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_assistant/participant_handler.py

@@ -1,7 +1,7 @@
 """AI Participant Handler — bridges the kommunikation plugin with the AI Assistant.
 
 When a message is received in a conversation that includes the 'ai' participant,
-this handler generates an LLM response using litellm.acompletion (non-streaming)
+this handler generates an LLM response using llm_complete (non-streaming)
 and returns it as a new message in the conversation.
 """
 
@@ -11,8 +11,7 @@ import logging
 import uuid
 from typing import Any
 
-import litellm
-
+from app.ai.llm_client import llm_complete
 from app.core.db import create_db_session
 from app.plugins.builtins.kommunikation.contracts import ParticipantHandler
 
@@ -136,11 +135,18 @@ class AIParticipantHandler(ParticipantHandler):
                     if provider.base_url:
                         params["api_base"] = provider.base_url
 
-                # Ensure non-streaming for acompletion
-                params["stream"] = False
+                # Ensure non-streaming
+                params.pop("stream", None)
 
-                response = await litellm.acompletion(**params)
-                response_text = response.choices[0].message.content or ""
+                result = await llm_complete(
+                    model=params.get("model", "gpt-4o-mini"),
+                    messages=params.get("messages", []),
+                    temperature=params.get("temperature", 0.7),
+                    max_tokens=params.get("max_tokens", 2048),
+                    api_key=params.get("api_key"),
+                    api_base=params.get("api_base"),
+                )
+                response_text = result["content"] or ""
 
                 if not response_text.strip():
                     response_text = "*(keine Antwort generiert)*"