anpassung des Orchestrator Workflows

math-tutor/backend/app/deterministic_services/orchestrator.py

@@ -24,13 +24,26 @@ def _bootstrap_context(sheet: dict, query_text: str, tool_log: list[dict]) -> No
         )
         _append_tool_log(tool_log, "sympy_solve", math_request, solution)
 
+def _extract_user_messages(messages: list[dict]) -> list[str]:
+    """
+    Extrahiert alle Inhalte von Messages mit der Rolle 'user' und gibt sie als Liste von Strings zurück.
+    Der letzte user-Content wird als letztes Element in der Liste enthalten sein.
+    """
+    user_contents = []
+    for message in messages:
+        if message.get("role") == "user":
+            content = message.get("content", "")
+            if content:  # Nur hinzufügen, wenn Inhalt vorhanden ist
+                user_contents.append(content)
+    return user_contents
+
 
 def run_chat(messages: list[dict], draft: str | None = None) -> dict:
     # Input-Fehlerbehandlung
     if not messages:
         raise ValueError("messages required")
 
-    last_user = next((m for m in reversed(messages) if m.get("role") == "user"), None)
+    last_user = _extract_user_messages(messages)[-1] if _extract_user_messages(messages) else None
     if not last_user:
         raise ValueError("last user message required")
 
@@ -45,25 +58,27 @@ def run_chat(messages: list[dict], draft: str | None = None) -> dict:
     tool_log: list[dict] = []
 
     if new_chat or not sheet.get("initialized"):
-        _bootstrap_context(sheet, last_user.get("content", ""), tool_log)
+        _bootstrap_context(sheet, last_user, tool_log)
         sheet["initialized"] = True
 
     history_text = context_store.format_history(messages)
     sheet_text = context_store.format_sheet(sheet)
-    llm_solution = solver_LLM.solve_question(last_user.get("content", ""), sheet_text)
-    _append_tool_log(tool_log, "LLM_Solution", last_user.get("content", ""), llm_solution)
+    if new_chat:
+        llm_solution = solver_LLM.solve_question(last_user, sheet_text)
+        _append_tool_log(tool_log, "LLM_Solution", {"question": last_user, "sheet": sheet_text}, llm_solution)
         context_store.add_LLM_solution(sheet, llm_solution)
+    else:
         decision = decision_LLM.needs_more_context(history_text, sheet_text)
-    _append_tool_log(tool_log, "decision", sheet_text, decision)
+        _append_tool_log(tool_log, "decision", {"sheet": sheet_text}, decision)
         context_store.add_decision(sheet, decision)
 
         if decision.get("needs_more_context"):
-        _bootstrap_context(sheet, last_user.get("content", ""), tool_log)
+            _bootstrap_context(sheet, ("\n").join(_extract_user_messages(messages)), tool_log)
             sheet_text = context_store.format_sheet(sheet)
 
     # Hinweis und Ausgaben generierung
     hint_args = {
-        "task": last_user.get("content", ""),
+        "task": _extract_user_messages(messages)[0],
         "solution": context_store.first_math_solution(sheet),
         "history": history_text,
         "retrival": context_store.get_retrival(sheet),