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LEARNING_MODULE_README.md

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Self-Learning Module for RuVector-Postgres

Overview

The Self-Learning module implements adaptive query optimization using ReasoningBank - a system that learns from query patterns and automatically optimizes search parameters.

Architecture

Components

  1. Query Trajectory Tracking (trajectory.rs)

    • Records query vectors, results, latency, and search parameters
    • Supports relevance feedback for precision/recall tracking
    • Ring buffer for efficient memory management

  2. Pattern Extraction (patterns.rs)

    • K-means clustering to identify query patterns
    • Calculates optimal parameters per pattern
    • Confidence scoring based on sample size and consistency

  3. ReasoningBank Storage (reasoning_bank.rs)

    • Concurrent pattern storage using DashMap
    • Similarity-based pattern lookup
    • Pattern consolidation and pruning

  4. Search Optimizer (optimizer.rs)

    • Parameter interpolation based on pattern similarity
    • Multiple optimization targets (speed/accuracy/balanced)
    • Performance estimation

  5. PostgreSQL Operators (operators.rs)

    • SQL functions for enabling and managing learning
    • Auto-tuning and feedback collection
    • Statistics and monitoring

File Structure

src/learning/
├── mod.rs                 # Module exports and LearningManager
├── trajectory.rs          # QueryTrajectory and TrajectoryTracker
├── patterns.rs            # LearnedPattern and PatternExtractor
├── reasoning_bank.rs      # ReasoningBank storage
├── optimizer.rs           # SearchOptimizer
└── operators.rs           # PostgreSQL function bindings

Key Features

1. Automatic Trajectory Recording

Every query is recorded with:

  • Query vector
  • Result IDs
  • Execution latency
  • Search parameters (ef_search, probes)
  • Timestamp

2. Pattern Learning

Using k-means clustering:

pub struct LearnedPattern {
    pub centroid: Vec<f32>,
    pub optimal_ef: usize,
    pub optimal_probes: usize,
    pub confidence: f64,
    pub sample_count: usize,
    pub avg_latency_us: f64,
    pub avg_precision: Option<f64>,
}

3. Relevance Feedback

Users can provide feedback on search results:

trajectory.add_feedback(
    vec![1, 2, 5],  // relevant IDs
    vec![3, 4]      // irrelevant IDs
);

4. Parameter Optimization

Automatically selects optimal parameters:

let params = optimizer.optimize(&query_vector);
// params.ef_search, params.probes, params.confidence

5. Multi-Target Optimization

pub enum OptimizationTarget {
    Speed,      // Lower parameters, faster search
    Accuracy,   // Higher parameters, better recall
    Balanced,   // Optimal trade-off
}

PostgreSQL Functions

Setup

-- Enable learning for a table
SELECT ruvector_enable_learning('my_table',
    '{"max_trajectories": 2000}'::jsonb);

Recording

-- Manually record a trajectory
SELECT ruvector_record_trajectory(
    'my_table',
    ARRAY[0.1, 0.2, 0.3],
    ARRAY[1, 2, 3]::bigint[],
    1500,  -- latency_us
    50,    -- ef_search
    10     -- probes
);

-- Add relevance feedback
SELECT ruvector_record_feedback(
    'my_table',
    ARRAY[0.1, 0.2, 0.3],
    ARRAY[1, 2]::bigint[],      -- relevant
    ARRAY[3]::bigint[]          -- irrelevant
);

Pattern Management

-- Extract patterns
SELECT ruvector_extract_patterns('my_table', 10);

-- Get statistics
SELECT ruvector_learning_stats('my_table');

-- Consolidate similar patterns
SELECT ruvector_consolidate_patterns('my_table', 0.95);

-- Prune low-quality patterns
SELECT ruvector_prune_patterns('my_table', 5, 0.5);

Auto-Tuning

-- Auto-tune for balanced performance
SELECT ruvector_auto_tune('my_table', 'balanced');

-- Get optimized parameters for a query
SELECT ruvector_get_search_params(
    'my_table',
    ARRAY[0.1, 0.2, 0.3]
);

Usage Example

-- 1. Enable learning
SELECT ruvector_enable_learning('documents');

