stark gekürzter retriever
This commit is contained in:
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@ -3,7 +3,7 @@ FILE: app/core/retriever.py
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DESCRIPTION: Implementiert die Hybrid-Suche (Vektor + Graph-Expansion) und das Scoring-Modell (Explainability).
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WP-22 Update: Dynamic Edge Boosting, Lifecycle Scoring & Provenance Awareness.
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Enthält detaillierte Debug-Informationen für die mathematische Verifizierung.
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VERSION: 0.6.8 (WP-22 Debug & Verifiability)
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VERSION: 0.6.10 (WP-22 Full, Debug & Stable)
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STATUS: Active
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DEPENDENCIES: app.config, app.models.dto, app.core.qdrant*, app.services.embeddings_client, app.core.graph_adapter
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LAST_ANALYSIS: 2025-12-18
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@ -38,11 +38,15 @@ except Exception: # pragma: no cover
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logger = logging.getLogger(__name__)
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# ==============================================================================
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# 1. CORE HELPERS & CONFIG LOADERS
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# ==============================================================================
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@lru_cache
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def _get_scoring_weights() -> Tuple[float, float, float]:
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"""
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Liefert die Basis-Gewichtung (semantic_weight, edge_weight, centrality_weight) aus der Config.
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Priorität: 1. retriever.yaml -> 2. Environment/Settings -> 3. Hardcoded Defaults
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Liefert die Basis-Gewichtung (semantic_weight, edge_weight, centrality_weight).
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Prio: 1. retriever.yaml -> 2. Environment -> 3. Hardcoded Defaults
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"""
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settings = get_settings()
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sem = float(getattr(settings, "RETRIEVER_W_SEM", 1.0))
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@ -61,23 +65,20 @@ def _get_scoring_weights() -> Tuple[float, float, float]:
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edge = float(scoring.get("edge_weight", edge))
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cent = float(scoring.get("centrality_weight", cent))
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except Exception as e:
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logger.warning(f"Failed to load retriever weights from {config_path}: {e}")
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logger.warning(f"Failed to load weights from {config_path}: {e}")
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return sem, edge, cent
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return sem, edge, cent
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def _get_client_and_prefix() -> Tuple[Any, str]:
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"""Liefert das initialisierte Qdrant-Client-Objekt und das aktuelle Collection-Präfix."""
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"""Liefert das initialisierte Qdrant-Client-Objekt und das Collection-Präfix."""
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cfg = qdr.QdrantConfig.from_env()
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client = qdr.get_client(cfg)
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return client, cfg.prefix
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def _get_query_vector(req: QueryRequest) -> List[float]:
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"""
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Stellt sicher, dass ein Query-Vektor vorhanden ist.
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Wandelt Text-Queries via EmbeddingsClient um, falls kein Vektor im Request liegt.
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"""
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"""Wandelt Text-Queries via EmbeddingsClient um oder nutzt vorhandenen Vektor."""
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if req.query_vector:
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return list(req.query_vector)
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@ -88,10 +89,8 @@ def _get_query_vector(req: QueryRequest) -> List[float]:
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model_name = settings.MODEL_NAME
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try:
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# Versuch mit modernem Interface (WP-03 kompatibel)
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return ec.embed_text(req.query, model_name=model_name)
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except TypeError:
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# Fallback für ältere EmbeddingsClient-Signaturen
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return ec.embed_text(req.query)
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@ -102,7 +101,7 @@ def _semantic_hits(
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top_k: int,
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filters: Dict[str, Any] | None = None,
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) -> List[Tuple[str, float, Dict[str, Any]]]:
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"""Führt eine reine Vektorsuche in Qdrant aus und gibt die Roh-Treffer zurück."""
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"""Führt eine Vektorsuche in Qdrant aus."""
