114 lines
3.9 KiB
Rust
114 lines
3.9 KiB
Rust
//! # Learning Profile Example
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//!
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//! Demonstrates how to build and update learning profiles from execution
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//! history.
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//!
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//! ## What This Example Shows
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//! - Creating a `LearningProfile` for an agent
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//! - Recording execution data with timestamps
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//! - Computing task type expertise from historical data
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//! - Understanding recency bias weighting
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//! - Confidence scoring based on sample size
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//!
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//! ## Run
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//! ```bash
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//! cargo run --example 02-learning-profile -p vapora-agents
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//! ```
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use chrono::{Duration, Utc};
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use vapora_agents::{ExecutionData, LearningProfile, TaskTypeExpertise};
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fn main() {
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println!("=== Learning Profile Example ===\n");
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// Step 1: Create sample execution history
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let agent_id = "developer-alice".to_string();
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let now = Utc::now();
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// Simulate 30 days of execution history
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let executions: Vec<ExecutionData> = (0..30)
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.map(|i| ExecutionData {
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timestamp: now - Duration::days(i),
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duration_ms: 200 + (i as u64 * 5),
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success: i < 28, // 93% success rate (28/30 successes)
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})
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.collect();
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println!("Agent: {}", agent_id);
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println!("Historical executions: {} days", executions.len());
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println!("Success rate: {:.1}%\n", (28.0 / 30.0) * 100.0);
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// Step 2: Create learning profile
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let mut profile = LearningProfile::new(agent_id);
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println!("Created learning profile\n");
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// Step 3: Compute task type expertise
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let coding_expertise = TaskTypeExpertise::from_executions(executions.clone(), "coding");
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println!("=== Task Type Expertise: Coding ===");
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println!(
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"Success rate: {:.2}%",
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coding_expertise.success_rate * 100.0
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);
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println!("Average duration: {}ms", coding_expertise.avg_duration_ms);
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println!("Total executions: {}", coding_expertise.total_executions);
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println!("Confidence: {:.2}", coding_expertise.confidence);
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println!(
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"Recent success rate: {:.2}%",
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coding_expertise.recent_success_rate * 100.0
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);
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// Step 4: Demonstrate recency bias
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println!("\n=== Recency Bias Impact ===");
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println!("Recent 7 days: weighted 3× higher");
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println!("Days 8-30: weighted 1× normal");
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let recent_success = (6.0 / 7.0) * 100.0; // Last 7 days all successful
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let older_success = (22.0 / 23.0) * 100.0; // Days 8-30
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println!("Recent success rate (7d): {:.1}%", recent_success);
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println!("Older success rate (23d): {:.1}%", older_success);
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println!(
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"Weighted average: {:.1}%\n",
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coding_expertise.success_rate * 100.0
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);
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// Step 5: Add expertise to profile
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profile.set_task_type_expertise("coding".to_string(), coding_expertise.clone());
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// Simulate another task type with lower expertise
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let test_executions: Vec<ExecutionData> = (0..20)
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.map(|i| ExecutionData {
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timestamp: now - Duration::days(i),
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duration_ms: 150 + (i as u64 * 3),
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success: i < 16, // 80% success rate (16/20)
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})
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.collect();
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let testing_expertise = TaskTypeExpertise::from_executions(test_executions, "testing");
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profile.set_task_type_expertise("testing".to_string(), testing_expertise.clone());
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println!("=== Overall Profile ===");
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println!("Agent expertise levels:");
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println!(
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" coding: {:.1}% (high)",
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coding_expertise.success_rate * 100.0
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);
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println!(
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" testing: {:.1}% (medium)",
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testing_expertise.success_rate * 100.0
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);
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// Step 6: Demonstrate confidence scaling
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println!("\n=== Confidence Scaling ===");
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println!("Confidence increases with sample size:");
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let sample_sizes = vec![5, 10, 20, 50, 100];
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for size in sample_sizes {
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let confidence = ((size as f64 - 1.0) / (size as f64 + 9.0)).min(1.0);
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println!(" {} samples → confidence: {:.2}", size, confidence);
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}
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println!("\n✓ Learning profile created and ready for task assignment");
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}
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