From 5278c75ac1f51806548dbbf9401c3dcf031da20b Mon Sep 17 00:00:00 2001
From: Lars <Lars@stommer.de>
Date: Tue, 23 Dec 2025 22:02:32 +0100
Subject: [PATCH] bug fix

---
 app/core/ingestion.py | 25 ++++++++++++++++++-------
 config/prompts.yaml   |  4 ++--
 2 files changed, 20 insertions(+), 9 deletions(-)

diff --git a/app/core/ingestion.py b/app/core/ingestion.py
index 5aa47e2..a86ed28 100644
--- a/app/core/ingestion.py
+++ b/app/core/ingestion.py
@@ -3,8 +3,8 @@ FILE: app/core/ingestion.py
 DESCRIPTION: Haupt-Ingestion-Logik. Transformiert Markdown in den Graphen.
              WP-20: Smart Edge Allocation via Hybrid LLM (OpenRouter/Gemini).
              WP-22: Content Lifecycle, Edge Registry Validation & Multi-Hash.
-FIX: Bulletproof JSON Extraction & Prompt Formatting Safety.
-VERSION: 2.11.6
+FIX: Behebung des AttributeError und Härtung des Prompt-Formattings.
+VERSION: 2.11.7
 STATUS: Active
 """
 import os
@@ -56,7 +56,7 @@ def extract_json_from_response(text: str) -> Any:
     try:
         return json.loads(clean_text.strip())
     except json.JSONDecodeError:
-        # Letzter Versuch: Alles vor der ersten [ und nach der letzten ] entfernen
+        # Versuch: Alles vor der ersten [ und nach der letzten ] entfernen
         start = clean_text.find('[')
         end = clean_text.rfind(']') + 1
         if start != -1 and end != 0:
@@ -120,6 +120,16 @@ class IngestionService:
         except Exception as e:
             logger.warning(f"DB init warning: {e}")
 
+    def _get_chunk_config_by_profile(self, profile_name: str, note_type: str) -> Dict[str, Any]:
+        """Holt die Chunker-Parameter für ein spezifisches Profil."""
+        profiles = self.registry.get("chunking_profiles", {})
+        if profile_name in profiles:
+            cfg = profiles[profile_name].copy()
+            if "overlap" in cfg and isinstance(cfg["overlap"], list): 
+                cfg["overlap"] = tuple(cfg["overlap"])
+            return cfg
+        return get_chunk_config(note_type)
+
     async def _perform_smart_edge_allocation(self, text: str, note_id: str) -> List[Dict]:
         """Nutzt das Hybrid LLM für die semantische Kanten-Extraktion."""
         provider = "openrouter" if self.settings.OPENROUTER_API_KEY else self.settings.MINDNET_LLM_PROVIDER
@@ -133,7 +143,7 @@ class IngestionService:
         template = self.llm.get_prompt("edge_extraction", provider)
         
         try:
-            # FIX: Format-Safety Block
+            # FIX: Format-Safety Block gegen KeyError: '"to"'
             try:
                 prompt = template.format(
                     text=text[:6000], 
@@ -141,7 +151,7 @@ class IngestionService:
                     valid_types=valid_types_str
                 )
             except KeyError as ke:
-                logger.error(f"❌ [Ingestion] Prompt-Template Fehler (Fehlende Maskierung in YAML?): {ke}")
+                logger.error(f"❌ [Ingestion] Prompt-Template Fehler (Variable {ke} fehlt). Prüfe prompts.yaml Maskierung.")
                 return []
 
             response_json = await self.llm.generate_raw_response(
@@ -149,7 +159,6 @@ class IngestionService:
                 provider=provider, model_override=model
             )
             
-            # FIX: Robustes JSON-Parsing
             raw_data = extract_json_from_response(response_json)
             
             if isinstance(raw_data, dict):
@@ -162,7 +171,7 @@ class IngestionService:
 
             processed = []
             for item in raw_data:
-                # FIX: Typ-Check zur Vermeidung von 'str' object assignment errors
+                # FIX: Schutz vor 'str' object does not support item assignment
                 if isinstance(item, dict) and "to" in item:
                     item["provenance"] = "semantic_ai"
                     item["line"] = f"ai-{provider}"
@@ -186,6 +195,7 @@ class IngestionService:
         force_replace: bool = False, apply: bool = False, purge_before: bool = False,
         note_scope_refs: bool = False, hash_source: str = "parsed", hash_normalize: str = "canonical"
     ) -> Dict[str, Any]:
+        """Verarbeitet eine Markdown-Datei und schreibt sie in den Graphen."""
         result = {"path": file_path, "status": "skipped", "changed": False, "error": None}
 
         try:
@@ -228,6 +238,7 @@ class IngestionService:
             body_text = getattr(parsed, "body", "") or ""
             if hasattr(edge_registry, "ensure_latest"): edge_registry.ensure_latest()
 
+            # FIX: Behebung des AttributeError durch korrekten Aufruf der Klassenmethode
             chunk_config = self._get_chunk_config_by_profile(effective_profile, note_type)
             chunks = await assemble_chunks(fm["id"], body_text, fm["type"], config=chunk_config)
             chunk_pls = make_chunk_payloads(fm, note_pl["path"], chunks, note_text=body_text)
diff --git a/config/prompts.yaml b/config/prompts.yaml
index 533f3d5..2dcea78 100644
--- a/config/prompts.yaml
+++ b/config/prompts.yaml
@@ -1,6 +1,6 @@
-# config/prompts.yaml — Final V2.5.1 (Hybrid & Multi-Provider Support)
+# config/prompts.yaml — Final V2.5.2 (Strict Hybrid Support)
 # WP-20: Optimierte Cloud-Templates. 
-# FIX: Technische Maskierung (Doppel-Klammern) in Cloud-Sektionen zur Vermeidung von KeyError.
+# FIX: Technische Maskierung (Doppel-Klammern) zur Vermeidung von KeyError: '"to"'.
 # OLLAMA: Unverändert laut Benutzeranweisung.
 
 system_prompt: |