""" app/services/discovery.py Service für Link-Vorschläge und Knowledge-Discovery (WP-11). Adaptiert für Async-Architecture (v2.4). """ import logging import os from typing import List, Dict, Any import yaml # Wir nutzen hier weiterhin die Low-Level Funktionen, da diese stabil sind 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]: """ Analysiert einen Draft-Text und schlägt Verlinkungen vor. Kombiniert Exact Match (Titel/Alias) und Semantic Match. """ suggestions = [] default_edge_type = self._get_default_edge_type(current_type) # 1. Exact Match: Finde Begriffe im Text, die als Notiz-Titel existieren # (Dies läuft synchron, ist aber sehr schnell durch Qdrant Scroll) 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 (Default für '{current_type}': {default_edge_type})" }) # 2. Semantic Match: Finde inhaltlich ähnliche Notizen # Wir filtern Ergebnisse heraus, die wir schon per Exact Match gefunden haben. semantic_hits = await self._get_semantic_suggestions_async(text) for hit in semantic_hits: if hit.node_id in existing_target_ids: continue if hit.total_score > 0.65: # FIX: Titel aus Payload lesen, nicht ID! 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 "")[:50] + "...", "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"Semantische Ähnlichkeit ({hit.total_score:.2f})" }) return { "draft_length": len(text), "suggestions_count": len(suggestions), "suggestions": suggestions } # --- Helpers --- async def _get_semantic_suggestions_async(self, text: str): """Async Wrapper um den Hybrid Retriever.""" req = QueryRequest(query=text, top_k=5, explain=False) try: # Da hybrid_retrieve (noch) sync ist, rufen wir es direkt auf. # In einer voll-async Umgebung würde man dies in einen Thread-Pool auslagern, # aber da Qdrant-Client sync ist, ist das hier okay. res = hybrid_retrieve(req) return res.results except Exception as e: logger.error(f"Semantic suggestion failed: {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: 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 = entity.get("title") if title and title.lower() in text_lower: found.append({"match": title, "title": title, "id": entity["id"]}) continue 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