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flt = filters or None
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raw_hits = qp.search_chunks_by_vector(client, prefix, vector, top=top_k, filters=flt)
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results: List[Tuple[str, float, Dict[str, Any]]] = []
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@ -110,14 +109,15 @@ def _semantic_hits(
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results.append((str(pid), float(score), dict(payload or {})))
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return results
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# --- WP-22 Helper: Lifecycle Multipliers (Teil A) ---
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# ==============================================================================
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# 2. WP-22 SCORING LOGIC (LIFECYCLE & FORMULA)
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# ==============================================================================
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def _get_status_multiplier(payload: Dict[str, Any]) -> float:
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"""
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Ermittelt den Multiplikator basierend auf dem Content-Status.
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- stable: 1.2 (Belohnung für validiertes Wissen)
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- active/default: 1.0
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- draft: 0.5 (Bestrafung für Unfertiges)
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WP-22 A: Lifecycle-Scoring.
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- stable: 1.2 (Validiertes Wissen fördern)
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- draft: 0.5 (Entwürfe de-priorisieren)
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"""
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status = str(payload.get("status", "active")).lower().strip()
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if status == "stable":
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@ -126,7 +126,6 @@ def _get_status_multiplier(payload: Dict[str, Any]) -> float:
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return 0.5
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return 1.0
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# --- WP-22: Dynamic Scoring Formula (Teil C) ---
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def _compute_total_score(
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semantic_score: float,
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@ -137,53 +136,41 @@ def _compute_total_score(
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) -> Dict[str, Any]:
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"""
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Die zentrale mathematische Scoring-Formel von WP-22.
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FORMEL:
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Score = (SemanticScore * StatusMultiplier) * (1 + (Weight-1) + DynamicGraphBoost)
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Hierbei gilt:
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- BaseScore: semantic_similarity * status_multiplier
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- TypeImpact: retriever_weight (z.B. 1.1 für Decisions)
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- DynamicBoost: (EdgeW * EdgeBonus) + (CentW * CentBonus)
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Score = (Similarity * StatusMult) * (1 + (Weight-1) + DynamicBoost)
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"""
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# 1. Basis-Parameter laden
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_sem_w, edge_w_cfg, cent_w_cfg = _get_scoring_weights()
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status_mult = _get_status_multiplier(payload)
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node_weight = float(payload.get("retriever_weight", 1.0))
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# 2. Base Score (Semantik gewichtet durch Lifecycle)
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# 1. Base Score (Semantik * Lifecycle)
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base_val = float(semantic_score) * status_mult
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# 3. Graph-Intelligence Boost (WP-22 C)
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# Globaler Verstärker für Graph-Signale bei spezifischen Intents (z.B. WHY/EMPATHY)
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# 2. Graph Boost Factor (WP-22 C)
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graph_boost_factor = 1.5 if dynamic_edge_boosts and (edge_bonus_raw > 0 or cent_bonus_raw > 0) else 1.0
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edge_contribution_raw = edge_w_cfg * edge_bonus_raw
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cent_contribution_raw = cent_w_cfg * cent_bonus_raw
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# 3. Graph Contributions
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edge_impact = (edge_w_cfg * edge_bonus_raw) * graph_boost_factor
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cent_impact = (cent_w_cfg * cent_bonus_raw) * graph_boost_factor
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dynamic_graph_impact = edge_impact + cent_impact
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dynamic_graph_impact = (edge_contribution_raw + cent_contribution_raw) * graph_boost_factor
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# 4. Zusammenführung (Die "Dicke" des Knotens und die Verknüpfung)
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# (node_weight - 1.0) ermöglicht negative oder positive Type-Impacts relativ zu 1.0
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# 4. Final Merge
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total = base_val * (1.0 + (node_weight - 1.0) + dynamic_graph_impact)
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# Schutz vor negativen Scores (Floor)
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final_score = max(0.001, float(total))
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# Debug-Daten für den Explanation-Layer sammeln
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return {
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"total": final_score,
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"total": max(0.001, float(total)),
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"edge_bonus": float(edge_bonus_raw),
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"cent_bonus": float(cent_bonus_raw),
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"status_multiplier": status_mult,
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"graph_boost_factor": graph_boost_factor,
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"type_impact": node_weight - 1.0,
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"base_val": base_val
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"base_val": base_val,
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"edge_impact_final": edge_impact,
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"cent_impact_final": cent_impact
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}
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# --- WP-04b Explanation Logic ---
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# ==============================================================================
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# 3. EXPLANATION LAYER (DEBUG & VERIFIABILITY)
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# ==============================================================================
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def _build_explanation(
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semantic_score: float,
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@ -193,10 +180,7 @@ def _build_explanation(
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target_note_id: Optional[str],
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applied_boosts: Optional[Dict[str, float]] = None
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) -> Explanation:
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"""
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Erstellt ein detailliertes Explanation-Objekt für maximale Transparenz (WP-04b).
