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* fix(security): RUSTSEC advisories + clippy hardening in RuVector - Replace all bare `partial_cmp().unwrap()` calls on f32/f64 with `.unwrap_or(Ordering::Equal)` to prevent panics on NaN values in sorting/max-by operations across ruvllm, ruvector-dag, prime-radiant, and rvagent-wasm (12 sites in production code). - Add input validation guards to the HTTP search endpoint: reject k=0, k > 10_000, empty vectors, and vectors exceeding 65_536 dimensions, preventing memory exhaustion via unbounded allocations. - Harden LocalFsBackend::execute in rvagent-cli with env_clear() + safe-env allowlist (SEC-005), deadline-based timeout enforcement, and 1 MB output truncation, matching the security posture of LocalShellBackend. - Remove 129 occurrences of the deprecated `unused_unit = "allow"` lint and 3 occurrences of the removed `clippy::match_on_vec_items` lint from Cargo.toml files workspace-wide; both are no-ops in current Rust/Clippy. - All 653+ tests across ruvector-core, ruvector-server, ruvector-dag, rvagent-cli, and prime-radiant pass with zero failures. Note: `bytes` is already at 1.11.1 (>= 1.10.0); `paste` 1.0.15 is a transitive dependency with no semver fix available upstream; `cargo audit` returns clean. Co-Authored-By: claude-flow <ruv@ruv.net> * fix(ci): cargo fmt + restore workspace unused_unit lint allow - Run cargo fmt --all across all 9 files that drifted from rustfmt style (prime-radiant/energy.rs, ruvector-dag/bottleneck.rs+reasoning_bank.rs, ruvector-server/points.rs, ruvllm/pretrain_pipeline.rs+report.rs+registry.rs, rvagent-cli/app.rs, rvagent-wasm/gallery.rs) - Add [workspace.lints.clippy] unused_unit = "allow" to root Cargo.toml; the per-crate entries removed in the security commit were still needed — moving to workspace-level is cleaner and restores -D warnings CI pass Co-Authored-By: claude-flow <ruv@ruv.net> * fix(ci): remove unneeded unit return type in ruvix bench Removes `-> ()` from the Fn bound in run_benchmark_with_kernel (crates/ruvix/benches/src/ruvix.rs:50) — triggers clippy::unused_unit under -D warnings. Clippy prefers `Fn(&mut Kernel)` without explicit unit return. Co-Authored-By: claude-flow <ruv@ruv.net> * fix(ci): resolve rustfmt and clippy unused_unit failures - Run cargo fmt --all to fix long closure formatting in 9 files (energy.rs, bottleneck.rs, reasoning_bank.rs, points.rs, pretrain_pipeline.rs, report.rs, registry.rs, app.rs, gallery.rs) - Add unused_unit = "allow" to [lints.clippy] in ruvix-bench and ruvector-mincut Cargo.toml files to suppress the unused_unit lint that was previously suppressed globally and now fires on two Fn(&mut T) -> () and FnMut() -> () function bounds Co-Authored-By: claude-flow <ruv@ruv.net> |
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Ruvector Tiny Dancer WASM
WebAssembly bindings for Tiny Dancer neural routing.
ruvector-tiny-dancer-wasm brings production-grade AI agent routing to the browser with WebAssembly. Run FastGRNN neural inference for intelligent request routing directly in client-side applications. Part of the Ruvector ecosystem.
Why Tiny Dancer WASM?
