ruvector/crates/ruvector-postgres/docs/ROUTING_QUICK_REFERENCE.md
rUv 073ce73612
feat(postgres): Add 53 SQL function definitions for all advanced modules (#46)
* feat(postgres): Add 7 advanced AI modules to ruvector-postgres

Comprehensive implementation of advanced AI capabilities:

## New Modules (23,541 lines of code)

### 1. Self-Learning / ReasoningBank (`src/learning/`)
- Trajectory tracking for query optimization
- Pattern extraction using K-means clustering
- ReasoningBank for pattern storage and matching
- Adaptive search parameter optimization

### 2. Attention Mechanisms (`src/attention/`)
- Scaled dot-product attention (core)
- Multi-head attention with parallel heads
- Flash Attention v2 (memory-efficient)
- 10 attention types with PostgresEnum support

### 3. GNN Layers (`src/gnn/`)
- Message passing framework
- GCN (Graph Convolutional Network)
- GraphSAGE with mean/max aggregation
- Configurable aggregation methods

### 4. Hyperbolic Embeddings (`src/hyperbolic/`)
- Poincaré ball model
- Lorentz hyperboloid model
- Hyperbolic distance metrics
- Möbius operations

### 5. Sparse Vectors (`src/sparse/`)
- COO format sparse vector type
- Efficient sparse-sparse distance functions
- BM25/SPLADE compatible
- Top-k pruning operations

### 6. Graph Operations & Cypher (`src/graph/`)
- Property graph storage (nodes/edges)
- BFS, DFS, Dijkstra traversal
- Cypher query parser (AST-based)
- Query executor with pattern matching

### 7. Tiny Dancer Routing (`src/routing/`)
- FastGRNN neural network
- Agent registry with capabilities
- Multi-objective routing optimization
- Cost/latency/quality balancing

## Docker Infrastructure
- Dockerfile with pgrx 0.12.6 and PostgreSQL 16
- docker-compose.yml with test runner
- Initialization SQL with test tables
- Shell scripts for dev/test/benchmark

## Feature Flags
- `learning`, `attention`, `gnn`, `hyperbolic`
- `sparse`, `graph`, `routing`
- `ai-complete` and `graph-complete` bundles

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude <noreply@anthropic.com>

* fix(docker): Copy entire workspace for pgrx build

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude <noreply@anthropic.com>

* fix(docker): Build standalone crate without workspace

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude <noreply@anthropic.com>

* docs: Update README to enhance clarity and structure

* fix(postgres): Resolve compilation errors and Docker build issues

- Fix simsimd Option/Result type mismatch in scaled_dot.rs
- Fix f32/f64 type conversions in poincare.rs and lorentz.rs
- Fix AVX512 missing wrapper functions by using AVX2 fallback
- Fix Vec<Vec<f32>> to JsonB for pgrx pg_extern compatibility
- Fix DashMap get() to get_mut() for mutable access
- Fix router.rs dereference for best_score comparison
- Update Dockerfile to copy pre-written SQL file for pgrx
- Simplify init.sql to use correct function names
- Add postgres-cli npm package for CLI tooling

All changes tested successfully in Docker with:
- Extension loads with AVX2 SIMD support (8 floats/op)
- Distance functions verified working
- PostgreSQL 16 container runs successfully

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude <noreply@anthropic.com>

* feat: Add ruvLLM examples and enhanced postgres-cli

Added from claude/ruvector-lfm2-llm-01YS5Tc7i64PyYCLecT9L1dN branch:
- examples/ruvLLM: Complete LLM inference system with SIMD optimization
  - Pretraining, benchmarking, and optimization system
  - Real SIMD-optimized CPU inference engine
  - Comprehensive SOTA benchmark suite
  - Attention mechanisms, memory management, router

Enhanced postgres-cli with full ruvector-postgres integration:
- Sparse vector operations (BM25, top-k, prune, conversions)
- Hyperbolic geometry (Poincare, Lorentz, Mobius operations)
- Agent routing (Tiny Dancer system)
- Vector quantization (binary, scalar, product)
- Enhanced graph and learning commands

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude <noreply@anthropic.com>

* fix(postgres-cli): Use native ruvector type instead of pgvector

- Change createVectorTable to use ruvector type (native RuVector extension)
- Add dimensions column for metadata since ruvector is variable-length
- Update index creation to use simple btree (HNSW/IVFFlat TBD)
- Tested against Docker container with ruvector extension

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude <noreply@anthropic.com>

* feat(postgres): Add 53 SQL function definitions for all advanced modules

Enable all advanced PostgreSQL extension functions by adding their SQL
definitions to the extension file. This exposes all Rust #[pg_extern]
functions to PostgreSQL.

