321 lines
14 KiB
Python
321 lines
14 KiB
Python
"""
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FILE: app/frontend/ui_graph_service.py
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DESCRIPTION: Data Layer für den Graphen. Greift direkt auf Qdrant zu (Performance), um Knoten/Kanten zu laden und Texte zu rekonstruieren ("Stitching").
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VERSION: 2.6.0
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STATUS: Active
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DEPENDENCIES: qdrant_client, streamlit_agraph, ui_config, re
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LAST_ANALYSIS: 2025-12-15
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"""
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import re
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from qdrant_client import QdrantClient, models
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from streamlit_agraph import Node, Edge
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from ui_config import GRAPH_COLORS, get_edge_color, SYSTEM_EDGES
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class GraphExplorerService:
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def __init__(self, url, api_key=None, prefix="mindnet"):
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self.client = QdrantClient(url=url, api_key=api_key)
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self.prefix = prefix
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self.notes_col = f"{prefix}_notes"
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self.chunks_col = f"{prefix}_chunks"
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self.edges_col = f"{prefix}_edges"
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self._note_cache = {}
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def get_note_with_full_content(self, note_id):
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"""
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Lädt die Metadaten der Note und rekonstruiert den gesamten Text
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aus den Chunks (Stitching). Wichtig für den Editor-Fallback.
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"""
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# 1. Metadaten holen
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meta = self._fetch_note_cached(note_id)
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if not meta: return None
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# 2. Volltext aus Chunks bauen
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full_text = self._fetch_full_text_stitched(note_id)
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# 3. Ergebnis kombinieren (Wir überschreiben das 'fulltext' Feld mit dem frischen Stitching)
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# Wir geben eine Kopie zurück, um den Cache nicht zu verfälschen
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complete_note = meta.copy()
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if full_text:
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complete_note['fulltext'] = full_text
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return complete_note
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def get_ego_graph(self, center_note_id: str, depth=2, show_labels=True):
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"""
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Erstellt den Ego-Graphen um eine zentrale Notiz.
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Lädt Volltext für das Zentrum und Snippets für Nachbarn.
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"""
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nodes_dict = {}
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unique_edges = {}
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# 1. Center Note laden
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center_note = self._fetch_note_cached(center_note_id)
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if not center_note: return [], []
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self._add_node_to_dict(nodes_dict, center_note, level=0)
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# Initialset für Suche
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level_1_ids = {center_note_id}
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# Suche Kanten für Center (L1)
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l1_edges = self._find_connected_edges([center_note_id], center_note.get("title"))
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for edge_data in l1_edges:
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src_id, tgt_id = self._process_edge(edge_data, nodes_dict, unique_edges, current_depth=1)
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if src_id: level_1_ids.add(src_id)
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if tgt_id: level_1_ids.add(tgt_id)
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# Level 2 Suche (begrenzt für Performance)
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if depth > 1 and len(level_1_ids) > 1 and len(level_1_ids) < 80:
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l1_subset = list(level_1_ids - {center_note_id})
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if l1_subset:
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l2_edges = self._find_connected_edges_batch(l1_subset)
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for edge_data in l2_edges:
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self._process_edge(edge_data, nodes_dict, unique_edges, current_depth=2)
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# --- SMART CONTENT LOADING ---
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# A. Fulltext für Center Node holen (Chunks zusammenfügen)
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center_text = self._fetch_full_text_stitched(center_note_id)
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if center_note_id in nodes_dict:
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orig_title = nodes_dict[center_note_id].title
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clean_full = self._clean_markdown(center_text[:2000])
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# Wir packen den Text in den Tooltip (title attribute)
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nodes_dict[center_note_id].title = f"{orig_title}\n\n📄 INHALT:\n{clean_full}..."
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# B. Previews für alle Nachbarn holen (Batch)
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all_ids = list(nodes_dict.keys())
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previews = self._fetch_previews_for_nodes(all_ids)
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for nid, node_obj in nodes_dict.items():
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if nid != center_note_id:
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prev_raw = previews.get(nid, "Kein Vorschau-Text.")
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clean_prev = self._clean_markdown(prev_raw[:600])
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node_obj.title = f"{node_obj.title}\n\n🔍 VORSCHAU:\n{clean_prev}..."
