Next.js 15 app that turns photos, PDFs, DOCX/PPTX and text into a swipeable feed of AI-generated activities (flashcards, multiple choice, true/false, fill-in-the-blank, matching, ordering, short answer) using the user's own Ollama models. Highlights: - All AI calls run in server route handlers; the browser never talks to Ollama. - Bring-your-own-key mode (default): the key lives only in the browser and is sent per-request; server, local-daemon and shared-key modes also supported. - Client-side document extraction (pdf.js, mammoth, jszip) with scanned-PDF page rendering for the vision model; notes stay in their original language. - ChatGPT-style shell: collapsible sidebar / mobile drawer, neutral token-based light + dark themes, folder organisation, PWA (manifest, SW, offline page). - Strict JSON validation with zod + auto-repair, rate limiting, and request guards (cross-origin, body-size, API-key sanitising). - setup.sh bootstraps deps, .env.local and a production build. Gitignore excludes .env*, the generated pdf.js worker and local scratch files.
71 lines
2.5 KiB
TypeScript
71 lines
2.5 KiB
TypeScript
import { z } from "zod";
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import { gradeShortAnswer } from "@/lib/ai";
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import { AUTH_ERROR_MESSAGES, authErrorKind, getServerConfig, resolveAuth } from "@/lib/config";
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import { friendlyErrorMessage, OllamaError } from "@/lib/ollama";
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import { rejectCrossOrigin, rejectOversizedBody } from "@/lib/http-guard";
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import { clientKey, rateLimit, rateLimitResponse } from "@/lib/rate-limit";
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export const runtime = "nodejs";
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export const dynamic = "force-dynamic";
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const requestSchema = z.object({
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question: z.string().min(1).max(1000),
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referenceAnswer: z.string().min(1).max(2000),
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keyPoints: z.array(z.string().max(300)).max(8).default([]),
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userAnswer: z.string().min(1).max(2000),
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/** Language of the deck so feedback comes back in the same language. */
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language: z.string().max(60).optional(),
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});
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/** Generous but finite: ~16 compressed images plus JSON overhead. */
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const MAX_BODY_BYTES = 65536;
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export async function POST(request: 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(`grade:${clientKey(request)}`, config.rateLimits.grade, config.rateLimits.windowMs);
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if (!limit.allowed) return rateLimitResponse(limit);
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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: AUTH_ERROR_MESSAGES[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: "Request body was not valid JSON." }, { 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({ error: "Invalid grading request." }, { status: 400 });
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}
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try {
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const result = await gradeShortAnswer({
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model: config.textModel,
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auth: auth.auth,
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...parsed.data,
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// Grading is quick; don't let a hung request block the feed for minutes.
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signal: AbortSignal.timeout(Math.min(config.timeoutMs, 120_000)),
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});
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return Response.json(result);
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} catch (error) {
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console.error("[api/grade]", error instanceof OllamaError ? `${error.kind}: ${error.message}` : error);
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return Response.json(
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{ error: friendlyErrorMessage(error, auth.auth.baseUrl), kind: error instanceof OllamaError ? error.kind : "unknown" },
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{ status: 502 },
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);
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}
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}
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