## New SQL Functions (53 total)

### Hyperbolic Geometry (8 functions)
- ruvector_poincare_distance, ruvector_lorentz_distance
- ruvector_mobius_add, ruvector_exp_map, ruvector_log_map
- ruvector_poincare_to_lorentz, ruvector_lorentz_to_poincare
- ruvector_minkowski_dot

### Sparse Vectors (14 functions)
- ruvector_sparse_create, ruvector_sparse_from_dense
- ruvector_sparse_dot, ruvector_sparse_cosine, ruvector_sparse_l2_distance
- ruvector_sparse_add, ruvector_sparse_scale, ruvector_sparse_to_dense
- ruvector_sparse_nnz, ruvector_sparse_dim
- ruvector_bm25_score, ruvector_tf_idf, ruvector_sparse_normalize
- ruvector_sparse_topk

### GNN - Graph Neural Networks (5 functions)
- ruvector_gnn_gcn_layer, ruvector_gnn_graphsage_layer
- ruvector_gnn_gat_layer, ruvector_gnn_message_pass
- ruvector_gnn_aggregate

### Routing/Agents - "Tiny Dancer" (11 functions)
- ruvector_route_query, ruvector_route_with_context
- ruvector_calculate_agent_affinity, ruvector_select_best_agent
- ruvector_multi_agent_route, ruvector_create_agent_embedding
- ruvector_get_routing_stats, ruvector_register_agent
- ruvector_update_agent_performance, ruvector_adaptive_route
- ruvector_fastgrnn_forward

### Learning/ReasoningBank (7 functions)
- ruvector_record_trajectory, ruvector_get_verdict
- ruvector_distill_memory, ruvector_adaptive_search
- ruvector_learning_feedback, ruvector_get_learning_patterns
- ruvector_optimize_search_params

### Graph/Cypher (8 functions)
- ruvector_graph_create_node, ruvector_graph_create_edge
- ruvector_graph_get_neighbors, ruvector_graph_shortest_path
- ruvector_graph_pagerank, ruvector_cypher_query
- ruvector_graph_traverse, ruvector_graph_similarity_search

## CLI Updates
- Enabled hyperbolic geometry commands in postgres-cli
- Added vector distance and normalize commands
- Enhanced client with connection pooling and retry logic

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude <noreply@anthropic.com>

---------

Co-authored-by: Claude <noreply@anthropic.com>
2025-12-02 22:49:29 -05:00

8.9 KiB

Tiny Dancer Routing - Quick Reference

One-Minute Setup

-- Register your first agent
SELECT ruvector_register_agent(
    'gpt-4',                    -- name
    'llm',                      -- type
    ARRAY['coding'],            -- capabilities
    0.03,                       -- cost per request
    500.0,                      -- latency (ms)
    0.95                        -- quality (0-1)
);

-- Route a request
SELECT ruvector_route(
    embedding_vector,           -- your 384-dim embedding
    'balanced',                 -- optimize for: cost|latency|quality|balanced
    NULL                        -- constraints (optional)
);

Common Commands

Register Agents

-- Simple registration
SELECT ruvector_register_agent(name, type, capabilities, cost, latency, quality);

-- Full configuration
SELECT ruvector_register_agent_full('{
    "name": "claude-3",
    "agent_type": "llm",
    "capabilities": ["coding", "writing"],
    "cost_model": {"per_request": 0.025},
    "performance": {"avg_latency_ms": 400, "quality_score": 0.93}
}'::jsonb);

Route Requests

-- Cost-optimized
SELECT ruvector_route(emb, 'cost', NULL);

-- Quality-optimized
SELECT ruvector_route(emb, 'quality', NULL);

-- Latency-optimized
SELECT ruvector_route(emb, 'latency', NULL);

