Inline Chinese gloss (offline) and a "say it more naturally" / tone-rewrite,
the two ESL features for the Mandarin-speaking writer.
Gloss: embedded English→Chinese dictionary (gloss.json.gz, 57k common words
built from ECDICT via scripts/build_gloss.py). lexicon gains Gloss()/Result.Gloss
and a lightweight GET /api/gloss/{word}; the right-click WordCard leads with the
中文; GlossTip shows it on a 350ms hover (reuses wordAt, so CJK is never glossed).
Offline + instant, works with the LLM down.
Rewrite: selecting text pops a SelectionBubble (✨更自然 + the tone vocabulary);
picking a style calls POST /api/docs/:id/rewrite (llm.RunRewrite, stateless,
owner-scoped) and shows a RewritePreview (original→rewrite, accept/cancel/retry).
Accept applies it in-editor.
Tests added in lexicon and suggestions. go build/vet/test, tsc, vite all clean;
live smoke vs a fake vLLM verified gloss + rewrite + 400/404/502 paths.
Claude-Session: https://claude.ai/code/session_016Yr6jELuRc7hyzYLccQKZd
Four enhancements to make the editor fit real school usage:
- Per-document tone (academic/professional/casual/humorous/creative/
persuasive/general): new documents.tone column (migration 0002), threaded
through the docs API, a bilingual ToneSelect dropdown on the title row, and
injected into the grammar-checkpoint LLM prompt so advice fits the register.
The voice pass stays tone-agnostic.
- Right-click word lookup: a new offline `lexicon` package serves definitions
(Wordset, modern ESL-friendly glosses) and synonyms (WordNet synsets first,
then frequency+stopword-ranked Moby for breadth) from gzipped embedded data,
behind /api/word/{word} with light morphology. The WordCard popover shows the
definition and tappable synonym pills that swap the word in place.
- Expanded writing stats: clicking the word count opens a StatsPanel with page
count, sentences, paragraphs, reading time, average word length, word variety,
and Flesch-Kincaid reading level — all computed client-side.
Claude-Session: https://claude.ai/code/session_016Yr6jELuRc7hyzYLccQKZd
Tier-1 voice-consistency pass: whole-document LLM review surfacing passages
that read tonally out of place (formal/over-polished/paraphrased-too-closely),
as honey-decorated `voice` flags with no correction (awareness-only).
- internal/llm/voice.go: RunVoice sends the whole document (no TruncateDoc),
MaxTokens 2048, 20s per-doc floor (VoiceInterval). Standalone voice prompt
in prompts.go (not bundled with the grammar checkpoint, per spec).
- internal/suggestions: POST /api/docs/:id/voice. replacePending is now
family-scoped (pendingScope) so grammar and voice never clobber each other's
pending flags; both passes return the unified pending set. check/voice share
one runPass helper. TestVoicePassCoexists covers both directions.
- Frontend: api.voiceDoc, useCheckpoint voicing/runVoice, honey "Check my
voice" toolbar pill, breathing honey dot in StatusBar.
Claude-Session: https://claude.ai/code/session_016Yr6jELuRc7hyzYLccQKZd
Conversational follow-up on a suggestion, streamed token-by-token.
Backend (interface-only; handlers never touch a concrete LLM client):
- internal/llm/chat.go: StreamAskPetal with conversational sampling
(max_tokens 512, temp 0.7, rep 1.15, top_p 0.92, stop "\n\n\n"),
reusing AskPetalSystemPrompt + TrimHistory.
- internal/suggestions/chat.go: POST /api/suggestions/:id/chat. One
user-scoped join loads the suggestion + parent content_text;
surroundingParagraph extracts the \n\n-bounded paragraph at from_pos
(whole-doc fallback when unlocated) and injects it server-side.
Streams event: token / event: done SSE frames with JSON-encoded data
so token newlines can't break framing; real http.Flusher per chunk.
LLM-unreachable -> 502 before SSE headers; unknown suggestion -> 404.
Frontend:
- streamSuggestionChat: fetch + ReadableStream SSE parser (not
EventSource, needs POST), abortable.
- AskPetal.tsx: whole conversation in component state (no persistence,
cleared on close), Petal's first bubble pre-seeded with the
explanation, rose/lavender bubbles, CJK font stack on the bubbles
only (Note #17), streaming caret.
- SuggestionCard "Ask Petal" pill pins the card open while chatting
(hover-close suppressed, click-away closes) and widens it to 340px.
Tests: chat_test.go covers streamed-text concat + done event,
server-side context injection on the system message, sampling params,
404, and surroundingParagraph. go build/vet/test clean, tsc clean,
vite build OK. Live SSE smoke-tested against a fake streaming vLLM:
tokens flushed individually through the chi middleware stack, done
terminator, 502 on LLM-down, 404 on unknown suggestion.
Claude-Session: https://claude.ai/code/session_016Yr6jELuRc7hyzYLccQKZd
Backend (internal/llm): backend-agnostic LLMClient interface + factory
with vLLM (OpenAI-compat) and Ollama (native) clients, each Complete +
Stream. prompts.go holds the checkpoint and Ask Petal templates;
checkpoint.go salvages JSON from model output (brace-matched), enforces a
per-doc 30s RateLimiter, and truncates the doc to a latency cap.
internal/suggestions: POST /api/docs/:id/check runs a checkpoint and
replaces the doc's pending suggestions in one tx (accepted/rejected kept
as history); GET /api/docs/:id/suggestions lists pending;
POST /api/suggestions/:id/{accept,dismiss} resolves one. Throttled checks
return the current set rather than erroring.
Frontend: useCheckpoint (4s debounce, loads existing on open, stale-guard
tokens); SuggestionHighlight renders ProseMirror decorations re-anchored
by the `original` string on every doc change (not stored marks), with
precise textblock-offset→PM-position mapping; SuggestionCard shows the
type tag + diff + explanation and applies the replacement in-editor on
accept; breathing rose checkpoint dot in the StatusBar; fade-float +
breathe animations.
Tests: llm parse/rate-limit/truncate; suggestions full flow + rate-limit
over httptest with a stub client. Smoke-tested end-to-end against a fake
vLLM endpoint (anchoring verified) and the LLM-unreachable 502 path.
Claude-Session: https://claude.ai/code/session_016Yr6jELuRc7hyzYLccQKZd
