bug fix

app/core/retriever.py

@@ -2,7 +2,7 @@
 FILE: app/core/retriever.py
 DESCRIPTION: Implementiert die Hybrid-Suche (Vektor + Graph-Expansion) und das Scoring-Modell (Explainability).
              WP-22 Update: Dynamic Edge Boosting, Lifecycle Scoring & Provenance Awareness.
-VERSION: 0.6.6 (WP-22 Scoring & Provenance)
+VERSION: 0.6.7 (WP-22 Scoring & Provenance Fix)
 STATUS: Active
 DEPENDENCIES: app.config, app.models.dto, app.core.qdrant*, app.services.embeddings_client, app.core.graph_adapter
 LAST_ANALYSIS: 2025-12-18
@@ -11,6 +11,7 @@ from __future__ import annotations
 
 import os
 import time
+import logging
 from functools import lru_cache
 from typing import Any, Dict, List, Tuple, Iterable, Optional
 
@@ -34,6 +35,7 @@ try:
 except Exception:  # pragma: no cover
     yaml = None  # type: ignore[assignment]
 
+logger = logging.getLogger(__name__)
 
 @lru_cache
 def _get_scoring_weights() -> Tuple[float, float, float]:
@@ -136,13 +138,21 @@ def _compute_total_score(
     
     # 3. Dynamic Boost (Graph-Signale)
     _sem_w, edge_w_cfg, cent_w_cfg = _get_scoring_weights()
-    dynamic_boost = (edge_w_cfg * edge_bonus_raw) + (cent_w_cfg * cent_bonus_raw)
     
-    # Falls Intent-Boosts vorliegen, verstärken wir den Dynamic Boost global
+    # Multiplikator für Intent-Boosting (Teil C)
-    if dynamic_edge_boosts and (edge_bonus_raw > 0 or cent_bonus_raw > 0):
+    graph_boost_factor = 1.5 if dynamic_edge_boosts and (edge_bonus_raw > 0 or cent_bonus_raw > 0) else 1.0
-        dynamic_boost *= 1.5
+    
+    edge_impact = (edge_w_cfg * edge_bonus_raw) * graph_boost_factor
+    cent_impact = (cent_w_cfg * cent_bonus_raw) * graph_boost_factor
+    
+    dynamic_boost = edge_impact + cent_impact
         
     total = base_score * (1.0 + config_weight + dynamic_boost)
+    
+    # Debug Logging für Berechnungs-Validierung
+    if logger.isEnabledFor(logging.DEBUG):
+        logger.debug(f"Scoring Node {payload.get('note_id')}: Base={base_score:.3f}, ConfigW={config_weight:.3f}, GraphB={dynamic_boost:.3f} -> Total={total:.3f}")
+        
     return float(total), float(edge_bonus_raw), float(cent_bonus_raw)
 
 
@@ -154,7 +164,7 @@ def _build_explanation(
     edge_bonus: float,
     cent_bonus: float,
     subgraph: Optional[ga.Subgraph],
-    node_key: Optional[str]
+    target_note_id: Optional[str]
 ) -> Explanation:
     """Erstellt ein Explanation-Objekt mit Provenance-Details."""
     _, edge_w_cfg, cent_w_cfg = _get_scoring_weights()
@@ -163,15 +173,15 @@ def _build_explanation(
     status_mult = _get_status_multiplier(payload)
     note_type = payload.get("type", "unknown")
 
-    # Breakdown für Explanation
+    # Breakdown für Explanation (Reflektiert die WP-22 Formel exakt)
-    config_w_impact = type_weight - 1.0
-    dynamic_b_impact = (edge_w_cfg * edge_bonus) + (cent_w_cfg * cent_bonus)
     base_val = semantic_score * status_mult
+    config_w_impact = type_weight - 1.0
     
+    # Zentrale Berechnung der Kontributionen für den Breakdown
     breakdown = ScoreBreakdown(
         semantic_contribution=base_val,
-        edge_contribution=base_val * dynamic_b_impact,
+        edge_contribution=base_val * (edge_w_cfg * edge_bonus),
-        centrality_contribution=0.0,
+        centrality_contribution=base_val * (cent_w_cfg * cent_bonus),
         raw_semantic=semantic_score,
         raw_edge_bonus=edge_bonus,
         raw_centrality=cent_bonus,
@@ -181,46 +191,62 @@ def _build_explanation(
     reasons: List[Reason] = []
     edges_dto: List[EdgeDTO] = []
 
+    # 1. Semantische Gründe
     if semantic_score > 0.85:
         reasons.append(Reason(kind="semantic", message="Sehr hohe textuelle Übereinstimmung.", score_impact=breakdown.semantic_contribution))
     elif semantic_score > 0.70:
         reasons.append(Reason(kind="semantic", message="Gute textuelle Übereinstimmung.", score_impact=breakdown.semantic_contribution))
 
+    # 2. Typ-Gründe
     if type_weight != 1.0:
         msg = "Bevorzugt" if type_weight > 1.0 else "Leicht abgewertet"
         reasons.append(Reason(kind="type", message=f"{msg} aufgrund des Typs '{note_type}'.", score_impact=base_val * config_w_impact))
 
-    # WP-22: Lifecycle Grund hinzufügen
+    # 3. Lifecycle-Gründe
     if status_mult != 1.0:
         msg = "Status-Bonus" if status_mult > 1.0 else "Status-Malus"
-        reasons.append(Reason(kind="lifecycle", message=f"{msg} ({payload.get('status', 'unknown')}).", score_impact=0.0))
+        reasons.append(Reason(kind="lifecycle", message=f"{msg} (Notiz ist '{payload.get('status', 'unknown')}').", score_impact=0.0))
 
