Restructure memory layers: USER.md/MEMORY.md cleanup, 4 new system pages, memory-layer-architecture concept
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---
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title: "Frigate NVR"
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category: systems
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tags: [frigate, nvr, camera, ai, mqtt, proxmox]
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created: "2026-09-27"
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modified: "2026-09-27"
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---
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# Frigate NVR
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> Frigate v0.18.0 auf Proxmox LXC CT151. KI-gestützte Objekterkennung für Kameras Einfahrt + Terrasse.
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## Infrastruktur
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- **Container:** CT151 auf proxmox6
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- **IP:** `10.0.30.104:5000`
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- **Version:** v0.18.0-
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- **Detector:** OpenVINO CPU (3.2 FPS)
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- **go2rtc:** v1.9.14
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- **GPU:** Intel iGPU `/dev/dri/renderD128` (gid=993)
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## Kameras
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| Name | IP | Substream | Detect FPS |
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|------|----|-----------|------------|
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| Einfahrt | `10.0.50.102` | Unterstream | 5.1 fps, 1.0 det |
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| Terrasse | `10.0.50.103` | Unterstream | 5.0 fps, 2.2 det |
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## MQTT
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- **Broker:** `10.0.30.10:1883` (Mosquitto auf HA)
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- **Prefix:** `frigate`
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- **User:** `frigate`
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- **Topic:** `frigate/events`
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## Frigate Plus Model
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- `plus://709bb8ad097786a7f2a37e27684c79a9`
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- Benötigt `PLUS_API_KEY` (Env-Var in `/config/frigate.env`)
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## Features
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- Face Recognition (groß): Sarah, Dominik, DHL1, Amazon1, Cleo, Lisa, Annemarie, Marcel Schnitzer, Uli, Eva, Postbote, Fr. Schnitzer
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- License Plate Recognition (CPU, threshold 0.5)
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- Semantic Search (groß)
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- Record: alerts+detections, retain 30 days
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## HA Automation
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- **ID:** `1714065780832` in `/config/automations.yaml`
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- **Mode:** `single`, cooldown 600s
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- **Dedup:** `input_text.frigate_last_event_id` speichert letzten Event
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- **Flow:** MQTT trigger → 10s delay → snapshot → notify → LLM Vision (optional)
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- **Labels:** person, car, license_plate, face, dog, cat, amazon, ups, package
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- **Sublabel-Extraktion:** `sub[0]` (Jinja, Array-Leck-Fix)
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## Bekannte Issues
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- Stationäre Autos triggerten wiederholt "new" Events → Flood → gefixt mit cooldown+dedup
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- MQTT ACL blockierte HA-Subscription (User `opendtu` hatte keine Subscribe-Rechte) → gefixt
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- v0.18 Breaking Changes: `clean_copy` entfernt, `license_plate.mask` Format geändert (list→dict)
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## Old Container
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- CT120 (frigate): gestoppt, wartet auf Deletion
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## Related
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- [[systems/homeassistant]] — MQTT, Automations, Notifications
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- [[systems/noris-ai]] — LLM Vision für Event-Klassifizierung
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- [[entities/infrastructure]] — CT151 auf proxmox6
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---
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title: "Home Assistant"
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category: systems
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tags: [homeassistant, smart-home, mqtt, automation]
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created: "2026-09-27"
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modified: "2026-09-27"
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---
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# Home Assistant
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> Smart Home Zentrale auf Proxmox. Steuerung, Automatisierung und Benachrichtigungen.
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## Infrastruktur
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- **Host:** `10.0.30.10` (HAOS VM)
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- **SSH:** `hassio@10.0.30.10` (PW: 1P Vault "Hermes")
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- **URL:** `https://homeassistant.familie-schoen.com`
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- **Container:** `homeassistant` (Docker)
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## MQTT
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- **Broker:** Mosquitto Add-on v7.1.1 auf localhost:1883
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- **HA MQTT User:** ehemals `opendtu` (BROKEN — ACL blockiert), gefixt
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- **ACL:** `/etc/mosquitto/acl` definiert `user homeassistant` + `user addons`
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- **Auth Plugin:** `go-auth.so` (files,http backends)
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## Automations
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- **File:** `/config/automations.yaml` (35 Automations)
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- **Schreibmethode:** SSH → `docker exec homeassistant chmod 666`, danach restore 644
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- **Reload:** REST API mit JWT (HS256, signed from `/config/.storage/auth`)
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### Frigate Einfahrt Notification (ID 1714065780832)
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- MQTT trigger `frigate/events` → filter `type=='new'` + cameras [Einfahrt,Terrasse] + labels [person,car,...]
