored.item.get_id().to_owned();
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target
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.entry(id)
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.and_modify(|existing| {
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if let Some(score) = scored.scores.vector {
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existing.scores.vector = Some(score);
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}
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if let Some(score) = scored.scores.fts {
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existing.scores.fts = Some(score);
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}
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if let Some(score) = scored.scores.graph {
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existing.scores.graph = Some(score);
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}
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})
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.or_insert_with(|| Scored {
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item: scored.item.clone(),
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scores: scored.scores,
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fused: scored.fused,
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});
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}
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}
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pub fn sort_by_fused_desc<T>(items: &mut [Scored<T>])
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where
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T: StoredObject,
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{
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items.sort_by(|a, b| {
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b.fused
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.partial_cmp(&a.fused)
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.unwrap_or(Ordering::Equal)
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.then_with(|| a.item.get_id().cmp(b.item.get_id()))
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});
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}
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