neue Funkktionen für Graphendarstellung
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@ -5,41 +5,50 @@ from datetime import datetime
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from streamlit_agraph import agraph, Config
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from qdrant_client import models
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# Importe aus den anderen Modulen
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from ui_utils import parse_markdown_draft, build_markdown_doc, load_history_from_logs, slugify
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from ui_api import save_draft_to_vault, analyze_draft_text, send_chat_message, submit_feedback
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from ui_config import HISTORY_FILE, COLLECTION_PREFIX
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from ui_config import HISTORY_FILE, COLLECTION_PREFIX, GRAPH_COLORS
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def render_sidebar():
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with st.sidebar:
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st.title("🧠 mindnet")
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st.caption("v2.6 | WP-19 Graph View")
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# Modus-Auswahl
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mode = st.radio("Modus", ["💬 Chat", "📝 Manueller Editor", "🕸️ Graph Explorer"], index=0)
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st.divider()
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st.subheader("⚙️ Settings")
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top_k = st.slider("Quellen (Top-K)", 1, 10, 5)
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explain = st.toggle("Explanation Layer", True)
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st.divider()
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st.subheader("🕒 Verlauf")
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# Historie laden
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for q in load_history_from_logs(HISTORY_FILE, 8):
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if st.button(f"🔎 {q[:25]}...", key=f"hist_{q}", use_container_width=True):
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st.session_state.messages.append({"role": "user", "content": q})
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st.rerun()
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return mode, top_k, explain
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def render_draft_editor(msg):
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"""
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Rendert den Editor für Drafts (genutzt im Chat bei INTERVIEW Intent oder im manuellen Modus).
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"""
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if "query_id" not in msg or not msg["query_id"]:
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msg["query_id"] = str(uuid.uuid4())
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qid = msg["query_id"]
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key_base = f"draft_{qid}"
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# State Keys
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# State Keys für Persistenz
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data_meta_key = f"{key_base}_data_meta"
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data_sugg_key = f"{key_base}_data_suggestions"
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widget_body_key = f"{key_base}_widget_body"
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data_body_key = f"{key_base}_data_body"
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# INIT STATE
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# --- INIT STATE ---
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if f"{key_base}_init" not in st.session_state:
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meta, body = parse_markdown_draft(msg["content"])
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if "type" not in meta: meta["type"] = "default"
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@ -50,16 +59,19 @@ def render_draft_editor(msg):
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st.session_state[data_meta_key] = meta
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st.session_state[data_sugg_key] = []
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st.session_state[data_body_key] = body.strip()
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# Widget States initialisieren
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st.session_state[f"{key_base}_wdg_title"] = meta["title"]
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st.session_state[f"{key_base}_wdg_type"] = meta["type"]
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st.session_state[f"{key_base}_wdg_tags"] = meta["tags_str"]
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st.session_state[f"{key_base}_init"] = True
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# RESURRECTION
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# --- STATE RESURRECTION ---
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# Falls Streamlit rerunt, stellen wir sicher, dass der Body nicht verloren geht
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if widget_body_key not in st.session_state and data_body_key in st.session_state:
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st.session_state[widget_body_key] = st.session_state[data_body_key]
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# CALLBACKS
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# --- CALLBACKS ---
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def _sync_meta():
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meta = st.session_state[data_meta_key]
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meta["title"] = st.session_state.get(f"{key_base}_wdg_title", "")
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@ -82,11 +94,13 @@ def render_draft_editor(msg):
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st.session_state[widget_body_key] = new_text
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st.session_state[data_body_key] = new_text
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# UI LAYOUT
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# --- UI LAYOUT ---
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st.markdown(f'<div class="draft-box">', unsafe_allow_html=True)
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st.markdown("### 📝 Entwurf bearbeiten")
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meta_ref = st.session_state[data_meta_key]
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# Metadaten Zeile
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c1, c2 = st.columns([2, 1])
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with c1:
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st.text_input("Titel", key=f"{key_base}_wdg_title", on_change=_sync_meta)
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@ -98,6 +112,7 @@ def render_draft_editor(msg):
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st.text_input("Tags", key=f"{key_base}_wdg_tags", on_change=_sync_meta)
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# Tabs für Bearbeitung
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tab_edit, tab_intel, tab_view = st.tabs(["✏️ Inhalt", "🧠 Intelligence", "👁️ Vorschau"])
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with tab_edit:
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@ -105,6 +120,7 @@ def render_draft_editor(msg):
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with tab_intel:
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st.info("Klicke auf 'Analysieren', um Verknüpfungen für den AKTUELLEN Text zu finden.")
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if st.button("🔍 Analyse starten", key=f"{key_base}_analyze"):
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st.session_state[data_sugg_key] = []
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text_to_analyze = st.session_state.get(widget_body_key, st.session_state.get(data_body_key, ""))
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@ -120,14 +136,18 @@ def render_draft_editor(msg):
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if not suggestions: st.warning("Keine Vorschläge gefunden.")
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else: st.success(f"{len(suggestions)} Vorschläge gefunden.")
