@mushi-mushi/wasm-classifier

@mushi-mushi/wasm-classifier

Your AI wrote it. Mushi tells you why it broke.

On-device spam filter — zero server cost before a report sends.

On-device pre-classification for Mushi Mushi reports — filter obvious junk before the report ever leaves the browser.

This is the V5.3 §2.12 lead-spec implementation of the WASM On-Device Pre-Classification layer. It runs in two modes:

1. Heuristic mode (default, zero deps, ~1 KB gz) — pattern-based score over the description, attached signals, and proactive triggers. Always available.
2. ONNX mode — lazy-loads onnxruntime-web (peer dep) and a quantized small language model (typically Phi-3-mini int4) hosted on a CDN. Falls back to heuristic mode automatically if the runtime, model, or WebGPU/WASM backend is unavailable.

Why

The V5 architecture ships every report to the server for two-stage LLM classification. That works, but:

• Cost — even at $0.002/report, a busy app with 100K reports/month is $200/month of LLM spend on reports the server is going to dismiss anyway ("hi", "test", "asdf", a single emoji).
• Privacy — reports flagged as obvious junk shouldn't be transmitted. Filtering them on-device keeps them out of audit logs and out of the LLM provider's hands.
• Latency — a sub-50 ms verdict in the widget lets us tell the user "we need a bit more detail" before they hit submit, which is a much better UX than a silent 200-ms server roundtrip.

The wasm-classifier sits between the widget's Submit button and the API client. If it returns block, the widget asks the user to elaborate. If it returns pass, the report is sent. If it returns unsure, the report is sent and the server LLM does the work.

Install

npm install @mushi-mushi/wasm-classifier
# Optional, only if you want the ONNX backend:
npm install onnxruntime-web

Usage — heuristic mode (zero deps)

import { createHeuristicClassifier } from '@mushi-mushi/wasm-classifier';

const classifier = createHeuristicClassifier();

const result = await classifier.classify({
  description: 'When I click checkout the page crashes with a 500',
  hasNetworkErrors: true,
});

if (result.verdict === 'block') {
  // Tell the user to elaborate — do NOT submit.
} else {
  await api.submitReport(report);
}

Usage — ONNX mode

import { createOnnxClassifier } from '@mushi-mushi/wasm-classifier';

const classifier = await createOnnxClassifier({
  modelUrl: 'https://cdn.your-app.com/mushi/phi-3-mini-int4.onnx',
  cacheKey: 'phi-3-mini-int4-v1',
  preload: true,
  classifyTimeoutMs: 750,
});

await classifier.ready;

const result = await classifier.classify({
  description: 'something feels off but I can\'t put my finger on it',
});

If onnxruntime-web is not installed, if the model fetch fails, or if a classify() call exceeds classifyTimeoutMs, the classifier transparently falls back to the heuristic backend so the widget never breaks.

Wiring into the widget

The browser SDK (@mushi-mushi/web) accepts a classifier in config.preFilter.wasmClassifier. When set, it is consulted before the existing pattern-based pre-filter:

import { Mushi } from '@mushi-mushi/web';
import { createHeuristicClassifier } from '@mushi-mushi/wasm-classifier';

Mushi.init({
  projectId: 'proj_…',
  apiKey: '…',
  preFilter: {
    wasmClassifier: createHeuristicClassifier(),
  },
});

Hosting the ONNX model

This package intentionally does not bundle the model file. Recommended workflow:

1. Train a small classification head on top of Phi-3-mini-4k-instruct-onnx using your own labelled report data (the LLM-as-Judge corpus from V5 §2.7 is a great starting set).
2. Quantize to int4 with onnxruntime quantization tools. Target file size: ≤ 100 MB so it caches in the browser CacheStorage.
3. Host on a CDN with long Cache-Control (e.g. public, max-age=31536000, immutable) and a versioned URL.
4. Pass the URL as modelUrl. Set cacheKey so re-visits skip re-downloading.

Until that custom head is in place, the ONNX backend delegates to the heuristic backend and reports modelId: 'phi-3-mini-onnx-int4' with the heuristic reason annotated.

Verdict semantics

Verdict	Meaning	Widget action
pass	High-confidence actionable bug	Submit the report.
block	High-confidence junk	Refuse submission, ask for more detail.
unsure	Ambiguous	Submit anyway — the server LLM is the source of truth.

The thresholds default to blockThreshold = 0.20 and passThreshold = 0.55. Both are tunable per project.

Privacy & telemetry

This package does not transmit any data. It executes entirely inside the browser and returns a result object to the caller. The caller (typically @mushi-mushi/web) decides what to do with it.

License

MIT.

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