import streamlit as st import requests import uuid import os import json import re import yaml import unicodedata from datetime import datetime from pathlib import Path from dotenv import load_dotenv # --- WP-19 GRAPH IMPORTS --- try: from streamlit_agraph import agraph, Node, Edge, Config from qdrant_client import QdrantClient, models except ImportError: st.error("Fehlende Bibliotheken! Bitte installiere: pip install streamlit-agraph qdrant-client") st.stop() # --- CONFIGURATION --- load_dotenv() API_BASE_URL = os.getenv("MINDNET_API_URL", "http://localhost:8002") CHAT_ENDPOINT = f"{API_BASE_URL}/chat" FEEDBACK_ENDPOINT = f"{API_BASE_URL}/feedback" INGEST_ANALYZE_ENDPOINT = f"{API_BASE_URL}/ingest/analyze" INGEST_SAVE_ENDPOINT = f"{API_BASE_URL}/ingest/save" HISTORY_FILE = Path("data/logs/search_history.jsonl") # Qdrant Config (Direct Access for Graph) QDRANT_URL = os.getenv("QDRANT_URL", "http://localhost:6333") QDRANT_KEY = os.getenv("QDRANT_API_KEY", None) if QDRANT_KEY == "": QDRANT_KEY = None COLLECTION_PREFIX = os.getenv("COLLECTION_PREFIX", "mindnet") # Timeout Strategy timeout_setting = os.getenv("MINDNET_API_TIMEOUT") or os.getenv("MINDNET_LLM_TIMEOUT") API_TIMEOUT = float(timeout_setting) if timeout_setting else 300.0 # --- PAGE SETUP --- st.set_page_config(page_title="mindnet v2.6", page_icon="🧠", layout="wide") # --- CSS STYLING --- st.markdown(""" """, unsafe_allow_html=True) # --- SESSION STATE --- if "messages" not in st.session_state: st.session_state.messages = [] if "user_id" not in st.session_state: st.session_state.user_id = str(uuid.uuid4()) # --- GRAPH STYLING CONFIG (WP-19) --- # Colors based on types.yaml and standard conventions GRAPH_COLORS = { "project": "#ff9f43", # Orange "concept": "#54a0ff", # Blue "decision": "#5f27cd", # Purple "risk": "#ff6b6b", # Red "person": "#1dd1a1", # Green "experience": "#feca57",# Yellow "value": "#00d2d3", # Cyan "goal": "#ff9ff3", # Pink "default": "#8395a7" # Grey } # Colors based on edge 'kind' EDGE_COLORS = { "depends_on": "#ff6b6b", # Red (Blocker) "blocks": "#ee5253", # Dark Red "caused_by": "#ff9ff3", # Pink "related_to": "#c8d6e5", # Light Grey "similar_to": "#c8d6e5", # Light Grey "next": "#54a0ff", # Blue "derived_from": "#ff9ff3",# Pink "references": "#bdc3c7", # Grey "belongs_to": "#2e86de" # Dark Blue "contributes_to": "#1dd1a1" } # --- HELPER FUNCTIONS --- def slugify(value): if not value: return "" value = str(value).lower() replacements = {'ä': 'ae', 'ö': 'oe', 'ü': 'ue', 'ß': 'ss', '&': 'und', '+': 'und'} for k, v in replacements.items(): value = value.replace(k, v) value = unicodedata.normalize('NFKD', value).encode('ascii', 'ignore').decode('ascii') value = re.sub(r'[^\w\s-]', '', value).strip() return re.sub(r'[-\s]+', '-', value) def normalize_meta_and_body(meta, body): ALLOWED_KEYS = {"title", "type", "status", "tags", "id", "created", "updated", "aliases", "lang"} clean_meta = {} extra_content = [] if "titel" in meta and "title" not in meta: meta["title"] = meta.pop("titel") tag_candidates = ["tags", "emotionale_keywords", "keywords", "schluesselwoerter"] all_tags = [] for key in tag_candidates: if key in meta: val = meta[key] if isinstance(val, list): all_tags.extend(val) elif isinstance(val, str): all_tags.extend([t.strip() for t in val.split(",")]) for key, val in meta.items(): if key in ALLOWED_KEYS: clean_meta[key] = val elif key in tag_candidates: pass else: if val and isinstance(val, str): header = key.replace("_", " ").title() extra_content.append(f"## {header}\n{val}\n") if all_tags: clean_tags = [] for t in all_tags: t_clean = str(t).replace("#", "").strip() if t_clean: clean_tags.append(t_clean) clean_meta["tags"] = list(set(clean_tags)) if extra_content: new_section = "\n".join(extra_content) final_body = f"{new_section}\n{body}" else: final_body = body return clean_meta, final_body def parse_markdown_draft(full_text): clean_text = full_text.strip() pattern_block = r"```(?:markdown|md|yaml)?\s*(.*?)\s*```" match_block = re.search(pattern_block, clean_text, re.DOTALL | re.IGNORECASE) if match_block: clean_text = match_block.group(1).strip() meta = {} body = clean_text yaml_str = "" parts = re.split(r"^---+\s*$", clean_text, maxsplit=2, flags=re.MULTILINE) if len(parts) >= 3: yaml_str = parts[1] body = parts[2] elif clean_text.startswith("---"): fallback_match = re.search(r"^---\s*(.*?)(?=\n#)", clean_text, re.DOTALL | re.MULTILINE) if fallback_match: yaml_str = fallback_match.group(1) body = clean_text.replace(f"---{yaml_str}", "", 1).strip() if yaml_str: yaml_str_clean = yaml_str.replace("#", "") try: parsed = yaml.safe_load(yaml_str_clean) if isinstance(parsed, dict): meta = parsed except Exception as e: print(f"YAML Parsing Warning: {e}") if not meta.get("title"): h1_match = re.search(r"^#\s+(.*)$", body, re.MULTILINE) if h1_match: meta["title"] = h1_match.group(1).strip() if meta.get("type") == "draft": meta["status"] = "draft" meta["type"] = "experience" return normalize_meta_and_body(meta, body) def build_markdown_doc(meta, body): if "id" not in meta or meta["id"] == "generated_on_save": raw_title = meta.get('title', 'note') clean_slug = slugify(raw_title)[:50] or "note" meta["id"] = f"{datetime.now().strftime('%Y%m%d')}-{clean_slug}" meta["updated"] = datetime.now().strftime("%Y-%m-%d") ordered_meta = {} prio_keys = ["id", "type", "title", "status", "tags"] for k in prio_keys: if k in meta: ordered_meta[k] = meta.pop(k) ordered_meta.update(meta) try: yaml_str = yaml.dump(ordered_meta, default_flow_style=None, sort_keys=False, allow_unicode=True).strip() except: yaml_str = "error: generating_yaml" return f"---\n{yaml_str}\n---\n\n{body}" def load_history_from_logs(limit=10): queries = [] if HISTORY_FILE.exists(): try: with open(HISTORY_FILE, "r", encoding="utf-8") as f: lines = f.readlines() for line in reversed(lines): try: entry = json.loads(line) q = entry.get("query_text") if q and q not in queries: queries.append(q) if len(queries) >= limit: break except: continue except: pass return queries # --- WP-19 GRAPH SERVICE (Advanced) --- class GraphExplorerService: def __init__(self, url, api_key=None, prefix="mindnet"): self.client = QdrantClient(url=url, api_key=api_key) self.prefix = prefix self.notes_col = f"{prefix}_notes" self.chunks_col = f"{prefix}_chunks" self.edges_col = f"{prefix}_edges" self._note_cache = {} def get_ego_graph(self, center_note_id: str): nodes_dict = {} unique_edges = {} center_note = self._fetch_note_cached(center_note_id) if not center_note: return [], [] self._add_node_to_dict(nodes_dict, center_note, is_center=True) center_title = center_note.get("title") # Chunks laden scroll_filter = models.Filter( must=[models.FieldCondition(key="note_id", match=models.MatchValue(value=center_note_id))] ) chunks, _ = self.client.scroll( collection_name=self.chunks_col, scroll_filter=scroll_filter, limit=100, with_payload=True ) center_chunk_ids = [c.id for c in chunks] raw_edges = [] # 1. OUTGOING: Source ist einer unserer Chunks if center_chunk_ids: out_filter = models.Filter( must=[models.FieldCondition(key="source_id", match=models.MatchAny(any=center_chunk_ids))] ) res_out, _ = self.client.scroll( collection_name=self.edges_col, scroll_filter=out_filter, limit=100, with_payload=True ) raw_edges.extend(res_out) # 2. INCOMING: Target ist Chunk, Titel oder exakte Note-ID # Hinweis: Target mit #Section (z.B. 'note#header') kann via Keyword-Index schwer gefunden werden, # wenn wir den Header-Teil nicht kennen. must_conditions = [] if center_chunk_ids: must_conditions.append(models.FieldCondition(key="target_id", match=models.MatchAny(any=center_chunk_ids))) if center_title: must_conditions.append(models.FieldCondition(key="target_id", match=models.MatchValue(value=center_title))) # NEU: Auch exakte Note-ID als Target prüfen must_conditions.append(models.FieldCondition(key="target_id", match=models.MatchValue(value=center_note_id))) if must_conditions: in_filter = models.Filter(should=must_conditions) # 'should' wirkt wie OR res_in, _ = self.client.scroll( collection_name=self.edges_col, scroll_filter=in_filter, limit=100, with_payload=True ) raw_edges.extend(res_in) # Verarbeitung for record in raw_edges: payload = record.payload src_ref = payload.get("source_id") tgt_ref = payload.get("target_id") kind = payload.get("kind", "related_to") provenance = payload.get("provenance", "explicit") 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['note_id'] tgt_id = tgt_note['note_id'] if src_id != tgt_id: self._add_node_to_dict(nodes_dict, src_note) self._add_node_to_dict(nodes_dict, tgt_note) key = (src_id, tgt_id) existing = unique_edges.get(key) is_current_explicit = (provenance == "explicit" or provenance == "rule") should_update = True if existing: is_existing_explicit = (existing['provenance'] == "explicit" or existing['provenance'] == "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 } final_edges = [] for (src, tgt), data in unique_edges.items(): kind = data['kind'] prov = data['provenance'] color = EDGE_COLORS.get(kind, "#bdc3c7") is_smart = (prov != "explicit" and prov != "rule") final_edges.append(Edge( source=src, target=tgt, label=kind, color=color, dashes=is_smart, title=f"Provenance: {prov}\nType: {kind}" )) return list(nodes_dict.values()), final_edges def _fetch_note_cached(self, note_id): if note_id in self._note_cache: return self._note_cache[note_id] 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: self._note_cache[note_id] = res[0].payload return res[0].payload return None def _resolve_note_from_ref(self, ref_str): if not ref_str: return None # Fall A: Chunk ID (Format: note_id#cXX) if "#" in ref_str: # 1. Versuch: Echte Chunk ID in DB suchen try: res = self.client.retrieve(collection_name=self.chunks_col, ids=[ref_str], with_payload=True) if res: parent_id = res[0].payload.get("note_id") return self._fetch_note_cached(parent_id) except: pass # 2. Versuch (NEU): Es ist ein Link auf eine Section (z.B. "note-id#Header") # Wir entfernen den Hash-Teil und suchen die Basis-Notiz possible_note_id = ref_str.split("#")[0] note_by_id = self._fetch_note_cached(possible_note_id) if note_by_id: return note_by_id # Fall B: Es ist direkt die Note ID note_by_id = self._fetch_note_cached(ref_str) if note_by_id: return note_by_id # Fall C: Es ist der Titel (Wikilink) res, _ = self.client.scroll( collection_name=self.notes_col, scroll_filter=models.Filter(must=[models.FieldCondition(key="title", match=models.MatchValue(value=ref_str))]), limit=1, with_payload=True ) if res: p = res[0].payload self._note_cache[p['note_id']] = p return p return None def _add_node_to_dict(self, node_dict, note_payload, is_center=False): nid = note_payload.get("note_id") if nid in node_dict: return ntype = note_payload.get("type", "default") color = GRAPH_COLORS.get(ntype, GRAPH_COLORS["default"]) size = 35 if is_center else 20 node_dict[nid] = Node( id=nid, label=note_payload.get("title", nid), size=size, color=color, shape="dot", title=f"Type: {ntype}\nTags: {note_payload.get('tags')}", font={'color': 'black'} ) # Init Graph Service graph_service = GraphExplorerService(QDRANT_URL, QDRANT_KEY, COLLECTION_PREFIX) # --- API CLIENT --- def send_chat_message(message: str, top_k: int, explain: bool): try: response = requests.post( CHAT_ENDPOINT, json={"message": message, "top_k": top_k, "explain": explain}, timeout=API_TIMEOUT ) response.raise_for_status() return response.json() except Exception as e: return {"error": str(e)} def analyze_draft_text(text: str, n_type: str): try: response = requests.post(INGEST_ANALYZE_ENDPOINT, json={"text": text, "type": n_type}, timeout=15) response.raise_for_status() return response.json() except Exception as e: return {"error": str(e)} def save_draft_to_vault(markdown_content: str, filename: str = None): try: response = requests.post(INGEST_SAVE_ENDPOINT, json={"markdown_content": markdown_content, "filename": filename}, timeout=API_TIMEOUT) response.raise_for_status() return response.json() except Exception as e: return {"error": str(e)} def submit_feedback(query_id, node_id, score, comment=None): try: requests.post(FEEDBACK_ENDPOINT, json={"query_id": query_id, "node_id": node_id, "score": score, "comment": comment}, timeout=2) st.toast(f"Feedback ({score}) gesendet!") except: pass # --- UI COMPONENTS --- def render_sidebar(): with st.sidebar: st.title("🧠 mindnet") st.caption("v2.6 | WP-19 Graph View") mode = st.radio("Modus", ["💬 Chat", "📝 Manueller Editor", "🕸️ Graph Explorer"], index=0) st.divider() st.subheader("⚙️ Settings") top_k = st.slider("Quellen (Top-K)", 1, 10, 5) explain = st.toggle("Explanation Layer", True) st.divider() st.subheader("🕒 Verlauf") for q in load_history_from_logs(8): if st.button(f"🔎 {q[:25]}...", key=f"hist_{q}", use_container_width=True): st.session_state.messages.append({"role": "user", "content": q}) st.rerun() return mode, top_k, explain def render_draft_editor(msg): if "query_id" not in msg or not msg["query_id"]: msg["query_id"] = str(uuid.uuid4()) qid = msg["query_id"] key_base = f"draft_{qid}" # State Keys data_meta_key = f"{key_base}_data_meta" data_sugg_key = f"{key_base}_data_suggestions" widget_body_key = f"{key_base}_widget_body" data_body_key = f"{key_base}_data_body" # INIT STATE if f"{key_base}_init" not in st.session_state: meta, body = parse_markdown_draft(msg["content"]) if "type" not in meta: meta["type"] = "default" if "title" not in meta: meta["title"] = "" tags = meta.get("tags", []) meta["tags_str"] = ", ".join(tags) if isinstance(tags, list) else str(tags) st.session_state[data_meta_key] = meta st.session_state[data_sugg_key] = [] st.session_state[data_body_key] = body.strip() st.session_state[f"{key_base}_wdg_title"] = meta["title"] st.session_state[f"{key_base}_wdg_type"] = meta["type"] st.session_state[f"{key_base}_wdg_tags"] = meta["tags_str"] st.session_state[f"{key_base}_init"] = True # RESURRECTION if widget_body_key not in st.session_state and data_body_key in st.session_state: st.session_state[widget_body_key] = st.session_state[data_body_key] # CALLBACKS def _sync_meta(): meta = st.session_state[data_meta_key] meta["title"] = st.session_state.get(f"{key_base}_wdg_title", "") meta["type"] = st.session_state.get(f"{key_base}_wdg_type", "default") meta["tags_str"] = st.session_state.get(f"{key_base}_wdg_tags", "") st.session_state[data_meta_key] = meta def _sync_body(): st.session_state[data_body_key] = st.session_state[widget_body_key] def _insert_text(text_to_insert): current = st.session_state.get(widget_body_key, "") new_text = f"{current}\n\n{text_to_insert}" st.session_state[widget_body_key] = new_text st.session_state[data_body_key] = new_text def _remove_text(text_to_remove): current = st.session_state.get(widget_body_key, "") new_text = current.replace(text_to_remove, "").strip() st.session_state[widget_body_key] = new_text st.session_state[data_body_key] = new_text # UI LAYOUT st.markdown(f'
