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docs(postgres): update README with v2 capabilities
- Update function count to 230+ - Add Gated Transformers section (13 functions) - Add Hybrid Search section (7 functions) - Add Multi-Tenancy section (17 functions) - Add Self-Healing section (23 functions) - Add Integrity Control section (4 functions) - Update feature comparison table with new modules 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
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@ -8,14 +8,18 @@
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[](https://www.npmjs.com/package/@ruvector/core)
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[](docs/SECURITY_AUDIT_REPORT.md)
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**The most advanced PostgreSQL vector database extension.** A drop-in pgvector replacement with 77+ SQL functions, SIMD acceleration, 39 attention mechanisms, GNN layers, hyperbolic embeddings, SPARQL/RDF support, and self-learning capabilities.
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**The most advanced PostgreSQL vector database extension.** A drop-in pgvector replacement with **230+ SQL functions**, SIMD acceleration, 39 attention mechanisms, GNN layers, hyperbolic embeddings, mincut-gated transformers, hybrid search, multi-tenancy, self-healing, and self-learning capabilities.
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## v2.0.0 Highlights (December 2025)
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- **Mincut-Gated Transformers**: Ultra-low-latency inference with coherence control via λ signals
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- **Hybrid Search**: Vector + BM25 fusion with RRF and linear blending
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- **Multi-Tenancy**: Row-level security with automatic tenant isolation
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- **Self-Healing**: Automated index repair with integrity validation
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- **Integrity Control**: Stoer-Wagner mincut-based quality assurance
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- **IVFFlat Index**: Full inverted list storage with proper page management
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- **HNSW Index**: Fixed query execution with heap scan integration
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- **Security Audit**: 3 critical SQL injection vulnerabilities fixed
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- **Multi-tenant**: Validated tenant isolation with parameterized queries
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- **GNN Module**: Complete Graph Neural Network operators
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## Why RuVector?
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@ -26,9 +30,14 @@
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| Distance Metrics | 3 | 8+ (including hyperbolic) |
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| **Local Embeddings** | - | **6 models (fastembed)** |
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| **Attention Mechanisms** | - | **39 types** |
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| **Gated Transformers** | - | **Mincut-coherence control** |
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| **Hybrid Search** | - | **RRF + Linear fusion** |
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| **Graph Neural Networks** | - | **GCN, GraphSAGE, GAT** |
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| **Hyperbolic Embeddings** | - | **Poincare, Lorentz** |
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| **Sparse Vectors / BM25** | Partial | **Full support** |
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| **Multi-Tenancy** | - | **Row-level isolation** |
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| **Self-Healing** | - | **Auto index repair** |
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| **Integrity Control** | - | **Stoer-Wagner mincut** |
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| **Self-Learning** | - | **ReasoningBank** |
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| **Agent Routing** | - | **Tiny Dancer** |
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| **Graph/Cypher** | - | **Full support** |
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@ -124,7 +133,7 @@ ORDER BY distance
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LIMIT 10;
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```
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## 67+ SQL Functions
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## 230+ SQL Functions
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RuVector exposes all advanced AI capabilities as native PostgreSQL functions.
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@ -449,6 +458,209 @@ SELECT ruvector_sparql_update('knowledge_graph', '
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- Result formats: JSON, XML, CSV, TSV
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- **~198K triples/sec** insertion, **~5.5M queries/sec** lookups
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### Gated Transformers (13 functions)
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Ultra-low-latency transformer inference with mincut-gated coherence control.
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```sql
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-- Get gate decision from integrity mincut signals
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SELECT gated_transformer_gate_decision(
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lambda := 150, -- Current mincut value
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lambda_prev := 160, -- Previous mincut
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boundary_count := 5, -- Witness edge count
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layer := 3 -- Current transformer layer
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);
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-- Returns: {"decision": "Allow", "reason": "None", "tier": 3, ...}
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-- Check early exit conditions
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SELECT gated_transformer_early_exit_check(
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lambda := 180,
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layer := 8,
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total_layers := 12
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);
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-- Returns: {"can_exit": true, "confidence": 0.92, "exit_layer": 8, ...}
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-- Mixture-of-Depths token routing (50% FLOPs reduction)
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SELECT gated_transformer_route_tokens(
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lambda := 150,
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token_count := 512,
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layer_capacity := 0.5 -- Route only 50% of tokens through compute
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);
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-- Returns: [{"index": 0, "route": "Compute"}, {"index": 1, "route": "Skip"}, ...]
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-- Configuration management
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SELECT gated_transformer_config(); -- Get current config
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SELECT gated_transformer_set_config(
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lambda_min := 50,
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lambda_critical := 20,
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check_interval := 64
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);
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-- Policy management
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SELECT gated_transformer_gate_policy(); -- Get current policy
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SELECT gated_transformer_set_policy(
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enable_tiering := true,
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enable_kv_flush := true,
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enable_freeze := false
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);
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-- Bridge with integrity module
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SELECT gated_transformer_from_integrity('my_hnsw_index');
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-- Get combined coherence score
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SELECT gated_transformer_coherence_score(
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lambda := 150,
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lambda_prev := 160,
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boundary_count := 5
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);
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-- Returns: 0.875 (normalized 0-1 coherence)
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```
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**Gated Transformer Features:**
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- **Dynamic Compute Allocation**: Mixture-of-Depths routes tokens for 50% FLOPs reduction
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- **Early Exit**: Layer-skipping with 30-50% latency reduction when coherence is high
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- **Tiered Decisions**: 5 tiers from Full→Reduced→Conservative→Minimal→Critical
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- **KV-Cache Management**: Automatic flush/freeze based on coherence signals
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- **Boundary Detection**: Witness edge tracking for structural integrity
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### Hybrid Search (7 functions)
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Vector + keyword fusion with multiple ranking strategies.
