2b0abad68e
Add a single-session AI flow alongside program generation: describe a
workout in plain words and get a ready-to-log workout back — exercises
with suggested weights, target reps, and set counts grounded in the
user's recent history. The suggestion can be inline-edited or refined
by sending a follow-up instruction back to the model, then "Use this
workout" pre-fills the normal New Workout form (nothing persists until
the user saves through the regular path).
Why reuse, not fork: the existing program-generation spine (detached
background runner, SSE streaming, lenient-JSON preview, 5 providers,
history context, library name->id mapping) already does the hard parts.
A new AIGeneration.kind discriminant ("program" | "workout", default
"program" via boot-time guarded ALTER) selects the parser and keeps the
ephemeral workout rows out of the program-shaped AI history. Refine is a
fresh generation seeded with the prior suggestion (validated through the
same schema before it re-enters the prompt).
Hand-off is sessionStorage -> /main/workouts/new?from=ai -> AiWorkoutPrefill,
which expands each suggestion into N sets and maps effort by cardio-ness
(Gear for cardio, RPE for strength). EditWorkoutData.id is now optional so
the prefill CREATEs rather than PATCHing a nonexistent id. The AI suggests
each weight in that exercise's effective logging unit (the library JSON
carries a per-exercise unit) so the stored number and unit never diverge.
Built + sideloaded to immense-voyage.local as 1.2.0:6; on-box ALTER and
non-root launch confirmed via start-cli. tsc clean (app + packaging),
251 tests pass, next build + s9pk build succeed.
166 lines
4.9 KiB
TypeScript
166 lines
4.9 KiB
TypeScript
import { NextRequest, NextResponse } from 'next/server';
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import { z } from 'zod';
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import { getCurrentUser } from '@/lib/auth';
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import { prisma } from '@/lib/prisma';
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import {
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PROGRAM_OUTPUT_SHAPE,
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} from '@/lib/ai/programSchema';
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import {
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buildHistorySummary,
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formatHistoryContext,
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} from '@/lib/ai/historyContext';
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import { buildBaseSystemPrompt } from '@/lib/ai/systemPromptBase';
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import { kickoffGeneration } from '@/lib/ai/generationRunner';
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/**
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* POST /api/ai/generate
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*
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* Body: { templateId?: string, userInput: string, includeHistory?: boolean }
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*
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* v1.1.0:4: this endpoint now KICKS OFF a background runner and returns
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* the new generation id immediately. The caller subscribes to live
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* deltas via GET /api/ai/generations/[id]/stream (SSE) or polls via
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* GET /api/ai/generations/[id]. Navigating away no longer cancels the
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* generation — the runner keeps writing to the row in the background.
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*
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* Response:
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* 201 { id: "...generationId..." }
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* 400 { error: "..." }
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*/
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const bodySchema = z.object({
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templateId: z.string().optional().nullable(),
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userInput: z.string().min(1),
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includeHistory: z.boolean().optional().default(false),
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});
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export const dynamic = 'force-dynamic';
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export async function POST(request: NextRequest) {
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const user = await getCurrentUser();
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if (!user) {
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return NextResponse.json({ error: 'Unauthorized' }, { status: 401 });
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}
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const body = await request.json().catch(() => ({}));
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const parsed = bodySchema.safeParse(body);
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if (!parsed.success) {
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return NextResponse.json(
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{ error: 'Invalid body', details: parsed.error.errors },
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{ status: 400 },
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);
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}
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const prefs = await prisma.userPreferences.findUnique({
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where: { userId: user.id },
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});
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if (!prefs?.aiProvider || !prefs?.aiModel) {
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return NextResponse.json(
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{
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error:
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'AI is not configured. Open Settings → AI integration and pick a provider + model.',
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},
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{ status: 400 },
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);
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}
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// Load the template if provided.
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let template:
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| { id: string; name: string; systemPrompt: string; userPromptTemplate: string }
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| null = null;
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if (parsed.data.templateId) {
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const t = await prisma.aIPromptTemplate.findFirst({
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where: {
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id: parsed.data.templateId,
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OR: [{ userId: user.id }, { userId: null }],
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},
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select: {
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id: true,
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name: true,
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systemPrompt: true,
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userPromptTemplate: true,
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},
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});
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if (!t) {
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return NextResponse.json({ error: 'Template not found.' }, { status: 404 });
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}
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template = t;
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}
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// Library for the prompt.
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const exercises = await prisma.exercise.findMany({
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where: { userId: user.id },
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select: { id: true, name: true, type: true, muscleGroups: true },
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});
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const libraryJson = JSON.stringify(
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exercises.map((e) => ({
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id: e.id,
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name: e.name,
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type: e.type,
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muscleGroups: (() => {
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try {
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return JSON.parse(e.muscleGroups);
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} catch {
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return [];
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}
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})(),
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})),
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);
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// History context if requested.
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let historyBlock = '';
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if (parsed.data.includeHistory) {
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const summary = await buildHistorySummary(prisma, user.id);
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historyBlock = formatHistoryContext(summary);
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}
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// v1.1.0:4 base prompt with output contract + weight rules. Stitched
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// BEFORE the template's coaching philosophy so output rules win when
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// they conflict.
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const weightUnit = (prefs.defaultWeightUnit as 'lbs' | 'kg') || 'lbs';
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const isLocalModel = prefs.aiProvider === 'ollama';
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const basePrompt = buildBaseSystemPrompt({
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weightUnit,
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hasHistoryContext: parsed.data.includeHistory,
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isLocalModel,
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});
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const templatePrompt = template?.systemPrompt ?? DEFAULT_TEMPLATE_PROMPT;
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const systemPrompt = `${basePrompt}
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# COACHING PHILOSOPHY (template-specific)
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${templatePrompt}
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# OUTPUT SHAPE
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${PROGRAM_OUTPUT_SHAPE}
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# LIBRARY (use these exerciseIds; do not invent ids)
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${libraryJson}${historyBlock}`;
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const userPromptBody =
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template?.userPromptTemplate.replace(/{{userInput}}/g, parsed.data.userInput) ??
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parsed.data.userInput;
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const id = await kickoffGeneration({
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prisma,
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userId: user.id,
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kind: 'program',
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templateId: template?.id ?? null,
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templateName: template?.name ?? null,
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userInput: parsed.data.userInput,
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systemPrompt,
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userPrompt: userPromptBody,
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provider: prefs.aiProvider,
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model: prefs.aiModel,
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apiKey: prefs.aiApiKey,
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baseUrl: prefs.aiBaseUrl,
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});
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return NextResponse.json({ id }, { status: 201 });
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}
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const DEFAULT_TEMPLATE_PROMPT = `You are a strength and conditioning coach. The user will describe what they want; design a program that matches their goal, experience, equipment, and time budget. Pick exercises from the LIBRARY and stay close to evidence-based programming for the requested goal (hypertrophy / strength / power / conditioning / general fitness).`;
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