', unsafe_allow_html=True) st.markdown("### 📝 Entwurf bearbeiten") meta_ref = st.session_state[data_meta_key] c1, c2 = st.columns([2, 1]) with c1: st.text_input("Titel", key=f"{key_base}_wdg_title", on_change=_sync_meta) with c2: known_types = ["concept", "project", "decision", "experience", "journal", "value", "goal", "principle", "risk", "belief"] curr_type = st.session_state.get(f"{key_base}_wdg_type", meta_ref["type"]) if curr_type not in known_types: known_types.append(curr_type) st.selectbox("Typ", known_types, key=f"{key_base}_wdg_type", on_change=_sync_meta) st.text_input("Tags", key=f"{key_base}_wdg_tags", on_change=_sync_meta) tab_edit, tab_intel, tab_view = st.tabs(["✏️ Inhalt", "🧠 Intelligence", "👁️ Vorschau"]) with tab_edit: st.text_area("Body", key=widget_body_key, height=500, on_change=_sync_body, label_visibility="collapsed") with tab_intel: st.info("Klicke auf 'Analysieren', um Verknüpfungen für den AKTUELLEN Text zu finden.") if st.button("🔍 Analyse starten", key=f"{key_base}_analyze"): st.session_state[data_sugg_key] = [] text_to_analyze = st.session_state.get(widget_body_key, st.session_state.get(data_body_key, "")) current_doc_type = st.session_state.get(f"{key_base}_wdg_type", "concept") with st.spinner("Analysiere..."): analysis = analyze_draft_text(text_to_analyze, current_doc_type) if "error" in analysis: st.error(f"Fehler: {analysis['error']}") else: suggestions = analysis.get("suggestions", []) st.session_state[data_sugg_key] = suggestions if not suggestions: st.warning("Keine Vorschläge gefunden.") else: st.success(f"{len(suggestions)} Vorschläge gefunden.") suggestions = st.session_state[data_sugg_key] if suggestions: current_text_state = st.session_state.get(widget_body_key, "") for idx, sugg in enumerate(suggestions): link_text = sugg.get('suggested_markdown', '') is_inserted = link_text in current_text_state bg_color = "#e6fffa" if is_inserted else "#ffffff" border = "3px solid #28a745" if is_inserted else "3px solid #1a73e8" st.markdown(f"""
{sugg.get('target_title')} ({sugg.get('type')})
{sugg.get('reason')}
{link_text}
""", unsafe_allow_html=True) if is_inserted: st.button("❌ Entfernen", key=f"del_{idx}_{key_base}", on_click=_remove_text, args=(link_text,)) else: st.button("➕ Einfügen", key=f"add_{idx}_{key_base}", on_click=_insert_text, args=(link_text,)) final_tags_str = st.session_state.get(f"{key_base}_wdg_tags", "") final_tags = [t.strip() for t in final_tags_str.split(",") if t.strip()] final_meta = { "id": "generated_on_save", "type": st.session_state.get(f"{key_base}_wdg_type", "default"), "title": st.session_state.get(f"{key_base}_wdg_title", "").strip(), "status": "draft", "tags": final_tags } final_body = st.session_state.get(widget_body_key, st.session_state[data_body_key]) if not final_meta["title"]: h1_match = re.search(r"^#\s+(.*)$", final_body, re.MULTILINE) if h1_match: final_meta["title"] = h1_match.group(1).strip() final_doc = build_markdown_doc(final_meta, final_body) with tab_view: st.markdown('
', unsafe_allow_html=True) st.markdown(final_doc) st.markdown('
', unsafe_allow_html=True) st.markdown("---") b1, b2 = st.columns([1, 1]) with b1: if st.button("💾 Speichern & Indizieren", type="primary", key=f"{key_base}_save"): with st.spinner("Speichere im Vault..."): raw_title = final_meta.get("title", "") if not raw_title: clean_body = re.sub(r"[#*_\[\]()]", "", final_body).strip() raw_title = clean_body[:40] if clean_body else "draft" safe_title = slugify(raw_title)[:60] or "draft" fname = f"{datetime.now().strftime('%Y%m%d')}-{safe_title}.md" result = save_draft_to_vault(final_doc, filename=fname) if "error" in result: st.error(f"Fehler: {result['error']}") else: st.success(f"Gespeichert: {result.get('file_path')}") st.balloons() with b2: if st.button("📋 Code anzeigen", key=f"{key_base}_btn_copy"): st.code(final_doc, language="markdown") st.markdown("
", unsafe_allow_html=True) def render_chat_interface(top_k, explain): for idx, msg in enumerate(st.session_state.messages): with st.chat_message(msg["role"]): if msg["role"] == "assistant": intent = msg.get("intent", "UNKNOWN") src = msg.get("intent_source", "?") icon = {"EMPATHY":"❤️", "DECISION":"⚖️", "CODING":"💻", "FACT":"📚", "INTERVIEW":"📝"}.get(intent, "🧠") st.markdown(f'
