🐘 RuVector PostgreSQL Bridge

Full PostgreSQL integration with advanced vector operations, attention mechanisms, GNN layers, and self-learning optimization.

Feature	Description	Performance
Vector Search	HNSW/IVF indexing with 12+ distance metrics	52,000+ inserts/sec, sub-ms queries
39 Attention Mechanisms	Multi-head, Flash, Sparse, Linear, Graph, Temporal	GPU-accelerated SQL functions
15 GNN Layer Types	GCN, GAT, GraphSAGE, MPNN, Transformer, PNA	Graph-aware vector queries
Hyperbolic Embeddings	Poincare, Lorentz, Klein models for hierarchical data	Native manifold operations
Self-Learning	Query optimizer, index tuner with EWC++	Continuous improvement

MCP Tools (8 tools):

Tool	Description
ruvector_search	Vector similarity search (cosine, euclidean, dot, etc.)
ruvector_insert	Insert vectors with batch support and upsert
ruvector_update	Update existing vectors and metadata
ruvector_delete	Delete vectors by ID or batch
ruvector_create_index	Create HNSW/IVF indices with tuning
ruvector_index_stats	Get index statistics and health
ruvector_batch_search	Batch vector searches with parallelism
ruvector_health	Connection pool health check

Configuration:

typescript
import { createRuVectorBridge } from '@claude-flow/plugins';

const bridge = createRuVectorBridge({
  host: 'localhost',
  port: 5432,
  database: 'vectors',
  user: 'postgres',
  password: 'secret',
  pool: { min: 2, max: 10 },
  ssl: true
});

// Enable the plugin
await registry.register(bridge);
await registry.loadAll();

Attention Mechanisms (39 types):

Category	Mechanisms
Core	multi_head, self_attention, cross_attention, causal, bidirectional
Efficient	flash_attention, flash_attention_v2, memory_efficient, chunk_attention
Sparse	sparse_attention, block_sparse, bigbird, longformer, local, global
Linear	linear_attention, performer, linformer, nystrom, reformer
Positional	relative_position, rotary_position, alibi, axial
Graph	graph_attention, hyperbolic_attention, spherical_attention
Temporal	temporal_attention, recurrent_attention, state_space
Multimodal	cross_modal, perceiver, flamingo
Retrieval	retrieval_attention, knn_attention, memory_augmented

GNN Layers (15 types):

Layer	Use Case
gcn	General graph convolution
gat / gatv2	Attention-weighted aggregation
sage	Inductive learning on large graphs
gin	Maximally expressive GNN
mpnn	Message passing with edge features
edge_conv	Point cloud processing
transformer	Full attention on graphs
pna	Principal neighborhood aggregation
rgcn / hgt / han	Heterogeneous graphs

Hyperbolic Operations:

typescript
import { createHyperbolicSpace } from '@claude-flow/plugins';

const space = createHyperbolicSpace('poincare', { curvature: -1.0 });

// Embed hierarchical data (trees, taxonomies)
const embedding = await space.embed(vector);
const distance = await space.distance(v1, v2);  // Geodesic distance
const midpoint = await space.geodesicMidpoint(v1, v2);

Self-Learning System:

typescript
import { createSelfLearningSystem } from '@claude-flow/plugins';

const learning = createSelfLearningSystem(bridge);

// Automatic optimization
await learning.startLearningLoop();  // Runs in background

// Manual optimization
const suggestions = await learning.queryOptimizer.analyze(query);
await learning.indexTuner.tune('my_index');

Hooks (auto-triggered):

Hook	Event	Purpose
ruvector-learn-pattern	PostMemoryStore	Learn from memory operations
ruvector-collect-stats	PostToolUse	Collect query statistics