212 lines
8.3 KiB
Python
212 lines
8.3 KiB
Python
"""
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app/services/discovery.py
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Service für Link-Vorschläge und Knowledge-Discovery (WP-11).
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Implementiert Sliding Window für lange Texte und Late Binding für Edge-Typen.
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"""
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import logging
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import asyncio
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import os # <--- Added missing import
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from typing import List, Dict, Any, Optional
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import yaml
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from app.core.qdrant import QdrantConfig, get_client
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from app.models.dto import QueryRequest
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# Hinweis: hybrid_retrieve ist aktuell synchron. In einer reinen Async-Welt
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# würde man dies refactorn, aber hier wrappen wir es.
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from app.core.retriever import hybrid_retrieve
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logger = logging.getLogger(__name__)
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class DiscoveryService:
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def __init__(self, collection_prefix: str = None):
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# 1. Config laden
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self.cfg = QdrantConfig.from_env()
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# Prefix Priorität: Argument > Env > Default
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self.prefix = collection_prefix or self.cfg.prefix or "mindnet"
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self.client = get_client(self.cfg)
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# 2. Registry für Late Binding laden (Edge Defaults)
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self.registry = self._load_type_registry()
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async def analyze_draft(self, text: str, current_type: str) -> Dict[str, Any]:
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"""
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Analysiert einen Draft-Text und schlägt Verlinkungen vor.
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Nutzt Sliding Window für Semantik und Full-Text Scan für Entity Recognition.
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"""
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suggestions = []
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# Default Edge Typ aus Config (z.B. 'depends_on' für Projekte)
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default_edge_type = self._get_default_edge_type(current_type)
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# ---------------------------------------------------------
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# 1. Exact Match: Finde Titel/Aliases im Text
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# ---------------------------------------------------------
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known_entities = self._fetch_all_titles_and_aliases()
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found_entities = self._find_entities_in_text(text, known_entities)
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existing_target_ids = set()
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for entity in found_entities:
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existing_target_ids.add(entity["id"])
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target_title = entity["title"]
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suggested_md = f"[[rel:{default_edge_type} {target_title}]]"
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suggestions.append({
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"type": "exact_match",
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"text_found": entity["match"],
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"target_title": target_title,
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"target_id": entity["id"],
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"suggested_edge_type": default_edge_type,
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"suggested_markdown": suggested_md,
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"confidence": 1.0,
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"reason": f"Exakter Treffer: '{entity['match']}'"
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})
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# ---------------------------------------------------------
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# 2. Semantic Match: Sliding Window Analyse
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# ---------------------------------------------------------
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# Zerlege Text in sinnvolle Abschnitte für das Embedding
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search_queries = self._generate_search_queries(text)
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# Parallel alle Abschnitte suchen
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tasks = [self._get_semantic_suggestions_async(q) for q in search_queries]
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results_list = await asyncio.gather(*tasks)
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# Ergebnisse zusammenführen
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seen_semantic_ids = set()
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for hits in results_list:
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for hit in hits:
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# Duplikate filtern (schon als Exact Match oder schon als anderer Semantic Hit)
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if hit.node_id in existing_target_ids or hit.node_id in seen_semantic_ids:
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continue
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# Schwellwert: Mit 'nomic-embed-text' sind Scores oft schärfer.
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# 0.50 ist ein guter Startwert für semantische Nähe.
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if hit.total_score > 0.50:
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seen_semantic_ids.add(hit.node_id)
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# Titel aus Payload holen (wurde in chunk_payload.py gefixt)
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target_title = hit.payload.get("title") or hit.node_id
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suggested_md = f"[[rel:{default_edge_type} {target_title}]]"
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suggestions.append({
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"type": "semantic_match",
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"text_found": (hit.source.get("text") or "")[:60] + "...",
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"target_title": target_title,
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"target_id": hit.node_id,
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"suggested_edge_type": default_edge_type,
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"suggested_markdown": suggested_md,
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"confidence": round(hit.total_score, 2),
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"reason": f"Semantisch ähnlich ({hit.total_score:.2f})"
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})
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# Sortieren nach Confidence (Höchste zuerst)
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suggestions.sort(key=lambda x: x["confidence"], reverse=True)
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return {
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"draft_length": len(text),
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"analyzed_windows": len(search_queries),
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"suggestions_count": len(suggestions),
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"suggestions": suggestions[:10] # Top 10 reichen
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}
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# --- Interne Helfer ---
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def _generate_search_queries(self, text: str) -> List[str]:
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"""Erzeugt Sliding Windows über den Text."""
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if not text: return []
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if len(text) < 600: return [text]
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queries = []
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# 1. Anfang (Kontext)
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queries.append(text[:500])
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# 2. Mitte
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mid = len(text) // 2
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queries.append(text[mid-250 : mid+250])
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# 3. Ende (Fazit)
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if len(text) > 800:
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queries.append(text[-500:])
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return queries
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async def _get_semantic_suggestions_async(self, text: str):
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"""Wrapper um den Retriever (sync)."""
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req = QueryRequest(query=text, top_k=5, explain=False)
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try:
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# Hier blockieren wir kurz den Loop, da hybrid_retrieve sync ist.
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# In High-Load Szenarien müsste das in einen ThreadPoolExecutor.
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res = hybrid_retrieve(req)
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return res.results
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except Exception as e:
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logger.error(f"Semantic suggestion error: {e}")
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return []
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def _load_type_registry(self) -> dict:
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path = os.getenv("MINDNET_TYPES_FILE", "config/types.yaml")
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if not os.path.exists(path):
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if os.path.exists("types.yaml"): path = "types.yaml"
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else: return {}
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try:
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with open(path, "r", encoding="utf-8") as f:
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return yaml.safe_load(f) or {}
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except Exception:
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return {}
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def _get_default_edge_type(self, note_type: str) -> str:
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types_cfg = self.registry.get("types", {})
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type_def = types_cfg.get(note_type, {})
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defaults = type_def.get("edge_defaults")
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if defaults and isinstance(defaults, list) and len(defaults) > 0:
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return defaults[0]
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return "related_to"
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def _fetch_all_titles_and_aliases(self) -> List[Dict]:
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notes = []
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next_page = None
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col_name = f"{self.prefix}_notes"
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try:
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while True:
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res, next_page = self.client.scroll(
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collection_name=col_name,
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limit=1000,
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offset=next_page,
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with_payload=True,
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with_vectors=False
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)
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for point in res:
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pl = point.payload or {}
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aliases = pl.get("aliases") or []
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if isinstance(aliases, str): aliases = [aliases]
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notes.append({
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"id": pl.get("note_id"),
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"title": pl.get("title"),
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"aliases": aliases
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})
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if next_page is None: break
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except Exception as e:
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logger.error(f"Error fetching titles: {e}")
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return []
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return notes
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def _find_entities_in_text(self, text: str, entities: List[Dict]) -> List[Dict]:
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found = []
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text_lower = text.lower()
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for entity in entities:
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# Title
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title = entity.get("title")
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if title and title.lower() in text_lower:
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found.append({"match": title, "title": title, "id": entity["id"]})
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continue
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# Aliases
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aliases = entity.get("aliases", [])
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for alias in aliases:
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if alias and str(alias).lower() in text_lower:
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found.append({"match": alias, "title": title, "id": entity["id"]})
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break
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return found |