""" app/services/discovery.py Service für Link-Vorschläge und Knowledge-Discovery (WP-11). Optimiert: Deduplizierung pro Notiz & Footer-Fokus für kurze Texte. """ import logging import asyncio import os from typing import List, Dict, Any, Optional, Set import yaml from app.core.qdrant import QdrantConfig, get_client from app.models.dto import QueryRequest from app.core.retriever import hybrid_retrieve logger = logging.getLogger(__name__) class DiscoveryService: def __init__(self, collection_prefix: str = None): self.cfg = QdrantConfig.from_env() self.prefix = collection_prefix or self.cfg.prefix or "mindnet" self.client = get_client(self.cfg) self.registry = self._load_type_registry() async def analyze_draft(self, text: str, current_type: str) -> Dict[str, Any]: suggestions = [] default_edge_type = self._get_default_edge_type(current_type) # Tracking-Sets für Deduplizierung (Wir merken uns NOTE-IDs, nicht Chunk-IDs) seen_target_note_ids = set() # --------------------------------------------------------- # 1. Exact Match: Titel/Aliases # --------------------------------------------------------- known_entities = self._fetch_all_titles_and_aliases() found_entities = self._find_entities_in_text(text, known_entities) for entity in found_entities: # Duplikate vermeiden if entity["id"] in seen_target_note_ids: continue seen_target_note_ids.add(entity["id"]) suggestions.append({ "type": "exact_match", "text_found": entity["match"], "target_title": entity["title"], "target_id": entity["id"], "suggested_edge_type": default_edge_type, "suggested_markdown": f"[[rel:{default_edge_type} {entity['title']}]]", "confidence": 1.0, "reason": f"Exakter Treffer: '{entity['match']}'" }) # --------------------------------------------------------- # 2. Semantic Match: Sliding Window & Footer Focus # --------------------------------------------------------- search_queries = self._generate_search_queries(text) # Async parallel abfragen tasks = [self._get_semantic_suggestions_async(q) for q in search_queries] results_list = await asyncio.gather(*tasks) # Ergebnisse verarbeiten for hits in results_list: for hit in hits: # WICHTIG: Note ID aus Payload holen (Chunk ID ist hit.node_id) note_id = hit.payload.get("note_id") # Fallback, falls Payload leer (sollte nicht passieren) if not note_id: continue # 1. Check: Haben wir diese NOTIZ schon? (Egal welcher Chunk) if note_id in seen_target_note_ids: continue # 2. Score Check (Threshold) if hit.total_score > 0.50: seen_target_note_ids.add(note_id) # Blockiere weitere Chunks dieser Notiz target_title = hit.payload.get("title") or "Unbekannt" suggested_md = f"[[rel:{default_edge_type} {target_title}]]" suggestions.append({ "type": "semantic_match", "text_found": (hit.source.get("text") or "")[:60] + "...", "target_title": target_title, "target_id": note_id, # Wir verlinken auf die Notiz, nicht den Chunk "suggested_edge_type": default_edge_type, "suggested_markdown": suggested_md, "confidence": round(hit.total_score, 2), "reason": f"Semantisch ähnlich ({hit.total_score:.2f})" }) # Sortieren nach Confidence suggestions.sort(key=lambda x: x["confidence"], reverse=True) return { "draft_length": len(text), "analyzed_windows": len(search_queries), "suggestions_count": len(suggestions), "suggestions": suggestions[:10] } # --- Optimierte Sliding Windows --- def _generate_search_queries(self, text: str) -> List[str]: """ Erzeugt intelligente Fenster. Besonderheit: Erzwingt 'Footer-Scan' auch bei kurzen Texten, damit "Referenzen am Ende" nicht im Kontext untergehen. """ text_len = len(text) if not text: return [] queries = [] # A) Der gesamte Text (oder Anfang) für den groben Kontext # Bei sehr kurzen Texten ist das alles. queries.append(text[:600]) # B) Der "Footer-Scan" (Das Ende) # Wenn der Text > 150 Zeichen ist, nehmen wir die letzten 200 Zeichen separat. # Grund: Oft steht dort "Gehört zu Projekt X". # Wenn wir das isolieren, ist der Vektor "Projekt X" sehr rein. if text_len > 150: footer = text[-250:] # Nur hinzufügen, wenn es sich signifikant vom Start unterscheidet if footer not in queries: queries.append(footer) # C) Sliding Window für lange Texte (> 800 Chars) if text_len > 800: window_size = 500 step = 1500 for i in range(window_size, text_len - window_size, step): end_pos = min(i + window_size, text_len) chunk = text[i:end_pos] if len(chunk) > 100: queries.append(chunk) return queries # --- Standard Helper (Unverändert) --- async def _get_semantic_suggestions_async(self, text: str): req = QueryRequest(query=text, top_k=5, explain=False) try: res = hybrid_retrieve(req) return res.results except Exception as e: logger.error(f"Semantic suggestion error: {e}") return [] def _load_type_registry(self) -> dict: path = os.getenv("MINDNET_TYPES_FILE", "config/types.yaml") if not os.path.exists(path): if os.path.exists("types.yaml"): path = "types.yaml" else: return {} try: with open(path, "r", encoding="utf-8") as f: return yaml.safe_load(f) or {} except Exception: return {} def _get_default_edge_type(self, note_type: str) -> str: types_cfg = self.registry.get("types", {}) type_def = types_cfg.get(note_type, {}) defaults = type_def.get("edge_defaults") return defaults[0] if defaults else "related_to" def _fetch_all_titles_and_aliases(self) -> List[Dict]: notes = [] next_page = None col = f"{self.prefix}_notes" try: while True: res, next_page = self.client.scroll(collection_name=col, limit=1000, offset=next_page, with_payload=True, with_vectors=False) for point in res: pl = point.payload or {} aliases = pl.get("aliases") or [] if isinstance(aliases, str): aliases = [aliases] notes.append({"id": pl.get("note_id"), "title": pl.get("title"), "aliases": aliases}) if next_page is None: break except Exception: pass return notes def _find_entities_in_text(self, text: str, entities: List[Dict]) -> List[Dict]: found = [] text_lower = text.lower() for entity in entities: title = entity.get("title") if title and title.lower() in text_lower: found.append({"match": title, "title": title, "id": entity["id"]}) continue for alias in entity.get("aliases", []): if str(alias).lower() in text_lower: found.append({"match": alias, "title": title, "id": entity["id"]}) break return found