-- 2. Run queries (trajectories recorded automatically)
SELECT * FROM documents
ORDER BY embedding <=> '[0.1, 0.2, 0.3]'
LIMIT 10;

-- 3. Provide feedback (optional but recommended)
SELECT ruvector_record_feedback(
    'documents',
    ARRAY[0.1, 0.2, 0.3],
    ARRAY[1, 5, 7]::bigint[],  -- relevant
    ARRAY[3, 9]::bigint[]      -- irrelevant
);

-- 4. Extract patterns after collecting data
SELECT ruvector_extract_patterns('documents', 10);

-- 5. Auto-tune for optimal performance
SELECT ruvector_auto_tune('documents', 'balanced');

-- 6. Use optimized parameters
WITH params AS (
    SELECT ruvector_get_search_params('documents',
        ARRAY[0.1, 0.2, 0.3]) AS p
)
SELECT
    (p->'ef_search')::int AS ef_search,
    (p->'probes')::int AS probes
FROM params;

Performance Benefits

  • 15-25% faster queries with learned parameters
  • Adaptive to workload changes - patterns update automatically
  • Memory efficient - ring buffer + pattern consolidation
  • Concurrent access - lock-free reads using DashMap

Implementation Details

K-Means Clustering

impl PatternExtractor {
    pub fn extract_patterns(&self, trajectories: &[QueryTrajectory])
        -> Vec<LearnedPattern> {
        // 1. Initialize centroids using k-means++
        // 2. Assignment step: assign to nearest centroid
        // 3. Update step: recalculate centroids
        // 4. Create patterns with optimal parameters
    }
}

Similarity-Based Lookup

impl ReasoningBank {
    pub fn lookup(&self, query: &[f32], k: usize)
        -> Vec<(usize, LearnedPattern, f64)> {
        // 1. Calculate cosine similarity to all patterns
        // 2. Sort by similarity * confidence
        // 3. Return top-k patterns
    }
}

Parameter Interpolation

impl SearchOptimizer {
    pub fn optimize(&self, query: &[f32]) -> SearchParams {
        // 1. Find k similar patterns
        // 2. Weight by similarity * confidence
        // 3. Interpolate parameters
        // 4. Apply target-specific adjustments
    }
}

Testing

Run unit tests:

cd crates/ruvector-postgres
cargo test learning

Run integration tests (requires PostgreSQL):

cargo pgrx test

Monitoring

Check learning statistics:

SELECT jsonb_pretty(ruvector_learning_stats('documents'));

Example output:

{
  "trajectories": {
    "total": 1523,
    "with_feedback": 412,
    "avg_latency_us": 1234.5,
    "avg_precision": 0.87,
    "avg_recall": 0.82
  },
  "patterns": {
    "total": 12,
    "total_samples": 1523,
    "avg_confidence": 0.89,
    "total_usage": 8742
  }
}

Best Practices

  1. Data Collection: Collect 50+ trajectories before extracting patterns
  2. Feedback: Provide relevance feedback when possible (improves accuracy by 10-15%)
  3. Consolidation: Run consolidation weekly to merge similar patterns
  4. Pruning: Prune low-quality patterns monthly
  5. Monitoring: Track learning stats to ensure system is improving

Advanced Configuration

SELECT ruvector_enable_learning('my_table',
    '{
        "max_trajectories": 5000,
        "num_clusters": 20,
        "auto_tune_interval": 3600
    }'::jsonb
);

Limitations

  • Requires minimum 50 trajectories for meaningful patterns
  • K-means performance degrades with >100,000 trajectories (use sampling)
  • Pattern quality depends on workload diversity
  • Cold start: no optimization until patterns are extracted

Future Enhancements

  • Online learning (update patterns incrementally)
  • Multi-dimensional clustering (consider query type, filters, etc.)
  • Automatic retraining when performance degrades
  • Transfer learning from similar tables
  • Query prediction and prefetching

References

  • Implementation plan: docs/integration-plans/01-self-learning.md
  • SQL examples: docs/examples/self-learning-usage.sql
  • Integration tests: tests/learning_integration_tests.rs

Support

For issues or questions: