221 lines
9.3 KiB
Python
221 lines
9.3 KiB
Python
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_ego_graph(self, center_note_id: str, depth=2):
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nodes_dict = {}
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unique_edges = {}
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# --- LEVEL 1: Center & direkte Nachbarn ---
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# 1. Center Note
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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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# Wir sammeln IDs für Level 2 Suche
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level_1_ids = {center_note_id}
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# Suche Kanten für Center
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l1_edges = self._find_connected_edges([center_note_id], center_note.get("title"))
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# Verarbeite L1 Kanten
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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: Nachbarn der Nachbarn ---
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if depth > 1 and level_1_ids:
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# Wir suchen Kanten, bei denen Source oder Target einer der L1 Nodes ist
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# Wichtig: Wir filtern System-Edges schon in der Query oder Python, um Traffic zu sparen
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# Um die Performance zu wahren, limitieren wir die L2 Suche auf die IDs, die wir schon haben (als Source)
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# Das ist ein "Ego-Network" Ansatz.
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# Wir nehmen alle IDs aus Level 1 (außer Center, das haben wir schon)
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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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# --- GRAPH CONSTRUCTION ---
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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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# Dynamische Farbe holen
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color = get_edge_color(kind)
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is_smart = (prov != "explicit" and prov != "rule")
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final_edges.append(Edge(
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source=src, target=tgt, label=kind, color=color, dashes=is_smart,
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title=f"Provenance: {prov}\nType: {kind}"
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))
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return list(nodes_dict.values()), final_edges
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def _find_connected_edges(self, note_ids, note_title=None):
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"""Findet In- und Outgoing Edges für eine Liste von Note-IDs."""
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# 1. Chunks zu diesen Notes finden
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scroll_filter = models.Filter(
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must=[models.FieldCondition(key="note_id", match=models.MatchAny(any=note_ids))]
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)
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chunks, _ = self.client.scroll(
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collection_name=self.chunks_col, scroll_filter=scroll_filter, limit=200
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)
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chunk_ids = [c.id for c in chunks]
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results = []
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# Outgoing (Source is Chunk)
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if chunk_ids:
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out_f = models.Filter(must=[
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models.FieldCondition(key="source_id", match=models.MatchAny(any=chunk_ids)),
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# Filter System Edges
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models.FieldCondition(key="kind", match=models.MatchExcept(except_=SYSTEM_EDGES))
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])
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res_out, _ = self.client.scroll(self.edges_col, scroll_filter=out_f, limit=100, with_payload=True)
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results.extend(res_out)
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# Incoming (Target is Chunk OR Title OR NoteID)
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shoulds = []
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if chunk_ids: shoulds.append(models.FieldCondition(key="target_id", match=models.MatchAny(any=chunk_ids)))
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if note_title: shoulds.append(models.FieldCondition(key="target_id", match=models.MatchValue(value=note_title)))
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shoulds.append(models.FieldCondition(key="target_id", match=models.MatchAny(any=note_ids)))
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if shoulds:
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in_f = 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_f, limit=100, 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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"""Batch-Suche für Level 2 (nur ausgehend und eingehend auf Note-Ebene, keine Title-Suche für Performance)."""
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# Vereinfachte Suche: Wir suchen Kanten, die direkt mit den note_ids (oder deren Chunks) zu tun haben
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# Um Performance zu sparen, machen wir hier einen simpleren Lookup, wenn möglich.
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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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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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# Resolve
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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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# Add Nodes
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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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# Add Edge (Deduplication Logic)
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key = (src_id, tgt_id)
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existing = unique_edges.get(key)
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# Update logic: Explicit > Smart
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should_update = True
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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] = {
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"source": src_id, "target": tgt_id, "kind": kind, "provenance": provenance
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}
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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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if not ref_str: return None
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# ... (Logik identisch zu vorher, hier gekürzt für Übersicht)
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# Fall A: Chunk ID / Section
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if "#" in ref_str:
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try:
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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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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
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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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# Wenn Node schon da ist, aber wir finden ihn auf einem "höheren" Level (näher am Zentrum), updaten wir ihn nicht zwingend,
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# außer wir wollen visuelle Eigenschaften ändern.
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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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# Größe & Label basierend auf Level
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if level == 0:
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size = 40
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label_prefix = ""
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elif level == 1:
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size = 25
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label_prefix = ""
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else:
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size = 15 # Level 2 kleiner
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label_prefix = ""
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node_dict[nid] = Node(
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id=nid,
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label=f"{label_prefix}{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=f"Type: {ntype}\nLevel: {level}\nTags: {note_payload.get('tags')}",
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font={'color': 'black', 'face': 'arial', 'size': 14 if level < 2 else 10}
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) |