feat(agent): refactor LLM configuration handling to use centralized config

- Replace separate user_id and jwt_token parameters with unified llm_config dict
- Simplify model factory to accept llm_config directly instead of fetching from API
- Update llm_call and generate_title nodes to extract llm_config from LangGraph config
- Remove deprecated API fetching logic from model factory
- Maintain backward compatibility for existing tool usage patterns

This change centralizes LLM configuration management, reducing API calls and improving performance by passing configuration directly from the API layer rather than fetching it repeatedly.

gns3server/api/routes/controller/chat.py

@@ -91,6 +91,18 @@ async def stream_chat(
     auth_header = http_request.headers.get("Authorization", "")
     jwt_token = auth_header.replace("Bearer ", "") if auth_header else None
 
+    # Get FastAPI app reference (for database access)
+    app = http_request.app
+
+    # Get user's LLM config (with decrypted API key)
+    from gns3server.db.tasks import get_user_llm_config_full
+    llm_config = await get_user_llm_config_full(user_id, app)
+    if not llm_config:
+        raise HTTPException(
+            status_code=status.HTTP_400_BAD_REQUEST,
+            detail="LLM configuration not found. Please configure your LLM settings first."
+        )
+
     # Get or create AgentService for this project
     agent_manager = await get_project_agent_manager()
     agent_service = await agent_manager.get_agent(str(project.id), project.path)
@@ -105,9 +117,9 @@ async def stream_chat(
                 message=request.message,
                 session_id=session_id,
                 project_id=str(project.id),
-                user_id=user_id,
                 jwt_token=jwt_token,
-                mode=request.mode
+                mode=request.mode,
+                llm_config=llm_config
             ):
                 try:
                     # Validate and serialize chunk