bvandeusen
1463794778
Surfaces earned auto-apply + its observability in Settings → Tagging → Concept heads: - Auto-apply section: an on/off switch (writes head_auto_apply_enabled), the precision-target + min-examples-to-fire tuning inputs, a Preview (dry-run → "would apply N", per-concept chips) and Apply-now button, with live run state. - "How auto-apply is landing": per-concept table from /api/heads/metrics — applied volume, misfires, realized misfire rate (green/amber/red), and missed (under-fires) — the signal to tune the precision target from. store: autoApply(dryRun) / autoApplyStatus() / metrics(). Card polls the sweep to completion, then refreshes counts + metrics. Completes the auto-apply task. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01Ttrj5P7upUTueSfoJcxEqa
42 lines
1.6 KiB
JavaScript
42 lines
1.6 KiB
JavaScript
import { defineStore } from 'pinia'
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import { useApi } from '../composables/useApi.js'
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// Heads (#114): the per-concept classifiers that LEARN from your tags and power
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// suggestions (replacing Camie + centroid). Training runs as a background task;
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// the card rehydrates status from GET /api/heads on mount so it survives
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// navigation (the run lives in head_training_run server-side).
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export const useHeadsStore = defineStore('heads', () => {
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const api = useApi()
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// Summary: head_count, graduated_count, last_trained_at, running_id, the
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// per-concept head table, and recent training runs.
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async function status() {
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return await api.get('/api/heads')
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}
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// (Re)train all eligible heads. One run at a time (409 if already running).
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async function train(params = {}) {
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return await api.post('/api/heads/train', { body: { params } })
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}
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// Earned auto-apply: trigger a sweep. dry_run previews (writes nothing);
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// a real sweep needs head_auto_apply_enabled on (else 400).
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async function autoApply(dryRun = false) {
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return await api.post('/api/heads/auto-apply', { body: { dry_run: dryRun } })
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}
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// Recent sweeps + per-concept report (volume / projected per head).
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async function autoApplyStatus() {
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return await api.get('/api/heads/auto-apply')
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}
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// Observability: per-concept counts (volume, misfires, under-fires, realized
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// misfire rate, head quality) + the daily time-series, to tune from.
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async function metrics() {
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return await api.get('/api/heads/metrics')
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}
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return { status, train, autoApply, autoApplyStatus, metrics }
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})
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