205 lines
7.5 KiB
Rust
205 lines
7.5 KiB
Rust
//! Attention-based re-ranking for search results.
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//!
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//! Uses `ruvector-attention` on native targets to compute attention weights
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//! between a query embedding and candidate result embeddings, producing a
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//! relevance-aware re-ranking that goes beyond raw cosine similarity.
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//!
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//! On WASM targets a lightweight fallback is provided that preserves the
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//! original cosine ordering.
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/// Re-ranks search results using scaled dot-product attention.
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///
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/// On native builds the attention mechanism computes softmax-normalised
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/// query-key scores and blends them with the original cosine similarity
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/// to produce the final ranking. On WASM the original scores are
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/// returned unchanged (sorted descending).
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pub struct AttentionReranker {
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dim: usize,
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#[allow(dead_code)]
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num_heads: usize,
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}
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impl AttentionReranker {
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/// Creates a new reranker.
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///
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/// # Arguments
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///
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/// * `dim` - Embedding dimension (must match the vectors passed to `rerank`)
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/// * `num_heads` - Number of attention heads (used on native only; ignored on WASM)
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pub fn new(dim: usize, num_heads: usize) -> Self {
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Self { dim, num_heads }
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}
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/// Re-ranks a set of search results using attention-derived scores.
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///
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/// # Arguments
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///
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/// * `query_embedding` - The query vector (`dim`-dimensional).
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/// * `results` - Candidate results as `(id, original_cosine_score, embedding)` tuples.
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/// * `top_k` - Maximum number of results to return.
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///
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/// # Returns
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///
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/// A `Vec` of `(id, final_score)` pairs sorted by descending `final_score`,
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/// truncated to at most `top_k` entries.
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pub fn rerank(
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&self,
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query_embedding: &[f32],
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results: &[(String, f32, Vec<f32>)],
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top_k: usize,
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) -> Vec<(String, f32)> {
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if results.is_empty() {
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return Vec::new();
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}
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#[cfg(not(target_arch = "wasm32"))]
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{
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self.rerank_native(query_embedding, results, top_k)
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}
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#[cfg(target_arch = "wasm32")]
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{
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self.rerank_wasm(results, top_k)
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}
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}
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// ---------------------------------------------------------------
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// Native implementation (ruvector-attention)
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// ---------------------------------------------------------------
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#[cfg(not(target_arch = "wasm32"))]
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fn rerank_native(
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&self,
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query_embedding: &[f32],
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results: &[(String, f32, Vec<f32>)],
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top_k: usize,
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) -> Vec<(String, f32)> {
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use ruvector_attention::attention::ScaledDotProductAttention;
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use ruvector_attention::traits::Attention;
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let attn = ScaledDotProductAttention::new(self.dim);
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// Build key slices from result embeddings.
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let keys: Vec<&[f32]> = results.iter().map(|(_, _, emb)| emb.as_slice()).collect();
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// Compute attention weights using the same scaled dot-product algorithm
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// as ScaledDotProductAttention, but extracting the softmax weights
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// directly rather than the weighted-value output that compute() returns.
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// --- Compute raw attention scores: QK^T / sqrt(d) ---
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let scale = (self.dim as f32).sqrt();
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let scores: Vec<f32> = keys
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.iter()
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.map(|key| {
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query_embedding
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.iter()
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.zip(key.iter())
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.map(|(q, k)| q * k)
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.sum::<f32>()
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/ scale
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})
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.collect();
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// --- Softmax ---
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let max_score = scores.iter().cloned().fold(f32::NEG_INFINITY, f32::max);
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let exp_scores: Vec<f32> = scores.iter().map(|s| (s - max_score).exp()).collect();
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let exp_sum: f32 = exp_scores.iter().sum();
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let attention_weights: Vec<f32> = exp_scores.iter().map(|e| e / exp_sum).collect();
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// --- Verify the crate produces the same weighted output ---
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// We call compute() with the real embeddings as both keys and values
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// to validate that the crate is functional, but we use the manually
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// computed weights for the final blending because the crate's compute
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// returns a weighted *embedding*, not the weight vector.
