# Self-Learning / ReasoningBank Integration Plan
## Overview
Integrate adaptive learning capabilities into ruvector-postgres, enabling the database to learn from query patterns, optimize search strategies, and improve recall/precision over time.
## Architecture
```
┌─────────────────────────────────────────────────────────────┐
│ PostgreSQL Extension │
├─────────────────────────────────────────────────────────────┤
│ ┌─────────────┐ ┌─────────────┐ ┌─────────────────────┐ │
│ │ Trajectory │ │ Verdict │ │ Memory Distillation│ │
│ │ Tracker │ │ Judgment │ │ Engine │ │
│ └──────┬──────┘ └──────┬──────┘ └──────────┬──────────┘ │
│ │ │ │ │
│ └────────────────┼─────────────────────┘ │
│ ▼ │
│ ┌───────────────────────┐ │
│ │ ReasoningBank │ │
│ │ (Pattern Storage) │ │
│ └───────────────────────┘ │
└─────────────────────────────────────────────────────────────┘
```
## Module Structure
```
src/
├── learning/
│ ├── mod.rs # Module exports
│ ├── trajectory.rs # Query trajectory tracking
│ ├── verdict.rs # Success/failure judgment
│ ├── distillation.rs # Pattern extraction
│ ├── reasoning_bank.rs # Pattern storage & retrieval
│ └── optimizer.rs # Search parameter optimization
```
## SQL Interface
### Configuration
```sql
-- Enable self-learning for a table
SELECT ruvector_enable_learning('embeddings',
trajectory_window := 1000,
learning_rate := 0.01,
min_samples := 100
);
-- View learning statistics
SELECT * FROM ruvector_learning_stats('embeddings');
-- Export learned patterns
SELECT ruvector_export_patterns('embeddings') AS patterns_json;
-- Import patterns from another instance
SELECT ruvector_import_patterns('embeddings', patterns_json);
```
### Automatic Optimization
```sql
-- Auto-tune HNSW parameters based on query patterns
SELECT ruvector_auto_tune('embeddings_idx',
optimize_for := 'recall', -- or 'latency', 'balanced'
sample_queries := 1000
);
-- Get recommended index parameters
SELECT * FROM ruvector_recommend_params('embeddings');
```
## Implementation Phases
### Phase 1: Trajectory Tracking (Week 1-2)
```rust
// src/learning/trajectory.rs
pub struct QueryTrajectory {
pub query_id: Uuid,
pub query_vector: Vec<f32>,
pub timestamp: DateTime<Utc>,
pub index_params: IndexParams,
pub results: Vec<SearchResult>,
pub latency_ms: f64,
pub recall_estimate: Option<f32>,
}
pub struct TrajectoryTracker {
buffer: RingBuffer<QueryTrajectory>,
storage: TrajectoryStorage,
}
impl TrajectoryTracker {
pub fn record(&mut self, trajectory: QueryTrajectory);
pub fn get_recent(&self, n: usize) -> Vec<&QueryTrajectory>;
pub fn analyze_patterns(&self) -> PatternAnalysis;
}
```
**SQL Functions:**
```sql
-- Record query feedback (user indicates relevance)
SELECT ruvector_record_feedback(
query_id := 'abc123',
relevant_ids := ARRAY[1, 5, 7],
irrelevant_ids := ARRAY[2, 3]
);
```
### Phase 2: Verdict Judgment (Week 3-4)
```rust
// src/learning/verdict.rs
pub struct VerdictEngine {
success_threshold: f32,
metrics: VerdictMetrics,
}
impl VerdictEngine {
/// Judge if a search was successful based on multiple signals
pub fn judge(&self, trajectory: &QueryTrajectory) -> Verdict {
let signals = vec![
self.latency_score(trajectory),
self.recall_score(trajectory),
self.diversity_score(trajectory),
self.user_feedback_score(trajectory),
];
Verdict {
success: signals.iter().sum::<f32>() / signals.len() as f32 > self.success_threshold,
confidence: self.compute_confidence(&signals),
recommendations: self.generate_recommendations(&signals),
}
}
}
```
### Phase 3: Memory Distillation (Week 5-6)
```rust
// src/learning/distillation.rs
pub struct DistillationEngine {
pattern_extractor: PatternExtractor,
compressor: PatternCompressor,
}
impl DistillationEngine {
/// Extract reusable patterns from trajectories
pub fn distill(&self, trajectories: &[QueryTrajectory]) -> Vec<LearnedPattern> {
let raw_patterns = self.pattern_extractor.extract(trajectories);
let compressed = self.compressor.compress(raw_patterns);
compressed
}
}
pub struct LearnedPattern {
pub query_cluster_centroid: Vec<f32>,
pub optimal_ef_search: u32,
pub optimal_probes: u32,
pub expected_recall: f32,
pub confidence: f32,
}
```
### Phase 4: ReasoningBank Storage (Week 7-8)
```rust
// src/learning/reasoning_bank.rs
pub struct ReasoningBank {
patterns: HnswIndex<LearnedPattern>,
metadata: HashMap<PatternId, PatternMetadata>,
}
impl ReasoningBank {
/// Find applicable patterns for a query
pub fn lookup(&self, query: &[f32], k: usize) -> Vec<&LearnedPattern> {
self.patterns.search(query, k)
}
/// Store a new pattern
pub fn store(&mut self, pattern: LearnedPattern) -> PatternId;
/// Merge similar patterns to prevent bloat
pub fn consolidate(&mut self);
/// Prune low-value patterns
pub fn prune(&mut self, min_usage: u32, min_confidence: f32);
}
```
### Phase 5: Search Optimizer (Week 9-10)
```rust
// src/learning/optimizer.rs
pub struct SearchOptimizer {
reasoning_bank: Arc<ReasoningBank>,
default_params: SearchParams,
}
impl SearchOptimizer {
/// Get optimized parameters for a query
pub fn optimize(&self, query: &[f32]) -> SearchParams {
match self.reasoning_bank.lookup(query, 3) {
patterns if !patterns.is_empty() => {
self.interpolate_params(query, patterns)
}
_ => self.default_params.clone()
}
}
fn interpolate_params(&self, query: &[f32], patterns: &[&LearnedPattern]) -> SearchParams {
// Weight patterns by similarity to query
let weights: Vec<f32> = patterns.iter()
.map(|p| cosine_similarity(query, &p.query_cluster_centroid))
.collect();
SearchParams {
ef_search: weighted_average(
patterns.iter().map(|p| p.optimal_ef_search as f32),
&weights
) as u32,
// ...
