angepasster retriever
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@ -131,9 +131,8 @@ def _build_explanation(
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) -> Explanation:
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"""
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Erstellt ein detailliertes Explanation-Objekt für einen Treffer.
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Analysiert Scores, Typen und eingehende Kanten.
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Analysiert Scores, Typen und Kanten (Incoming & Outgoing).
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"""
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# 1. Weights & Config laden
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sem_w, edge_w, cent_w = _get_scoring_weights()
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try:
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@ -143,7 +142,6 @@ def _build_explanation(
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note_type = payload.get("type", "unknown")
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# 2. Score Breakdown berechnen
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breakdown = ScoreBreakdown(
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semantic_contribution=(sem_w * semantic_score * type_weight),
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edge_contribution=(edge_w * edge_bonus),
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@ -157,76 +155,87 @@ def _build_explanation(
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reasons: List[Reason] = []
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edges_dto: List[EdgeDTO] = []
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# 3. Semantische Gründe
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# 1. Semantische Gründe
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if semantic_score > 0.85:
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reasons.append(Reason(
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kind="semantic",
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message="Sehr hohe textuelle Übereinstimmung mit der Anfrage.",
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message="Sehr hohe textuelle Übereinstimmung.",
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score_impact=breakdown.semantic_contribution
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))
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elif semantic_score > 0.75:
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elif semantic_score > 0.70:
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reasons.append(Reason(
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kind="semantic",
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message="Gute textuelle Übereinstimmung.",
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score_impact=breakdown.semantic_contribution
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))
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# 4. Typ-Gründe
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if type_weight > 1.0:
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# 2. Typ-Gründe
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if type_weight != 1.0:
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msg = "Bevorzugt" if type_weight > 1.0 else "Leicht abgewertet"
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reasons.append(Reason(
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kind="type",
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message=f"Bevorzugt aufgrund des Typs '{note_type}' (Gewicht: {type_weight}).",
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score_impact=(sem_w * semantic_score * (type_weight - 1.0)) # Delta
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))
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elif type_weight < 1.0:
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reasons.append(Reason(
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kind="type",
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message=f"Abgewertet aufgrund des Typs '{note_type}' (Gewicht: {type_weight}).",
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score_impact=None
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message=f"{msg} aufgrund des Typs '{note_type}' (Gewicht: {type_weight}).",
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score_impact=(sem_w * semantic_score * (type_weight - 1.0))
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))
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# 5. Graph-Gründe (Incoming Edges)
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# 3. Graph-Gründe (Edges)
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if subgraph and node_key and edge_bonus > 0:
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# Wir suchen nach eingehenden Kanten, die diesen Bonus verursacht haben.
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# graph_adapter.py (v0.4.0) muss get_incoming_edges bereitstellen.
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# Wir sammeln die stärksten Kanten (egal ob rein oder raus),
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# die zum Score beitragen.
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# A) Outgoing (Ich verweise auf...) - Das ist oft der Hub-Score
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if hasattr(subgraph, "get_outgoing_edges"):
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outgoing = subgraph.get_outgoing_edges(node_key)
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for edge in outgoing:
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target = edge.get("target", "Unknown")
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kind = edge.get("kind", "edge")
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weight = edge.get("weight", 0.0)
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# Nur relevante Kanten aufnehmen
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if weight > 0.05:
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edges_dto.append(EdgeDTO(
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id=f"{node_key}->{target}:{kind}",
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kind=kind, source=node_key, target=target, weight=weight, direction="out"
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))
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# B) Incoming (Ich werde verwiesen von...)
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if hasattr(subgraph, "get_incoming_edges"):
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incoming = subgraph.get_incoming_edges(node_key)
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# Sortieren nach Gewicht (stärkste zuerst)
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incoming_sorted = sorted(incoming, key=lambda e: e.get("weight", 0.0), reverse=True)
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# Top-3 Gründe extrahieren
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for idx, edge in enumerate(incoming_sorted[:3]):
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for edge in incoming:
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src = edge.get("source", "Unknown")
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kind = edge.get("kind", "edge")
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weight = edge.get("weight", 0.0)
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msg = f"Verbunden mit '{src}' via '{kind}'"
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reasons.append(Reason(
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kind="edge",
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message=msg,
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score_impact=(edge_w * weight),
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details={"source": src, "kind": kind, "weight": weight}
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))
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if weight > 0.05:
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edges_dto.append(EdgeDTO(
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id=f"{src}->{node_key}:{kind}",
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kind=kind, source=src, target=node_key, weight=weight, direction="in"
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))
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# EdgeDTO für die API
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edges_dto.append(EdgeDTO(
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id=f"{src}->{node_key}:{kind}", # Synthetische ID für Anzeige
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kind=kind,
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source=src,
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target=node_key,
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weight=weight,
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direction="in"
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))
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else:
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# Fallback, falls GraphAdapter noch alt
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reasons.append(Reason(kind="edge", message="Knoten ist im Kontext-Graphen vernetzt."))
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# Sortieren nach Gewicht und Top-3 als Reasons generieren
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all_edges = sorted(edges_dto, key=lambda e: e.weight, reverse=True)
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# 6. Centrality Gründe
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if cent_bonus > 0.05:
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for top_edge in all_edges[:3]:
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# Impact schätzen (grob, da Edge-Bonus eine Summe ist)
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impact = edge_w * top_edge.weight
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if top_edge.direction == "out":
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msg = f"Verweist auf '{top_edge.target}' via '{top_edge.kind}'"
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else:
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msg = f"Referenziert von '{top_edge.source}' via '{top_edge.kind}'"
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reasons.append(Reason(
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kind="edge",
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message=msg,
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score_impact=impact,
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details={"kind": top_edge.kind, "weight": top_edge.weight}
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))
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# 4. Centrality
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if cent_bonus > 0.01:
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reasons.append(Reason(
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kind="centrality",
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message="Knoten ist ein zentraler Hub im Kontext der Anfrage.",
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message="Knoten liegt zentral im Kontext.",
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score_impact=breakdown.centrality_contribution
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))
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@ -235,7 +244,6 @@ def _build_explanation(
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reasons=reasons,
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related_edges=edges_dto if edges_dto else None
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)
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# --- End Explanation Logic ---
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