""" FILE: app/core/retrieval/retriever.py DESCRIPTION: Haupt-Schnittstelle für die Suche. Orchestriert Vektorsuche und Graph-Expansion. WP-15c Update: Note-Level Diversity Pooling & Super-Edge Aggregation. VERSION: 0.7.0 STATUS: Active DEPENDENCIES: app.config, app.models.dto, app.core.database*, app.core.graph_adapter """ from __future__ import annotations import os import time import logging from typing import Any, Dict, List, Tuple, Iterable, Optional from collections import defaultdict from app.config import get_settings from app.models.dto import ( QueryRequest, QueryResponse, QueryHit, Explanation, ScoreBreakdown, Reason, EdgeDTO ) # MODULARISIERUNG: Neue Import-Pfade für die Datenbank-Ebene import app.core.database.qdrant as qdr import app.core.database.qdrant_points as qp import app.services.embeddings_client as ec import app.core.graph.graph_subgraph as ga # Mathematische Engine importieren from app.core.retrieval.retriever_scoring import get_weights, compute_wp22_score logger = logging.getLogger(__name__) # ============================================================================== # 1. CORE HELPERS & CONFIG LOADERS # ============================================================================== def _get_client_and_prefix() -> Tuple[Any, str]: """Initialisiert Qdrant Client und lädt Collection-Prefix via database-Paket.""" cfg = qdr.QdrantConfig.from_env() return qdr.get_client(cfg), cfg.prefix def _get_query_vector(req: QueryRequest) -> List[float]: """ Vektorisiert die Anfrage. FIX: Enthält try-except Block für unterschiedliche Signaturen von ec.embed_text. """ if req.query_vector: return list(req.query_vector) if not req.query: raise ValueError("Kein Text oder Vektor für die Suche angegeben.") settings = get_settings() try: # Versuch mit modernem Interface (WP-03 kompatibel) return ec.embed_text(req.query, model_name=settings.MODEL_NAME) except TypeError: # Fallback für Signaturen, die 'model_name' nicht als Keyword akzeptieren logger.debug("ec.embed_text does not accept 'model_name' keyword. Falling back.") return ec.embed_text(req.query) def _semantic_hits( client: Any, prefix: str, vector: List[float], top_k: int, filters: Optional[Dict] = None ) -> List[Tuple[str, float, Dict[str, Any]]]: """Führt die Vektorsuche via database-Points-Modul durch.""" raw_hits = qp.search_chunks_by_vector(client, prefix, vector, top=top_k, filters=filters) # Strikte Typkonvertierung für Stabilität return [(str(hit[0]), float(hit[1]), dict(hit[2] or {})) for hit in raw_hits] # ============================================================================== # 2. EXPLANATION LAYER (DEBUG & VERIFIABILITY) # ============================================================================== def _build_explanation( semantic_score: float, payload: Dict[str, Any], scoring_debug: Dict[str, Any], subgraph: Optional[ga.Subgraph], target_note_id: Optional[str], applied_boosts: Optional[Dict[str, float]] = None ) -> Explanation: """ Transformiert mathematische Scores und Graph-Signale in eine menschenlesbare Erklärung. """ _, edge_w_cfg, _ = get_weights() base_val = scoring_debug["base_val"] # 1. Detaillierter mathematischer Breakdown breakdown = ScoreBreakdown( semantic_contribution=base_val, edge_contribution=base_val * scoring_debug["edge_impact_final"], centrality_contribution=base_val * scoring_debug["cent_impact_final"], raw_semantic=semantic_score, raw_edge_bonus=scoring_debug["edge_bonus"], raw_centrality=scoring_debug["cent_bonus"], node_weight=float(payload.get("retriever_weight", 1.0)), status_multiplier=scoring_debug["status_multiplier"], graph_boost_factor=scoring_debug["graph_boost_factor"] ) reasons: List[Reason] = [] edges_dto: List[EdgeDTO] = [] # 2. Gründe für Semantik hinzufügen if semantic_score > 0.85: reasons.append(Reason(kind="semantic", message="Sehr hohe textuelle Übereinstimmung.", score_impact=base_val)) elif semantic_score > 0.70: reasons.append(Reason(kind="semantic", message="Inhaltliche Übereinstimmung.", score_impact=base_val)) # 3. Gründe für Typ und Lifecycle (WP-25 