422 lines
16 KiB
Python
422 lines
16 KiB
Python
"""
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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: 0.6.11 (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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"""
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from __future__ import annotations
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import os
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import time
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import logging
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from functools import lru_cache
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from typing import Any, Dict, List, Tuple, Iterable, Optional
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from app.config import get_settings
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from app.models.dto import (
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QueryRequest,
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QueryResponse,
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QueryHit,
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Explanation,
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ScoreBreakdown,
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Reason,
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EdgeDTO
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)
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import app.core.qdrant as qdr
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import app.core.qdrant_points as qp
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import app.services.embeddings_client as ec
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import app.core.graph_adapter as ga
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try:
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import yaml # type: ignore[import]
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except Exception: # pragma: no cover
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yaml = None # type: ignore[assignment]
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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).
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Priorität: 1. retriever.yaml -> 2. Environment/Settings -> 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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edge = float(getattr(settings, "RETRIEVER_W_EDGE", 0.0))
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cent = float(getattr(settings, "RETRIEVER_W_CENT", 0.0))
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config_path = os.getenv("MINDNET_RETRIEVER_CONFIG", "config/retriever.yaml")
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if yaml is None:
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return sem, edge, cent
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try:
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if os.path.exists(config_path):
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with open(config_path, "r", encoding="utf-8") as f:
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data = yaml.safe_load(f) or {}
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scoring = data.get("scoring", {}) or {}
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sem = float(scoring.get("semantic_weight", sem))
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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 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 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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"""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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if not req.query:
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raise ValueError("QueryRequest benötigt entweder 'query' oder 'query_vector'")
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settings = get_settings()
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model_name = settings.MODEL_NAME
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try:
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return ec.embed_text(req.query, model_name=model_name)
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except TypeError:
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return ec.embed_text(req.query)
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def _semantic_hits(
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client: Any,
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prefix: str,
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vector: List[float],
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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 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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for pid, score, payload in raw_hits:
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results.append((str(pid), float(score), dict(payload or {})))
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return results
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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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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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return 1.2
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if status == "draft":
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return 0.5
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return 1.0
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def _compute_total_score(
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semantic_score: float,
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payload: Dict[str, Any],
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edge_bonus_raw: float = 0.0,
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cent_bonus_raw: float = 0.0,
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dynamic_edge_boosts: Dict[str, float] = None
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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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Score = (Similarity * StatusMult) * (1 + (Weight-1) + DynamicBoost)
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"""
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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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# 1. Base Score (Semantik * Lifecycle)
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base_val = float(semantic_score) * status_mult
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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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# 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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# 4. Final Merge
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total = base_val * (1.0 + (node_weight - 1.0) + dynamic_graph_impact)
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# Floor-Schutz
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final_score = max(0.001, float(total))
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return {
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"total": final_score,
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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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"edge_impact_final": edge_impact,
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"cent_impact_final": cent_impact
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}
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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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payload: Dict[str, Any],
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scoring_debug: Dict[str, Any],
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subgraph: Optional[ga.Subgraph],
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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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"""Erstellt ein detailliertes Explanation-Objekt inkl. 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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status_mult = scoring_debug["status_multiplier"]
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graph_bf = scoring_debug["graph_boost_factor"]
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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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breakdown = ScoreBreakdown(
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semantic_contribution=base_val,
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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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node_weight=type_weight,
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status_multiplier=status_mult,
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graph_boost_factor=graph_bf
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)
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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 > 0.70:
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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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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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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 (WP-22 B) - Beachtet eingehende UND ausgehende Kanten
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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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raw_edges.extend(subgraph.get_incoming_edges(target_note_id) or [])
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if hasattr(subgraph, "get_outgoing_edges"):
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raw_edges.extend(subgraph.get_outgoing_edges(target_note_id) or [])
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for edge in raw_edges:
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src = edge.get("source")
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tgt = edge.get("target")
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k = edge.get("kind", "edge")
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prov = edge.get("provenance", "rule")
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conf = float(edge.get("confidence", 1.0))
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is_incoming = (tgt == target_note_id)
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direction = "in" if is_incoming else "out"
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# neighbor_id FIX: Variable sicher innerhalb der Schleife definieren
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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=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_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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# e.source/e.target sind durch e.direction eindeutig zugeordnet
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peer_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 '{peer_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 liegt zentral im aktuellen 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_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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expand = getattr(req, "expand", None)
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if not expand:
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return 0, None
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if isinstance(expand, dict):
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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 1, None
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def _build_hits_from_semantic(
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hits: Iterable[Tuple[str, float, Dict[str, Any]]],
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top_k: int,
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used_mode: str,
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subgraph: ga.Subgraph | None = None,
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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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"""Wandelt semantische Roh-Treffer in strukturierte QueryHits um und berechnet WP-22 Scores."""
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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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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:
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pass
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# Messbare Scoring-Daten berechnen
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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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)
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enriched.append((pid, float(semantic_score), payload, debug_data))
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# Sortierung nach finalem 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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results: List[QueryHit] = []
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for pid, semantic_score, payload, debug in limited_hits:
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explanation_obj = None
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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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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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semantic_score=float(semantic_score),
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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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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)
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# ==============================================================================
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# 4. PUBLIC INTERFACE
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# ==============================================================================
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def hybrid_retrieve(req: QueryRequest) -> QueryResponse:
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"""Hybrid-Suche: Semantik + WP-22 Graph Intelligence."""
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client, prefix = _get_client_and_prefix()
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vector = list(req.query_vector) if req.query_vector else _get_query_vector(req)
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top_k = req.top_k or 10
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# 1. Semantische Suche
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hits = _semantic_hits(client, prefix, vector, top_k=top_k, filters=req.filters)
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# 2. Graph Expansion & Custom Weighting
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expand_depth, edge_types = _extract_expand_options(req)
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boost_edges = getattr(req, "boost_edges", {}) or {}
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subgraph: ga.Subgraph | None = None
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if expand_depth > 0 and hits:
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seed_ids = list({h[2].get("note_id") for h in hits if h[2].get("note_id")})
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if seed_ids:
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try:
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subgraph = ga.expand(client, prefix, seed_ids, depth=expand_depth, edge_types=edge_types)
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# WP-22: Transformation der Gewichte im RAM-Graphen vor Bonus-Berechnung
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if subgraph and hasattr(subgraph, "graph"):
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for u, v, data in subgraph.graph.edges(data=True):
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# Provenance Weighting (Concept 2.6)
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prov = data.get("provenance", "rule")
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prov_w = 1.0 if prov == "explicit" else (0.9 if prov == "smart" else 0.7)
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# Intent Boost Multiplikator
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k = data.get("kind")
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intent_multiplier = boost_edges.get(k, 1.0)
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# Finales Gewicht setzen
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data["weight"] = data.get("weight", 1.0) * prov_w * intent_multiplier
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except Exception as e:
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logger.error(f"Graph expansion failed: {e}")
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subgraph = None
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# 3. Scoring & Result Generation
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return _build_hits_from_semantic(
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hits,
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top_k,
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"hybrid",
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subgraph,
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req.explain,
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boost_edges
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)
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class Retriever:
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"""Wrapper-Klasse für FastAPI-Integration."""
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async def search(self, request: QueryRequest) -> QueryResponse:
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return hybrid_retrieve(request) |