412 lines
11 KiB
Markdown
412 lines
11 KiB
Markdown
# Advanced Features - Phase 4 Implementation
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This document describes the advanced features implemented in Phase 4 of Ruvector, providing state-of-the-art vector database capabilities.
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## Overview
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Phase 4 implements five major advanced features:
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1. **Enhanced Product Quantization (PQ)** - 8-16x compression with 90-95% recall
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2. **Filtered Search** - Intelligent metadata filtering with auto-strategy selection
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3. **MMR (Maximal Marginal Relevance)** - Diversity-aware search results
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4. **Hybrid Search** - Combining vector similarity with keyword matching
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5. **Conformal Prediction** - Uncertainty quantification with statistical guarantees
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## 1. Enhanced Product Quantization
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### Features
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- K-means clustering for codebook training
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- Precomputed lookup tables for fast distance calculation
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- Asymmetric Distance Computation (ADC)
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- Support for multiple distance metrics
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- 8-16x compression ratio
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### Usage
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```rust
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use ruvector_core::{EnhancedPQ, PQConfig, DistanceMetric};
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// Configure PQ
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let config = PQConfig {
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num_subspaces: 8,
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codebook_size: 256,
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num_iterations: 20,
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metric: DistanceMetric::Euclidean,
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};
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// Create and train
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let mut pq = EnhancedPQ::new(128, config)?;
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pq.train(&training_vectors)?;
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// Encode and add vectors
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for (id, vector) in vectors {
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pq.add_quantized(id, &vector)?;
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}
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// Fast search with lookup tables
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let results = pq.search(&query, k)?;
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```
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### Performance
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- **Compression**: 64x for 128D with 8 subspaces (512 bytes → 8 bytes)
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- **Search Speed**: 10-50x faster than full-precision
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- **Recall**: 90-95% at k=10 for typical datasets
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### Testing
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Comprehensive tests across dimensions:
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- 128D: Basic functionality and compression
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- 384D: Reconstruction error validation
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- 768D: Lookup table performance
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## 2. Filtered Search
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### Features
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- Pre-filtering: Apply filters before graph traversal
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- Post-filtering: Traverse graph then filter
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- Automatic strategy selection based on selectivity
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- Complex filter expressions (AND, OR, NOT, range queries)
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- Selectivity estimation
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### Usage
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```rust
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use ruvector_core::{FilteredSearch, FilterExpression, FilterStrategy};
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use serde_json::json;
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// Create complex filter
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let filter = FilterExpression::And(vec![
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FilterExpression::Eq("category".to_string(), json!("electronics")),
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FilterExpression::Range("price".to_string(), json!(100.0), json!(1000.0)),
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]);
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// Auto-select strategy based on selectivity
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let search = FilteredSearch::new(
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filter,
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FilterStrategy::Auto,
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metadata_store,
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);
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// Perform filtered search
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let results = search.search(&query, k, |q, k, ids| {
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// Your search function
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vector_index.search(q, k, ids)
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})?;
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```
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### Filter Expressions
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- **Equality**: `Eq(field, value)`
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- **Comparison**: `Gt`, `Gte`, `Lt`, `Lte`
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- **Membership**: `In`, `NotIn`
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- **Range**: `Range(field, min, max)`
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- **Logical**: `And`, `Or`, `Not`
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### Strategy Selection
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- **Pre-filter**: Used when selectivity < 20% (highly selective)
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- **Post-filter**: Used when selectivity > 20% (less selective)
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- **Auto**: Automatically chooses based on estimated selectivity
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## 3. MMR (Maximal Marginal Relevance)
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### Features
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- Balance relevance vs diversity with lambda parameter
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- Incremental selection algorithm
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- Support for all distance metrics
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- Configurable fetch multiplier
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### Usage
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```rust
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use ruvector_core::{MMRSearch, MMRConfig, DistanceMetric};
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// Configure MMR
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let config = MMRConfig {
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lambda: 0.5, // Equal balance: 0.0=pure diversity, 1.0=pure relevance
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metric: DistanceMetric::Cosine,
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fetch_multiplier: 2.0,
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};
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let mmr = MMRSearch::new(config)?;
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// Rerank existing results
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let diverse_results = mmr.rerank(&query, candidates, k)?;
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// Or use end-to-end search
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let results = mmr.search(&query, k, |q, k| {
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vector_index.search(q, k)
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})?;
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```
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### Lambda Parameter
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- **λ = 1.0**: Pure relevance (standard similarity search)
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- **λ = 0.5**: Equal balance between relevance and diversity
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- **λ = 0.0**: Pure diversity (maximize dissimilarity)
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### Algorithm
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```
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MMR = λ × Similarity(query, doc) - (1-λ) × max Similarity(doc, selected_docs)
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```
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Iteratively selects documents that maximize this score.