Vorbereitung) type_weight = float(payload.get("retriever_weight", 1.0)) if type_weight != 1.0: msg = "Bevorzugt" if type_weight > 1.0 else "De-priorisiert" reasons.append(Reason(kind="type", message=f"{msg} durch Typ-Profil.", score_impact=base_val * (type_weight - 1.0))) # NEU: Explizite Ausweisung des Lifecycle-Status (WP-22) status_mult = scoring_debug.get("status_multiplier", 1.0) if status_mult != 1.0: status_msg = "Belohnt (Stable)" if status_mult > 1.0 else "De-priorisiert (Draft)" reasons.append(Reason( kind="status", message=f"{status_msg} durch Content-Lifecycle.", score_impact=semantic_score * (status_mult - 1.0) )) # 4. Kanten-Verarbeitung (Graph-Intelligence) if subgraph and target_note_id and scoring_debug["edge_bonus"] > 0: raw_edges = [] if hasattr(subgraph, "get_incoming_edges"): raw_edges.extend(subgraph.get_incoming_edges(target_note_id) or []) if hasattr(subgraph, "get_outgoing_edges"): raw_edges.extend(subgraph.get_outgoing_edges(target_note_id) or []) for edge in raw_edges: src = str(edge.get("source") or "note_root") tgt = str(edge.get("target") or target_note_id or "unknown_target") kind = str(edge.get("kind", "related_to")) prov = str(edge.get("provenance", "rule")) conf = float(edge.get("confidence", 1.0)) direction = "in" if tgt == target_note_id else "out" edge_obj = EdgeDTO( id=f"{src}->{tgt}:{kind}", kind=kind, source=src, target=tgt, weight=conf, direction=direction, provenance=prov, confidence=conf ) edges_dto.append(edge_obj) # Die 3 wichtigsten Kanten als Begründung formulieren top_edges = sorted(edges_dto, key=lambda e: e.confidence, reverse=True) for e in top_edges[:3]: peer = e.source if e.direction == "in" else e.target prov_txt = "Bestätigte" if e.provenance == "explicit" else "KI-basierte" boost_txt = f" [Boost x{applied_boosts.get(e.kind)}]" if applied_boosts and e.kind in applied_boosts else "" reasons.append(Reason( kind="edge", message=f"{prov_txt} Kante '{e.kind}'{boost_txt} von/zu '{peer}'.", score_impact=edge_w_cfg * e.confidence )) if scoring_debug["cent_bonus"] > 0.01: reasons.append(Reason(kind="centrality", message="Die Notiz ist ein zentraler Informations-Hub.", score_impact=breakdown.centrality_contribution)) return Explanation( breakdown=breakdown, reasons=reasons, related_edges=edges_dto if edges_dto else None, applied_boosts=applied_boosts ) # ============================================================================== # 3. CORE RETRIEVAL PIPELINE # ============================================================================== def _build_hits_from_semantic( hits: Iterable[Tuple[str, float, Dict[str, Any]]], top_k: int, used_mode: str, subgraph: ga.Subgraph | None = None, explain: bool = False, dynamic_edge_boosts: Dict[str, float] = None ) -> QueryResponse: """ Wandelt semantische Roh-Treffer in bewertete QueryHits um. WP-15c: Implementiert Note-Level Diversity Pooling. """ t0 = time.time() enriched = [] # Erstes Scoring für alle Kandidaten for pid, semantic_score, payload in hits: edge_bonus, cent_bonus = 0.0, 0.0 target_id = payload.get("note_id") if subgraph and target_id: try: edge_bonus = float(subgraph.edge_bonus(target_id)) cent_bonus = float(subgraph.centrality_bonus(target_id)) except Exception: pass debug_data = compute_wp22_score( semantic_score, payload, edge_bonus, cent_bonus, dynamic_edge_boosts ) enriched.append((pid, semantic_score, payload, debug_data)) # 1. Sortierung nach finalem mathematischen Score enriched_sorted = sorted(enriched, key=lambda h: h[3]["total"], reverse=True) # 2. WP-15c: Note-Level Diversity Pooling # Wir behalten pro note_id nur den Hit mit dem höchsten total_score. # Dies verhindert, dass 10 Chunks derselben Note andere KeyNotes verdrängen. unique_note_hits = [] seen_notes = set() for item in enriched_sorted: _, _, payload, _ = item note_id = str(payload.get("note_id", "unknown")) if note_id not in seen_notes: unique_note_hits.append(item) seen_notes.add(note_id) # 3. Begrenzung auf top_k nach dem Diversity-Pooling limited_hits = unique_note_hits[: max(1, top_k)] results: List[QueryHit] = [] for