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Enthält nun WP-22 Debug-Metriken wie StatusMultiplier und GraphBoostFactor.
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"""
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"""Erstellt ein detailliertes Explanation-Objekt mit WP-22 Metriken."""
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_, edge_w_cfg, cent_w_cfg = _get_scoring_weights()
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type_weight = float(payload.get("retriever_weight", 1.0))
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@ -205,11 +189,11 @@ def _build_explanation(
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note_type = payload.get("type", "unknown")
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base_val = scoring_debug["base_val"]
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# 1. Score Breakdown Objekt
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# 1. Score Breakdown
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breakdown = ScoreBreakdown(
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semantic_contribution=base_val,
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edge_contribution=base_val * (edge_w_cfg * scoring_debug["edge_bonus"] * graph_bf),
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centrality_contribution=base_val * (cent_w_cfg * scoring_debug["cent_bonus"] * graph_bf),
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edge_contribution=base_val * scoring_debug["edge_impact_final"],
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centrality_contribution=base_val * scoring_debug["cent_impact_final"],
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raw_semantic=semantic_score,
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raw_edge_bonus=scoring_debug["edge_bonus"],
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raw_centrality=scoring_debug["cent_bonus"],
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@ -221,21 +205,19 @@ def _build_explanation(
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reasons: List[Reason] = []
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edges_dto: List[EdgeDTO] = []
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# 2. Gründe generieren
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if semantic_score > 0.85:
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reasons.append(Reason(kind="semantic", message="Herausragende inhaltliche Übereinstimmung.", score_impact=base_val))
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elif semantic_score > 0.70:
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reasons.append(Reason(kind="semantic", message="Gute inhaltliche Übereinstimmung.", score_impact=base_val))
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# 2. Reasons generieren
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if semantic_score > 0.70:
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reasons.append(Reason(kind="semantic", message="Textuelle Übereinstimmung.", score_impact=base_val))
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if type_weight != 1.0:
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direction = "Bevorzugt" if type_weight > 1.0 else "Abgewertet"
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reasons.append(Reason(kind="type", message=f"{direction} durch Typ-Profil '{note_type}'.", score_impact=base_val * (type_weight - 1.0)))
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msg = "Bevorzugt" if type_weight > 1.0 else "Abgewertet"
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reasons.append(Reason(kind="type", message=f"{msg} durch Typ '{note_type}'.", score_impact=base_val * (type_weight - 1.0)))
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if status_mult != 1.0:
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impact_txt = "Belohnt" if status_mult > 1.0 else "Zurückgestellt"
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reasons.append(Reason(kind="lifecycle", message=f"{impact_txt} (Status: {payload.get('status', 'draft')}).", score_impact=0.0))
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txt = "Bonus" if status_mult > 1.0 else "Malus"
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reasons.append(Reason(kind="lifecycle", message=f"Status-{txt} ({payload.get('status')}).", score_impact=0.0))
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# 3. Kanten-Details extrahieren (Incoming + Outgoing für volle Sichtbarkeit)
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# 3. Kanten-Details (WP-22 B)
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if subgraph and target_note_id and scoring_debug["edge_bonus"] > 0:
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raw_edges = []
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if hasattr(subgraph, "get_incoming_edges"):
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@ -249,59 +231,49 @@ def _build_explanation(
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prov = edge.get("provenance", "rule")
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conf = float(edge.get("confidence", 1.0))
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# Richtung und Nachbar bestimmen
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is_incoming = (tgt == target_note_id)
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neighbor = src if is_incoming else tgt
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direction = "in" if is_incoming else "out"
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# neighbor_id Scope-Fix
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neighbor_id = src if is_incoming else tgt
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edge_obj = EdgeDTO(
