freezmo/app/api/generate/route.ts
Martin Dvorak 3ea2cea100 Freezmo: self-hosted notes-to-activities study app
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.
2026-10-06 20:56:55 +00:00

226 lines
7.7 KiB
TypeScript

import { z } from "zod";
import { extractNotes, generateActivities, notesToText, type PipelineStage } from "@/lib/ai";
import { AUTH_ERROR_MESSAGES, authErrorKind, getServerConfig, resolveAuth, resolveModelName } from "@/lib/config";
import { friendlyErrorMessage, OllamaError } from "@/lib/ollama";
import { rejectCrossOrigin, rejectOversizedBody } from "@/lib/http-guard";
import { clientKey, rateLimit, rateLimitResponse } from "@/lib/rate-limit";
import { difficultySchema } from "@/lib/schemas";
import { uid } from "@/lib/utils";
import { xpForActivity } from "@/lib/xp";
export const runtime = "nodejs";
export const dynamic = "force-dynamic";
export const maxDuration = 300;
/** ~8 MB per image after client-side resizing; up to 16 images (photos + PDF pages). */
const MAX_IMAGE_CHARS = 8_000_000;
const requestSchema = z.object({
images: z.array(z.string().min(32).max(MAX_IMAGE_CHARS)).max(16).optional(),
text: z.string().max(120_000).optional(),
difficulty: difficultySchema.default("medium"),
count: z.number().int().min(4).max(18).optional(),
focus: z.string().max(300).optional(),
/** Language the deck must be written in (usually detected by the vision step). */
language: z.string().max(60).optional(),
mode: z.enum(["create", "more"]).default("create"),
existing: z
.object({
title: z.string().max(120),
types: z.array(z.string()).max(30),
count: z.number().int().min(0).max(100),
})
.optional(),
});
type StreamEvent =
| { type: "stage"; stage: PipelineStage; message: string }
| { type: "deck"; deck: unknown; meta: Record<string, unknown> }
| { type: "error"; error: string; kind?: string };
function encoder() {
const enc = new TextEncoder();
return (event: StreamEvent) => enc.encode(`${JSON.stringify(event)}\n`);
}
/** Generous but finite: ~16 compressed images plus JSON overhead. */
const MAX_BODY_BYTES = 33554432;
export async function POST(request: Request) {
const crossOrigin = rejectCrossOrigin(request);
if (crossOrigin) return crossOrigin;
const oversized = rejectOversizedBody(request, MAX_BODY_BYTES);
if (oversized) return oversized;
const config = getServerConfig();
// Cheap requests are rejected before any work happens.
const limit = rateLimit(`generate:${clientKey(request)}`, config.rateLimits.generate, config.rateLimits.windowMs);
if (!limit.allowed) return rateLimitResponse(limit);
const auth = resolveAuth(config, request);
if ("error" in auth) {
return Response.json(
{ error: AUTH_ERROR_MESSAGES[auth.error], kind: authErrorKind(auth.error) },
{ status: 401 },
);
}
let payload: unknown;
try {
payload = await request.json();
} catch {
return Response.json({ error: "Request body was not valid JSON." }, { status: 400 });
}
const parsed = requestSchema.safeParse(payload);
if (!parsed.success) {
return Response.json(
{
error: `Invalid request: ${parsed.error.issues
.slice(0, 3)
.map((issue) => `${issue.path.join(".") || "body"} ${issue.message}`)
.join("; ")}`,
},
{ status: 400 },
);
}
const { images, text, difficulty, count, focus, mode, existing, language } = parsed.data;
const imageList = (images ?? []).map(stripDataUrlPrefix).filter(Boolean);
if (imageList.length === 0 && !text?.trim()) {
return Response.json(
{ error: "Add at least one photo of your notes or paste some text to build a deck." },
{ status: 400 },
);
}
const requestedCount = count ?? config.defaultActivityCount;
const encode = encoder();
const abort = new AbortController();
// A single hard deadline for the whole pipeline (two sequential model calls).
const globalTimer = setTimeout(() => abort.abort(), config.timeoutMs * 2);
const stream = new ReadableStream<Uint8Array>({
async start(controller) {
const send = (event: StreamEvent) => {
try {
controller.enqueue(encode(event));
} catch {
/* client went away */
}
};
try {
let sourceText: string;
let deckTitle: string | undefined;
let subject: string | undefined;
let summary: string | undefined;
let detectedLanguage: string | undefined = language;
let repaired = false;
if (imageList.length > 0) {
send({ type: "stage", stage: "vision", message: "Reading your notes…" });
const extraction = await extractNotes({
model: config.visionModel,
repairModel: config.textModel,
auth: auth.auth,
images: imageList,
text,
signal: abort.signal,
onProgress: (message) => send({ type: "stage", stage: "vision", message }),
});
sourceText = notesToText(extraction.notes);
deckTitle = extraction.notes.title;
subject = extraction.notes.subject;
summary = extraction.notes.summary;
detectedLanguage = extraction.notes.language || language;
repaired = repaired || extraction.repaired || extraction.languageRepaired;
send({
type: "stage",
stage: "generation",
message: "Finding the key ideas…",
});
} else {
sourceText = text!.trim();
send({ type: "stage", stage: "generation", message: "Finding the key ideas…" });
}
const generated = await generateActivities({
model: config.textModel,
auth: auth.auth,
notes: sourceText,
difficulty,
count: requestedCount,
language: detectedLanguage,
existing: mode === "more" ? existing : undefined,
focus,
signal: abort.signal,
});
const activities = generated.deck.activities.map((activity) => ({
...activity,
id: uid("act"),
xp: xpForActivity(activity.type, difficulty),
}));
send({
type: "deck",
deck: {
title: generated.deck.title || deckTitle || "Your study deck",
subject: generated.deck.subject ?? subject,
summary: generated.deck.summary ?? summary,
language: generated.deck.language || detectedLanguage,
sourceText,
difficulty,
activities,
},
meta: {
requested: generated.requested,
generated: activities.length,
repaired,
language: generated.deck.language || detectedLanguage || null,
visionModel: imageList.length ? resolveModelName(config.visionModel, auth.auth.directCloud) : null,
textModel: resolveModelName(config.textModel, auth.auth.directCloud),
},
});
} catch (error) {
const message = friendlyErrorMessage(error, auth.auth.baseUrl);
// Never log the request body: it may contain the user's notes.
console.error("[api/generate]", error instanceof OllamaError ? `${error.kind}: ${error.message}` : error);
send({
type: "error",
error: message,
kind: error instanceof OllamaError ? error.kind : "unknown",
});
} finally {
clearTimeout(globalTimer);
try {
controller.close();
} catch {
/* already closed */
}
}
},
cancel() {
clearTimeout(globalTimer);
abort.abort();
},
});
return new Response(stream, {
headers: {
"Content-Type": "application/x-ndjson; charset=utf-8",
"Cache-Control": "no-store, no-transform",
Connection: "keep-alive",
},
});
}
function stripDataUrlPrefix(value: string): string {
const commaIndex = value.indexOf(",");
if (value.startsWith("data:") && commaIndex !== -1) return value.slice(commaIndex + 1);
return value;
}