""" app/services/discovery.py Service für Link-Vorschläge und Knowledge-Discovery (WP-11). Implementiert Sliding Window für lange Texte und Late Binding für Edge-Typen. """ import logging import asyncio import os # <--- Added missing import from typing import List, Dict, Any, Optional import yaml from app.core.qdrant import QdrantConfig, get_client from app.models.dto import QueryRequest # Hinweis: hybrid_retrieve ist aktuell synchron. In einer reinen Async-Welt # würde man dies refactorn, aber hier wrappen wir es. from app.core.retriever import hybrid_retrieve logger = logging.getLogger(__name__) class DiscoveryService: def __init__(self, collection_prefix: str = None): # 1. Config laden self.cfg = QdrantConfig.from_env() # Prefix Priorität: Argument > Env > Default self.prefix = collection_prefix or self.cfg.prefix or "mindnet" self.client = get_client(self.cfg) # 2. Registry für Late Binding laden (Edge Defaults) self.registry = self._load_type_registry() async def analyze_draft(self, text: str, current_type: str) -> Dict[str, Any]: """ Analysiert einen Draft-Text und schlägt Verlinkungen vor. Nutzt Sliding Window für Semantik und Full-Text Scan für Entity Recognition. """ suggestions = [] # Default Edge Typ aus Config (z.B. 'depends_on' für Projekte) default_edge_type = self._get_default_edge_type(current_type) # --------------------------------------------------------- # 1. Exact Match: Finde Titel/Aliases im Text # --------------------------------------------------------- known_entities = self._fetch_all_titles_and_aliases() found_entities = self._find_entities_in_text(text, known_entities) existing_target_ids = set() for entity in found_entities: existing_target_ids.add(entity["id"]) target_title = entity["title"] suggested_md = f"[[rel:{default_edge_type} {target_title}]]" suggestions.append({ "type": "exact_match", "text_found": entity["match"], "target_title": target_title, "target_id": entity["id"], "suggested_edge_type": default_edge_type, "suggested_markdown": suggested_md, "confidence": 1.0, "reason": f"Exakter Treffer: '{entity['match']}'" }) # --------------------------------------------------------- # 2. Semantic Match: Sliding Window Analyse # --------------------------------------------------------- # Zerlege Text in sinnvolle Abschnitte für das Embedding search_queries = self._generate_search_queries(text) # Parallel alle Abschnitte suchen tasks = [self._get_semantic_suggestions_async(q) for q in search_queries] results_list = await asyncio.gather(*tasks) # Ergebnisse zusammenführen seen_semantic_ids = set() for hits in results_list: for hit in hits: # Duplikate filtern (schon als Exact Match oder schon als anderer Semantic Hit) if hit.node_id in existing_target_ids or hit.node_id in seen_semantic_ids: continue # Schwellwert: Mit 'nomic-embed-text' sind Scores oft schärfer. # 0.50 ist ein guter Startwert für semantische Nähe. if hit.total_score > 0.50: seen_semantic_ids.add(hit.node_id) # Titel aus Payload holen (wurde in chunk_payload.py gefixt) target_title = hit.payload.get("title") or hit.node_id 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": hit.node_id, "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 (Höchste zuerst) 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] # Top 10 reichen } # --- Interne Helfer --- def _generate_search_queries(self, text: str) -> List[str]: """Erzeugt Sliding Windows über den Text.""" if not text: return [] if len(text) < 600: return [text] queries = [] # 1. Anfang (Kontext) queries.append(text[:500]) # 2. Mitte mid = len(text) // 2 queries.append(text[mid-250 : mid+250]) # 3. Ende (Fazit) if len(text) > 800: queries.append(text[-500:]) return queries async def _get_semantic_suggestions_async(self, text: str): """Wrapper um den Retriever (sync).""" req = QueryRequest(query=text, top_k=5, explain=False) try: # Hier blockieren wir kurz den Loop, da hybrid_retrieve sync ist. # In High-Load Szenarien müsste das in einen ThreadPoolExecutor. 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") if defaults and isinstance(defaults, list) and len(defaults) > 0: return defaults[0] return "related_to" def _fetch_all_titles_and_aliases(self) -> List[Dict]: notes = [] next_page = None col_name = f"{self.prefix}_notes" try: while True: res, next_page = self.client.scroll( collection_name=col_name, 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 as e: logger.error(f"Error fetching titles: {e}") return [] 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 title = entity.get("title") if title and title.lower() in text_lower: found.append({"match": title, "title": title, "id": entity["id"]}) continue # Aliases aliases = entity.get("aliases", []) for alias in aliases: if alias and str(alias).lower() in text_lower: found.append({"match": alias, "title": title, "id": entity["id"]}) break return found