Squashed 'vendor/ruvector/' content from commit b64c2172
git-subtree-dir: vendor/ruvector git-subtree-split: b64c21726f2bb37286d9ee36a7869fef60cc6900
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ruv
495
crates/ruvector-postgres/sql/routing_example.sql
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495
crates/ruvector-postgres/sql/routing_example.sql
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-- Tiny Dancer Routing Module - SQL Examples
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--
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-- Complete examples for agent registration, routing, and monitoring
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-- ============================================================================
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-- Setup: Create supporting tables
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-- ============================================================================
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-- Table for storing requests with embeddings
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CREATE TABLE ai_requests (
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id BIGSERIAL PRIMARY KEY,
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query_text TEXT NOT NULL,
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embedding vector(384), -- Request embedding
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task_type TEXT, -- 'coding', 'writing', 'analysis', etc.
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priority TEXT, -- 'low', 'medium', 'high', 'critical'
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created_at TIMESTAMPTZ DEFAULT NOW()
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);
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-- Table for tracking request completions
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CREATE TABLE request_completions (
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id BIGSERIAL PRIMARY KEY,
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request_id BIGINT REFERENCES ai_requests(id),
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agent_name TEXT NOT NULL,
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latency_ms FLOAT NOT NULL,
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cost FLOAT NOT NULL,
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quality_score FLOAT,
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success BOOLEAN DEFAULT true,
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error_message TEXT,
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completed_at TIMESTAMPTZ DEFAULT NOW()
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);
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-- ============================================================================
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-- Agent Registration
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-- ============================================================================
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-- Register OpenAI models
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SELECT ruvector_register_agent(
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'gpt-4',
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'llm',
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ARRAY['coding', 'reasoning', 'math', 'writing', 'analysis'],
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0.03, -- $0.03 per request
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500.0, -- 500ms average latency
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0.95 -- 0.95 quality score
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);
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SELECT ruvector_register_agent(
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'gpt-4-turbo',
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'llm',
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ARRAY['coding', 'reasoning', 'fast', 'multimodal'],
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0.02,
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300.0,
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0.93
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);
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SELECT ruvector_register_agent(
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'gpt-3.5-turbo',
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'llm',
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ARRAY['general', 'fast', 'chat'],
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0.002,
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150.0,
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0.75
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);
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-- Register Anthropic models
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SELECT ruvector_register_agent(
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'claude-3-opus',
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'llm',
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ARRAY['coding', 'reasoning', 'analysis', 'writing'],
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0.025,
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400.0,
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0.93
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);
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SELECT ruvector_register_agent(
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'claude-3-sonnet',
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'llm',
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ARRAY['coding', 'balanced', 'analysis'],
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0.01,
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250.0,
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0.88
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);
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SELECT ruvector_register_agent(
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'claude-3-haiku',
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'llm',
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ARRAY['fast', 'general', 'chat'],
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0.003,
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100.0,
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0.80
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);
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-- Register open-source models
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SELECT ruvector_register_agent(
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'llama-2-70b',
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'llm',
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ARRAY['local', 'private', 'coding', 'general'],
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0.0, -- Free (self-hosted)
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800.0,
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0.72
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);
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SELECT ruvector_register_agent(
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'mixtral-8x7b',
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'llm',
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ARRAY['local', 'private', 'fast', 'coding'],
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0.0,
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600.0,
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0.78
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);
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-- Register specialized models
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SELECT ruvector_register_agent(
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'codellama-34b',
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'specialized',
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ARRAY['coding', 'local', 'specialized'],
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0.0,
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700.0,
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0.82
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);
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SELECT ruvector_register_agent(
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'deepseek-coder',
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'specialized',
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ARRAY['coding', 'specialized', 'fast'],
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0.005,
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200.0,
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0.85
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);
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-- ============================================================================