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- Mode: `single`, cooldown 600s
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- Dedup: `input_text.frigate_last_event_id`
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- Flow: delay 10s → snapshot → notify iPhone → optional LLM Vision
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- Siehe [[systems/frigate]]
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## Notify Services
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| Service | Status |
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|---------|--------|
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| `notify.mobile_app_iphone_dominik` | ✅ aktiv |
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| `notify.mobile_app_sarahs_iphone_app` | verfügbar |
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| `notify.mobile_app_ipad_2` | verfügbar |
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| `notify.mobile_app_sm_x205` | verfügbar |
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## Entitäten
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- Kameras: `camera.einfahrt_2`, `camera.terrasse_2` (Suffix `_2` wegen verwaister Integration)
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- Motion: `binary_sensor.einfahrt_motion_2`, `binary_sensor.terrasse_motion_2`
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- Input Text: `input_text.frigate_last_event_id` (dedup storage, max 255 chars)
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## Snapshots
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- Gespeichert: `/config/www/snapshots/{camera}_latest.jpg`
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- URL: `/local/snapshots/` (HTTP 200, keine Auth)
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## JWT Auth
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1. `jwt_key` aus `/config/.storage/auth` lesen
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2. Client `Hermes_202606` (token id `1444b2c6757b4d66a6f8f8e5899b4e6a`)
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3. HS256 signieren, Bearer Header
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4. `?return_response=true` für Service-Call Responses
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## Integrations
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- Frigate (MQTT)
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- LLM Vision (noris AI `gemma-4-31b-it`)
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- Tibber (Strom)
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- Marstek Speicher (VENUS-E, IP 10.0.50.113)
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- Various sensors (Xiaomi BLE, etc.)
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## Related
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- [[systems/frigate]] — NVR Integration
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- [[systems/noris-ai]] — LLM Vision Provider
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- [[reference/ip-map]] — IP Assignments
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# Laya Decision Model
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> 421M param ModernBERT-large Classifier (Apache 2.0) auf CT152, CPU-only.
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> Nutze in normalen Sessions fuer schnelle Klassifizierung, Binaerentscheidungen und Pre-Filter.
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## Zugang
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- **Endpoint:** `POST http://10.0.30.152:8000/predict`
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- **Payload:** `{"state": "<text>", "questions": {...}}`
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- **Health:** `GET http://10.0.30.152:8000/health`
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- **Presets:** `GET http://10.0.30.152:8000/presets/{router|guard|moderation|triage}`
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## Entscheidungstypen (Primitives)
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| Typ |用途 | Return Fields |
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|-----|------|---------------|
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| `choice` | Klassifizierung in N Labels | `choice`, `answer_confidence`, `probabilities` |
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| `noul` | Ja/Nein mit Wahrscheinlichkeit | `noul` (0..1), `answer_confidence` |
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| `score` | Ordinale Bewertung | `score`, `answer_confidence` |
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## Verwendung in Sessions
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**Praeferieren fuer:**
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- Pre-Filter vor teuren LLM-Calls (z.B. "ist diese Email eine Rechnung?" → nur bei "ja" LLM aufrufen)
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- Binaerentscheidungen: alert/skip, escalate/ignore, move/keep
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- Multi-Kategorie-Klassifizierung mit Confidence
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- Gatekeeping: Notification-Suppression, Alert-Filtering
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**NICHT geeignet fuer:**
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- Textgenerierung / Zusammenfassungen (dafür LLM verwenden)
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- Komplexe Reasoning-Tasks
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- Embeddings / Semantische Suche (dafür Harrier/Hindsight)
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## Example Call
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```python
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import json, urllib.request
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payload = json.dumps({
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"state": "Von: amazon.de\nBetreff: Bestellbestätigung #12345",
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"questions": {
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"kategorie": {
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"type": "choice",
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"instructions": "Welche Kategorie?",
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"criteria": {
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"rechnung": "Rechnung, Invoice",
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"bestellung": "Bestellbestätigung, Order",
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"werbung": "Newsletter, Marketing"
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}
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},
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"ignorieren": {
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"type": "noul",
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"instructions": "Soll ignoriert werden?"
