# RuVector-Postgres Integration Plans

Comprehensive implementation plans for integrating advanced capabilities into the ruvector-postgres PostgreSQL extension.

## Overview

These documents outline the roadmap to transform ruvector-postgres from a pgvector-compatible extension into a full-featured AI database with self-learning, attention mechanisms, GNN layers, and more.

## Current State

ruvector-postgres v0.1.0 includes:
- ✅ SIMD-optimized distance functions (AVX-512, AVX2, NEON)
- ✅ HNSW index with configurable parameters
- ✅ IVFFlat index for memory-efficient search
- ✅ Scalar (SQ8), Binary, and Product quantization
- ✅ pgvector-compatible SQL interface
- ✅ Parallel query execution

## Planned Integrations

| Feature | Document | Priority | Complexity | Est. Weeks |
|---------|----------|----------|------------|------------|
| Self-Learning / ReasoningBank | [01-self-learning.md](./01-self-learning.md) | High | High | 10 |
| Attention Mechanisms (39 types) | [02-attention-mechanisms.md](./02-attention-mechanisms.md) | High | Medium | 12 |
| GNN Layers | [03-gnn-layers.md](./03-gnn-layers.md) | High | High | 12 |
| Hyperbolic Embeddings | [04-hyperbolic-embeddings.md](./04-hyperbolic-embeddings.md) | Medium | Medium | 10 |
| Sparse Vectors | [05-sparse-vectors.md](./05-sparse-vectors.md) | High | Medium | 10 |
| Graph Operations & Cypher | [06-graph-operations.md](./06-graph-operations.md) | High | High | 14 |
| Tiny Dancer Routing | [07-tiny-dancer-routing.md](./07-tiny-dancer-routing.md) | Medium | Medium | 12 |

## Supporting Documents

| Document | Description |
|----------|-------------|
| [Optimization Strategy](./08-optimization-strategy.md) | SIMD, memory, query optimization techniques |
| [Benchmarking Plan](./09-benchmarking-plan.md) | Performance testing and comparison methodology |

## Architecture Principles

### Modularity
Each feature is implemented as a separate module with feature flags:

```toml
[features]
# Core (always enabled)
default = ["pg16"]

# Advanced features (opt-in)
learning = []
attention = []
gnn = []
hyperbolic = []
sparse = []
graph = []
routing = []

# Feature bundles
ai-complete = ["learning", "attention", "gnn", "routing"]
graph-complete = ["hyperbolic", "sparse", "graph"]
all = ["ai-complete", "graph-complete"]
```

### Dependency Strategy

```
ruvector-postgres
├── ruvector-core (shared types, SIMD)
├── ruvector-attention (optional)
├── ruvector-gnn (optional)
├── ruvector-graph (optional)
├── ruvector-tiny-dancer-core (optional)
└── External
    ├── pgrx (PostgreSQL FFI)
    ├── simsimd (SIMD operations)
    └── rayon (parallelism)
```

### SQL Interface Design

All features follow consistent SQL patterns:

```sql
-- Enable features
SELECT ruvector_enable_feature('learning', table_name := 'embeddings');

-- Configuration via GUCs
SET ruvector.learning_rate = 0.01;
SET ruvector.attention_type = 'flash';

-- Feature-specific functions prefixed with ruvector_
SELECT ruvector_attention_score(a, b, 'scaled_dot');
SELECT ruvector_gnn_search(query, 'edges', num_hops := 2);
SELECT ruvector_route(request, optimize_for := 'cost');

-- Cypher queries via dedicated function
SELECT * FROM ruvector_cypher('graph_name', $$
    MATCH (n:Person)-[:KNOWS]->(friend)
    RETURN friend.name
$$);
```

## Implementation Roadmap

### Phase 1: Foundation (Months 1-3)
- [ ] Sparse vectors (BM25, SPLADE support)
- [ ] Hyperbolic embeddings (Poincaré ball model)
- [ ] Basic attention operations (scaled dot-product)

### Phase 2: Graph (Months 4-6)
- [ ] Property graph storage
- [ ] Cypher query parser
- [ ] Basic graph algorithms (BFS, shortest path)
- [ ] Vector-guided traversal

### Phase 3: Neural (Months 7-9)
- [ ] GNN message passing framework
- [ ] GCN, GraphSAGE, GAT layers
- [ ] Multi-head attention
- [ ] Flash attention

### Phase 4: Intelligence (Months 10-12)
- [ ] Self-learning trajectory tracking
- [ ] ReasoningBank pattern storage
- [ ] Adaptive search optimization
- [ ] AI agent routing (Tiny Dancer)

### Phase 5: Production (Months 13-15)
- [ ] Performance optimization
- [ ] Comprehensive benchmarking
- [ ] Documentation and examples
- [ ] Production hardening

## Performance Targets

| Metric | Target | Notes |
|--------|--------|-------|
| Vector search (1M, 768d) | <2ms p50 | HNSW with ef=64 |
| Recall@10 | >0.95 | At target latency |
| GNN forward (10K nodes) | <20ms | Single layer |
| Cypher simple query | <5ms | Pattern match |
| Memory overhead | <20% | vs raw vectors |
| Build throughput | >50K vec/s | HNSW M=16 |

## Contributing

Each integration plan includes:
1. Architecture diagrams
2. Module structure
3. SQL interface specification
4. Implementation phases with timelines
5. Code examples
6. Benchmark targets
7. Dependencies and feature flags

When implementing:
1. Start with the module structure
2. Implement core functionality with tests
3. Add PostgreSQL integration
4. Write benchmarks
5. Document SQL interface
6. Update this README

## License

MIT License - See main repository for details.