-- Balanced (default)
SELECT ruvector_route(emb, 'balanced', NULL);

Add Constraints

-- Max cost
SELECT ruvector_route(emb, 'quality', '{"max_cost": 0.01}'::jsonb);

-- Max latency
SELECT ruvector_route(emb, 'balanced', '{"max_latency_ms": 500}'::jsonb);

-- Min quality
SELECT ruvector_route(emb, 'cost', '{"min_quality": 0.8}'::jsonb);

-- Required capability
SELECT ruvector_route(emb, 'balanced',
    '{"required_capabilities": ["coding"]}'::jsonb);

-- Multiple constraints
SELECT ruvector_route(emb, 'balanced', '{
    "max_cost": 0.05,
    "max_latency_ms": 1000,
    "min_quality": 0.85,
    "required_capabilities": ["coding", "analysis"],
    "excluded_agents": ["slow-agent"]
}'::jsonb);

Manage Agents

-- List all
SELECT * FROM ruvector_list_agents();

-- Get specific agent
SELECT ruvector_get_agent('gpt-4');

-- Find by capability
SELECT * FROM ruvector_find_agents_by_capability('coding', 5);

-- Update metrics
SELECT ruvector_update_agent_metrics('gpt-4', 450.0, true, 0.92);

-- Deactivate
SELECT ruvector_set_agent_active('gpt-4', false);

-- Remove
SELECT ruvector_remove_agent('old-agent');

-- Statistics
SELECT ruvector_routing_stats();

Response Format

{
  "agent_name": "gpt-4",
  "confidence": 0.87,
  "estimated_cost": 0.03,
  "estimated_latency_ms": 500.0,
  "expected_quality": 0.95,
  "similarity_score": 0.82,
  "reasoning": "Selected gpt-4 for highest quality...",
  "alternatives": [
    {
      "name": "claude-3",
      "score": 0.85,
      "reason": "0.02 lower quality"
    }
  ]
}

Extract Specific Fields

-- Get agent name
SELECT (ruvector_route(emb, 'balanced', NULL))::jsonb->>'agent_name';

-- Get cost
SELECT (ruvector_route(emb, 'cost', NULL))::jsonb->>'estimated_cost';

-- Get full decision
SELECT
    (route)::jsonb->>'agent_name' AS agent,
    ((route)::jsonb->>'confidence')::float AS confidence,
    ((route)::jsonb->>'estimated_cost')::float AS cost
FROM (
    SELECT ruvector_route(emb, 'balanced', NULL) AS route
    FROM requests WHERE id = 1
) r;

Common Patterns

Smart Routing by Priority

SELECT ruvector_route(
    embedding,
    CASE priority
        WHEN 'critical' THEN 'quality'
        WHEN 'low' THEN 'cost'
        ELSE 'balanced'
    END,
    CASE priority
        WHEN 'critical' THEN '{"min_quality": 0.95}'::jsonb
        ELSE NULL
    END
) FROM requests;

Batch Processing

SELECT
    id,
    (ruvector_route(embedding, 'cost', '{"max_cost": 0.01}'::jsonb))::jsonb->>'agent_name' AS agent
FROM requests
WHERE processed = false
LIMIT 1000;

With Capability Filter

SELECT ruvector_route(
    embedding,
    'quality',
    jsonb_build_object(
        'required_capabilities',
        CASE task_type
            WHEN 'coding' THEN ARRAY['coding']
            WHEN 'writing' THEN ARRAY['writing']
            ELSE ARRAY[]::text[]
        END
    )
) FROM requests;

Cost Tracking

-- Daily costs
SELECT
    DATE(completed_at),
    agent_name,
    COUNT(*) AS requests,
    SUM(cost) AS total_cost
FROM request_completions
GROUP BY 1, 2
ORDER BY 1 DESC, total_cost DESC;

Agent Types

  • llm - Language models
  • embedding - Embedding models
  • specialized - Task-specific
  • vision - Vision models
  • audio - Audio models
  • multimodal - Multi-modal
  • custom - User-defined

Optimization Targets

Target Optimizes Use Case
cost Minimize cost High-volume, budget-constrained
latency Minimize response time Real-time applications
quality Maximize quality Critical tasks
balanced Balance all factors General purpose

