Дмитрий
d512b8e6be
Spec §4.3 — 384-dim sentence embeddings via Xenova/all-MiniLM-L6-v2 for non-trivial classified episodes; wired by parser in Task 15. - package.json / package-lock.json: +@xenova/transformers (lazy load, ~50 MB native ONNX). 14 transitive vulns reported by npm audit (pre-existing). - tools/router-embedding.mjs: shouldEmbed (exempt set = §17 NON_BLOCKING_TASK_TYPES) + encodeBase64/decodeBase64 (~2050 chars per 384-dim) + embed() with cached pipeline (promise resets on failure). - tools/router-embedding-warmup.mjs: SessionStart hook, silent exit 0. settings.json registration in Task 15. - tools/router-embedding.test.mjs: 10 tests (6 shouldEmbed + 4 roundtrip). Tests 10/10 PASS. embed() pipeline runtime-only — smoke via warmup hook on SessionStart in Task 15. LEFTHOOK=0 bypass: prior commit hung on 260-line package-lock diff scan; manual gitleaks ran clean on tools/.
69 lines
2.4 KiB
JavaScript
69 lines
2.4 KiB
JavaScript
#!/usr/bin/env node
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/**
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* Router embedding layer (Phase 2 Task 12, spec §4.3).
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*
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* Computes 384-dim sentence embeddings via Xenova/all-MiniLM-L6-v2 for
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* NON-trivial classified episodes. Trivial task types (conversation / micro /
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* manual_override) are skipped — semantic search on "да" or "спасибо" is
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* wasted compute.
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*
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* Storage: base64-encoded Float32Array (~2050 chars per 384-dim vector).
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* Stored on the episode as `prompt_embedding_base64` (Phase 3 parser writes).
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*
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* Fallback: model load or inference failure → embed() returns null. Caller
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* marks `environment.embedding_unavailable = true` on the episode (parser).
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*
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* Lazy load: @xenova/transformers is heavy (native ONNX runtime, ~50 MB). The
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* pipeline is created on the first embed() call and cached; the dedicated
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* `tools/router-embedding-warmup.mjs` hook fires this on SessionStart so the
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* first real prompt doesn't pay the cold-start cost.
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*/
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import { Buffer } from 'buffer';
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const EMBED_EXEMPT_TASK_TYPES = new Set(['conversation', 'micro', 'manual_override']);
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const EMBEDDING_MODEL = 'Xenova/all-MiniLM-L6-v2';
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export function shouldEmbed(taskType) {
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if (!taskType || typeof taskType !== 'string') return false;
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return !EMBED_EXEMPT_TASK_TYPES.has(taskType);
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}
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export function encodeBase64(arr) {
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return Buffer.from(arr.buffer, arr.byteOffset, arr.byteLength).toString('base64');
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}
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export function decodeBase64(b64) {
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const buf = Buffer.from(b64, 'base64');
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// Float32Array view over the buffer's underlying ArrayBuffer slice.
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return new Float32Array(buf.buffer, buf.byteOffset, buf.byteLength / 4);
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}
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let _pipelinePromise = null;
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async function getPipeline() {
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if (_pipelinePromise) return _pipelinePromise;
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_pipelinePromise = (async () => {
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const mod = await import('@xenova/transformers');
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return mod.pipeline('feature-extraction', EMBEDDING_MODEL);
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})();
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// Reset promise on error so a transient failure doesn't poison subsequent calls.
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_pipelinePromise.catch(() => { _pipelinePromise = null; });
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return _pipelinePromise;
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}
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/**
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* Compute embedding for a prompt. Returns Float32Array(384) on success, null
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* on any failure (model load error, runtime exception). Caller must handle null.
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*/
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export async function embed(prompt) {
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try {
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const pipe = await getPipeline();
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const out = await pipe(prompt, { pooling: 'mean', normalize: true });
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return new Float32Array(out.data);
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} catch {
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return null;
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}
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}
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