Vapora/crates/vapora-llm-router/tests/cost_optimization_test.rs
Jesús Pérez d14150da75 feat: Phase 5.3 - Multi-Agent Learning Infrastructure
Implement intelligent agent learning from Knowledge Graph execution history
with per-task-type expertise tracking, recency bias, and learning curves.

## Phase 5.3 Implementation

### Learning Infrastructure ( Complete)
- LearningProfileService with per-task-type expertise metrics
- TaskTypeExpertise model tracking success_rate, confidence, learning curves
- Recency bias weighting: recent 7 days weighted 3x higher (exponential decay)
- Confidence scoring prevents overfitting: min(1.0, executions / 20)
- Learning curves computed from daily execution windows

### Agent Scoring Service ( Complete)
- Unified AgentScore combining SwarmCoordinator + learning profiles
- Scoring formula: 0.3*base + 0.5*expertise + 0.2*confidence
- Rank agents by combined score for intelligent assignment
- Support for recency-biased scoring (recent_success_rate)
- Methods: rank_agents, select_best, rank_agents_with_recency

### KG Integration ( Complete)
- KGPersistence::get_executions_for_task_type() - query by agent + task type
- KGPersistence::get_agent_executions() - all executions for agent
- Coordinator::load_learning_profile_from_kg() - core KG→Learning integration
- Coordinator::load_all_learning_profiles() - batch load for multiple agents
- Convert PersistedExecution → ExecutionData for learning calculations

### Agent Assignment Integration ( Complete)
- AgentCoordinator uses learning profiles for task assignment
- extract_task_type() infers task type from title/description
- assign_task() scores candidates using AgentScoringService
- Fallback to load-based selection if no learning data available
- Learning profiles stored in coordinator.learning_profiles RwLock

### Profile Adapter Enhancements ( Complete)
- create_learning_profile() - initialize empty profiles
- add_task_type_expertise() - set task-type expertise
- update_profile_with_learning() - update swarm profiles from learning

## Files Modified

### vapora-knowledge-graph/src/persistence.rs (+30 lines)
- get_executions_for_task_type(agent_id, task_type, limit)
- get_agent_executions(agent_id, limit)

### vapora-agents/src/coordinator.rs (+100 lines)
- load_learning_profile_from_kg() - core KG integration method
- load_all_learning_profiles() - batch loading for agents
- assign_task() already uses learning-based scoring via AgentScoringService

### Existing Complete Implementation
- vapora-knowledge-graph/src/learning.rs - calculation functions
- vapora-agents/src/learning_profile.rs - data structures and expertise
- vapora-agents/src/scoring.rs - unified scoring service
- vapora-agents/src/profile_adapter.rs - adapter methods

## Tests Passing
- learning_profile: 7 tests 
- scoring: 5 tests 
- profile_adapter: 6 tests 
- coordinator: learning-specific tests 

## Data Flow
1. Task arrives → AgentCoordinator::assign_task()
2. Extract task_type from description
3. Query KG for task-type executions (load_learning_profile_from_kg)
4. Calculate expertise with recency bias
5. Score candidates (SwarmCoordinator + learning)
6. Assign to top-scored agent
7. Execution result → KG → Update learning profiles

## Key Design Decisions
 Recency bias: 7-day half-life with 3x weight for recent performance
 Confidence scoring: min(1.0, total_executions / 20) prevents overfitting
 Hierarchical scoring: 30% base load, 50% expertise, 20% confidence
 KG query limit: 100 recent executions per task-type for performance
 Async loading: load_learning_profile_from_kg supports concurrent loads

## Next: Phase 5.4 - Cost Optimization
Ready to implement budget enforcement and cost-aware provider selection.
2026-01-11 13:03:53 +00:00

