🐘 RuVector PostgreSQL Bridge

Feature	Description	Performance
pgvector Integration	Native PostgreSQL vector operations	150x faster than in-memory
Attention Mechanisms	Self, multi-head, cross-attention in SQL	GPU-accelerated
Graph Neural Networks	GNN operations via SQL functions	Message passing, aggregation
Hyperbolic Embeddings	Poincaré ball model in PostgreSQL	Better hierarchy representation
Quantization	Int8/Float16 compression	3.92x memory reduction
Streaming	Large dataset processing	Batch + async support
Migrations	Version-controlled schema	7 migration scripts

bash
# Initialize RuVector in PostgreSQL
ruflo ruvector init --database mydb --user admin

# Check connection and schema status
ruflo ruvector status --verbose

# Run pending migrations
ruflo ruvector migrate --up

# Performance benchmark
ruflo ruvector benchmark --iterations 1000

# Optimize indices and vacuum
ruflo ruvector optimize --analyze

# Backup vector data
ruflo ruvector backup --output ./backup.sql

Migration	Purpose	Features
001_create_extension	Enable pgvector	Vector type, operators
002_create_vector_tables	Core tables	embeddings, patterns, agents
003_create_indices	HNSW indices	150x faster search
004_create_functions	Vector functions	Similarity, clustering
005_create_attention_functions	Attention ops	Self/multi-head attention
006_create_gnn_functions	GNN operations	Message passing, aggregation
007_create_hyperbolic_functions	Hyperbolic geometry	Poincaré operations