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# Graphen bauen (Nodes & Edges finalisieren)
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final_edges = []
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for (src, tgt), data in unique_edges.items():
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kind = data['kind']
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prov = data['provenance']
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color = get_edge_color(kind)
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is_smart = (prov != "explicit" and prov != "rule")
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# Label Logik
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label_text = kind if show_labels else " "
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final_edges.append(Edge(
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source=src, target=tgt, label=label_text, color=color, dashes=is_smart,
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title=f"Relation: {kind}\nProvenance: {prov}"
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))
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return list(nodes_dict.values()), final_edges
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def _clean_markdown(self, text):
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"""Entfernt Markdown-Sonderzeichen für saubere Tooltips im Browser."""
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if not text: return ""
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# Entferne Header Marker (## )
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text = re.sub(r'#+\s', '', text)
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# Entferne Bold/Italic (** oder *)
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text = re.sub(r'\*\*|__|\*|_', '', text)
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# Entferne Links [Text](Url) -> Text
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text = re.sub(r'\[([^\]]+)\]\([^\)]+\)', r'\1', text)
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# Entferne Wikilinks [[Link]] -> Link
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text = re.sub(r'\[\[([^\]]+)\]\]', r'\1', text)
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return text
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def _fetch_full_text_stitched(self, note_id):
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"""Lädt alle Chunks einer Note und baut den Text zusammen."""
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try:
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scroll_filter = models.Filter(
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must=[models.FieldCondition(key="note_id", match=models.MatchValue(value=note_id))]
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)
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# Limit hoch genug setzen
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chunks, _ = self.client.scroll(self.chunks_col, scroll_filter=scroll_filter, limit=100, with_payload=True)
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# Sortieren nach 'ord' (Reihenfolge im Dokument)
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chunks.sort(key=lambda x: x.payload.get('ord', 999))
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full_text = []
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for c in chunks:
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# 'text' ist der reine Inhalt ohne Overlap
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txt = c.payload.get('text', '')
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if txt: full_text.append(txt)
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return "\n\n".join(full_text)
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except:
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return "Fehler beim Laden des Volltexts."
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def _fetch_previews_for_nodes(self, node_ids):
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"""Holt Batch-weise den ersten Chunk für eine Liste von Nodes."""
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if not node_ids: return {}
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previews = {}
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try:
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scroll_filter = models.Filter(must=[models.FieldCondition(key="note_id", match=models.MatchAny(any=node_ids))])
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# Limit = Anzahl Nodes * 3 (Puffer)
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chunks, _ = self.client.scroll(self.chunks_col, scroll_filter=scroll_filter, limit=len(node_ids)*3, with_payload=True)
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for c in chunks:
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nid = c.payload.get("note_id")
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# Nur den ersten gefundenen Chunk pro Note nehmen
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if nid and nid not in previews:
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previews[nid] = c.payload.get("window") or c.payload.get("text") or ""
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except: pass
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return previews
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def _find_connected_edges(self, note_ids, note_title=None):
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"""Findet eingehende und ausgehende Kanten."""
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results = []
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# 1. OUTGOING EDGES (Der "Owner"-Fix)
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# Wir suchen Kanten, die im Feld 'note_id' (Owner) eine unserer Notizen haben.
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# Das findet ALLE ausgehenden Kanten, egal ob sie an einem Chunk oder der Note hängen.
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if note_ids:
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out_filter = models.Filter(must=[
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models.FieldCondition(key="note_id", match=models.MatchAny(any=note_ids)),
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models.FieldCondition(key="kind", match=models.MatchExcept(**{"except": SYSTEM_EDGES}))
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])
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# Limit hoch, um alles zu finden
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res_out, _ = self.client.scroll(self.edges_col, scroll_filter=out_filter, limit=500, with_payload=True)
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results.extend(res_out)
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# 2. INCOMING EDGES (Ziel = Chunk ID oder Titel oder Note ID)
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# Hier müssen wir Chunks auflösen, um Treffer auf Chunks zu finden.
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# Chunk IDs der aktuellen Notes holen
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chunk_ids = []
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if note_ids:
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c_filter = models.Filter(must=[models.FieldCondition(key="note_id", match=models.MatchAny(any=note_ids))])
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chunks, _ = self.client.scroll(self.chunks_col, scroll_filter=c_filter, limit=300)
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chunk_ids = [c.id for c in chunks]
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shoulds = []
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# Case A: Edge zeigt auf einen unserer Chunks
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if chunk_ids:
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shoulds.append(models.FieldCondition(key="target_id", match=models.MatchAny(any=chunk_ids)))
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# Case B: Edge zeigt direkt auf unsere Note ID
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if note_ids:
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shoulds.append(models.FieldCondition(key="target_id", match=models.MatchAny(any=note_ids)))
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# Case C: Edge zeigt auf unseren Titel (Wikilinks)
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if note_title:
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shoulds.append(models.FieldCondition(key="target_id", match=models.MatchValue(value=note_title)))
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if shoulds:
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in_filter = models.Filter(
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must=[models.FieldCondition(key="kind", match=models.MatchExcept(**{"except": SYSTEM_EDGES}))],
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should=shoulds
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)
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res_in, _ = self.client.scroll(self.edges_col, scroll_filter=in_filter, limit=500, with_payload=True)
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results.extend(res_in)
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return results
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def _find_connected_edges_batch(self, note_ids):
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# Wrapper für Level 2 Suche
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return self._find_connected_edges(note_ids)
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def _process_edge(self, record, nodes_dict, unique_edges, current_depth):
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"""Verarbeitet eine rohe Edge, löst IDs auf und fügt sie den Dictionaries hinzu."""
