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312 lines
12 KiB
Python
312 lines
12 KiB
Python
"""
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FILE: app/core/retrieval/retriever.py
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DESCRIPTION: Haupt-Schnittstelle für die Suche. Orchestriert Vektorsuche und Graph-Expansion.
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Nutzt retriever_scoring.py für die WP-22 Logik.
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MODULARISIERUNG: Verschoben in das retrieval-Paket für WP-14.
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VERSION: 0.6.16
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STATUS: Active
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DEPENDENCIES: app.config, app.models.dto, app.core.database*, app.core.graph_adapter
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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 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, QueryResponse, QueryHit,
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Explanation, ScoreBreakdown, Reason, EdgeDTO
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)
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# MODULARISIERUNG: Neue Import-Pfade für die Datenbank-Ebene
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import app.core.database.qdrant as qdr
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import app.core.database.qdrant_points as qp
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import app.services.embeddings_client as ec
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import app.core.graph.graph_subgraph as ga
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# Mathematische Engine importieren
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from app.core.retrieval.retriever_scoring import get_weights, compute_wp22_score
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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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def _get_client_and_prefix() -> Tuple[Any, str]:
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"""Initialisiert Qdrant Client und lädt Collection-Prefix via database-Paket."""
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cfg = qdr.QdrantConfig.from_env()
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return qdr.get_client(cfg), cfg.prefix
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def _get_query_vector(req: QueryRequest) -> List[float]:
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"""
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Vektorisiert die Anfrage.
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FIX: Enthält try-except Block für unterschiedliche Signaturen von ec.embed_text.
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"""
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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("Kein Text oder Vektor für die Suche angegeben.")
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settings = get_settings()
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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=settings.MODEL_NAME)
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except TypeError:
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# Fallback für Signaturen, die 'model_name' nicht als Keyword akzeptieren
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logger.debug("ec.embed_text does not accept 'model_name' keyword. Falling back.")
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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: Optional[Dict] = None
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) -> List[Tuple[str, float, Dict[str, Any]]]:
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"""Führt die Vektorsuche via database-Points-Modul durch."""
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raw_hits = qp.search_chunks_by_vector(client, prefix, vector, top=top_k, filters=filters)
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# Strikte Typkonvertierung für Stabilität
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return [(str(hit[0]), float(hit[1]), dict(hit[2] or {})) for hit in raw_hits]
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# ==============================================================================
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# 2. 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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"""
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Transformiert mathematische Scores und Graph-Signale in eine menschenlesbare Erklärung.
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Behebt Pydantic ValidationErrors durch explizite String-Sicherung.
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"""
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_, edge_w_cfg, _ = get_weights()
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base_val = scoring_debug["base_val"]
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# 1. Detaillierter mathematischer Breakdown
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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=float(payload.get("retriever_weight", 1.0)),
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status_multiplier=scoring_debug["status_multiplier"],
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graph_boost_factor=scoring_debug["graph_boost_factor"]
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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 für Semantik hinzufügen
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if semantic_score > 0.85:
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reasons.append(Reason(kind="semantic", message="Sehr hohe textuelle Übereinstimmung.", score_impact=base_val))
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elif semantic_score > 0.70:
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reasons.append(Reason(kind="semantic", message="Inhaltliche Übereinstimmung.", score_impact=base_val))
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# 3. Gründe für Typ und Lifecycle
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type_weight = float(payload.get("retriever_weight", 1.0))
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if type_weight != 1.0:
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msg = "Bevorzugt" if type_weight > 1.0 else "De-priorisiert"
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reasons.append(Reason(kind="type", message=f"{msg} durch Typ-Profil.", score_impact=base_val * (type_weight - 1.0)))
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# 4. Kanten-Verarbeitung (Graph-Intelligence)
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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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# FIX: Zwingende String-Konvertierung für Pydantic-Stabilität
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src = str(edge.get("source") or "note_root")
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tgt = str(edge.get("target") or target_note_id or "unknown_target")
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kind = str(edge.get("kind", "related_to"))
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prov = str(edge.get("provenance", "rule"))
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conf = float(edge.get("confidence", 1.0))
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direction = "in" if tgt == target_note_id else "out"
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edge_obj = EdgeDTO(
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id=f"{src}->{tgt}:{kind}",
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kind=kind,
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source=src,
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target=tgt,
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weight=conf,
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direction=direction,
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provenance=prov,
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confidence=conf
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)
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edges_dto.append(edge_obj)
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# Die 3 wichtigsten Kanten als Begründung 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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peer = e.source if e.direction == "in" else e.target