271 lines
8.3 KiB
Rust

use vapora_llm_router::{CostRanker, ProviderConfig};
fn create_provider_configs() -> Vec<(String, ProviderConfig)> {
vec![
(
"claude".to_string(),
ProviderConfig {
enabled: true,
api_key: None,
url: None,
model: "claude-opus-4-5".to_string(),
max_tokens: 4096,
temperature: 0.7,
cost_per_1m_input: 3.0, // $3 per 1M input
cost_per_1m_output: 15.0, // $15 per 1M output
},
),
(
"gpt4".to_string(),
ProviderConfig {
enabled: true,
api_key: None,
url: None,
model: "gpt-4".to_string(),
max_tokens: 4096,
temperature: 0.7,
cost_per_1m_input: 2.5,
cost_per_1m_output: 10.0,
},
),
(
"gemini".to_string(),
ProviderConfig {
enabled: true,
api_key: None,
url: None,
model: "gemini-pro".to_string(),
max_tokens: 4096,
temperature: 0.7,
cost_per_1m_input: 0.30,
cost_per_1m_output: 1.20,
},
),
(
"ollama".to_string(),
ProviderConfig {
enabled: true,
api_key: None,
url: Some("http://localhost:11434".to_string()),
model: "llama2".to_string(),
max_tokens: 4096,
temperature: 0.7,
cost_per_1m_input: 0.0,
cost_per_1m_output: 0.0,
},
),
]
}
#[test]
fn test_cost_estimation_accuracy() {
let config = ProviderConfig {
enabled: true,
api_key: None,
url: None,
model: "test".to_string(),
max_tokens: 4096,
temperature: 0.7,
cost_per_1m_input: 1.0, // $1 per 1M input
cost_per_1m_output: 2.0, // $2 per 1M output
};
// 1000 input + 500 output tokens
let cost = CostRanker::estimate_cost(&config, 1000, 500);
// (1000 * 1 / 1M) * 100 + (500 * 2 / 1M) * 100 = 0.1 + 0.1 = 0.2 cents ≈ 0
assert!(cost <= 1); // Small rounding acceptable
}
#[test]
fn test_efficiency_ranking_prioritizes_value() {
let configs = create_provider_configs();
let ranked = CostRanker::rank_by_efficiency(configs, "coding", 10000, 2000);
assert_eq!(ranked.len(), 4);
// Ollama should rank first (free + decent quality)
assert_eq!(ranked[0].provider, "ollama");
// Claude should rank last (most expensive)
assert_eq!(ranked[ranked.len() - 1].provider, "claude");
// Efficiency should be descending
for i in 1..ranked.len() {
assert!(
ranked[i - 1].cost_efficiency >= ranked[i].cost_efficiency,
"Efficiency should be descending"
);
}
}
#[test]
fn test_cost_ranking_cheapest_first() {
let configs = create_provider_configs();
let ranked = CostRanker::rank_by_cost(configs, 10000, 2000);
assert_eq!(ranked.len(), 4);
// Ollama (free) should be first
assert_eq!(ranked[0].provider, "ollama");
assert_eq!(ranked[0].estimated_cost_cents, 0);
// Costs should be ascending
for i in 1..ranked.len() {
assert!(
ranked[i - 1].estimated_cost_cents <= ranked[i].estimated_cost_cents,
"Costs should be ascending"
);
}
}
#[test]
fn test_quality_score_differentiation() {
let claude_quality = CostRanker::get_quality_score("claude", "coding", None);
let gpt4_quality = CostRanker::get_quality_score("gpt4", "coding", None);
let gemini_quality = CostRanker::get_quality_score("gemini", "coding", None);
let ollama_quality = CostRanker::get_quality_score("ollama", "coding", None);
// Quality should reflect realistic differences
assert!(claude_quality > gpt4_quality);
assert!(gpt4_quality > gemini_quality);
assert!(gemini_quality > ollama_quality);
}
#[test]
fn test_cost_benefit_ratio_ordering() {
let configs = create_provider_configs();
let ratios = CostRanker::cost_benefit_ratio(configs, "coding", 5000, 1000);
assert_eq!(ratios.len(), 4);
// First item should have best efficiency
let best = &ratios[0];
let worst = &ratios[ratios.len() - 1];
assert!(best.2 >= worst.2, "First should have better efficiency than last");