-    if subgraph and node_key and edge_bonus > 0:
+    # 4. Graph-Gründe (Edges) - FIX: Beachtet eingehende UND ausgehende Kanten
-        # WP-22: Detaillierte Provenance-Gründe (Basis für WP-08)
+    if subgraph and target_note_id and edge_bonus > 0:
-        incoming_raw = subgraph.get_incoming_edges(node_key) or []
+        # Sammle alle relevanten Kanten (Incoming + Outgoing)
-        for edge in incoming_raw:
+        edges_raw = []
-            src = edge.get("source", "Unknown")
+        if hasattr(subgraph, "get_incoming_edges"):
+             edges_raw.extend(subgraph.get_incoming_edges(target_note_id) or [])
+        if hasattr(subgraph, "get_outgoing_edges"):
+             edges_raw.extend(subgraph.get_outgoing_edges(target_note_id) or [])
+             
+        for edge in edges_raw:
+            src = edge.get("source", target_note_id)
+            tgt = edge.get("target", target_note_id)
             k = edge.get("kind", "edge")
             prov = edge.get("provenance", "rule")
             conf = float(edge.get("confidence", 1.0))
             
+            # Richtung bestimmen
+            direction = "in" if tgt == target_note_id else "out"
+            peer_id = src if direction == "in" else tgt
+            
             edges_dto.append(EdgeDTO(
-                id=f"{src}->{node_key}:{k}", kind=k, source=src, target=node_key, 
+                id=f"{src}->{tgt}:{k}", kind=k, source=src, target=tgt, 
-                weight=conf, direction="in", provenance=prov, confidence=conf
+                weight=conf, direction=direction, provenance=prov, confidence=conf
             ))
 
+        # Die 3 stärksten Kanten als Begründung auflisten
         all_edges = sorted(edges_dto, key=lambda e: e.confidence, reverse=True)
-        for top_edge in all_edges[:3]:
+        for top_e in all_edges[:3]:
-            prov_txt = "Bestätigt durch" if top_edge.provenance == "explicit" else "Vermutet durch"
+            prov_txt = "Bestätigte" if top_e.provenance == "explicit" else "Vermutete (KI)"
+            dir_txt = "Referenz von" if top_e.direction == "in" else "Verweis auf"
             reasons.append(Reason(
                 kind="edge", 
-                message=f"{prov_txt} Kante '{top_edge.kind}' von '{top_edge.source}'.", 
+                message=f"{prov_txt} Kante '{top_e.kind}': {dir_txt} '{top_e.peer_id if hasattr(top_e, 'peer_id') else (top_e.source if top_e.direction=='in' else top_e.target)}'.", 
-                score_impact=edge_w_cfg * top_edge.confidence,
+                score_impact=edge_w_cfg * top_e.confidence,
-                details={"provenance": top_edge.provenance}
+                details={"provenance": top_e.provenance, "kind": top_e.kind}
             ))
 
+    # 5. Zentralitäts-Gründe
     if cent_bonus > 0.01:
-        reasons.append(Reason(kind="centrality", message="Knoten liegt zentral im Kontext.", score_impact=cent_w_cfg * cent_bonus))
+        reasons.append(Reason(kind="centrality", message="Knoten liegt zentral im aktuellen Kontext-Graphen.", score_impact=breakdown.centrality_contribution))
 
     return Explanation(breakdown=breakdown, reasons=reasons, related_edges=edges_dto if edges_dto else None)
 
@@ -266,16 +292,17 @@ def _build_hits_from_semantic(
     for pid, semantic_score, payload in hits:
         edge_bonus = 0.0
         cent_bonus = 0.0
-        node_key = payload.get("chunk_id") or payload.get("note_id")
+        # WICHTIG für WP-22: Graph-Abfragen IMMER über die Note-ID, nicht Chunk-ID
+        target_note_id = payload.get("note_id")
         
-        if subgraph is not None and node_key:
+        if subgraph is not None and target_note_id:
             try:
-                # WP-22: edge_bonus nutzt intern bereits die confidence-gewichteten Pfade
+                # edge_bonus nutzt intern bereits die confidence-gewichteten Pfade
-                edge_bonus = float(subgraph.edge_bonus(node_key))
+                edge_bonus = float(subgraph.edge_bonus(target_note_id))
             except Exception:
                 edge_bonus = 0.0
             try:
-                cent_bonus = float(subgraph.centrality_bonus(node_key))
+                cent_bonus = float(subgraph.centrality_bonus(target_note_id))
             except Exception:
                 cent_bonus = 0.0
 
@@ -288,6 +315,7 @@ def _build_hits_from_semantic(
         )
         enriched.append((pid, float(semantic_score), payload, total, eb, cb))
 
+    # Sortierung nach finalem Score
     enriched_sorted = sorted(enriched, key=lambda h: h[3], reverse=True)
     limited = enriched_sorted[: max(1, top_k)]
 
@@ -301,7 +329,7 @@ def _build_hits_from_semantic(
                 edge_bonus=eb,
                 cent_bonus=cb,
                 subgraph=subgraph,
-                node_key=payload.get("chunk_id") or payload.get("note_id")
+                target_note_id=payload.get("note_id")
             )
 
         text_content = payload.get("page_content") or payload.get("text") or payload.get("content")