mindnet/app/frontend/ui_graph_service.py

"""
FILE: app/frontend/ui_graph_service.py
DESCRIPTION: Data Layer für den Graphen. Greift direkt auf Qdrant zu (Performance), um Knoten/Kanten zu laden und Texte zu rekonstruieren ("Stitching").
VERSION: 2.6.1 (Fix: Anchor-Link & Fragment Resolution)
STATUS: Active
DEPENDENCIES: qdrant_client, streamlit_agraph, ui_config, re
LAST_ANALYSIS: 2025-12-28
"""

import re
from qdrant_client import QdrantClient, models
from streamlit_agraph import Node, Edge
from ui_config import COLLECTION_PREFIX, GRAPH_COLORS, get_edge_color, SYSTEM_EDGES

class GraphExplorerService:
    def __init__(self, url, api_key=None, prefix=None):
        """
        Initialisiert den Service. Nutzt COLLECTION_PREFIX aus der Config, 
        sofern kein spezifischer Prefix übergeben wurde.
        """
        self.client = QdrantClient(url=url, api_key=api_key)
        self.prefix = prefix if prefix else COLLECTION_PREFIX
        self.notes_col = f"{self.prefix}_notes"
        self.chunks_col = f"{self.prefix}_chunks"
        self.edges_col = f"{self.prefix}_edges"
        self._note_cache = {} 
        self._ref_resolution_cache = {} 

    def get_note_with_full_content(self, note_id):
        """
        Lädt die Metadaten der Note und rekonstruiert den gesamten Text 
        aus den Chunks (Stitching). Wichtig für den Editor-Fallback.
        """
        # 1. Metadaten holen
        meta = self._fetch_note_cached(note_id)
        if not meta: return None
        
        # 2. Volltext aus Chunks bauen
        full_text = self._fetch_full_text_stitched(note_id)
        
        # 3. Ergebnis kombinieren (Kopie zurückgeben)
        complete_note = meta.copy()
        if full_text:
            complete_note['fulltext'] = full_text
            
        return complete_note

    def get_ego_graph(self, center_note_id: str, depth=2, show_labels=True):
        """
        Erstellt den Ego-Graphen um eine zentrale Notiz.
        Lädt Volltext für das Zentrum und Snippets für Nachbarn.
        """
        nodes_dict = {} 
        unique_edges = {} 
        
        # 1. Center Note laden
        center_note = self._fetch_note_cached(center_note_id)
        if not center_note: return [], []
        self._add_node_to_dict(nodes_dict, center_note, level=0)
        
        # Initialset für Suche
        level_1_ids = {center_note_id}
        
        # Suche Kanten für Center (L1) inkl. Titel für Anchor-Suche
        l1_edges = self._find_connected_edges([center_note_id], center_note.get("title"))
        
        for edge_data in l1_edges:
            src_id, tgt_id = self._process_edge(edge_data, nodes_dict, unique_edges, current_depth=1)
            if src_id: level_1_ids.add(src_id)
            if tgt_id: level_1_ids.add(tgt_id)

        # Level 2 Suche (begrenzt für Performance)
        if depth > 1 and len(level_1_ids) > 1 and len(level_1_ids) < 80: 
            l1_subset = list(level_1_ids - {center_note_id})
            if l1_subset:
                l2_edges = self._find_connected_edges_batch(l1_subset)
                for edge_data in l2_edges:
                    self._process_edge(edge_data, nodes_dict, unique_edges, current_depth=2)

        # --- SMART CONTENT LOADING ---
        
        # A. Fulltext für Center Node holen (Chunks zusammenfügen)
        center_text = self._fetch_full_text_stitched(center_note_id)
        if center_note_id in nodes_dict:
            orig_title = nodes_dict[center_note_id].title
            clean_full = self._clean_markdown(center_text[:2000])
            nodes_dict[center_note_id].title = f"{orig_title}\n\n📄 INHALT:\n{clean_full}..."

        # B. Previews für alle Nachbarn holen (Batch)
        all_ids = list(nodes_dict.keys())
        previews = self._fetch_previews_for_nodes(all_ids)
        
        for nid, node_obj in nodes_dict.items():
            if nid != center_note_id:
                prev_raw = previews.get(nid, "Kein Vorschau-Text.")
                clean_prev = self._clean_markdown(prev_raw[:600])
                node_obj.title = f"{node_obj.title}\n\n🔍 VORSCHAU:\n{clean_prev}..."

        # Graphen bauen (Nodes & Edges finalisieren)
        final_edges = []
        for (src, tgt), data in unique_edges.items():
            kind = data['kind']
            prov = data['provenance']
            color = get_edge_color(kind)
            is_smart = (prov != "explicit" and prov != "rule")
            label_text = kind if show_labels else " "
            
            final_edges.append(Edge(
                source=src, target=tgt, label=label_text, color=color, dashes=is_smart,
                title=f"Relation: {kind}\nProvenance: {prov}"
            ))

        return list(nodes_dict.values()), final_edges

    def _clean_markdown(self, text):
        """Entfernt Markdown-Sonderzeichen für saubere Tooltips."""
        if not text: return ""
        text = re.sub(r'#+\s', '', text)
        text = re.sub(r'\*\*|__|\*|_', '', text)
        text = re.sub(r'\[([^\]]+)\]\([^\)]+\)', r'\1', text)
        text = re.sub(r'\[\[([^\]]+)\]\]', r'\1', text)
        return text

    def _fetch_full_text_stitched(self, note_id):
        """Lädt alle Chunks einer Note und baut den Text zusammen."""
        try:
            scroll_filter = models.Filter(
                must=[models.FieldCondition(key="note_id", match=models.MatchValue(value=note_id))]
            )
            chunks, _ = self.client.scroll(self.chunks_col, scroll_filter=scroll_filter, limit=100, with_payload=True)
            chunks.sort(key=lambda x: x.payload.get('ord', 999))
            full_text = [c.payload.get('text', '') for c in chunks if c.payload.get('text')]
            return "\n\n".join(full_text)
        except:
            return "Fehler beim Laden des Volltexts."
    def _fetch_previews_for_nodes(self, node_ids):
        """
        Holt Batch-weise den ersten relevanten Textabschnitt für eine Liste von Nodes.
        Optimiert die Ladezeit durch Reduzierung der API-Calls.
        """
        if not node_ids: 
            return {}
        previews = {}
        try:
            scroll_filter = models.Filter(
                must=[models.FieldCondition(key="note_id", match=models.MatchAny(any=node_ids))]
            )
            # Genügend Chunks laden, um für jede ID eine Vorschau zu finden
            chunks, _ = self.client.scroll(self.chunks_col, scroll_filter=scroll_filter, limit=len(node_ids)*3, with_payload=True)
            
            for c in chunks:
                nid = c.payload.get("note_id")
                # Wir nehmen den ersten gefundenen Chunk
                if nid and nid not in previews:
                    previews[nid] = c.payload.get("window") or c.payload.get("text") or ""
        except Exception: 
            pass
        return previews

    def _find_connected_edges(self, note_ids, note_title=None):
        """
        Findet ein- und ausgehende Kanten für eine Liste von IDs.
        Implementiert den Fix für Anker-Links [[Titel#Abschnitt]] durch Präfix-Suche in der target_id.
        """
        results = []
        if not note_ids:
            return results
        
        # 1. AUSGEHENDE KANTEN (Outgoing)
        # Suche über 'note_id' als Besitzer der Kante.
        out_filter = models.Filter(must=[
            models.FieldCondition(key="note_id", match=models.MatchAny(any=note_ids)),
            models.FieldCondition(key="kind", match=models.MatchExcept(**{"except": SYSTEM_EDGES}))
        ])
        res_out, _ = self.client.scroll(self.edges_col, scroll_filter=out_filter, limit=2000, with_payload=True)
        results.extend(res_out)