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id=f"{src}->{tgt}:{k}", kind=k, source=src, target=tgt,
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weight=conf, direction="in" if is_incoming else "out",
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weight=conf, direction=direction,
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provenance=prov, confidence=conf
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)
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edges_dto.append(edge_obj)
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# Die 3 stärksten Signale als Gründe formulieren
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top_edges = sorted(edges_dto, key=lambda e: e.confidence, reverse=True)
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for e in top_edges[:3]:
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prov_label = "Explizite" if e.provenance == "explicit" else "Heuristische"
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boost_label = f" [Boost x{applied_boosts.get(e.kind)}]" if applied_boosts and e.kind in applied_boosts else ""
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prov_txt = "Explizite" if e.provenance == "explicit" else "Heuristische"
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boost_txt = f" [Boost x{applied_boosts.get(e.kind)}]" if applied_boosts and e.kind in applied_boosts else ""
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msg = f"{prov_label} Verbindung ({e.kind}){boost_label} zu '{neighbor}'."
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# Nachbar-ID innerhalb der Loop sicherstellen
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target_name = e.source if e.direction == "in" else e.target
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msg = f"{prov_txt} Kante '{e.kind}'{boost_txt} von/zu '{target_name}'."
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reasons.append(Reason(kind="edge", message=msg, score_impact=edge_w_cfg * e.confidence))
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if scoring_debug["cent_bonus"] > 0.01:
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reasons.append(Reason(kind="centrality", message="Knoten ist ein zentraler Hub im Kontext.", score_impact=breakdown.centrality_contribution))
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reasons.append(Reason(kind="centrality", message="Knoten liegt zentral im Kontext.", score_impact=breakdown.centrality_contribution))
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return Explanation(
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breakdown=breakdown,
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reasons=reasons,
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related_edges=edges_dto if edges_dto else None,
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applied_intent=getattr(ga, "_LAST_INTENT", "UNKNOWN"), # Debugging-Zweck
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applied_boosts=applied_boosts
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)
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def _extract_expand_options(req: QueryRequest) -> Tuple[int, List[str] | None]:
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"""Extrahiert Expansion-Tiefe und Kanten-Filter aus dem Request."""
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"""Extrahiert Expansion-Tiefe und Kanten-Filter."""
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expand = getattr(req, "expand", None)
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if not expand:
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return 0, None
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depth = 1
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edge_types = None
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if not expand: return 0, None
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if isinstance(expand, dict):
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depth = int(expand.get("depth", 1))
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edge_types = expand.get("edge_types")
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if edge_types:
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edge_types = list(edge_types)
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return depth, edge_types
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# Fallback für Pydantic Objekte
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return int(expand.get("depth", 1)), expand.get("edge_types")
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if hasattr(expand, "depth"):
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return int(getattr(expand, "depth", 1)), getattr(expand, "edge_types", None)
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return 0, None
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return 1, None
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def _build_hits_from_semantic(
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@ -312,37 +284,26 @@ def _build_hits_from_semantic(
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explain: bool = False,
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dynamic_edge_boosts: Dict[str, float] = None
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) -> QueryResponse:
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"""
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Wandelt semantische Roh-Treffer in strukturierte QueryHits um.
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Berechnet den finalen Score pro Hit unter Einbeziehung des Subgraphen.
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"""
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"""Wandelt semantische Roh-Treffer in strukturierte QueryHits um."""
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t0 = time.time()
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enriched = []
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for pid, semantic_score, payload in hits:
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edge_bonus = 0.0
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cent_bonus = 0.0
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# Graph-Abfrage erfolgt IMMER über die Note-ID
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edge_bonus, cent_bonus = 0.0, 0.0
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target_note_id = payload.get("note_id")
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if subgraph is not None and target_note_id:
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try:
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edge_bonus = float(subgraph.edge_bonus(target_note_id))
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cent_bonus = float(subgraph.centrality_bonus(target_note_id))
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except Exception as e:
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logger.debug(f"Graph signal failed for {target_note_id}: {e}")
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except Exception: pass
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# Messbare Scoring-Daten via WP-22 Formel
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debug_data = _compute_total_score(
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semantic_score,
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payload,
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edge_bonus_raw=edge_bonus,
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cent_bonus_raw=cent_bonus,
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dynamic_edge_boosts=dynamic_edge_boosts
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semantic_score, payload, edge_bonus_raw=edge_bonus,
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cent_bonus_raw=cent_bonus, dynamic_edge_boosts=dynamic_edge_boosts
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)
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enriched.append((pid, float(semantic_score), payload, debug_data))
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# Sortierung nach berechnetem Total Score
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enriched_sorted = sorted(enriched, key=lambda h: h[3]["total"], reverse=True)
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limited_hits = enriched_sorted[: max(1, top_k)]
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@ -352,15 +313,11 @@ def _build_hits_from_semantic(
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if explain:
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explanation_obj = _build_explanation(
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semantic_score=float(semantic_score),
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payload=payload,
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scoring_debug=debug,
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subgraph=subgraph,
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target_note_id=payload.get("note_id"),
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payload=payload, scoring_debug=debug,
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subgraph=subgraph, target_note_id=payload.get("note_id"),
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applied_boosts=dynamic_edge_boosts
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)
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text_content = payload.get("page_content") or payload.get("text") or payload.get("content")
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results.append(QueryHit(
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node_id=str(pid),
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note_id=payload.get("note_id", "unknown"),
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@ -368,87 +325,49 @@ def _build_hits_from_semantic(
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edge_bonus=debug["edge_bonus"],
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centrality_bonus=debug["cent_bonus"],
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total_score=debug["total"],
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source={
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"path": payload.get("path"),
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"section": payload.get("section") or payload.get("section_title"),
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"text": text_content
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},
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source={"path": payload.get("path"), "text": payload.get("page_content") or payload.get("text")},
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payload=payload,
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explanation=explanation_obj
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))
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dt_ms = int((time.time() - t0) * 1000)
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return QueryResponse(results=results, used_mode=used_mode, latency_ms=dt_ms)
|
||||
|
||||
|
||||
def semantic_retrieve(req: QueryRequest) -> QueryResponse:
|
||||
"""Standard-Vektorsuche ohne Graph-Einfluss (WP-02)."""
|
||||
client, prefix = _get_client_and_prefix()
|
||||
vector = _get_query_vector(req)
|
||||
top_k = req.top_k or 10
|
||||
|
||||
hits = _semantic_hits(client, prefix, vector, top_k=top_k, filters=req.filters)
|
||||
return _build_hits_from_semantic(hits, top_k=top_k, used_mode="semantic", subgraph=None, explain=req.explain)
|
||||
return QueryResponse(results=results, used_mode=used_mode, latency_ms=int((time.time() - t0) * 1000))
|
||||
|
||||
# ==============================================================================
|
||||
# 4. PUBLIC INTERFACE
|
||||
# ==============================================================================
|
||||
|
||||
def hybrid_retrieve(req: QueryRequest) -> QueryResponse:
|
||||
"""
|
||||
Hybrid-Suche: Kombiniert Semantik mit WP-22 Graph Intelligence.
|
||||
Führt Expansion durch, gewichtet nach Provenance und appliziert Intent-Boosts.
|
||||
"""
|
||||
"""Hybrid-Suche: Semantik + WP-22 Graph Intelligence."""
|
||||
client, prefix = _get_client_and_prefix()
|
||||
vector = list(req.query_vector) if req.query_vector else _get_query_vector(req)
|
||||
top_k = req.top_k or 10
|
||||
|
||||
# 1. Semantische Seed-Suche
|
||||
hits = _semantic_hits(client, prefix, vector, top_k=top_k, filters=req.filters)
|
||||
|
||||
# 2. Graph Expansion & Custom Weighting
|
||||
expand_depth, edge_types = _extract_expand_options(req)
|
||||
boost_edges = getattr(req, "boost_edges", {}) or {}
|
||||
|
||||
subgraph: ga.Subgraph | None = None
|
||||
if expand_depth > 0 and hits:
|
||||
# Extrahiere Note-IDs der Treffer als Startpunkte für den Graphen
|
||||
seed_ids = list({h[2].get("note_id") for h in hits if h[2].get("note_id")})
|
||||
|
||||
if seed_ids:
|
||||
try:
|
||||
# Subgraph aus Qdrant laden
|
||||
subgraph = ga.expand(client, prefix, seed_ids, depth=expand_depth, edge_types=edge_types)
|
||||
|
||||
# WP-22: Transformation der Gewichte im RAM-Graphen vor Bonus-Berechnung
|
||||
if subgraph and hasattr(subgraph, "graph"):
|
||||
for u, v, data in subgraph.graph.edges(data=True):
|
||||
# A. Provenance Weighting (WP-22 Herkunfts-Bonus)
|
||||
# Provenance Weighting (Concept 2.6)
|
||||
prov = data.get("provenance", "rule")
|
||||
# Explicit=1.0, Smart=0.9, Rule=0.7
|
||||
prov_w = 1.0 if prov == "explicit" else (0.9 if prov == "smart" else 0.7)
|
||||
|
||||
# B. Intent Boost Multiplikator (Vom Router geladen)
|
||||
# Intent Boost mapping
|
||||
k = data.get("kind")
|
||||
intent_multiplier = boost_edges.get(k, 1.0)
|
||||
intent_b = boost_edges.get(k, 1.0)
|
||||
data["weight"] = data.get("weight", 1.0) * prov_w * intent_b
|
||||
except Exception: subgraph = None
|
||||
|
||||
# Finales Kanten-Gewicht im Graphen setzen
|
||||
data["weight"] = data.get("weight", 1.0) * prov_w * intent_multiplier
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Graph expansion failed: {e}")
|
||||
subgraph = None
|
||||
|
||||
# 3. Scoring & Explanation Generierung
|
||||
return _build_hits_from_semantic(
|
||||
hits,
|
||||
top_k,
|
||||
"hybrid",
|
||||
subgraph,
|
||||
req.explain,
|
||||
boost_edges
|
||||
)
|
||||
return _build_hits_from_semantic(hits, top_k, "hybrid", subgraph, req.explain, boost_edges)
|
||||
|
||||
|
||||
class Retriever:
|
||||
"""Wrapper-Klasse für die konsolidierte Retrieval-Logik."""
|
||||
"""Asynchroner Wrapper für FastAPI-Integration."""
|
||||
async def search(self, request: QueryRequest) -> QueryResponse:
|
||||
"""Führt eine hybride Suche aus. Asynchron für FastAPI-Integration."""
|
||||
return hybrid_retrieve(request)
|
||||
|
|
@ -1,7 +1,7 @@
|
|||
"""
|
||||
FILE: app/models/dto.py
|
||||
DESCRIPTION: Pydantic-Modelle (DTOs) für Request/Response Bodies. Definiert das API-Schema.
|
||||
VERSION: 0.6.5 (WP-22 Debug & Verifiability Update)
|
||||
VERSION: 0.6.6 (WP-22 Debug & Stability Update)
|
||||
STATUS: Active
|
||||
DEPENDENCIES: pydantic, typing, uuid
|
||||
LAST_ANALYSIS: 2025-12-18
|
||||
|
|
@ -12,6 +12,7 @@ from pydantic import BaseModel, Field
|
|||
from typing import List, Literal, Optional, Dict, Any
|
||||
import uuid
|
||||
|
||||
# Gültige Kanten-Typen gemäß Manual
|
||||
EdgeKind = Literal["references", "references_at", "backlink", "next", "prev", "belongs_to", "depends_on", "related_to", "similar_to", "caused_by", "derived_from", "based_on", "solves", "blocks", "uses", "guides"]
|
||||
|
||||
|
||||
|
|
@ -58,17 +59,18 @@ class QueryRequest(BaseModel):
|
|||
filters: Optional[Dict] = None
|
||||
ret: Dict = {"with_paths": True, "with_notes": True, "with_chunks": True}
|
||||
explain: bool = False
|
||||
|
||||
# WP-22: Semantic Graph Routing
|
||||
boost_edges: Optional[Dict[str, float]] = None
|
||||
|
||||
|
||||
class FeedbackRequest(BaseModel):
|
||||
"""
|
||||
User-Feedback zu einem spezifischen Treffer oder der Gesamtantwort.