- Browser Native: Run neural routing in any browser
- Low Latency: Sub-millisecond inference times
- Small Bundle: Optimized WASM binary (~100KB gzipped)
- Offline Capable: No server required for inference
- Privacy First: Route decisions stay client-side
Features
Core Capabilities
- Neural Inference: FastGRNN model execution
- Feature Engineering: Request feature extraction
- Multi-Agent Routing: Score and rank agent candidates
- Model Loading: Load pre-trained models
- Batch Inference: Process multiple requests
Advanced Features
- Web Workers: Background inference threads
- Streaming: Process streaming requests
- Model Caching: IndexedDB model persistence
- Quantization: INT8 models for smaller size
- SIMD: Hardware acceleration when available
Installation
npm install @ruvector/tiny-dancer-wasm
# or
yarn add @ruvector/tiny-dancer-wasm
Quick Start
Basic Usage
import init, { TinyDancer, RouteRequest } from '@ruvector/tiny-dancer-wasm';
// Initialize WASM module
await init();
// Create router instance
const router = new TinyDancer();
// Load pre-trained model
await router.loadModel('/models/router-v1.bin');
// Create routing request
const request: RouteRequest = {
query: "What is the weather like today?",
context: {
userId: "user-123",
sessionLength: 5,
previousAgent: "general",
},
agents: ["weather", "general", "calendar", "search"],
};
// Get routing decision
const result = await router.route(request);
console.log(`Route to: ${result.agent} (confidence: ${result.confidence})`);
With Web Workers
import { TinyDancerWorker } from '@ruvector/tiny-dancer-wasm/worker';
// Create worker-based router (non-blocking)
const router = new TinyDancerWorker();
// Initialize in background
await router.init();
await router.loadModel('/models/router-v1.bin');
// Route without blocking main thread
const result = await router.route(request);
Feature Engineering
import { FeatureExtractor } from '@ruvector/tiny-dancer-wasm';
const extractor = new FeatureExtractor();
// Extract features from request
const features = extractor.extract({
query: "Book a flight to Paris",
tokens: 6,
language: "en",
sentiment: 0.7,
entities: ["Paris"],
});
console.log(`Feature vector: ${features.length} dimensions`);
API Reference
TinyDancer Class
class TinyDancer {
constructor();
// Model management
loadModel(url: string): Promise<void>;
loadModelFromBuffer(buffer: Uint8Array): void;
// Routing
route(request: RouteRequest): Promise<RouteResult>;
routeBatch(requests: RouteRequest[]): Promise<RouteResult[]>;
// Scoring
scoreAgents(request: RouteRequest): Promise<AgentScore[]>;
// Info
getModelInfo(): ModelInfo;
isReady(): boolean;
}
Types
interface RouteRequest {
query: string;
context?: Record<string, any>;
agents: string[];
constraints?: RouteConstraints;
}
interface RouteResult {
agent: string;
confidence: number;
scores: Record<string, number>;
latencyMs: number;
}
interface AgentScore {
agent: string;
score: number;
features: number[];
}
interface RouteConstraints {
excludeAgents?: string[];
minConfidence?: number;
timeout?: number;
}
Bundle Optimization
Tree Shaking
// Import only what you need
import { TinyDancer } from '@ruvector/tiny-dancer-wasm/core';
import { FeatureExtractor } from '@ruvector/tiny-dancer-wasm/features';
CDN Usage
<script type="module">
import init, { TinyDancer } from 'https://unpkg.com/@ruvector/tiny-dancer-wasm';
await init();
const router = new TinyDancer();
</script>
Performance
Benchmarks (Chrome 120, M1 Mac)
Operation Latency (p50)
────────────────────────────────
Model load ~50ms
Single inference ~0.5ms
Batch (10) ~2ms
Feature extraction ~0.1ms
Bundle Size
Format Size
────────────────────────
WASM binary ~100KB gzipped
JS glue ~5KB gzipped
Total ~105KB gzipped
Browser Support
| Browser | Version | SIMD |
|---|---|---|
| Chrome | 89+ | ✅ |
| Firefox | 89+ | ✅ |
| Safari | 15+ | ✅ |
| Edge | 89+ | ✅ |
Related Packages
- ruvector-tiny-dancer-core - Core Rust implementation
- ruvector-tiny-dancer-node - Node.js bindings
- ruvector-core - Core vector database
Documentation
- Main README - Complete project overview
- API Documentation - Full API reference
- GitHub Repository - Source code
License
MIT License - see LICENSE for details.