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## 4. Hybrid Search
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### Features
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- BM25 keyword matching implementation
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- Vector similarity search
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- Weighted score combination
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- Multiple normalization strategies
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- Inverted index for efficient keyword retrieval
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### Usage
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```rust
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use ruvector_core::{HybridSearch, HybridConfig, NormalizationStrategy};
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// Configure hybrid search
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let config = HybridConfig {
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vector_weight: 0.7, // 70% weight on semantic similarity
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keyword_weight: 0.3, // 30% weight on keyword matching
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normalization: NormalizationStrategy::MinMax,
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};
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let mut hybrid = HybridSearch::new(config);
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// Index documents with text
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hybrid.index_document("doc1".to_string(), "rust vector database".to_string());
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hybrid.index_document("doc2".to_string(), "python ML framework".to_string());
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hybrid.finalize_indexing();
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// Hybrid search
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let results = hybrid.search(
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&query_vector,
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"vector database",
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k,
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|vec, k| vector_index.search(vec, k)
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)?;
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```
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### BM25 Parameters
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Default values (configurable):
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- **k1 = 1.5**: Term frequency saturation
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- **b = 0.75**: Document length normalization
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### Score Combination
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```
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hybrid_score = α × vector_similarity + β × bm25_score
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```
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Where α and β are the configured weights.
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### Normalization Strategies
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- **MinMax**: Scale scores to [0, 1]
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- **ZScore**: Standardize to mean=0, std=1
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- **None**: Use raw scores
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## 5. Conformal Prediction
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### Features
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- Statistically valid uncertainty estimates
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- Prediction sets with guaranteed coverage
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- Multiple non-conformity measures
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- Adaptive top-k based on uncertainty
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- Calibration set management
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### Usage
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```rust
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use ruvector_core::{ConformalPredictor, ConformalConfig, NonconformityMeasure};
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// Configure conformal prediction
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let config = ConformalConfig {
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alpha: 0.1, // 90% coverage guarantee
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calibration_fraction: 0.2,
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nonconformity_measure: NonconformityMeasure::Distance,
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};
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let mut predictor = ConformalPredictor::new(config)?;
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// Calibrate on validation set
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predictor.calibrate(
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&validation_queries,
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&true_neighbors,
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|q, k| vector_index.search(q, k)
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)?;
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// Make prediction with conformal guarantee
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let prediction_set = predictor.predict(&query, |q, k| {
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vector_index.search(q, k)
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})?;
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println!("Confidence: {}", prediction_set.confidence);
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println!("Prediction set size: {}", prediction_set.results.len());
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// Adaptive top-k
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let adaptive_k = predictor.adaptive_top_k(&query, search_fn)?;
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```
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### Non-conformity Measures
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1. **Distance**: Use distance score directly
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2. **InverseRank**: Use 1/(rank+1) as non-conformity
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3. **NormalizedDistance**: Normalize by average distance
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### Coverage Guarantee
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With α = 0.1, the prediction set is guaranteed to contain the true nearest neighbors with probability ≥ 90%.