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payload = record.payload
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src_ref = payload.get("source_id")
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tgt_ref = payload.get("target_id")
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kind = payload.get("kind")
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provenance = payload.get("provenance", "explicit")
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# IDs zu Notes auflösen
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src_note = self._resolve_note_from_ref(src_ref)
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tgt_note = self._resolve_note_from_ref(tgt_ref)
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if src_note and tgt_note:
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src_id = src_note['note_id']
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tgt_id = tgt_note['note_id']
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if src_id != tgt_id:
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# Nodes hinzufügen
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self._add_node_to_dict(nodes_dict, src_note, level=current_depth)
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self._add_node_to_dict(nodes_dict, tgt_note, level=current_depth)
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# Kante hinzufügen (mit Deduplizierung)
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key = (src_id, tgt_id)
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existing = unique_edges.get(key)
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should_update = True
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# Bevorzuge explizite Kanten vor Smart Kanten
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is_current_explicit = (provenance in ["explicit", "rule"])
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if existing:
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is_existing_explicit = (existing['provenance'] in ["explicit", "rule"])
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if is_existing_explicit and not is_current_explicit:
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should_update = False
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if should_update:
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unique_edges[key] = {"source": src_id, "target": tgt_id, "kind": kind, "provenance": provenance}
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return src_id, tgt_id
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return None, None
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def _fetch_note_cached(self, note_id):
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if note_id in self._note_cache: return self._note_cache[note_id]
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res, _ = self.client.scroll(
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collection_name=self.notes_col,
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scroll_filter=models.Filter(must=[models.FieldCondition(key="note_id", match=models.MatchValue(value=note_id))]),
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limit=1, with_payload=True
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)
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if res:
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self._note_cache[note_id] = res[0].payload
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return res[0].payload
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return None
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def _resolve_note_from_ref(self, ref_str):
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"""Löst eine ID (Chunk, Note oder Titel) zu einer Note Payload auf."""
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if not ref_str: return None
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# Fall A: Chunk ID (enthält #)
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if "#" in ref_str:
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try:
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# Versuch 1: Chunk ID direkt
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res = self.client.retrieve(self.chunks_col, ids=[ref_str], with_payload=True)
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if res: return self._fetch_note_cached(res[0].payload.get("note_id"))
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except: pass
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# Versuch 2: NoteID#Section (Hash abtrennen)
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possible_note_id = ref_str.split("#")[0]
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if self._fetch_note_cached(possible_note_id): return self._fetch_note_cached(possible_note_id)
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# Fall B: Note ID direkt
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if self._fetch_note_cached(ref_str): return self._fetch_note_cached(ref_str)
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# Fall C: Titel
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res, _ = self.client.scroll(
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collection_name=self.notes_col,
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scroll_filter=models.Filter(must=[models.FieldCondition(key="title", match=models.MatchValue(value=ref_str))]),
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limit=1, with_payload=True
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)
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if res:
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self._note_cache[res[0].payload['note_id']] = res[0].payload
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return res[0].payload
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return None
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def _add_node_to_dict(self, node_dict, note_payload, level=1):
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nid = note_payload.get("note_id")
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if nid in node_dict: return
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ntype = note_payload.get("type", "default")
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color = GRAPH_COLORS.get(ntype, GRAPH_COLORS["default"])
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# Basis-Tooltip (wird später erweitert)
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tooltip = f"Titel: {note_payload.get('title')}\nTyp: {ntype}"
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if level == 0: size = 45
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elif level == 1: size = 25
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else: size = 15
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node_dict[nid] = Node(
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id=nid,
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label=note_payload.get('title', nid),
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size=size,
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color=color,
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shape="dot" if level > 0 else "diamond",
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title=tooltip,
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font={'color': 'black', 'face': 'arial', 'size': 14 if level < 2 else 0}
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) |