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.
193 lines
5.9 KiB
TypeScript
193 lines
5.9 KiB
TypeScript
import { z } from "zod";
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import { authErrorMessage, authErrorKind, getServerConfig, resolveAuth, resolveModelName } from "@/lib/config";
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import { friendlyErrorMessage, ollamaChatStream, 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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import { buildChatSystemPrompt } from "@/lib/prompts";
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import { estimateTokens, trimHistory } from "@/lib/chat-trim";
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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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/** Context chunks (~7k tokens) plus a trimmed window plus JSON overhead. */
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const MAX_BODY_BYTES = 1_000_000;
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/** Hard cap on the attached context, whatever the client sends. */
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const CONTEXT_TOKEN_BUDGET = 9000;
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const requestSchema = z.object({
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model: z.string().min(1).max(120).optional(),
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messages: z
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.array(
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z.object({
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role: z.enum(["user", "assistant"]),
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content: z.string().min(1).max(20_000),
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}),
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)
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.min(1)
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.max(60),
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context: z
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.array(
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z.object({
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deckId: z.string().max(120),
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deckTitle: z.string().max(200),
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section: z.string().max(200),
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text: z.string().min(1).max(12_000),
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}),
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)
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.max(40)
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.optional(),
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});
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type StreamEvent =
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| { type: "token"; text: string }
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| { type: "thinking"; text: string }
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| { type: "done"; model: string }
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| { type: "error"; error: string; kind?: string };
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function encoder() {
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const enc = new TextEncoder();
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return (event: StreamEvent) => enc.encode(`${JSON.stringify(event)}\n`);
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}
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export async function POST(request: Request) {
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const locale = requestLocale(request);
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const crossOrigin = rejectCrossOrigin(request);
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if (crossOrigin) return crossOrigin;
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const oversized = rejectOversizedBody(request, MAX_BODY_BYTES);
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if (oversized) return oversized;
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const config = getServerConfig();
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const limit = rateLimit(`chat:${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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{
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error: translate(locale, "server.invalidRequest", {
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detail: parsed.error.issues
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.slice(0, 3)
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.map((issue) => `${issue.path.join(".") || "body"} ${issue.message}`)
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.join("; "),
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}),
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},
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{ status: 400 },
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);
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}
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const { messages, context } = parsed.data;
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// The model must come from the server's allowlist — never from the client.
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const allowed = config.chatModels.length > 0 ? config.chatModels : [config.textModel];
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const model = parsed.data.model ?? allowed[0];
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if (!allowed.some((entry) => entry.toLowerCase() === model.toLowerCase())) {
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return Response.json(
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{ error: translate(locale, "server.chatModelNotAllowed"), kind: "bad_model" },
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{ status: 400 },
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);
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}
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const last = messages[messages.length - 1];
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if (last.role !== "user") {
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return Response.json({ error: translate(locale, "server.chatNeedsQuestion") }, { status: 400 });
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}
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// Cap the context, then trim the conversation window.
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const keptContext: typeof context = [];
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let contextTokens = 0;
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for (const chunk of context ?? []) {
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const cost = estimateTokens(chunk.text);
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if (contextTokens + cost > CONTEXT_TOKEN_BUDGET) break;
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keptContext.push(chunk);
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contextTokens += cost;
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}
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const history = trimHistory(messages);
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const system = buildChatSystemPrompt({
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context: keptContext.map((chunk) => ({
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deckTitle: chunk.deckTitle,
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section: chunk.section,
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text: chunk.text,
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})),
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});
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const encode = encoder();
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const abort = new AbortController();
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const globalTimer = setTimeout(() => abort.abort(), config.timeoutMs);
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const stream = new ReadableStream<Uint8Array>({
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async start(controller) {
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const send = (event: StreamEvent) => {
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try {
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controller.enqueue(encode(event));
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} catch {
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/* client went away */
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}
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};
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try {
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const result = await ollamaChatStream({
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model,
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auth: auth.auth,
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messages: [{ role: "system", content: system }, ...history],
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signal: abort.signal,
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onToken: (text) => send({ type: "token", text }),
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onThinking: (text) => send({ type: "thinking", text }),
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});
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send({ type: "done", model: resolveModelName(result.model, auth.auth.directCloud) });
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} catch (error) {
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// A client-side stop aborts the upstream call: nothing to report.
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if (abort.signal.aborted && request.signal.aborted) {
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/* stopped by the user */
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} else {
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const message = friendlyErrorMessage(error, auth.auth.baseUrl, locale);
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console.error("[api/chat]", error instanceof OllamaError ? `${error.kind}: ${error.message}` : error);
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send({
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type: "error",
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error: message,
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kind: error instanceof OllamaError ? error.kind : "unknown",
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});
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}
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} finally {
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clearTimeout(globalTimer);
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try {
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controller.close();
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} catch {
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/* already closed */
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}
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}
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},
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cancel() {
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clearTimeout(globalTimer);
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abort.abort();
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},
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});
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return new Response(stream, {
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headers: {
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"Content-Type": "application/x-ndjson; charset=utf-8",
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"Cache-Control": "no-store, no-transform",
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Connection: "keep-alive",
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},
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});
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}
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