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ruvector/docs/dag/00-INDEX.md
Claude 9923056af2 docs(dag): add comprehensive Neural DAG Learning implementation plan 
Add complete documentation for 15-agent swarm implementation of self-learning
DAG system integrating RuVector with QuDAG quantum-resistant consensus.

Documents created:
- 00-INDEX.md: Document index and priority matrix
- 01-ARCHITECTURE.md: 7-layer system architecture
- 02-DAG-ATTENTION-MECHANISMS.md: 7 novel attention mechanisms
- 03-SONA-INTEGRATION.md: Self-Optimizing Neural Architecture
- 04-POSTGRES-INTEGRATION.md: pgrx extension integration
- 05-QUERY-PLAN-DAG.md: Query plan to DAG conversion
- 06-MINCUT-OPTIMIZATION.md: Subpolynomial O(n^0.12) algorithms
- 07-SELF-HEALING.md: Autonomous anomaly detection and repair
- 08-QUDAG-INTEGRATION.md: Quantum-resistant distributed consensus
- 09-SQL-API.md: Complete SQL function reference (50+ functions)
- 10-TESTING-STRATEGY.md: Unit, integration, property tests
- 11-AGENT-TASKS.md: 15-agent task breakdown and dependencies
- 12-MILESTONES.md: 8-phase implementation milestones

Key features documented:
- 7 DAG-centric attention mechanisms (Topological, Causal Cone, etc.)
- SONA integration with MicroLoRA (<100μs adaptation)
- ReasoningBank with K-means++ clustering
- EWC++ for catastrophic forgetting prevention
- ML-KEM-768 and ML-DSA quantum-resistant cryptography
- rUv token integration for distributed pattern learning
2025-12-29 22:15:55 +00:00

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

Quick Start for Agents

  1. Read 01-ARCHITECTURE.md for system overview
  2. Check 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

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