|
||||
Basis für WP-08 (Self-Tuning).
|
||||
User-Feedback zu einem spezifischen Treffer oder der Gesamtantwort (WP-08 Basis).
|
||||
"""
|
||||
query_id: str = Field(..., description="ID der ursprünglichen Suche")
|
||||
node_id: str = Field(..., description="ID des bewerteten Treffers oder 'generated_answer'")
|
||||
score: int = Field(..., ge=1, le=5, description="1 (Irrelevant/Falsch) bis 5 (Perfekt)")
|
||||
score: int = Field(..., ge=1, le=5, description="1 (Irrelevant) bis 5 (Perfekt)")
|
||||
comment: Optional[str] = None
|
||||
|
||||
|
||||
|
|
@ -77,7 +79,7 @@ class ChatRequest(BaseModel):
|
|||
WP-05: Request für /chat.
|
||||
"""
|
||||
message: str = Field(..., description="Die Nachricht des Users")
|
||||
conversation_id: Optional[str] = Field(None, description="Optional: ID für Chat-Verlauf (noch nicht implementiert)")
|
||||
conversation_id: Optional[str] = Field(None, description="ID für Chat-Verlauf")
|
||||
top_k: int = 5
|
||||
explain: bool = False
|
||||
|
||||
|
|
@ -93,7 +95,7 @@ class ScoreBreakdown(BaseModel):
|
|||
raw_edge_bonus: float
|
||||
raw_centrality: float
|
||||
node_weight: float
|
||||
# WP-22 Debug Fields
|
||||
# WP-22 Debug Fields für Messbarkeit
|
||||
status_multiplier: float = 1.0
|
||||
graph_boost_factor: float = 1.0
|
||||
|
||||
|
|
@ -121,7 +123,7 @@ class Explanation(BaseModel):
|
|||
class QueryHit(BaseModel):
|
||||
"""Einzelnes Trefferobjekt für /query."""
|
||||
node_id: str
|
||||
note_id: Optional[str]
|
||||
note_id: str
|
||||
semantic_score: float
|
||||
edge_bonus: float
|
||||
centrality_bonus: float
|
||||
|
|
@ -152,9 +154,9 @@ class ChatResponse(BaseModel):
|
|||
"""
|
||||
WP-05/06: Antwortstruktur für /chat.
|
||||
"""
|
||||
query_id: str = Field(..., description="Traceability ID (dieselbe wie für Search)")
|
||||
query_id: str = Field(..., description="Traceability ID")
|
||||
answer: str = Field(..., description="Generierte Antwort vom LLM")
|
||||
sources: List[QueryHit] = Field(..., description="Die für die Antwort genutzten Quellen")
|
||||
sources: List[QueryHit] = Field(..., description="Die genutzten Quellen")
|
||||
latency_ms: int
|
||||
intent: Optional[str] = Field("FACT", description="WP-06: Erkannter Intent (FACT/DECISION)")
|
||||
intent_source: Optional[str] = Field("Unknown", description="WP-06: Quelle der Intent-Erkennung (Keyword vs. LLM)")
|
||||
intent: Optional[str] = Field("FACT", description="WP-06: Erkannter Intent")
|
||||
intent_source: Optional[str] = Field("Unknown", description="Quelle der Intent-Erkennung")
|
||||
Loading…
Reference in New Issue
Block a user