Entry points (no sixth tab): Ask in the Library header (desktop button, phone icon), Ask about this deck in the deck menu, Ask a question on study/homework results (compact result travels as context), and a Chat row in the desktop sidebar. Phones get a full-screen route with back + New chat; the tab bar steps aside. - Client-side retrieval: chunk scoped decks (~800 tokens), BM25 against the question + recent turns, top chunks under ~7k tokens, each labelled with deck + section. Notes come from the deck's stored sourceText (already kept for Regenerate); decks without it fall back to generated content and the UI says so. - Stateless server: /api/chat validates against the CHAT_MODELS allowlist, trims the history to ~6k tokens, caps context at ~9k and streams token/thinking/done/error NDJSON. /api/chat/extract reduces attachments to text through the existing vision step. - UI: user bubbles + full-width assistant text, streaming with stop, auto-scroll that pauses with a jump-to-bottom button, safe markdown (React nodes, no HTML injection; KaTeX dynamically imported only when a message has math), Thinking row for reasoning, copy/regenerate/ edit-and-resend, model + context pickers (last model remembered), composer with visualViewport keyboard handling, safe-area, 16px textarea, Enter-to-send on desktop. - Usage-limit failures keep the draft and show the shared message with Settings/Ollama actions; stopping maps the reader abort to a clean stopped state; offline disables sending with a reason. - Settings: Keep notes text for chat toggle; deck menu: Delete notes text. RATE_LIMIT_CHAT + CHAT_MODELS in .env.example and README. - Tests: chat suite (63 checks, stub Ollama) wired into run-all. Full set: 12/12 suites, 487 checks. SW -> freezmo-v32. Also: tests/model-compare.mjs (diagnostic) benchmarks two models on the same notes (latency, repair rounds, activity types) so the gpt-oss vs gemma default can be decided on numbers; defaults unchanged.
107 lines
3.7 KiB
TypeScript
107 lines
3.7 KiB
TypeScript
import { z } from "zod";
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import { extractNotes, notesToText } from "@/lib/ai";
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import { authErrorMessage, authErrorKind, getServerConfig, resolveAuth } from "@/lib/config";
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import { friendlyErrorMessage, OllamaError } from "@/lib/ollama";
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import { rejectCrossOrigin, rejectOversizedBody, requestLocale } from "@/lib/http-guard";
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import { clientKey, rateLimit, rateLimitResponse } from "@/lib/rate-limit";
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import { translate } from "@/lib/i18n-dict";
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export const runtime = "nodejs";
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export const dynamic = "force-dynamic";
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export const maxDuration = 300;
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/** Same ceiling as deck generation: up to 16 resized photos. */
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const MAX_BODY_BYTES = 33554432;
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const MAX_IMAGE_CHARS = 8_000_000;
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const requestSchema = z.object({
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images: z.array(z.string().min(32).max(MAX_IMAGE_CHARS)).max(16).optional(),
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text: z.string().max(120_000).optional(),
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});
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/**
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* Turns chat attachments (photos, scanned pages, pasted document text) into
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* plain text with the existing vision pipeline, so any chat model — including
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* text-only ones — can use them. Never stores anything.
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*/
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export async function POST(request: Request) {
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const locale = requestLocale(request);
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const crossOrigin = rejectCrossOrigin(request);
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if (crossOrigin) return crossOrigin;
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const oversized = rejectOversizedBody(request, MAX_BODY_BYTES);
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if (oversized) return oversized;
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const config = getServerConfig();
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const limit = rateLimit(`chat:${clientKey(request)}`, config.rateLimits.chat, config.rateLimits.windowMs);
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if (!limit.allowed) return rateLimitResponse(limit, locale);
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const auth = resolveAuth(config, request);
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if ("error" in auth) {
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return Response.json(
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{ error: authErrorMessage(locale, auth.error), kind: authErrorKind(auth.error) },
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{ status: 401 },
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);
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}
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let payload: unknown;
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try {
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payload = await request.json();
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} catch {
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return Response.json({ error: translate(locale, "server.bodyNotJson") }, { status: 400 });
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}
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const parsed = requestSchema.safeParse(payload);
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if (!parsed.success) {
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return Response.json(
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{ error: translate(locale, "server.invalidRequest", { detail: parsed.error.issues[0]?.message ?? "" }) },
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{ status: 400 },
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);
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}
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const images = (parsed.data.images ?? []).map(stripDataUrlPrefix).filter(Boolean);
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const text = parsed.data.text?.trim() ?? "";
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if (images.length === 0 && !text) {
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return Response.json({ error: translate(locale, "server.needContent") }, { status: 400 });
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}
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const abort = new AbortController();
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const timer = setTimeout(() => abort.abort(), config.timeoutMs);
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try {
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if (images.length === 0) {
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// Plain text needs no model call at all.
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return Response.json({ text: text.slice(0, 48_000) });
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}
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const extraction = await extractNotes({
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model: config.visionModel,
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repairModel: config.textModel,
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auth: auth.auth,
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images,
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text: text || undefined,
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signal: abort.signal,
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});
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const extracted = notesToText(extraction.notes).slice(0, 48_000);
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if (!extracted.trim()) {
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return Response.json({ error: translate(locale, "chat.attachEmpty"), kind: "bad_response" }, { status: 422 });
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}
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return Response.json({ text: extracted });
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} catch (error) {
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console.error("[api/chat/extract]", error instanceof OllamaError ? `${error.kind}: ${error.message}` : error);
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return Response.json(
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{
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error: friendlyErrorMessage(error, auth.auth.baseUrl, locale),
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kind: error instanceof OllamaError ? error.kind : "unknown",
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},
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{ status: 502 },
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);
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} finally {
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clearTimeout(timer);
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}
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}
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function stripDataUrlPrefix(value: string): string {
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const commaIndex = value.indexOf(",");
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if (value.startsWith("data:") && commaIndex !== -1) return value.slice(commaIndex + 1);
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return value;
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}
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