# Neural Self-Learning DAG Implementation Plan

## Project Overview

This document set provides a complete implementation plan for integrating a Neural Self-Learning DAG system into RuVector-Postgres, with optional QuDAG distributed consensus integration.

## Document Index

| Document | Description | Priority |
|----------|-------------|----------|
| [01-ARCHITECTURE.md](./01-ARCHITECTURE.md) | System architecture and component overview | P0 |
| [02-DAG-ATTENTION-MECHANISMS.md](./02-DAG-ATTENTION-MECHANISMS.md) | 7 specialized DAG attention implementations | P0 |
| [03-SONA-INTEGRATION.md](./03-SONA-INTEGRATION.md) | Self-Optimizing Neural Architecture integration | P0 |
| [04-POSTGRES-INTEGRATION.md](./04-POSTGRES-INTEGRATION.md) | PostgreSQL extension integration details | P0 |
| [05-QUERY-PLAN-DAG.md](./05-QUERY-PLAN-DAG.md) | Query plan as learnable DAG structure | P1 |
| [06-MINCUT-OPTIMIZATION.md](./06-MINCUT-OPTIMIZATION.md) | Min-cut based bottleneck detection | P1 |
| [07-SELF-HEALING.md](./07-SELF-HEALING.md) | Self-healing and adaptive repair | P1 |
| [08-QUDAG-INTEGRATION.md](./08-QUDAG-INTEGRATION.md) | QuDAG distributed consensus integration | P2 |
| [09-SQL-API.md](./09-SQL-API.md) | Complete SQL API specification | P0 |
| [10-TESTING-STRATEGY.md](./10-TESTING-STRATEGY.md) | Testing approach and benchmarks | P1 |
| [11-AGENT-TASKS.md](./11-AGENT-TASKS.md) | 15-agent swarm task breakdown | P0 |
| [12-MILESTONES.md](./12-MILESTONES.md) | Implementation milestones and timeline | P0 |

## Quick Start for Agents

1. Read [01-ARCHITECTURE.md](./01-ARCHITECTURE.md) for system overview
2. Check [11-AGENT-TASKS.md](./11-AGENT-TASKS.md) for your assigned tasks
3. Follow task-specific documents as referenced
4. Coordinate via shared memory patterns in [03-SONA-INTEGRATION.md](./03-SONA-INTEGRATION.md)

## Project Goals

### Primary Goals
- Create self-learning query optimization for RuVector-Postgres
- Implement 7 DAG-centric attention mechanisms
- Integrate SONA two-tier learning system
- Provide adaptive cost estimation
- Enable bottleneck detection via min-cut analysis

### Secondary Goals
- QuDAG distributed consensus for federated learning
- Self-healing index maintenance
- HDC state compression for efficient sync
- Production-ready SQL API

## Success Metrics

| Metric | Target | Measurement |
|--------|--------|-------------|
| Query latency improvement | 30-50% | Benchmark suite |
| Pattern recall accuracy | >95% | Test coverage |
| Learning overhead | <5% | Per-query timing |
| Bottleneck detection | O(n^0.12) | Algorithmic analysis |
| Memory overhead | <100MB | Per-table measurement |

## Dependencies

### Required Crates (Internal)
- `ruvector-postgres` - PostgreSQL extension framework
- `ruvector-attention` - 39 attention mechanisms
- `ruvector-gnn` - Graph neural network layers
- `ruvector-graph` - Query execution DAG
- `ruvector-mincut` - Subpolynomial min-cut
- `ruvector-nervous-system` - BTSP, HDC, spiking networks
- `sona` - Self-Optimizing Neural Architecture

### Required Crates (External)
- `pgrx` - PostgreSQL Rust extension framework
- `dashmap` - Concurrent hashmap
- `parking_lot` - Fast synchronization primitives
- `ndarray` - N-dimensional arrays
- `rayon` - Parallel iterators

### Optional (QuDAG Integration)
- `qudag` - Quantum-resistant DAG consensus
- `ml-kem` - Post-quantum key encapsulation
- `ml-dsa` - Post-quantum signatures

## Version

- Plan Version: 1.0.0
- Target RuVector Version: 0.5.0
- Last Updated: 2025-12-29