mirror of
https://github.com/Memo-2023/mana-monorepo.git
synced 2026-05-18 02:09:39 +02:00
0c0e31d2f3
Replaces hand-rolled fetch + JSON.parse + cast-to-any with generateObject
from the AI SDK. The model is constrained to the shared Zod schemas in
@mana/shared-types, so the response is validated at the boundary instead
of trusting Gemini to emit the right shape.
Routes refactored:
- nutriphi/analysis/photo (image_url → multimodal `image:` content)
- nutriphi/analysis/text (free-text meal description)
- planta/analysis/identify (plant photo identification)
Why this is materially better than the old code:
- Runtime validation: if Gemini drifts, the AI SDK throws before the
response leaves the route. Frontend never sees malformed payloads.
- Provider-portable: createOpenAICompatible({ baseURL: MANA_LLM_URL })
keeps mana-llm as the central routing/auth/observability point. The
AI SDK speaks the OpenAI dialect to mana-llm. If we ever swap the
backend (e.g. claude-sonnet-4-6 for plant ID), it's a one-line model
name change.
- System prompts moved from a multi-line example-laden string to a
short instruction. The schema itself (with .describe() field hints)
now carries the structural contract that the JSON-by-example
paragraph used to encode. Token cost goes down, accuracy goes up.
- Drops manual fetch error handling (status checks, JSON.parse, cast)
in favour of try/catch around generateObject. Errors are typed.
mana-llm itself is unchanged — it's still the OpenAI-compatible proxy
in front of Gemini Vision. The AI SDK just gives us a typed client and
a schema-aware decoder on top of it.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
93 lines
3.1 KiB
TypeScript
93 lines
3.1 KiB
TypeScript
/**
|
|
* Planta module — Photo upload + AI plant identification.
|
|
*
|
|
* CRUD for plants, photos, watering is handled by mana-sync. This
|
|
* module owns the server-only operations: photo upload to mana-media
|
|
* and structured plant identification via the Vercel AI SDK
|
|
* (`generateObject`) using the shared PlantIdentificationSchema in
|
|
* @mana/shared-types. See nutriphi/routes.ts for the rationale behind
|
|
* the AI SDK + Zod approach.
|
|
*/
|
|
|
|
import { Hono } from 'hono';
|
|
import { generateObject } from 'ai';
|
|
import { createOpenAICompatible } from '@ai-sdk/openai-compatible';
|
|
import { PlantIdentificationSchema } from '@mana/shared-types';
|
|
import { logger, type AuthVariables } from '@mana/shared-hono';
|
|
|
|
const LLM_URL = process.env.MANA_LLM_URL || 'http://localhost:3025';
|
|
const VISION_MODEL = process.env.VISION_MODEL || 'gemini-2.0-flash';
|
|
|
|
const llm = createOpenAICompatible({
|
|
name: 'mana-llm',
|
|
baseURL: `${LLM_URL}/api/v1`,
|
|
});
|
|
|
|
const IDENTIFICATION_PROMPT = `Du bist ein Pflanzenexperte. Analysiere das Pflanzenfoto und liefere eine strukturierte Identifikation mit lateinischem Namen, deutschen Trivialnamen, Pflegehinweisen und einer Gesundheitseinschätzung. Antworte auf Deutsch.`;
|
|
|
|
const routes = new Hono<{ Variables: AuthVariables }>();
|
|
|
|
// ─── Photo Upload (server-only: S3 storage) ─────────────────
|
|
|
|
routes.post('/photos/upload', async (c) => {
|
|
const userId = c.get('userId');
|
|
const formData = await c.req.formData();
|
|
const file = formData.get('file') as File | null;
|
|
const plantId = formData.get('plantId') as string | null;
|
|
|
|
if (!file) return c.json({ error: 'No file provided' }, 400);
|
|
if (file.size > 10 * 1024 * 1024) return c.json({ error: 'File too large (max 10MB)' }, 400);
|
|
|
|
try {
|
|
const { uploadImageToMedia } = await import('../../lib/media');
|
|
const buffer = await file.arrayBuffer();
|
|
const result = await uploadImageToMedia(buffer, file.name, { app: 'planta', userId });
|
|
|
|
return c.json(
|
|
{
|
|
storagePath: result.id,
|
|
publicUrl: result.urls.original,
|
|
mediaId: result.id,
|
|
plantId,
|
|
},
|
|
201
|
|
);
|
|
} catch (err) {
|
|
logger.error('planta.upload_failed', {
|
|
error: err instanceof Error ? err.message : String(err),
|
|
});
|
|
return c.json({ error: 'Upload failed' }, 500);
|
|
}
|
|
});
|
|
|
|
// ─── AI Analysis (Gemini Vision on uploaded URL) ─────────────
|
|
|
|
routes.post('/analysis/identify', async (c) => {
|
|
const { photoUrl } = await c.req.json();
|
|
if (!photoUrl) return c.json({ error: 'photoUrl required' }, 400);
|
|
|
|
try {
|
|
const { object } = await generateObject({
|
|
model: llm(VISION_MODEL),
|
|
schema: PlantIdentificationSchema,
|
|
system: IDENTIFICATION_PROMPT,
|
|
messages: [
|
|
{
|
|
role: 'user',
|
|
content: [
|
|
{ type: 'text', text: 'Analysiere diese Pflanze.' },
|
|
{ type: 'image', image: new URL(photoUrl) },
|
|
],
|
|
},
|
|
],
|
|
});
|
|
return c.json(object);
|
|
} catch (err) {
|
|
logger.error('planta.analysis_failed', {
|
|
error: err instanceof Error ? err.message : String(err),
|
|
});
|
|
return c.json({ error: 'Analysis failed' }, 500);
|
|
}
|
|
});
|
|
|
|
export { routes as plantaRoutes };
|