84 lines
3.4 KiB
Markdown
84 lines
3.4 KiB
Markdown
# Neural Self-Learning DAG Implementation Plan
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## Project Overview
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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.
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## Document Index
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| Document | Description | Priority |
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|----------|-------------|----------|
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| [01-ARCHITECTURE.md](./01-ARCHITECTURE.md) | System architecture and component overview | P0 |
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| [02-DAG-ATTENTION-MECHANISMS.md](./02-DAG-ATTENTION-MECHANISMS.md) | 7 specialized DAG attention implementations | P0 |
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| [03-SONA-INTEGRATION.md](./03-SONA-INTEGRATION.md) | Self-Optimizing Neural Architecture integration | P0 |
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| [04-POSTGRES-INTEGRATION.md](./04-POSTGRES-INTEGRATION.md) | PostgreSQL extension integration details | P0 |
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| [05-QUERY-PLAN-DAG.md](./05-QUERY-PLAN-DAG.md) | Query plan as learnable DAG structure | P1 |
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| [06-MINCUT-OPTIMIZATION.md](./06-MINCUT-OPTIMIZATION.md) | Min-cut based bottleneck detection | P1 |
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| [07-SELF-HEALING.md](./07-SELF-HEALING.md) | Self-healing and adaptive repair | P1 |
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| [08-QUDAG-INTEGRATION.md](./08-QUDAG-INTEGRATION.md) | QuDAG distributed consensus integration | P2 |
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| [09-SQL-API.md](./09-SQL-API.md) | Complete SQL API specification | P0 |
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| [10-TESTING-STRATEGY.md](./10-TESTING-STRATEGY.md) | Testing approach and benchmarks | P1 |
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| [11-AGENT-TASKS.md](./11-AGENT-TASKS.md) | 15-agent swarm task breakdown | P0 |
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| [12-MILESTONES.md](./12-MILESTONES.md) | Implementation milestones and timeline | P0 |
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## Quick Start for Agents
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1. Read [01-ARCHITECTURE.md](./01-ARCHITECTURE.md) for system overview
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2. Check [11-AGENT-TASKS.md](./11-AGENT-TASKS.md) for your assigned tasks
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3. Follow task-specific documents as referenced
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4. Coordinate via shared memory patterns in [03-SONA-INTEGRATION.md](./03-SONA-INTEGRATION.md)
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## Project Goals
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### Primary Goals
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- Create self-learning query optimization for RuVector-Postgres
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- Implement 7 DAG-centric attention mechanisms
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- Integrate SONA two-tier learning system
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- Provide adaptive cost estimation
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- Enable bottleneck detection via min-cut analysis
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### Secondary Goals
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- QuDAG distributed consensus for federated learning
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- Self-healing index maintenance
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- HDC state compression for efficient sync
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- Production-ready SQL API
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## Success Metrics
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| Metric | Target | Measurement |
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|--------|--------|-------------|
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| Query latency improvement | 30-50% | Benchmark suite |
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| Pattern recall accuracy | >95% | Test coverage |
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| Learning overhead | <5% | Per-query timing |
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| Bottleneck detection | O(n^0.12) | Algorithmic analysis |
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| Memory overhead | <100MB | Per-table measurement |
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## Dependencies
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### Required Crates (Internal)
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- `ruvector-postgres` - PostgreSQL extension framework
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- `ruvector-attention` - 39 attention mechanisms
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- `ruvector-gnn` - Graph neural network layers
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- `ruvector-graph` - Query execution DAG
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- `ruvector-mincut` - Subpolynomial min-cut
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- `ruvector-nervous-system` - BTSP, HDC, spiking networks
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- `sona` - Self-Optimizing Neural Architecture
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### Required Crates (External)
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- `pgrx` - PostgreSQL Rust extension framework
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- `dashmap` - Concurrent hashmap
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- `parking_lot` - Fast synchronization primitives
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- `ndarray` - N-dimensional arrays
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- `rayon` - Parallel iterators
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### Optional (QuDAG Integration)
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- `qudag` - Quantum-resistant DAG consensus
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- `ml-kem` - Post-quantum key encapsulation
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- `ml-dsa` - Post-quantum signatures
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## Version
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- Plan Version: 1.0.0
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- Target RuVector Version: 0.5.0
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- Last Updated: 2025-12-29
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