pid, s_score, pl, dbg in limited_hits: explanation_obj = None if explain: explanation_obj = _build_explanation( semantic_score=float(s_score), payload=pl, scoring_debug=dbg, subgraph=subgraph, target_note_id=pl.get("note_id"), applied_boosts=dynamic_edge_boosts ) text_content = pl.get("page_content") or pl.get("text") or pl.get("content", "[Kein Text]") results.append(QueryHit( node_id=str(pid), note_id=str(pl.get("note_id", "unknown")), semantic_score=float(s_score), edge_bonus=dbg["edge_bonus"], centrality_bonus=dbg["cent_bonus"], total_score=dbg["total"], source={ "path": pl.get("path"), "section": pl.get("section") or pl.get("section_title"), "text": text_content }, payload=pl, explanation=explanation_obj )) return QueryResponse(results=results, used_mode=used_mode, latency_ms=int((time.time() - t0) * 1000)) def hybrid_retrieve(req: QueryRequest) -> QueryResponse: """ Die Haupt-Einstiegsfunktion für die hybride Suche. WP-15c: Implementiert Edge-Aggregation (Super-Kanten). """ 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 (Wir laden etwas mehr für das Pooling) hits = _semantic_hits(client, prefix, vector, top_k=top_k * 3, filters=req.filters) # 2. Graph Expansion Konfiguration expand_cfg = req.expand if isinstance(req.expand, dict) else {} depth = int(expand_cfg.get("depth", 1)) boost_edges = getattr(req, "boost_edges", {}) or {} subgraph: ga.Subgraph | None = None if depth > 0 and hits: seed_ids = list({h[2].get("note_id") for h in hits if h[2].get("note_id")}) if seed_ids: try: subgraph = ga.expand(client, prefix, seed_ids, depth=depth, edge_types=expand_cfg.get("edge_types")) # --- WP-15c: Edge-Aggregation & Deduplizierung (Super-Kanten) --- # Verhindert Score-Explosion durch multiple Links auf versch. Abschnitte. # Logik: 1. Kante zählt voll, weitere dämpfen auf Faktor 0.1. if subgraph and hasattr(subgraph, "adj"): for src, edge_list in subgraph.adj.items(): # Gruppiere Kanten nach Ziel-Note (Deduplizierung ID_A -> ID_B) by_target = defaultdict(list) for e in edge_list: by_target[e["target"]].append(e) aggregated_list = [] for tgt, edges in by_target.items(): if len(edges) > 1: # Sortiere: Stärkste Kante zuerst sorted_edges = sorted(edges, key=lambda x: x.get("weight", 0.0), reverse=True) primary = sorted_edges[0] # Aggregiertes Gewicht berechnen (Sättigungs-Logik) total_w = primary.get("weight", 0.0) for secondary in sorted_edges[1:]: total_w += secondary.get("weight", 0.0) * 0.1 primary["weight"] = total_w primary["is_super_edge"] = True # Flag für Explanation Layer primary["edge_count"] = len(edges) aggregated_list.append(primary) else: aggregated_list.append(edges[0]) # In-Place Update der Adjazenzliste des Graphen subgraph.adj[src] = aggregated_list # Re-Sync der In-Degrees für Centrality-Bonus (Aggregation konsistent halten) subgraph.in_degree = defaultdict(int) for src, edges in subgraph.adj.items(): for e in edges: subgraph.in_degree[e["target"]] += 1 # --- WP-22: Kanten-Gewichtung (Provenance & Intent Boost) --- if subgraph and hasattr(subgraph, "adj"): for src, edges in subgraph.adj.items(): for e in edges: # A. Provenance Weighting prov = e.get("provenance", "rule") prov_w = 1.0 if prov == "explicit" else (0.9 if prov == "smart" else 0.7) # B. Intent Boost Multiplikator kind = e.get("kind") intent_multiplier = boost_edges.get(kind, 1.0) # Gewichtung anpassen e["weight"] = e.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 # top_k wird erst hier final angewandt return _build_hits_from_semantic(hits, top_k, "hybrid", subgraph, req.explain, boost_edges) def semantic_retrieve(req: QueryRequest) -> QueryResponse: """Standard Vektorsuche ohne Graph-Einfluss.""" client, prefix = _get_client_and_prefix() vector = _get_query_vector(req) hits = _semantic_hits(client, prefix, vector, req.top_k or 10, req.filters) return _build_hits_from_semantic(hits, req.top_k or 10, "semantic", explain=req.explain) class Retriever: """Schnittstelle für die asynchrone Suche.""" async def search(self, request: QueryRequest) -> QueryResponse: return hybrid_retrieve(request)