}
#[test]
fn test_cost_calculation_with_large_tokens() {
let configs = create_provider_configs();
let ranked = CostRanker::rank_by_cost(configs, 1_000_000, 100_000);
// For claude: (1M * $3) + (100k * $15/1M) = $3 + $1.50 = $4.50 = 450 cents
let claude_cost = ranked
.iter()
.find(|s| s.provider == "claude")
.unwrap()
.estimated_cost_cents;
assert!(claude_cost > 400); // Approximately $4.50
// For ollama: $0
let ollama_cost = ranked
.iter()
.find(|s| s.provider == "ollama")
.unwrap()
.estimated_cost_cents;
assert_eq!(ollama_cost, 0);
}
#[test]
fn test_efficiency_with_fallback_strategy() {
let configs = create_provider_configs();
// High-quality task (e.g., architecture) - use best
let premium = CostRanker::rank_by_efficiency(configs.clone(), "architecture", 5000, 2000);
// Top provider should have reasonable quality score
assert!(premium[0].quality_score >= 0.75);
// Low-cost task (e.g., simple formatting) - use cheap
let budget = CostRanker::rank_by_cost(configs.clone(), 1000, 500);
// Ollama should be in the zero-cost group (first position or tied for first)
let ollama_index = budget.iter().position(|s| s.provider == "ollama").unwrap();
assert!(ollama_index == 0 || budget[0].estimated_cost_cents == budget[ollama_index].estimated_cost_cents);
}
#[test]
fn test_empty_provider_list() {
let ranked = CostRanker::rank_by_efficiency(Vec::new(), "coding", 5000, 1000);
assert_eq!(ranked.len(), 0);
let ranked_cost = CostRanker::rank_by_cost(Vec::new(), 5000, 1000);
assert_eq!(ranked_cost.len(), 0);
}
#[test]
fn test_single_provider() {
let single = vec![(
"ollama".to_string(),
ProviderConfig {
enabled: true,
api_key: None,
url: Some("http://localhost:11434".to_string()),
model: "llama2".to_string(),
max_tokens: 4096,
temperature: 0.7,
cost_per_1m_input: 0.0,
cost_per_1m_output: 0.0,
},
)];
let ranked = CostRanker::rank_by_efficiency(single.clone(), "coding", 1000, 500);
assert_eq!(ranked.len(), 1);
assert_eq!(ranked[0].provider, "ollama");
let ranked_cost = CostRanker::rank_by_cost(single, 1000, 500);
assert_eq!(ranked_cost.len(), 1);
}
#[test]
fn test_zero_token_cost() {
let config = ProviderConfig {
enabled: true,
api_key: None,
url: None,
model: "test".to_string(),
max_tokens: 4096,
temperature: 0.7,
cost_per_1m_input: 1.0,
cost_per_1m_output: 2.0,
};
// Zero tokens should cost zero
let cost = CostRanker::estimate_cost(&config, 0, 0);
assert_eq!(cost, 0);
}
#[test]
fn test_efficiency_division_by_zero_protection() {
// Even free providers shouldn't cause division errors
let configs = create_provider_configs();
let ranked = CostRanker::rank_by_efficiency(configs, "coding", 5000, 1000);
// All should have valid efficiency scores
for score in ranked {
assert!(score.cost_efficiency.is_finite());
assert!(score.cost_efficiency >= 0.0);
}
}
#[test]
fn test_cost_accuracy_matches_provider_rates() {
let claude_config = ProviderConfig {
enabled: true,
api_key: None,
url: None,
model: "claude-opus-4-5".to_string(),
max_tokens: 4096,
temperature: 0.7,
cost_per_1m_input: 3.0,
cost_per_1m_output: 15.0,
};
// 1M input tokens = $3.00 = 300 cents
let cost_1m_input = CostRanker::estimate_cost(&claude_config, 1_000_000, 0);
assert_eq!(cost_1m_input, 300);
// 1M output tokens = $15.00 = 1500 cents
let cost_1m_output = CostRanker::estimate_cost(&claude_config, 0, 1_000_000);
assert_eq!(cost_1m_output, 1500);
// Combined
let cost_combined = CostRanker::estimate_cost(&claude_config, 1_000_000, 1_000_000);
assert_eq!(cost_combined, 1800);
}