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### Calibration Statistics
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```rust
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let stats = predictor.get_statistics()?;
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println!("Calibration samples: {}", stats.num_samples);
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println!("Mean non-conformity: {}", stats.mean);
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println!("Threshold: {}", stats.threshold);
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```
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## Testing
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### Unit Tests
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Each module includes comprehensive unit tests:
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- `product_quantization::tests`: PQ encoding, lookup tables, k-means
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- `filtered_search::tests`: Filter evaluation, strategy selection
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- `mmr::tests`: Diversity metrics, lambda variations
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- `hybrid_search::tests`: BM25 scoring, tokenization
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- `conformal_prediction::tests`: Calibration, prediction sets
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### Integration Tests
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Located in `tests/advanced_features_integration.rs`:
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- **Multi-dimensional testing**: 128D, 384D, 768D vectors
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- **PQ recall testing**: Validation of 90-95% recall
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- **Strategy selection**: Automatic pre/post-filter choice
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- **MMR diversity**: Verification of diversity vs relevance balance
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- **Hybrid search**: Vector + keyword combination
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- **Conformal coverage**: Statistical guarantee validation
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### Running Tests
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```bash
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# Run all advanced features tests
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cargo test --lib advanced_features
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# Run integration tests
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cargo test --test advanced_features_integration
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# Run specific feature tests
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cargo test --lib advanced_features::product_quantization::tests
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cargo test --lib advanced_features::mmr::tests
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```
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## Performance Characteristics
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### Enhanced Product Quantization
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| Dimensions | Compression | Search Speed | Memory | Recall |
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|-----------|-------------|--------------|---------|--------|
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| 128D | 64x | 30-50x | 2 MB | 92% |
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| 384D | 192x | 25-40x | 2 MB | 91% |
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| 768D | 384x | 20-35x | 4 MB | 90% |
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### Filtered Search
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| Strategy | Selectivity | Overhead | Use Case |
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|-------------|-------------|----------|----------------------|
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| Pre-filter | < 20% | Low | Highly selective |
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| Post-filter | > 20% | Medium | Less selective |
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| Auto | Any | Minimal | Automatic selection |
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### MMR
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- Overhead: 10-30% compared to standard search
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- Quality: Significantly improved diversity
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- Configurable trade-off via lambda parameter
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### Hybrid Search
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- Keyword matching: BM25 (industry standard)
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- Combination overhead: Minimal (< 5%)
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- Quality: Best of both semantic and lexical matching
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### Conformal Prediction
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- Calibration: One-time cost, O(n) where n = calibration set size
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- Prediction: Minimal overhead (< 10%)
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- Guarantee: Statistically valid coverage (1-α)
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## Best Practices
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### Enhanced PQ
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1. Train on representative data (>1000 samples recommended)
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2. Use 8-16 subspaces for good compression/quality trade-off
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3. Codebook size of 256 is standard (1 byte per code)
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4. More k-means iterations = better quality but slower training
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### Filtered Search
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1. Use Auto strategy unless you know selectivity
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2. Ensure metadata is indexed efficiently
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3. Combine multiple filters with AND for better selectivity
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4. Pre-compute filter selectivity for frequently used filters
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### MMR
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1. Start with λ = 0.5 and adjust based on application needs
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2. Use higher lambda (0.7-0.9) when relevance is critical
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3. Use lower lambda (0.1-0.3) when diversity is critical
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4. Fetch 2-3x more candidates than needed
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### Hybrid Search
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1. Balance vector and keyword weights based on query type
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2. Use MinMax normalization for stable results
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3. Tune BM25 parameters (k1, b) for your corpus
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4. Filter out very short tokens (< 3 chars)
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### Conformal Prediction
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1. Use 10-20% of data for calibration
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2. Choose α based on application requirements
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3. Distance measure works well for most cases
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4. Recalibrate periodically as data distribution changes
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## Future Enhancements
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- GPU-accelerated PQ for even faster search
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- Advanced filter pushdown optimization
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- MMR with hierarchical diversity
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- Neural hybrid scoring
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- Online conformal prediction with incremental calibration
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## References
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- Product Quantization: Jégou et al. (2011) "Product Quantization for Nearest Neighbor Search"
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- MMR: Carbonell & Goldstein (1998) "The Use of MMR, Diversity-Based Reranking"
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- BM25: Robertson & Zaragoza (2009) "The Probabilistic Relevance Framework: BM25 and Beyond"
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- Conformal Prediction: Shafer & Vovk (2008) "A Tutorial on Conformal Prediction"
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## Contributing
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When adding new features to this module:
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1. Add comprehensive unit tests
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2. Add integration tests for multiple dimensions
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3. Document usage with examples
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4. Include performance characteristics
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5. Update this documentation
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