mirror of
https://github.com/ruvnet/RuVector.git
synced 2026-05-27 00:25:10 +00:00
758fce1a22
Root-level `cargo fmt --all` doesn't recurse into nested workspaces
(crates/rvf/, examples/onnx-embeddings/, examples/data/, …), but
CI's `cargo fmt --all -- --check` was failing on files inside them
(e.g. crates/rvf/rvf-wire/src/hash.rs).
Ran `cargo fmt --all` inside each nested workspace. Mechanical-only
whitespace, no semantic change.
Touched nested workspaces:
crates/rvf/*
examples/onnx-embeddings/*
examples/data/*
examples/mincut/*
examples/exo-ai-2025/*
examples/prime-radiant/*
examples/rvf/*
examples/ultra-low-latency-sim/*
examples/edge/*
examples/vibecast-7sense/*
examples/onnx-embeddings-wasm/*
Combined with previous commit (96d8fdc17), the full workspace tree
should now pass `cargo fmt --all -- --check` in CI.
Co-Authored-By: claude-flow <ruv@ruv.net>
78 lines
2.4 KiB
Rust
78 lines
2.4 KiB
Rust
//! Distributed Learning Example
|
|
//!
|
|
//! Demonstrates distributed Q-learning across multiple agents.
|
|
|
|
use ruvector_edge::prelude::*;
|
|
use ruvector_edge::IntelligenceSync;
|
|
use std::sync::Arc;
|
|
use tokio::sync::RwLock;
|
|
|
|
#[tokio::main]
|
|
async fn main() -> Result<()> {
|
|
tracing_subscriber::fmt().with_env_filter("info").init();
|
|
|
|
println!("Distributed Learning Example");
|
|
println!("============================\n");
|
|
|
|
// Create intelligence sync for aggregated learning
|
|
let sync = Arc::new(RwLock::new(IntelligenceSync::new("swarm-coordinator")));
|
|
|
|
// Simulate multiple learning agents with their own experiences
|
|
let scenarios = vec![
|
|
("learner-001", "edit_code", "coder", 0.9),
|
|
("learner-001", "review_code", "reviewer", 0.85),
|
|
("learner-002", "test_code", "tester", 0.88),
|
|
("learner-002", "debug_error", "debugger", 0.92),
|
|
("learner-003", "deploy_app", "devops", 0.87),
|
|
("learner-003", "edit_code", "coder", 0.95), // Another agent learns edit_code
|
|
];
|
|
|
|
println!("Distributed learning phase:");
|
|
for (agent, state, action, reward) in &scenarios {
|
|
let sync_guard = sync.write().await;
|
|
sync_guard.update_pattern(state, action, *reward);
|
|
println!(
|
|
" {} learned: {} -> {} ({:.2})",
|
|
agent, state, action, reward
|
|
);
|
|
}
|
|
|
|
// Query merged intelligence
|
|
let sync_guard = sync.read().await;
|
|
let states_to_query = vec![
|
|
"edit_code",
|
|
"review_code",
|
|
"test_code",
|
|
"debug_error",
|
|
"deploy_app",
|
|
];
|
|
|
|
println!("\nMerged intelligence queries:");
|
|
for state in states_to_query {
|
|
if let Some((action, confidence)) = sync_guard.get_best_action(
|
|
state,
|
|
&["coder", "reviewer", "tester", "debugger", "devops"]
|
|
.iter()
|
|
.map(|s| s.to_string())
|
|
.collect::<Vec<_>>(),
|
|
) {
|
|
println!(
|
|
" {} -> {} (confidence: {:.1}%)",
|
|
state,
|
|
action,
|
|
confidence * 100.0
|
|
);
|
|
}
|
|
}
|
|
|
|
// Get swarm stats
|
|
let stats = sync_guard.get_swarm_stats();
|
|
println!("\nSwarm statistics:");
|
|
println!(" Total patterns: {}", stats.total_patterns);
|
|
println!(" Total visits: {}", stats.total_visits);
|
|
println!(" Avg confidence: {:.1}%", stats.avg_confidence * 100.0);
|
|
|
|
println!("\nDistributed learning example complete!");
|
|
|
|
Ok(())
|
|
}
|