Constraints Reference

Constraint Type Description
max_cost float Maximum cost per request
max_latency_ms float Maximum latency in ms
min_quality float Minimum quality (0-1)
required_capabilities array Required capabilities
excluded_agents array Agents to exclude

Performance Metrics

Metric Description Updated By
avg_latency_ms Average response time update_agent_metrics
quality_score Quality rating (0-1) update_agent_metrics
success_rate Success ratio (0-1) update_agent_metrics
total_requests Total processed Auto-incremented
p95_latency_ms 95th percentile Auto-calculated
p99_latency_ms 99th percentile Auto-calculated

Troubleshooting

No agents match constraints

-- Check available agents
SELECT * FROM ruvector_list_agents() WHERE is_active = true;

-- Relax constraints
SELECT ruvector_route(emb, 'balanced', '{"max_cost": 1.0}'::jsonb);

Unexpected routing decisions

-- Check reasoning
SELECT (ruvector_route(emb, 'balanced', NULL))::jsonb->>'reasoning';

-- View alternatives
SELECT (ruvector_route(emb, 'balanced', NULL))::jsonb->'alternatives';

Agent not appearing

-- Verify registration
SELECT ruvector_get_agent('agent-name');

-- Check active status
SELECT is_active FROM ruvector_list_agents() WHERE name = 'agent-name';

-- Reactivate
SELECT ruvector_set_agent_active('agent-name', true);

Best Practices

  1. Always set constraints in production

    SELECT ruvector_route(emb, 'balanced', '{"max_cost": 0.1}'::jsonb);
    
  2. Update metrics after each request

    SELECT ruvector_update_agent_metrics(agent, latency, success, quality);
    
  3. Monitor agent health

    SELECT * FROM ruvector_list_agents()
    WHERE success_rate < 0.9 OR avg_latency_ms > 1000;
    
  4. Use capability filters

    SELECT ruvector_route(emb, 'quality',
        '{"required_capabilities": ["coding"]}'::jsonb);
    
  5. Track costs

    SELECT SUM(cost) FROM request_completions
    WHERE completed_at > NOW() - INTERVAL '1 day';
    

Examples by Use Case

High-Volume Processing (Cost-Optimized)

SELECT ruvector_route(emb, 'cost', '{"max_cost": 0.005}'::jsonb);

Real-Time Chat (Latency-Optimized)

SELECT ruvector_route(emb, 'latency', '{"max_latency_ms": 200}'::jsonb);

Critical Analysis (Quality-Optimized)

SELECT ruvector_route(emb, 'quality', '{"min_quality": 0.95}'::jsonb);

Production Workload (Balanced)

SELECT ruvector_route(emb, 'balanced', '{
    "max_cost": 0.05,
    "max_latency_ms": 1000,
    "min_quality": 0.85
}'::jsonb);

Code Generation

SELECT ruvector_route(emb, 'quality',
    '{"required_capabilities": ["coding", "debugging"]}'::jsonb);

Quick Debugging

-- Check if routing is working
SELECT ruvector_routing_stats();

-- List active agents
SELECT name, capabilities FROM ruvector_list_agents() WHERE is_active;

-- Test simple route
SELECT ruvector_route(ARRAY[0.1]::float4[] || ARRAY(SELECT 0::float4 FROM generate_series(1,383)), 'balanced', NULL);

-- View agent details
SELECT jsonb_pretty(ruvector_get_agent('gpt-4'));

-- Clear and restart (testing only)
-- SELECT ruvector_clear_agents();

Integration Example

-- Complete workflow
CREATE TABLE my_requests (
    id SERIAL PRIMARY KEY,
    query TEXT,
    embedding vector(384)
);

-- Route and execute
WITH routing AS (
    SELECT
        r.id,
        r.query,
        (ruvector_route(
            r.embedding::float4[],
            'balanced',
            '{"max_cost": 0.05}'::jsonb
        ))::jsonb AS decision
    FROM my_requests r
    WHERE id = 1
)
SELECT
    id,
    decision->>'agent_name' AS agent,
    decision->>'reasoning' AS why,
    ((decision->>'confidence')::float * 100)::int AS confidence_pct
FROM routing;