feat(model): wählbares LLM-Modell pro User & pro Job

Mac-Worker:
- /api/summarize, /api/protocol, /api/preload akzeptieren optionales
  model-Feld → überschreibt den .env-Default pro Call, kein Restart nötig
- /api/models listet die lokal verfügbaren Ollama-Modelle + Default

LXC:
- User.default_model + Job.model (Migration für SQLite)
- /api/auth/me liefert default_model, PATCH speichert ihn sofort
- /api/mac/models proxiet die Liste an die UI
- Pipeline reicht job.model an Mac-Worker durch; leer = Worker-Default
- create_job & reprocess akzeptieren model-Override
- Snapshot-Meta nutzt job.model (genauer als /health-Snapshot)

UI:
- Header-Dropdown "Modell:" neben "Profil:" — sofort-speichert User-Default
- Jeder Job zeigt sein Modell als Tag (lila); leer bedeutet Worker-Default
- Reprocess öffnet einen Modal-Picker (statt simplem confirm), Auswahl
  aller verfügbaren Modelle inkl. "Worker-Default"

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

mac-worker/app/core/ollama_client.py

@@ -52,7 +52,15 @@ def render(template: str, **vars: str) -> str:
     return out
 
 
-async def _chat(prompt: str) -> str:
+def _resolve_model(model: str | None) -> str:
+    """Wählt das tatsächlich zu verwendende Ollama-Modell.
+
+    Leerer oder fehlender `model`-Parameter → Worker-Default aus .env.
+    """
+    return (model or "").strip() or settings.ollama_model
+
+
+async def _chat(prompt: str, model: str | None = None) -> str:
     """Ruft Ollama im Streaming-Modus auf.
 
     Streaming hat zwei wichtige Vorteile:
@@ -64,15 +72,16 @@ async def _chat(prompt: str) -> str:
     `keep_alive`: das Modell bleibt 30 min im RAM zwischen Calls,
     damit nachfolgende Anfragen nicht erneut die Ladezeit zahlen.
     """
+    use_model = _resolve_model(model)
     url = f"{settings.ollama_url.rstrip('/')}/api/generate"
     payload = {
-        "model": settings.ollama_model,
+        "model": use_model,
         "prompt": prompt,
         "stream": True,
         "keep_alive": "30m",
         "options": {"temperature": 0.2},
     }
-    log.info("Ollama generate model=%s prompt_len=%d (streaming)", settings.ollama_model, len(prompt))
+    log.info("Ollama generate model=%s prompt_len=%d (streaming)", use_model, len(prompt))
     timeout = httpx.Timeout(
         connect=15.0,
         read=settings.ollama_timeout,   # pro Chunk — großzügig wegen initial Modell-Load
@@ -100,15 +109,17 @@ async def _chat(prompt: str) -> str:
     return "".join(chunks)
 
 
-async def summarize(transcript: str, title: str = "", profile_name: str | None = None) -> dict[str, Any]:
+async def summarize(
+    transcript: str, title: str = "", profile_name: str | None = None, model: str | None = None,
+) -> dict[str, Any]:
     profile = profiles.get(profile_name)
     prompt = render(
         profile.summary_prompt,
         title=title or "(ohne Titel)",
         transcript=_truncate(transcript),
     )
-    log.info("Summarize profile=%s", profile.name)
-    raw = await _chat(prompt)
+    log.info("Summarize profile=%s model=%s", profile.name, _resolve_model(model))
+    raw = await _chat(prompt, model=model)
     try:
         return _parse_json(raw)
     except Exception:  # noqa: BLE001
@@ -117,7 +128,8 @@ async def summarize(transcript: str, title: str = "", profile_name: str | None =
 
 
 async def make_protocol(
-    transcript: str, summary: dict, title: str = "", profile_name: str | None = None
+    transcript: str, summary: dict, title: str = "", profile_name: str | None = None,
+    model: str | None = None,
 ) -> dict:
     profile = profiles.get(profile_name)
     prompt = render(
@@ -126,8 +138,8 @@ async def make_protocol(
         summary_json=json.dumps(summary, ensure_ascii=False),
         transcript=_truncate(transcript),
     )
-    log.info("Protocol profile=%s", profile.name)
-    raw = await _chat(prompt)
+    log.info("Protocol profile=%s model=%s", profile.name, _resolve_model(model))
+    raw = await _chat(prompt, model=model)
     try:
         return _parse_json(raw)
     except Exception:  # noqa: BLE001
@@ -143,3 +155,23 @@ async def health() -> dict:
         r = await c.get(url)
         r.raise_for_status()
         return r.json()
+
+
+async def list_models() -> list[dict]:
+    """Liefert die lokal gepullten Ollama-Modelle inkl. Größe und Mod-Zeit.
+
+    Antwort-Form pro Eintrag: {"name", "size", "modified_at"} (Größe in Bytes).
+    """
+    url = f"{settings.ollama_url.rstrip('/')}/api/tags"
+    async with httpx.AsyncClient(timeout=10) as c:
+        r = await c.get(url)
+        r.raise_for_status()
+        data = r.json()
+    out: list[dict] = []
+    for m in data.get("models", []):
+        out.append({
+            "name": m.get("name") or m.get("model") or "",
+            "size": m.get("size", 0),
+            "modified_at": m.get("modified_at", ""),
+        })
+    return [m for m in out if m["name"]]