{icon} Intent: {intent} ({src})
', unsafe_allow_html=True) with st.expander("🐞 Debug Raw Payload", expanded=False): st.json(msg) if intent == "INTERVIEW": render_draft_editor(msg) else: st.markdown(msg["content"]) if "sources" in msg and msg["sources"]: for hit in msg["sources"]: with st.expander(f"📄 {hit.get('note_id', '?')} ({hit.get('total_score', 0):.2f})"): st.markdown(f"_{hit.get('source', {}).get('text', '')[:300]}..._") if hit.get('explanation'): st.caption(f"Grund: {hit['explanation']['reasons'][0]['message']}") def _cb(qid=msg.get("query_id"), nid=hit.get('node_id')): val = st.session_state.get(f"fb_src_{qid}_{nid}") if val is not None: submit_feedback(qid, nid, val+1) st.feedback("faces", key=f"fb_src_{msg.get('query_id')}_{hit.get('node_id')}", on_change=_cb) if "query_id" in msg: qid = msg["query_id"] st.feedback("stars", key=f"fb_glob_{qid}", on_change=lambda: submit_feedback(qid, "generated_answer", st.session_state[f"fb_glob_{qid}"]+1)) else: st.markdown(msg["content"]) if prompt := st.chat_input("Frage Mindnet..."): st.session_state.messages.append({"role": "user", "content": prompt}) st.rerun() if len(st.session_state.messages) > 0 and st.session_state.messages[-1]["role"] == "user": with st.chat_message("assistant"): with st.spinner("Thinking..."): resp = send_chat_message(st.session_state.messages[-1]["content"], top_k, explain) if "error" in resp: st.error(resp["error"]) else: st.session_state.messages.append({ "role": "assistant", "content": resp.get("answer"), "intent": resp.get("intent", "FACT"), "intent_source": resp.get("intent_source", "Unknown"), "sources": resp.get("sources", []), "query_id": resp.get("query_id") }) st.rerun() def render_manual_editor(): mock_msg = { "content": "---\ntype: concept\ntitle: Neue Notiz\nstatus: draft\ntags: []\n---\n# Titel\n", "query_id": "manual_mode_v2" } render_draft_editor(mock_msg) def render_graph_explorer(): st.header("🕸️ Graph Explorer (WP-19)") col_ctrl, col_graph = st.columns([1, 3]) with col_ctrl: st.subheader("Fokus setzen") search_term = st.text_input("Suche Notiz (Titel)", placeholder="z.B. Project Alpha") selected_note_id = None if search_term: # Suche nach Titel für Autocomplete hits, _ = graph_service.client.scroll( collection_name=f"{COLLECTION_PREFIX}_notes", scroll_filter=models.Filter( must=[models.FieldCondition(key="title", match=models.MatchText(text=search_term))] ), limit=10 ) options = {h.payload['title']: h.payload['note_id'] for h in hits} if options: selected_title = st.selectbox("Wähle Notiz:", list(options.keys())) selected_note_id = options[selected_title] else: st.warning("Keine Notiz gefunden.") st.markdown("---") st.markdown("**Legende:**") st.markdown(f"🔴 **Blocker** (Risk/Block)") st.markdown(f"🔵 **Konzept/Struktur**") st.markdown(f"🟣 **Entscheidung**") st.markdown(f"--- **Solid**: Explicit Link") st.markdown(f"- - **Dashed**: Smart/AI Link") with col_graph: if selected_note_id: with st.spinner(f"Lade Graph für {selected_note_id}..."): nodes, edges = graph_service.get_ego_graph(selected_note_id) if not nodes: st.error("Knoten konnte nicht geladen werden.") else: config = Config( width=900, height=700, directed=True, physics=True, hierarchical=False, nodeHighlightBehavior=True, highlightColor="#F7A7A6", collapsible=False ) # Rendering the Graph st.caption(f"Graph zeigt {len(nodes)} Knoten und {len(edges)} Kanten.") return_value = agraph(nodes=nodes, edges=edges, config=config) if return_value: st.info(f"Auswahl: {return_value}") else: st.info("👈 Bitte wähle links eine Notiz aus, um den Graphen zu starten.") # --- MAIN --- mode, top_k, explain = render_sidebar() if mode == "💬 Chat": render_chat_interface(top_k, explain) elif mode == "📝 Manueller Editor": render_manual_editor() elif mode == "🕸️ Graph Explorer": render_graph_explorer()