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}
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}
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}).encode()
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req = urllib.request.Request(
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"http://10.0.30.152:8000/predict",
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data=payload,
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headers={"Content-Type": "application/json"}
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)
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result = json.loads(urllib.request.urlopen(req, timeout=30).read())
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# result["answers"]["kategorie"]["choice"] → "bestellung"
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# result["answers"]["kategorie"]["answer_confidence"] → 0.99
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```
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## Performance
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- Latenz: ~1.5-1.7s warm cache (CPU)
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- Load time: ~18s (cold start)
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- systemd service: `laya.service` (enabled, onboot)
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## Einsatzgebiete (aktiv)
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- **Rechnungen-Organizer** (Cron `f773f8c23230`): 12-Kategorie Email-Klassifizierung, 3-Schichten-Safety
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- Weitere Kandidaten: Beleg-Sammler, SRE Network Recon, Backup Digest, Frigate Event Gate
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## Constraints
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- 421M Modell → niedrige Confidence bei ambiguous Inputs (Feature, nicht Bug!)
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- Batch funktioniert nicht — ein Request pro Input-Instanz
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- `noul` ist nicht zuverlaessig fuer kritische Entscheidungen allein — immer mit `choice` kombinieren
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- Confidence-Threshold empfohlen (≥0.6 fuer Moves, ≥0.5 fuer Ignores)
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## Related
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- [[concepts/email-organization]] — Rechnungs-Organizer Architecture
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- [[entities/infrastructure]] — CT152 auf proxmox6
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- Solution Doc: `docs/solutions/architecture/2026-09-27-laya-email-organizer-migration.md`
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---
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title: "noris AI Platform"
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category: systems
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tags: [ai, llm, noris, gpu, embeddings]
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created: "2026-09-27"
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modified: "2026-09-27"
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---
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# noris AI Platform (ai.noris.de)
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> Interne AI-Plattform der noris Network AG. Bereitstellung von LLMs, Embeddings und Image Generation.
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## Endpoints
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- **Chat:** `https://ai.noris.de/v1/chat/completions`
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- **Embeddings:** `https://ai.noris.de/v1/embeddings`
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- **Images:** `https://ai.noris.de/v1/images/generations` (b64_json)
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## Modelle
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| Typ | Modell-ID | Hinweise |
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|-----|-----------|----------|
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| Flagship LLM | `glm-5-2` | Primary, OpenRouter-kompatibel |
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| General | `gemma-4-31b-it` | Vision-fähig, genutzt von LLM Vision |
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| Large MoE | `gpt-oss-120b` | |
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| Mid-range | `qwen3.6-27b` | |
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| Mid-range | `qwen3.8-27b` | |
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| Fast | `ds-v4-flash` | Low-latency, Paperless OCR |
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| Embedding | `harrier` | Vektorembeddings |
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| Image Gen | `qwen-image` | via `/v1/images/gen` |
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| Image Gen | `qsu` | |
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| Image Gen | `qwen-image-2-1` | |
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## Verbraucher
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- **Hermes Agent** — Primärmodell `glm-5-2` via OpenRouter
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- **HA LLM Vision** — `gemma-4-31b-it` für Bildanalyse (Frigate Events)
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- **Paperless** — `ds-v4-flash` für OCR/Kategorisierung
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- **Personal Coach Bot** — `glm-5-2` via noris direkt
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## Related
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- [[systems/frigate]] — nutzt noris AI für Event-Klassifizierung
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- [[systems/homeassistant]] — LLM Vision Integration
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- [[systems/paperless]] — OCR via ds-v4-flash
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---
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title: "Paperless-ngx"
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category: systems
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tags: [paperless, documents, oidc, ocr]
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created: "2026-09-27"
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modified: "2026-09-27"
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---
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# Paperless-ngx
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> Dokumentenmanagement mit OCR, OIDC-Login und AI-Kategorisierung.
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## Zugriff
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- **URL:** `https://dokumente.familie-schoen.com`
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- **mTLS:** `https://dokumente-mtls.familie-schoen.com` (auto-login als `dominik`)
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- **PKCS12:** `dominik-dokumente-mtls.p12` (PW: siehe 1P Vault "Hermes")
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## Auth
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- OIDC via Authelia (`dominik@schoen.eu`)
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- Break-Glass lokaler User: `dominik`
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- mTLS Client Cert: CN=dominik, gültig bis Juli 2028
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## Konfiguration
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- **AI Backend:** `ds-v4-flash@ai.noris.de` (noris AI)
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- **Memory:** 4Gi (PAT-002)
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- **Mail Import:** `dokumente@familie-schoen.com` (iCloud mailbox, max 30 Tage)
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- **Owner:** dominik
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## Known Issue
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- PAT-002: Memory-Limit 4Gi erforderlich, sonst OOM bei großen OCR-Batches
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## Related
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- [[concepts/credential-policy]] — 1Password, mTLS Zertifikate
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- [[systems/noris-ai]] — ds-v4-flash für OCR
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