Martin Dvorak
e4a8e0ba01
Answer history and schedules - Every answer is recorded (deck, activity, topic, correct/wrong, time) in a versioned IndexedDB log (freezmo-learning v1, stores answers + srs), mirrored in memory for synchronous React reads and capped at 20k answers (oldest pruned). StudyFeed records as it grades. - Light SM-2 schedule per activity: 1 day, 3 days, then interval x ease; wrong answers return in ten minutes with the ease nudged down. Review, mistakes, stats - Library 'Smart review' card: due count, mistakes count (latest answer wrong — clears itself when answered correctly), today's goal progress. Review sessions freeze their item list at start. - New /review (due across decks, ?mistakes=1 global or ?deck= per deck) built on a shared SessionFeed with the study feed's snap/keyboard mechanics, wake lock and a results slide. - New /stats: day streak, five-week calendar, accuracy per subject and per deck, weakest topics (>=3 answers), daily-goal progress. - Daily goal configurable in Settings (10/20/30/50). Exam mode - New /exam: pick decks, question count, optional timer; no hints (a NoHints context hides hint rows and the self-check escape); results with score, per-deck and per-topic breakdown; wrong answers land in the mistakes set through the normal history. Reminders (conditional) - VAPID-gated: GET/POST/DELETE /api/push and POST /api/push/test with the standard route pattern, RATE_LIMIT_PUSH, zod, auth before body. Subscriptions stored server-side as endpoint + keys only (PUSH_DIR). UI hidden entirely without keys; iOS install note; service worker push/notificationclick handlers; scripts/send-reminders.mjs for a cron/systemd timer; web-push added as the one server dependency. Verified: new phase-2 suite 42/42 (history/SRS shapes from a real study run, mistakes clearing, review session, stats math, exam timer and breakdowns, VAPID-less hidden state plus the configured subscribe/validate/test/delete path against a second instance) plus regression 74/74, mobile 52/52, wizard 80/80, declutter 116/116, animation 8/8, native 35/35, phase1 38/38, i18n 25/25. Key parity 709/709. Lint + build clean. SW freezmo-v26.
78 lines
3.7 KiB
Bash
78 lines
3.7 KiB
Bash
# ---------------------------------------------------------------------------
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# Freezmo - Ollama configuration
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# Copy this file to `.env.local` and adjust if needed.
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# ---------------------------------------------------------------------------
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# WHO PROVIDES THE API KEY -----------------------------------------------------
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# user-key (default) Every browser supplies its own key. The key is typed
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# into the settings dialog, kept in that browser's local
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# storage and sent with each request in the
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# `x-ollama-key` header. The server never stores it.
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# Point OLLAMA_BASE_URL at https://ollama.com.
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# server One key for everyone, read from OLLAMA_API_KEY below
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# (single-user installs). Nothing is sent from the client.
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# local No key at all: a local daemon signed in with
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# `ollama signin` proxies the cloud models.
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OLLAMA_AUTH=user-key
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# Ollama endpoint. https://ollama.com for cloud, http://localhost:11434 for a
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# local daemon. In user-key mode this is the endpoint the browser's key is sent
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# to; in server/local mode it is used directly.
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OLLAMA_BASE_URL=https://ollama.com
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# Only used when OLLAMA_AUTH=server. Never commit a real key.
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OLLAMA_API_KEY=
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# Model used for the vision/transcription step (reads photos of notes).
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# Must be multimodal — gpt-oss and other text-only models cannot be used here.
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# Free-tier friendly: gemma4:31b-cloud, kimi-k3:cloud (paid), mistral-large-3 (paid).
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VISION_MODEL=gemma4:31b-cloud
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# Model used to generate activities, grade short answers and restore the
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# language if a transcription came back translated. gpt-oss:120b is free-tier
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# friendly, very good at strict JSON and the strongest multilingual model in the
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# free plan. NOTE: it is text-only, so it can't be used as VISION_MODEL.
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# Free alternatives: nemotron-3-super:cloud, gpt-oss:20b-cloud.
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# Paid/credit: glm-5.3-flash:cloud, kimi-k3:cloud.
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TEXT_MODEL=gpt-oss:120b-cloud
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# RATE LIMITS (per client IP, sliding window, in-memory) ----------------------
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# Generation decks are expensive, so they are limited tightly by default.
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# RATE_LIMIT_WINDOW_MS=300000
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# RATE_LIMIT_GENERATE=12
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# RATE_LIMIT_GRADE=40
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# RATE_LIMIT_HOMEWORK=12
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# RATE_LIMIT_HEALTH=90
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# Encrypted sync (no model calls, but shouldn't be hammered).
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# RATE_LIMIT_SYNC=60
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# Push subscription changes and test sends.
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# RATE_LIMIT_PUSH=30
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# PUSH REMINDERS (optional) ----------------------------------------------------
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# Web Push reminders only appear in the UI when BOTH VAPID keys are set.
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# Generate them once with: npx web-push generate-vapid-keys
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# VAPID_PUBLIC_KEY=
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# VAPID_PRIVATE_KEY=
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# VAPID_SUBJECT=mailto:you@example.com
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# Where subscriptions are stored (endpoint + keys only, no personal data).
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# PUSH_DIR=.freezmo/push
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# The daily sender is a script you schedule yourself (cron/systemd timer):
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# node scripts/send-reminders.mjs
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# PUSH_REMINDER_TITLE=Time to review
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# PUSH_REMINDER_BODY=Your decks are waiting — a few due cards keep the streak alive.
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# ENCRYPTED SYNC STORAGE -------------------------------------------------------
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# Opt-in sync uploads an opaque AES-GCM blob; the server can't read it and
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# never sees the passphrase or the API key. One small JSON file per sync ID.
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# SYNC_DIR=.freezmo/sync
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# Per-blob cap in bytes (larger uploads are refused with HTTP 413).
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# SYNC_MAX_BYTES=12582912
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# Total cap for the directory in bytes (new blobs refused with HTTP 507).
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# SYNC_TOTAL_BYTES=1073741824
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# Optional tuning ------------------------------------------------------------
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# OLLAMA_TIMEOUT_MS=300000
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# OLLAMA_HEALTH_TIMEOUT_MS=8000
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# Send `think: false` to thinking models for faster JSON output.
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# OLLAMA_THINK=false
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# FREEZMO_DEFAULT_ACTIVITY_COUNT=10
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