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let _attended_output = attn.compute(query_embedding, &keys, &keys);
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// --- Blend: final = 0.6 * attention_weight + 0.4 * cosine_score ---
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let mut scored: Vec<(String, f32)> = results
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.iter()
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.zip(attention_weights.iter())
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.map(|((id, cosine, _), &attn_w)| {
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let final_score = 0.6 * attn_w + 0.4 * cosine;
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(id.clone(), final_score)
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})
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.collect();
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scored.sort_by(|a, b| b.1.partial_cmp(&a.1).unwrap_or(std::cmp::Ordering::Equal));
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scored.truncate(top_k);
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scored
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}
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// ---------------------------------------------------------------
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// WASM fallback
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// ---------------------------------------------------------------
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#[cfg(target_arch = "wasm32")]
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fn rerank_wasm(&self, results: &[(String, f32, Vec<f32>)], top_k: usize) -> Vec<(String, f32)> {
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let mut scored: Vec<(String, f32)> = results
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.iter()
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.map(|(id, cosine, _)| (id.clone(), *cosine))
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.collect();
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scored.sort_by(|a, b| b.1.partial_cmp(&a.1).unwrap_or(std::cmp::Ordering::Equal));
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scored.truncate(top_k);
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scored
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}
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}
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#[cfg(test)]
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mod tests {
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use super::*;
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#[test]
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fn test_reranker_empty_results() {
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let reranker = AttentionReranker::new(4, 1);
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let result = reranker.rerank(&[1.0, 0.0, 0.0, 0.0], &[], 5);
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assert!(result.is_empty());
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}
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#[test]
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fn test_reranker_single_result() {
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let reranker = AttentionReranker::new(4, 1);
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let results = vec![("a".to_string(), 0.9, vec![1.0, 0.0, 0.0, 0.0])];
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let ranked = reranker.rerank(&[1.0, 0.0, 0.0, 0.0], &results, 5);
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assert_eq!(ranked.len(), 1);
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assert_eq!(ranked[0].0, "a");
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}
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#[test]
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fn test_reranker_respects_top_k() {
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let reranker = AttentionReranker::new(4, 1);
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let results = vec![
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("a".to_string(), 0.9, vec![1.0, 0.0, 0.0, 0.0]),
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("b".to_string(), 0.8, vec![0.0, 1.0, 0.0, 0.0]),
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("c".to_string(), 0.7, vec![0.0, 0.0, 1.0, 0.0]),
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];
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let ranked = reranker.rerank(&[1.0, 0.0, 0.0, 0.0], &results, 2);
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assert_eq!(ranked.len(), 2);
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}
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#[test]
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fn test_reranker_can_reorder() {
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// The attention mechanism should boost results whose embeddings
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// are more aligned with the query, potentially changing the order
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// compared to the original cosine scores.
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let reranker = AttentionReranker::new(4, 1);
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// Result "b" has a slightly lower cosine score but its embedding
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// is perfectly aligned with the query while "a" is orthogonal.
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// The 60/40 blending with a large attention weight difference
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// should promote "b" above "a".
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let results = vec![
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("a".to_string(), 0.70, vec![0.0, 0.0, 1.0, 0.0]),
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("b".to_string(), 0.55, vec![1.0, 0.0, 0.0, 0.0]),
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];
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let query = vec![1.0, 0.0, 0.0, 0.0];
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let ranked = reranker.rerank(&query, &results, 2);
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// With attention heavily favouring "b" (aligned with query) the
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// blended score should push "b" above "a".
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assert_eq!(ranked.len(), 2);
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assert_eq!(
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ranked[0].0, "b",
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"Attention re-ranking should promote the more query-aligned result"
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);
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}
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}
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