ruvector/crates/ruvector-postgres/docs/integration-plans

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	High	High	10
Attention Mechanisms (39 types)	02-attention-mechanisms.md	High	Medium	12
GNN Layers	03-gnn-layers.md	High	High	12
Hyperbolic Embeddings	04-hyperbolic-embeddings.md	Medium	Medium	10
Sparse Vectors	05-sparse-vectors.md	High	Medium	10
Graph Operations & Cypher	06-graph-operations.md	High	High	14
Tiny Dancer Routing	07-tiny-dancer-routing.md	Medium	Medium	12

Supporting Documents

Document	Description
Optimization Strategy	SIMD, memory, query optimization techniques
Benchmarking Plan	Performance testing and comparison methodology

Architecture Principles

Modularity

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

[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:

-- 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.