d803bfe2b1
git-subtree-dir: vendor/ruvector git-subtree-split: b64c21726f2bb37286d9ee36a7869fef60cc6900
166 lines
5.0 KiB
Markdown
166 lines
5.0 KiB
Markdown
# 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.
|