Vapora/crates/vapora-backend/tests/integration_tests.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

141 lines
4.2 KiB
Rust

// Integration tests for VAPORA backend
// These tests verify the complete API functionality
use axum::http::StatusCode;
use axum_test::TestServer;
use chrono::Utc;
use vapora_shared::models::{Agent, AgentRole, AgentStatus, Project, ProjectStatus, Task, TaskPriority, TaskStatus};
/// Helper function to create a test project
fn create_test_project() -> Project {
Project {
id: None,
tenant_id: "test-tenant".to_string(),
title: "Test Project".to_string(),
description: Some("A test project".to_string()),
status: ProjectStatus::Active,
features: vec!["feature1".to_string()],
created_at: Utc::now(),
updated_at: Utc::now(),
}
}
/// Helper function to create a test task
fn create_test_task(project_id: String) -> Task {
Task {
id: None,
tenant_id: "test-tenant".to_string(),
project_id,
title: "Test Task".to_string(),
description: Some("A test task".to_string()),
status: TaskStatus::Todo,
assignee: "unassigned".to_string(),
priority: TaskPriority::Medium,
task_order: 0,
feature: Some("feature1".to_string()),
created_at: Utc::now(),
updated_at: Utc::now(),
}
}
/// Helper function to create a test agent
fn create_test_agent() -> Agent {
Agent {
id: "test-agent-1".to_string(),
role: AgentRole::Developer,
name: "Test Developer Agent".to_string(),
version: "1.0.0".to_string(),
status: AgentStatus::Active,
capabilities: vec!["rust".to_string(), "async".to_string()],
skills: vec!["backend".to_string()],
llm_provider: "claude".to_string(),
llm_model: "claude-sonnet-4".to_string(),
max_concurrent_tasks: 3,
created_at: Utc::now(),
}
}
#[tokio::test]
async fn test_health_endpoint() {
// Note: This test doesn't require a running server
// It's a placeholder for actual integration tests
// Real tests would use TestServer and require SurrealDB to be running
}
#[tokio::test]
async fn test_project_lifecycle() {
// Note: This test requires a running SurrealDB instance
// For now, it's a placeholder demonstrating the test structure
// Real implementation would:
// 1. Create a TestServer with the app
// 2. POST /api/v1/projects - create project
// 3. GET /api/v1/projects/:id - verify creation
// 4. PUT /api/v1/projects/:id - update project
// 5. DELETE /api/v1/projects/:id - delete project
}
#[tokio::test]
async fn test_task_lifecycle() {
// Note: Placeholder test
// Real implementation would test:
// 1. Create task
// 2. List tasks
// 3. Update task status
// 4. Reorder task
// 5. Delete task
}
#[tokio::test]
async fn test_agent_registration() {
// Note: Placeholder test
// Real implementation would test:
// 1. Register agent
// 2. List agents
// 3. Update agent status
// 4. Check agent health
// 5. Deregister agent
}
#[tokio::test]
async fn test_kanban_operations() {
// Note: Placeholder test
// Real implementation would test:
// 1. Create multiple tasks in different columns
// 2. Move task between columns
// 3. Reorder tasks within a column
// 4. Verify task order is maintained
}
#[tokio::test]
async fn test_error_handling() {
// Note: Placeholder test
// Real implementation would test:
// 1. Not found errors (404)
// 2. Invalid input errors (400)
// 3. Unauthorized errors (401)
// 4. Database errors (500)
}
// Note: To run these tests properly, you would need:
// 1. A test SurrealDB instance running
// 2. Test fixtures and cleanup
// 3. TestServer setup from axum_test
//
// Example of a real test structure:
//
// #[tokio::test]
// async fn test_create_project_real() {
// let app = build_test_app().await;
// let server = TestServer::new(app).unwrap();
//
// let project = create_test_project();
// let response = server
// .post("/api/v1/projects")
// .json(&project)
// .await;
//
// assert_eq!(response.status_code(), StatusCode::CREATED);
// let created: Project = response.json();
// assert_eq!(created.title, project.title);
// }