        # 2. EINGEHENDE KANTEN (Incoming)
        # Suche über target_id (Ziel der Kante).
        
        # Sammele alle Chunk-IDs für exakte Treffer auf Segment-Ebene
        c_filter = models.Filter(must=[models.FieldCondition(key="note_id", match=models.MatchAny(any=note_ids))])
        chunks, _ = self.client.scroll(self.chunks_col, scroll_filter=c_filter, limit=1000, with_payload=False)
        chunk_ids = [c.id for c in chunks]

        should_conditions = []
        if chunk_ids: 
            should_conditions.append(models.FieldCondition(key="target_id", match=models.MatchAny(any=chunk_ids)))
        should_conditions.append(models.FieldCondition(key="target_id", match=models.MatchAny(any=note_ids)))
            
        # TITEL-BASIERTE SUCHE (Inkl. Anker-Fix)
        titles_to_check = []
        if note_title:
            titles_to_check.append(note_title)
            # Aliase laden für robuste Verlinkung
            for nid in note_ids:
                note = self._fetch_note_cached(nid)
                if note:
                    aliases = note.get("aliases", [])
                    if isinstance(aliases, str): aliases = [aliases]
                    titles_to_check.extend([a for a in aliases if a not in titles_to_check])

        # Exakte Titel-Matches hinzufügen
        for t in titles_to_check:
            should_conditions.append(models.FieldCondition(key="target_id", match=models.MatchValue(value=t)))
        
        if should_conditions:
            in_filter = models.Filter(
                must=[models.FieldCondition(key="kind", match=models.MatchExcept(**{"except": SYSTEM_EDGES}))],
                should=should_conditions
            )
            res_in, _ = self.client.scroll(self.edges_col, scroll_filter=in_filter, limit=2000, with_payload=True)
            results.extend(res_in)

        # FIX FÜR [[Titel#Abschnitt]]: Suche nach Fragmenten
        if titles_to_check:
            for t in titles_to_check:
                anchor_filter = models.Filter(must=[
                    models.FieldCondition(key="target_id", match=models.MatchText(text=t)),
                    models.FieldCondition(key="kind", match=models.MatchExcept(**{"except": SYSTEM_EDGES}))
                ])
                res_anchor, _ = self.client.scroll(self.edges_col, scroll_filter=anchor_filter, limit=1000, with_payload=True)
                
                existing_ids = {r.id for r in results}
                for edge in res_anchor:
                    tgt = edge.payload.get("target_id", "")
                    # Client-seitige Filterung: Nur Kanten nehmen, die mit Titel# beginnen
                    if edge.id not in existing_ids and (tgt == t or tgt.startswith(f"{t}#")):
                        results.append(edge)
        
        return results

    def _find_connected_edges_batch(self, note_ids):
        """Wrapper für die Suche in tieferen Ebenen des Graphen."""
        first_note = self._fetch_note_cached(note_ids[0]) if note_ids else None
        title = first_note.get("title") if first_note else None
        return self._find_connected_edges(note_ids, note_title=title)

    def _process_edge(self, record, nodes_dict, unique_edges, current_depth):
        """
        Verarbeitet eine rohe Kante, löst Quell- und Ziel-Referenzen auf 
        und fügt sie den Dictionaries für den Graphen hinzu.
        """
        if not record or not record.payload:
            return None, None
            
        payload = record.payload
        src_ref = payload.get("source_id")
        tgt_ref = payload.get("target_id")
        kind = payload.get("kind")
        provenance = payload.get("provenance", "explicit")

        if not src_ref or not tgt_ref:
            return None, None

        # IDs zu Notes auflösen (Hier greift der Fragment-Fix)
        src_note = self._resolve_note_from_ref(src_ref)
        tgt_note = self._resolve_note_from_ref(tgt_ref)

        if src_note and tgt_note:
            src_id = src_note.get('note_id')
            tgt_id = tgt_note.get('note_id')
            
            if src_id and tgt_id and src_id != tgt_id:
                # Knoten zum Set hinzufügen
                self._add_node_to_dict(nodes_dict, src_note, level=current_depth)
                self._add_node_to_dict(nodes_dict, tgt_note, level=current_depth)
                