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prov_txt = "Bestätigte" if e.provenance == "explicit" else "KI-basierte"
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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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reasons.append(Reason(
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kind="edge",
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message=f"{prov_txt} Kante '{e.kind}'{boost_txt} von/zu '{peer}'.",
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score_impact=edge_w_cfg * e.confidence
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))
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if scoring_debug["cent_bonus"] > 0.01:
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reasons.append(Reason(kind="centrality", message="Die Notiz ist ein zentraler Informations-Hub.", 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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# ==============================================================================
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# 3. CORE RETRIEVAL PIPELINE
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# ==============================================================================
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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 bewertete 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, cent_bonus = 0.0, 0.0
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target_id = payload.get("note_id")
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if subgraph and target_id:
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try:
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edge_bonus = float(subgraph.edge_bonus(target_id))
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cent_bonus = float(subgraph.centrality_bonus(target_id))
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except Exception:
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pass
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# Mathematisches Scoring via WP-22 Engine
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debug_data = compute_wp22_score(
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semantic_score, payload, edge_bonus, cent_bonus, dynamic_edge_boosts
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)
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enriched.append((pid, semantic_score, payload, debug_data))
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# Sortierung nach finalem mathematischen 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, s_score, pl, dbg 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(s_score),
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payload=pl,
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scoring_debug=dbg,
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subgraph=subgraph,
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target_note_id=pl.get("note_id"),
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applied_boosts=dynamic_edge_boosts
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)
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# Payload Text-Feld normalisieren
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text_content = pl.get("page_content") or pl.get("text") or pl.get("content", "[Kein Text]")
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results.append(QueryHit(
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node_id=str(pid),
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note_id=str(pl.get("note_id", "unknown")),
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semantic_score=float(s_score),
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edge_bonus=dbg["edge_bonus"],
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centrality_bonus=dbg["cent_bonus"],
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total_score=dbg["total"],
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source={
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"path": pl.get("path"),
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"section": pl.get("section") or pl.get("section_title"),
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"text": text_content
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},
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payload=pl,
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explanation=explanation_obj
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))
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return QueryResponse(results=results, used_mode=used_mode, latency_ms=int((time.time() - t0) * 1000))
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def hybrid_retrieve(req: QueryRequest) -> QueryResponse:
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"""
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Die Haupt-Einstiegsfunktion für die hybride Suche.
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Kombiniert Vektorsuche mit Graph-Expansion und WP-22 Gewichtung.
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"""
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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 Seed-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 Konfiguration
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expand_cfg = req.expand if isinstance(req.expand, dict) else {}
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depth = int(expand_cfg.get("depth", 1))
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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 depth > 0 and hits:
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# Start-IDs für den Graph-Traversal sammeln
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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 aus RAM/DB laden
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subgraph = ga.expand(client, prefix, seed_ids, depth=depth, edge_types=expand_cfg.get("edge_types"))
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# --- WP-22: Kanten-Gewichtung im RAM-Graphen vor Bonus-Berechnung ---
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if subgraph and hasattr(subgraph, "graph"):
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for _, _, data in subgraph.graph.edges(data=True):
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# A. Provenance Weighting (WP-22 Bonus für Herkunft)
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prov = data.get("provenance", "rule")
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# Belohnung: Explizite Links (1.0) > Smart (0.9) > Rule (0.7)
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prov_w = 1.0 if prov == "explicit" else (0.9 if prov == "smart" else 0.7)
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# B. Intent Boost Multiplikator (Vom Router dynamisch injiziert)
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kind = data.get("kind")
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intent_multiplier = boost_edges.get(kind, 1.0)
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# Finales Gewicht setzen (Basis * Provenance * Intent)
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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 & Explanation Generierung
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return _build_hits_from_semantic(hits, top_k, "hybrid", subgraph, req.explain, boost_edges)
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def semantic_retrieve(req: QueryRequest) -> QueryResponse:
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"""Standard Vektorsuche ohne Graph-Einfluss (WP-02 Fallback)."""
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client, prefix = _get_client_and_prefix()
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vector = _get_query_vector(req)
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hits = _semantic_hits(client, prefix, vector, req.top_k or 10, req.filters)
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return _build_hits_from_semantic(hits, req.top_k or 10, "semantic", explain=req.explain)
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class Retriever:
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"""Schnittstelle für die asynchrone Suche."""
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async def search(self, request: QueryRequest) -> QueryResponse:
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"""Führt eine hybride Suche aus."""
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return hybrid_retrieve(request) |