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-- Basic Routing Examples
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-- ============================================================================
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-- Example 1: Balanced routing (default)
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SELECT ruvector_route(
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(SELECT embedding FROM ai_requests WHERE id = 1),
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'balanced',
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NULL
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) AS routing_decision;
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-- Example 2: Cost-optimized routing
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SELECT ruvector_route(
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(SELECT embedding FROM ai_requests WHERE id = 2),
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'cost',
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NULL
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) AS routing_decision;
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-- Example 3: Quality-optimized routing
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SELECT ruvector_route(
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(SELECT embedding FROM ai_requests WHERE id = 3),
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'quality',
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'{"min_quality": 0.9}'::jsonb
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) AS routing_decision;
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-- Example 4: Latency-optimized routing
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SELECT ruvector_route(
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(SELECT embedding FROM ai_requests WHERE id = 4),
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'latency',
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'{"max_latency_ms": 300.0}'::jsonb
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) AS routing_decision;
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-- ============================================================================
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-- Constraint-Based Routing
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-- ============================================================================
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-- Example 5: Routing with cost constraint
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SELECT
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r.id,
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r.query_text,
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(ruvector_route(
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r.embedding,
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'quality',
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'{"max_cost": 0.01}'::jsonb
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))::jsonb->>'agent_name' AS selected_agent,
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(ruvector_route(
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r.embedding,
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'quality',
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'{"max_cost": 0.01}'::jsonb
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))::jsonb->>'estimated_cost' AS estimated_cost
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FROM ai_requests r
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WHERE r.id = 5;
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-- Example 6: Routing with multiple constraints
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SELECT ruvector_route(
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(SELECT embedding FROM ai_requests WHERE id = 6),
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'balanced',
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'{
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"max_cost": 0.02,
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"max_latency_ms": 500.0,
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"min_quality": 0.85,
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"required_capabilities": ["coding", "analysis"]
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}'::jsonb
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) AS routing_decision;
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-- Example 7: Exclude specific agents
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SELECT ruvector_route(
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(SELECT embedding FROM ai_requests WHERE id = 7),
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'quality',
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'{
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"excluded_agents": ["gpt-3.5-turbo", "llama-2-70b"],
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"min_quality": 0.9
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}'::jsonb
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) AS routing_decision;
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-- ============================================================================
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-- Capability-Based Routing
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-- ============================================================================
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-- Example 8: Route coding tasks
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SELECT
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r.id,
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r.query_text,
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(ruvector_route(
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r.embedding,
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'quality',
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'{"required_capabilities": ["coding"]}'::jsonb
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))::jsonb AS routing
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FROM ai_requests r
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WHERE r.task_type = 'coding'
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LIMIT 10;
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-- Example 9: Route with multiple required capabilities
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SELECT ruvector_route(
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(SELECT embedding FROM ai_requests WHERE task_type = 'complex_analysis' LIMIT 1),
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'balanced',
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'{
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"required_capabilities": ["coding", "reasoning", "analysis"],
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"min_quality": 0.85
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}'::jsonb
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) AS routing_decision;
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-- ============================================================================
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-- Batch Routing
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-- ============================================================================
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-- Example 10: Process batch of requests
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CREATE TEMP TABLE batch_routing_results AS
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SELECT
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r.id,
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r.query_text,
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r.task_type,
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r.priority,
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(ruvector_route(
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r.embedding,
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CASE
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WHEN r.priority = 'critical' THEN 'quality'
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WHEN r.priority = 'high' THEN 'balanced'
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ELSE 'cost'
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END,
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CASE
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WHEN r.priority = 'critical' THEN '{"min_quality": 0.95}'::jsonb
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WHEN r.priority = 'high' THEN '{"min_quality": 0.85, "max_latency_ms": 500.0}'::jsonb
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ELSE '{"max_cost": 0.005}'::jsonb
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END
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))::jsonb AS routing_decision
