feat(B-LLM): Zentraler LLM Client — llm_complete() + llm_embed() + Migration + Tests + Doku

B-LLM: llm_client.py um generische llm_complete() und llm_embed() erweitert
- Provider-Auswahl, API-Key-Auflösung, Error-Handling, Cost-Tracking
- Retry mit Exponential-Backoff für transient errors
- Timeout konfigurierbar
- Helper: get_api_credentials(), build_model(), _classify_error()

B-LLM-MIG: Alle 8 direkten litellm.acompletion() Calls auf llm_complete() umgestellt
- agent_runner.py, query_understanding.py (2x), ai_proactive (3x), ai_assistant (2x)
- 0 verbleibende direkte litellm.acompletion() Calls außerhalb llm_client.py

B-LLM-TEST: 39 Tests in test_llm_client.py — alle grün
- Mock mode, error handling, embed, helpers, backward compat

B-LLM-DOC: Plugin-Dev-Guide Kapitel 7 (LLM Integration) hinzugefügt

app/plugins/builtins/ai_proactive/services.py

@@ -15,6 +15,7 @@ from datetime import UTC, datetime, timedelta
 from typing import Any
 
 import litellm
+from app.ai.llm_client import llm_complete
 from sqlalchemy import func, select, text
 from sqlalchemy.ext.asyncio import AsyncSession
 
@@ -421,27 +422,23 @@ async def generate_suggestion(
         model_parts = model.split("/", 1)
         model = f"{provider_type}/{model_parts[-1]}"
 
-    litellm_kwargs: dict[str, Any] = dict(
-        model=model,
-        messages=[
-            {"role": "system", "content": SYSTEM_PROMPT},
-            {
-                "role": "user",
-                "content": json.dumps(context_data, default=str, ensure_ascii=False),
-            },
-        ],
-        temperature=0.3,
-        max_tokens=1500,
-        response_format={"type": "json_object"},
-    )
-    if api_key:
-        litellm_kwargs["api_key"] = api_key
-    if api_base:
-        litellm_kwargs["api_base"] = api_base
-
     try:
-        response = await litellm.acompletion(**litellm_kwargs)
-        content = response.choices[0].message.content
+        result = await llm_complete(
+            model=model,
+            messages=[
+                {"role": "system", "content": SYSTEM_PROMPT},
+                {
+                    "role": "user",
+                    "content": json.dumps(context_data, default=str, ensure_ascii=False),
+                },
+            ],
+            temperature=0.3,
+            max_tokens=1500,
+            response_format={"type": "json_object"},
+            api_key=api_key,
+            api_base=api_base,
+        )
+        content = result["content"]
         if not content:
             return None
         # Strip markdown code fences if present (e.g. ```json ... ```)