}
}
}
```
## PostgreSQL Integration
### Background Worker
```rust
// src/learning/bgworker.rs
#[pg_guard]
pub extern "C" fn learning_bgworker_main(_arg: pg_sys::Datum) {
BackgroundWorker::attach_signal_handlers(SignalWakeFlags::SIGHUP | SignalWakeFlags::SIGTERM);
loop {
// Process trajectory buffer
let trajectories = TRAJECTORY_BUFFER.drain();
if trajectories.len() >= MIN_BATCH_SIZE {
// Distill patterns
let patterns = DISTILLATION_ENGINE.distill(&trajectories);
// Store in reasoning bank
for pattern in patterns {
REASONING_BANK.store(pattern);
}
// Periodic consolidation
if should_consolidate() {
REASONING_BANK.consolidate();
}
}
// Sleep until next batch
BackgroundWorker::wait_latch(LEARNING_INTERVAL_MS);
}
}
```
### GUC Configuration
```rust
static LEARNING_ENABLED: GucSetting<bool> = GucSetting::new(false);
static LEARNING_RATE: GucSetting<f64> = GucSetting::new(0.01);
static TRAJECTORY_BUFFER_SIZE: GucSetting<i32> = GucSetting::new(10000);
static PATTERN_CONSOLIDATION_INTERVAL: GucSetting<i32> = GucSetting::new(3600);
```
## Optimization Strategies
### 1. Adaptive ef_search
```sql
-- Before: Static ef_search
SET ruvector.ef_search = 40;
SELECT * FROM items ORDER BY embedding <-> query_vec LIMIT 10;
-- After: Adaptive ef_search based on learned patterns
SELECT * FROM items
ORDER BY embedding <-> query_vec
LIMIT 10
WITH (adaptive_search := true);
```
### 2. Query-Aware Probing
For IVFFlat, learn optimal probe counts per query cluster:
```rust
pub fn adaptive_probes(&self, query: &[f32]) -> u32 {
let cluster_id = self.assign_cluster(query);
self.learned_probes.get(&cluster_id).unwrap_or(&self.default_probes)
}
```
### 3. Index Selection
Learn when to use HNSW vs IVFFlat:
```rust
pub fn select_index(&self, query: &[f32], k: usize) -> IndexType {
let features = QueryFeatures::extract(query, k);
self.index_selector.predict(&features)
}
```
## Benchmarks
### Metrics to Track
| Recall@10 | 0.95 | 0.98 | After 10K queries |
| p99 Latency | 5ms | 3ms | After learning |
| Memory Overhead | 0 | <100MB | Pattern storage |
| Learning Time | N/A | <1s/1K queries | Background processing |
### Benchmark Queries
```sql
-- Measure recall improvement
SELECT ruvector_benchmark_recall(
table_name := 'embeddings',
ground_truth_table := 'embeddings_ground_truth',
num_queries := 1000,
k := 10
);
-- Measure latency improvement
SELECT ruvector_benchmark_latency(
table_name := 'embeddings',
num_queries := 10000,
k := 10,
percentiles := ARRAY[50, 90, 99]
);
```
## Dependencies
```toml
[dependencies]
# Existing ruvector crates (optional integration)
# ruvector-core = { path = "../ruvector-core", optional = true }
# Pattern storage
dashmap = "6.0"
parking_lot = "0.12"
# Statistics
statrs = "0.16"
# Clustering for pattern extraction
linfa = "0.7"
linfa-clustering = "0.7"
# Serialization for pattern export/import
serde = { version = "1.0", features = ["derive"] }
serde_json = "1.0"
```
## Feature Flags
```toml
[features]
learning = []
learning-advanced = ["learning", "linfa", "linfa-clustering"]
learning-distributed = ["learning", "ruvector-replication"]
```
## Migration Path
1. **v0.2.0**: Basic trajectory tracking, manual feedback
2. **v0.3.0**: Verdict judgment, automatic pattern extraction
3. **v0.4.0**: Full ReasoningBank, adaptive search
4. **v0.5.0**: Distributed learning across replicas
## Security Considerations
- Pattern data is stored locally, no external transmission
- Trajectory data can be anonymized (hash query vectors)
- Learning can be disabled per-table for sensitive data
- Export/import requires superuser privileges