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FROM ai_requests r
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WHERE created_at > NOW() - INTERVAL '1 hour'
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AND r.id NOT IN (SELECT request_id FROM request_completions);
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-- View batch results
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SELECT
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id,
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task_type,
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priority,
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routing_decision->>'agent_name' AS agent,
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(routing_decision->>'confidence')::float AS confidence,
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(routing_decision->>'estimated_cost')::float AS cost,
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(routing_decision->>'estimated_latency_ms')::float AS latency_ms,
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routing_decision->>'reasoning' AS reasoning
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FROM batch_routing_results
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ORDER BY priority DESC, id;
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-- Calculate batch statistics
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SELECT
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task_type,
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routing_decision->>'agent_name' AS agent,
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COUNT(*) AS requests,
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AVG((routing_decision->>'estimated_cost')::float) AS avg_cost,
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AVG((routing_decision->>'estimated_latency_ms')::float) AS avg_latency,
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AVG((routing_decision->>'confidence')::float) AS avg_confidence
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FROM batch_routing_results
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GROUP BY task_type, routing_decision->>'agent_name'
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ORDER BY requests DESC;
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-- ============================================================================
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-- Performance Tracking
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-- ============================================================================
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-- Example 11: Record request completion
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INSERT INTO request_completions (request_id, agent_name, latency_ms, cost, quality_score, success)
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VALUES (1, 'gpt-4', 450.0, 0.03, 0.92, true);
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-- Update agent metrics after completion
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SELECT ruvector_update_agent_metrics(
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'gpt-4',
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450.0,
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true,
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0.92
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);
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-- Example 12: Track performance over time
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SELECT
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agent_name,
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DATE_TRUNC('hour', completed_at) AS hour,
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COUNT(*) AS requests,
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AVG(latency_ms) AS avg_latency,
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AVG(cost) AS avg_cost,
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AVG(quality_score) AS avg_quality,
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SUM(CASE WHEN success THEN 1 ELSE 0 END)::float / COUNT(*) AS success_rate
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FROM request_completions
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WHERE completed_at > NOW() - INTERVAL '24 hours'
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GROUP BY agent_name, DATE_TRUNC('hour', completed_at)
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ORDER BY hour DESC, requests DESC;
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-- ============================================================================
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-- Agent Management
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-- ============================================================================
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-- Example 13: List all agents with statistics
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SELECT
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name,
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agent_type,
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capabilities,
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cost_per_request,
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avg_latency_ms,
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quality_score,
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success_rate,
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total_requests,
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is_active
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FROM ruvector_list_agents()
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ORDER BY total_requests DESC;
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-- Example 14: Find best agents by capability
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SELECT * FROM ruvector_find_agents_by_capability('coding', 5);
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SELECT * FROM ruvector_find_agents_by_capability('writing', 5);
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SELECT * FROM ruvector_find_agents_by_capability('fast', 5);
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-- Example 15: Get detailed agent information
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SELECT ruvector_get_agent('gpt-4') AS agent_details;
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SELECT ruvector_get_agent('claude-3-opus') AS agent_details;
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-- Example 16: View routing statistics
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SELECT ruvector_routing_stats() AS stats;
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-- ============================================================================
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-- Advanced Routing Patterns
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-- ============================================================================
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-- Example 17: Create smart routing function
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CREATE OR REPLACE FUNCTION smart_route(
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request_embedding vector,
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task_type TEXT,
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priority TEXT DEFAULT 'medium',
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max_budget FLOAT DEFAULT NULL
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) RETURNS jsonb AS $$
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DECLARE
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optimization_target TEXT;
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constraints jsonb;
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BEGIN
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-- Determine optimization strategy
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optimization_target := CASE
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WHEN priority = 'critical' THEN 'quality'
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WHEN priority = 'high' THEN 'balanced'
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WHEN priority = 'low' THEN 'cost'
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ELSE 'balanced'
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END;
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-- Build constraints
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constraints := jsonb_build_object(
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'max_cost', COALESCE(max_budget, 1.0),
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'min_quality', CASE
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WHEN priority = 'critical' THEN 0.95
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WHEN priority = 'high' THEN 0.85
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ELSE 0.70