                # Kante registrieren (Deduplizierung)
                key = (src_id, tgt_id)
                existing = unique_edges.get(key)
                
                is_current_explicit = (provenance in ["explicit", "rule"])
                should_update = True
                
                if existing:
                    is_existing_explicit = (existing.get('provenance', '') in ["explicit", "rule"])
                    if is_existing_explicit and not is_current_explicit:
                        should_update = False
                
                if should_update:
                    unique_edges[key] = {
                        "source": src_id, 
                        "target": tgt_id, 
                        "kind": kind, 
                        "provenance": provenance
                    }
                return src_id, tgt_id
        return None, None

    def _fetch_note_cached(self, note_id):
        """Lädt eine Note aus Qdrant mit Session-Caching."""
        if not note_id:
            return None
        if note_id in self._note_cache:
            return self._note_cache[note_id]
        
        try:
            res, _ = self.client.scroll(
                collection_name=self.notes_col,
                scroll_filter=models.Filter(must=[
                    models.FieldCondition(key="note_id", match=models.MatchValue(value=note_id))
                ]),
                limit=1, with_payload=True
            )
            if res and res[0].payload:
                payload = res[0].payload
                self._note_cache[note_id] = payload
                return payload
        except Exception: 
            pass
        return None

    def _resolve_note_from_ref(self, ref_str):
        """
        Löst eine Referenz (ID, Chunk-ID oder Wikilink mit Anker) auf eine Note auf.
        Bereinigt Anker (#) vor der Suche.
        """
        if not ref_str: 
            return None
        
        if ref_str in self._ref_resolution_cache:
            return self._ref_resolution_cache[ref_str]
        
        # Fragment-Behandlung: Trenne Anker ab
        base_ref = ref_str.split("#")[0].strip()
        
        # 1. Versuch: Direkte Note-ID Suche
        note = self._fetch_note_cached(base_ref)
        if note:
            self._ref_resolution_cache[ref_str] = note
            return note
            
        # 2. Versuch: Titel-Suche (Keyword-Match)
        try:
            res, _ = self.client.scroll(
                collection_name=self.notes_col,
                scroll_filter=models.Filter(must=[
                    models.FieldCondition(key="title", match=models.MatchValue(value=base_ref))
                ]),
                limit=1, with_payload=True
            )
            if res and res[0].payload:
                payload = res[0].payload
                self._ref_resolution_cache[ref_str] = payload
                return payload
        except Exception: 
            pass

        # 3. Versuch: Auflösung über Chunks
        if "#" in ref_str:
            try:
                res_chunk = self.client.retrieve(self.chunks_col, ids=[ref_str], with_payload=True)
                if res_chunk and res_chunk[0].payload:
                    note_id = res_chunk[0].payload.get("note_id")
                    note = self._fetch_note_cached(note_id)
                    if note:
                        self._ref_resolution_cache[ref_str] = note
                        return note
            except Exception: 
                pass
        
        return None

    def _add_node_to_dict(self, node_dict, note_payload, level=1):
        """Erstellt ein Node-Objekt für streamlit-agraph mit Styling."""
        nid = note_payload.get("note_id")
        if not nid or nid in node_dict: 
            return
        
        ntype = note_payload.get("type", "default")
        color = GRAPH_COLORS.get(ntype, GRAPH_COLORS.get("default", "#8395a7"))
        tooltip = f"Titel: {note_payload.get('title')}\nTyp: {ntype}"
        
        size = 45 if level == 0 else (25 if level == 1 else 15)
        node_dict[nid] = Node(
            id=nid,
            label=note_payload.get('title', nid),
            size=size,
            color=color,
            shape="dot" if level > 0 else "diamond",
            title=tooltip, 
            font={'color': 'black', 'face': 'arial', 'size': 14 if level < 2 else 0} 
        )