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```sql
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-- Linear fusion (alpha blending)
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SELECT ruvector_hybrid_linear(
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vector_results, -- Array of (id, score) from vector search
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keyword_results, -- Array of (id, score) from BM25
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alpha := 0.7 -- 0.7 vector weight, 0.3 keyword weight
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);
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-- Reciprocal Rank Fusion (RRF)
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SELECT ruvector_hybrid_rrf(
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vector_results,
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keyword_results,
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k := 60 -- RRF constant
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);
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-- Combined search with auto-fusion
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SELECT ruvector_hybrid_search(
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query_text := 'machine learning optimization',
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query_embedding := $embedding,
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table_name := 'documents',
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text_column := 'content',
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vector_column := 'embedding',
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limit_k := 10
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);
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-- Get/Set hybrid search parameters
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SELECT ruvector_get_hybrid_alpha(); -- Returns current alpha
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SELECT ruvector_set_hybrid_alpha(0.6);
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SELECT ruvector_get_hybrid_rrf_k();
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SELECT ruvector_set_hybrid_rrf_k(40);
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```
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### Multi-Tenancy (17 functions)
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Row-level security with automatic tenant isolation.
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```sql
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-- Set current tenant context
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SELECT ruvector_set_tenant('tenant_123');
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SELECT ruvector_get_tenant();
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-- Create tenant-isolated table
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SELECT ruvector_create_tenant_table(
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'documents',
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'id SERIAL PRIMARY KEY, content TEXT, embedding ruvector(384)'
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);
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-- Automatic tenant filtering (via RLS policies)
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INSERT INTO documents (content, embedding)
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VALUES ('Hello', '[0.1, 0.2, ...]'::ruvector);
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-- Automatically tagged with tenant_id
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-- Query only sees current tenant's data
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SELECT * FROM documents
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WHERE embedding <-> $query < 0.5;
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-- Tenant management
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SELECT ruvector_list_tenants();
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SELECT ruvector_tenant_stats('tenant_123');
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SELECT ruvector_migrate_tenant('old_tenant', 'new_tenant');
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-- Cross-tenant queries (admin only)
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SELECT ruvector_admin_query_all_tenants('documents', 'SELECT count(*) FROM documents');
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```
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### Self-Healing (23 functions)
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Automated index repair with integrity validation.
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```sql
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-- Check index health
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SELECT ruvector_index_health('documents_embedding_idx');
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-- Returns: {"status": "healthy", "fragmentation": 0.05, "orphaned_nodes": 0}
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-- Automatic repair
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SELECT ruvector_auto_repair('documents_embedding_idx');
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-- Schedule maintenance
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SELECT ruvector_schedule_maintenance(
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'documents_embedding_idx',
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interval := '1 day',
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repair_threshold := 0.1 -- Repair if fragmentation > 10%
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);
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-- Self-healing operations
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SELECT ruvector_compact_index('documents_embedding_idx');
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SELECT ruvector_rebalance_hnsw('documents_embedding_idx');
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SELECT ruvector_rebuild_ivf_centroids('documents_embedding_idx');
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SELECT ruvector_validate_graph_connectivity('documents_embedding_idx');
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-- Monitor healing status
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SELECT ruvector_healing_status();
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SELECT ruvector_last_repair_log('documents_embedding_idx');
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-- Integrity checks
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SELECT ruvector_check_orphaned_vectors('documents');
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SELECT ruvector_check_duplicate_vectors('documents', threshold := 0.001);
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```
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### Integrity Control (4 functions)
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Stoer-Wagner mincut-based quality assurance for vector indices.
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```sql
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-- Get integrity status
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SELECT ruvector_integrity_status();
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-- Returns: {"enabled": true, "active_contracts": 1, "contracts": ["default"]}
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-- Create integrity contract (SLA)
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SELECT ruvector_integrity_create_contract(
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id := 'production_sla',
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name := 'Production SLA',
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min_recall := 0.95, -- Minimum recall requirement
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max_latency_ms := 100, -- Maximum query latency
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min_mincut := 0.1 -- Minimum graph connectivity
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);
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-- Validate against contract
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SELECT ruvector_integrity_validate(
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'production_sla',
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recall := 0.97,
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latency_ms := 45,
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mincut := 0.15
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);
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-- Returns: {"passed": true, "recall": 0.97, "latency_ms": 45, "mincut": 0.15, "failures": []}
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-- Compute mincut for graph connectivity
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SELECT ruvector_mincut(
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n := 100, -- Number of nodes
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edges_json := '[{"u": 0, "v": 1, "w": 1.0}, ...]'::jsonb
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);
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-- Returns minimum cut value (Stoer-Wagner algorithm)
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```
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## Vector Types
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### `ruvector(n)` - Dense Vector
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