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END,
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'required_capabilities', CASE
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WHEN task_type = 'coding' THEN ARRAY['coding']
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WHEN task_type = 'writing' THEN ARRAY['writing']
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WHEN task_type = 'analysis' THEN ARRAY['analysis', 'reasoning']
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ELSE ARRAY[]::text[]
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END
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);
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RETURN ruvector_route(
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request_embedding::float4[],
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optimization_target,
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constraints
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);
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END;
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$$ LANGUAGE plpgsql;
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-- Use smart routing
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SELECT smart_route(
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(SELECT embedding FROM ai_requests WHERE id = 100),
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'coding',
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'high',
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0.05
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) AS routing_decision;
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-- Example 18: Cost-aware view with fallback
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CREATE VIEW cost_optimized_routing AS
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SELECT
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r.id,
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r.query_text,
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r.task_type,
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r.priority,
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-- Try cost-optimized first
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COALESCE(
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(SELECT ruvector_route(r.embedding, 'cost', '{"max_cost": 0.01, "min_quality": 0.8}'::jsonb)),
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-- Fallback to balanced if no cheap option
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ruvector_route(r.embedding, 'balanced', '{"max_cost": 0.05}'::jsonb)
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) AS routing_decision
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FROM ai_requests r;
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-- Example 19: A/B testing framework
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CREATE TABLE routing_experiments (
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id BIGSERIAL PRIMARY KEY,
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request_id BIGINT REFERENCES ai_requests(id),
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agent_a TEXT,
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agent_b TEXT,
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selected_agent TEXT,
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a_score FLOAT,
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b_score FLOAT,
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actual_quality FLOAT,
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created_at TIMESTAMPTZ DEFAULT NOW()
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);
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-- Run A/B test
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INSERT INTO routing_experiments (request_id, agent_a, agent_b, selected_agent, a_score, b_score)
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SELECT
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r.id,
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'gpt-4' AS agent_a,
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'claude-3-opus' AS agent_b,
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CASE WHEN random() < 0.5 THEN 'gpt-4' ELSE 'claude-3-opus' END AS selected_agent,
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(ruvector_route(r.embedding, 'quality', '{"excluded_agents": ["claude-3-opus"]}'::jsonb))::jsonb->>'expected_quality' AS a_score,
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(ruvector_route(r.embedding, 'quality', '{"excluded_agents": ["gpt-4"]}'::jsonb))::jsonb->>'expected_quality' AS b_score
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FROM ai_requests r
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WHERE created_at > NOW() - INTERVAL '1 hour'
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LIMIT 100;
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-- ============================================================================
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-- Monitoring and Alerts
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-- ============================================================================
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-- Example 20: Monitor agent health
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CREATE VIEW agent_health AS
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SELECT
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name,
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avg_latency_ms,
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quality_score,
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success_rate,
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total_requests,
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CASE
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WHEN NOT is_active THEN 'inactive'
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WHEN success_rate < 0.90 THEN 'critical'
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WHEN avg_latency_ms > 1000 THEN 'slow'
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WHEN quality_score < 0.75 THEN 'low_quality'
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ELSE 'healthy'
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END AS health_status
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FROM ruvector_list_agents();
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-- Find unhealthy agents
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SELECT * FROM agent_health WHERE health_status != 'healthy';
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-- Example 21: Cost tracking
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CREATE VIEW daily_routing_costs AS
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SELECT
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DATE_TRUNC('day', completed_at) AS day,
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agent_name,
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COUNT(*) AS requests,
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SUM(cost) AS total_cost,
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AVG(cost) AS avg_cost_per_request,
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AVG(quality_score) AS avg_quality
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FROM request_completions
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WHERE completed_at > NOW() - INTERVAL '30 days'
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GROUP BY DATE_TRUNC('day', completed_at), agent_name
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ORDER BY day DESC, total_cost DESC;
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-- ============================================================================
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-- Cleanup
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-- ============================================================================
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-- Example 22: Deactivate underperforming agents
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UPDATE ruvector_list_agents()
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SET is_active = false
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WHERE success_rate < 0.80;
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-- Example 23: Remove inactive agents
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SELECT ruvector_remove_agent(name)
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FROM ruvector_list_agents()
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WHERE NOT is_active
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AND total_requests = 0;
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-- Example 24: Clear all agents (testing only)
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||||
-- SELECT ruvector_clear_agents();
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