freezmo/lib/chat-client.ts
Martin Dvorak 6036238bf0 Teach it back: the AI becomes the curious student
A new mode where the user explains a topic in their own words and the AI
plays a curious classmate: short reactions, exactly one follow-up question
at a time, never "correct"/"wrong", never lecturing, and everything it knows
comes from the user's own decks and notes.

- Entry points: a sidebar row next to Exam mode and a "Teach it back" action
  in every deck's menu (which prefills the topic and scope).
- Start screen: deck or typed topic, friendly/skeptical student, the same
  context and model chips as Chat (/teach?deck=<id> pre-selects the deck).
- Session: the student prompt is a mode of the existing /api/chat route, so
  model resolution, allowlists, rate limits and the streaming client are the
  same code; the personality lives in lib/prompts.ts (TEACH_*). The deck's
  cards travel as a numbered checklist and the student is told to ask about
  the ideas that were skipped; it must say so rather than guess when the
  notes cover nothing. One constant (TEACH_MAX_EXCHANGES = 10) ends the
  session, or the Finish button does.
- Recap: a teach_summary call (JSON, one repair retry) yields well / gaps
  with card references / next steps; each gap links to the card it means.
  "Add missed cards to review" schedules those activities due now; "Turn
  gaps into cards" generates one flashcard per gap from the deck's notes and
  shows an editable review list before saving.
- Sessions are chat conversations with a mode:"teach" marker + session
  record: same IndexedDB history, backup and sync, filtered out of the Chat
  screens; the recent list reopens a recap or continues a session.
- Reused pieces: MessageRow, Composer (now optional attach/scope chips and a
  mic), Markdown/KaTeX, AiDisclaimer, ContextPicker/ModelPicker, plus a
  shared useChatModels hook and useKeyboardInset extracted from Chat.
- Dictation: Web Speech API recognition (lib/speech-input.ts); the mic only
  renders where the browser supports it, and dictated phrases land in the
  draft.
- All strings in en + cs. teach-test: 44 checks (API prompts and guards,
  both entry points, the session, recap actions, history, exchange limit,
  360/390px layouts, mic on/off). Version 0.8.1, SW v103.
2026-10-16 23:05:24 +00:00

123 lines
4.6 KiB
TypeScript

"use client";
import { ApiError, consumeNdjson } from "./client-api";
import { localeHeader, t } from "./i18n";
import { authHeaders } from "./settings";
import { providerHeaders, providersStore } from "./providers-client";
import type { ChatSource } from "./chat";
/**
* Client for the streaming chat route. The conversation window and the chosen
* context chunks travel with every request (the server never stores either);
* the API key only ever goes to our own route in its usual header.
*/
export type ChatStreamEvent =
| { type: "token"; text: string }
| { type: "thinking"; text: string }
| { type: "done"; model: string }
| { type: "error"; error: string; kind?: string };
export type StreamChatInput = {
model?: string;
/** A cloud provider the model belongs to (Ollama when omitted). */
provider?: string;
messages: { role: "user" | "assistant"; content: string }[];
context: (ChatSource & { text: string })[];
/** "teach" runs a Teach it back turn; "teach_summary" asks for the recap JSON. */
mode?: "teach" | "teach_summary";
teach?: {
topic: string;
difficulty: "friendly" | "skeptical";
/** Exchanges answered so far, and how many the session plans. */
exchange: number;
limit: number;
/** "[n] question → answer" checklist lines (lib/teach.ts). */
cards: string[];
};
signal?: AbortSignal;
onToken: (text: string) => void;
onThinking: (text: string) => void;
onDone: (model: string) => void;
};
export async function streamChat(input: StreamChatInput): Promise<void> {
let response: Response;
try {
response = await fetch("/api/chat", {
method: "POST",
headers: { "Content-Type": "application/json", ...authHeaders(), ...providerHeaders(input.provider), ...localeHeader() },
body: JSON.stringify({
model: input.model || undefined,
messages: input.messages,
context: input.context,
mode: input.mode,
teach: input.teach,
}),
signal: input.signal,
});
} catch (error) {
if (error instanceof Error && error.name === "AbortError") {
throw new ApiError(t("api.cancelled"), "aborted");
}
throw new ApiError(t("api.unreachable"), "network");
}
if (!response.ok) {
const body = (await response.json().catch(() => null)) as { error?: string; kind?: string } | null;
throw new ApiError(body?.error ?? t("api.httpError", { status: response.status }), body?.kind);
}
let streamError: ApiError | null = null;
try {
await consumeNdjson(response, (raw) => {
const event = raw as ChatStreamEvent;
if (event.type === "token") input.onToken(event.text);
else if (event.type === "thinking") input.onThinking(event.text);
else if (event.type === "done") input.onDone(event.model);
else if (event.type === "error") streamError = new ApiError(event.error, event.kind);
});
} catch (error) {
// Aborting while the body is being read rejects the reader with an
// AbortError, not a fetch rejection — treat it as a user stop.
if (error instanceof Error && error.name === "AbortError") {
throw new ApiError(t("api.cancelled"), "aborted");
}
throw error;
}
if (streamError) throw streamError;
}
/**
* Turns attached photos (and any client-extracted document text) into plain
* text through the existing vision pipeline, so the chat model itself never
* needs to be multimodal.
*/
export async function extractChatAttachment(
input: { images?: string[]; text?: string },
options: { signal?: AbortSignal } = {},
): Promise<string> {
// Attachment transcription follows the study-generation provider, if set.
const generation = providersStore.getSnapshot().generation;
let response: Response;
try {
response = await fetch("/api/chat/extract", {
method: "POST",
headers: { "Content-Type": "application/json", ...authHeaders(), ...providerHeaders(generation?.provider), ...localeHeader() },
body: JSON.stringify(generation ? { ...input, model: generation.visionModel || generation.textModel } : input),
signal: options.signal,
});
} catch (error) {
if (error instanceof Error && error.name === "AbortError") {
throw new ApiError(t("api.cancelled"), "aborted");
}
throw new ApiError(t("api.unreachable"), "network");
}
const body = (await response.json().catch(() => null)) as { text?: string; error?: string; kind?: string } | null;
if (!response.ok) {
throw new ApiError(body?.error ?? t("api.httpError", { status: response.status }), body?.kind);
}
if (!body?.text?.trim()) throw new ApiError(t("chat.attachEmpty"), "bad_response");
return body.text.trim();
}