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# Vorschläge anzeigen
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suggestions = st.session_state[data_sugg_key]
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if suggestions:
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current_text_state = st.session_state.get(widget_body_key, "")
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for idx, sugg in enumerate(suggestions):
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link_text = sugg.get('suggested_markdown', '')
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is_inserted = link_text in current_text_state
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# Styling je nach Status
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bg_color = "#e6fffa" if is_inserted else "#ffffff"
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border = "3px solid #28a745" if is_inserted else "3px solid #1a73e8"
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st.markdown(f"""
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<div style="border-left: {border}; background-color: {bg_color}; padding: 10px; margin-bottom: 8px; border-radius: 4px; box-shadow: 0 1px 3px rgba(0,0,0,0.1);">
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<b>{sugg.get('target_title')}</b> <small>({sugg.get('type')})</small><br>
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@ -135,13 +155,16 @@ def render_draft_editor(msg):
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<code>{link_text}</code>
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</div>
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""", unsafe_allow_html=True)
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if is_inserted:
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st.button("❌ Entfernen", key=f"del_{idx}_{key_base}", on_click=_remove_text, args=(link_text,))
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else:
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st.button("➕ Einfügen", key=f"add_{idx}_{key_base}", on_click=_insert_text, args=(link_text,))
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# Dokument zusammenbauen für Speicherung/Vorschau
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final_tags_str = st.session_state.get(f"{key_base}_wdg_tags", "")
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final_tags = [t.strip() for t in final_tags_str.split(",") if t.strip()]
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final_meta = {
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"id": "generated_on_save",
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"type": st.session_state.get(f"{key_base}_wdg_type", "default"),
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@ -150,6 +173,8 @@ def render_draft_editor(msg):
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"tags": final_tags
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}
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final_body = st.session_state.get(widget_body_key, st.session_state[data_body_key])
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# Fallback Title aus H1
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if not final_meta["title"]:
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h1_match = re.search(r"^#\s+(.*)$", final_body, re.MULTILINE)
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if h1_match: final_meta["title"] = h1_match.group(1).strip()
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@ -162,6 +187,8 @@ def render_draft_editor(msg):
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st.markdown('</div>', unsafe_allow_html=True)
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st.markdown("---")
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# Footer Buttons
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b1, b2 = st.columns([1, 1])
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with b1:
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if st.button("💾 Speichern & Indizieren", type="primary", key=f"{key_base}_save"):
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@ -172,6 +199,7 @@ def render_draft_editor(msg):
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raw_title = clean_body[:40] if clean_body else "draft"
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safe_title = slugify(raw_title)[:60] or "draft"
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fname = f"{datetime.now().strftime('%Y%m%d')}-{safe_title}.md"
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result = save_draft_to_vault(final_doc, filename=fname)
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if "error" in result: st.error(f"Fehler: {result['error']}")
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else:
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@ -180,46 +208,59 @@ def render_draft_editor(msg):
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with b2:
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if st.button("📋 Code anzeigen", key=f"{key_base}_btn_copy"):
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st.code(final_doc, language="markdown")
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st.markdown("</div>", unsafe_allow_html=True)
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def render_chat_interface(top_k, explain):
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"""
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Rendert den Chat-Verlauf und das Eingabefeld.
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"""
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for idx, msg in enumerate(st.session_state.messages):
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with st.chat_message(msg["role"]):
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if msg["role"] == "assistant":
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# Intent Badge
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intent = msg.get("intent", "UNKNOWN")
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src = msg.get("intent_source", "?")
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icon = {"EMPATHY":"❤️", "DECISION":"⚖️", "CODING":"💻", "FACT":"📚", "INTERVIEW":"📝"}.get(intent, "🧠")
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st.markdown(f'<div class="intent-badge">{icon} Intent: {intent} <span style="opacity:0.6; font-size:0.8em">({src})</span></div>', unsafe_allow_html=True)
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# Debug Info
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with st.expander("🐞 Debug Raw Payload", expanded=False):
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st.json(msg)
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# Special Renderers
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if intent == "INTERVIEW":
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render_draft_editor(msg)
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else:
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st.markdown(msg["content"])
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# Quellen-Anzeige
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if "sources" in msg and msg["sources"]:
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for hit in msg["sources"]:
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with st.expander(f"📄 {hit.get('note_id', '?')} ({hit.get('total_score', 0):.2f})"):
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st.markdown(f"_{hit.get('source', {}).get('text', '')[:300]}..._")
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if hit.get('explanation'):
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st.caption(f"Grund: {hit['explanation']['reasons'][0]['message']}")
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# Source Feedback
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def _cb(qid=msg.get("query_id"), nid=hit.get('node_id')):
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val = st.session_state.get(f"fb_src_{qid}_{nid}")
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if val is not None: submit_feedback(qid, nid, val+1)
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st.feedback("faces", key=f"fb_src_{msg.get('query_id')}_{hit.get('node_id')}", on_change=_cb)
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# Global Feedback
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if "query_id" in msg:
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qid = msg["query_id"]
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st.feedback("stars", key=f"fb_glob_{qid}", on_change=lambda: submit_feedback(qid, "generated_answer", st.session_state[f"fb_glob_{qid}"]+1))
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else:
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st.markdown(msg["content"])
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# Chat Input
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if prompt := st.chat_input("Frage Mindnet..."):
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st.session_state.messages.append({"role": "user", "content": prompt})
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st.rerun()
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# Antwort generieren (falls User zuletzt gefragt hat)
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if len(st.session_state.messages) > 0 and st.session_state.messages[-1]["role"] == "user":
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with st.chat_message("assistant"):
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with st.spinner("Thinking..."):
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@ -238,6 +279,7 @@ def render_chat_interface(top_k, explain):
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st.rerun()
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def render_manual_editor():
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"""Rendert einen leeren Editor für manuelle Eingaben."""
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mock_msg = {
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"content": "---\ntype: concept\ntitle: Neue Notiz\nstatus: draft\ntags: []\n---\n# Titel\n",
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"query_id": "manual_mode_v2"
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@ -245,15 +287,24 @@ def render_manual_editor():
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render_draft_editor(mock_msg)
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def render_graph_explorer(graph_service):
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st.header("🕸️ Graph Explorer (WP-19)")
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"""
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Rendert den erweiterten Graph Explorer (WP-19).
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"""
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st.header("🕸️ Graph Explorer")
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col_ctrl, col_graph = st.columns([1, 3])
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# State Management für Graph Navigation
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if "graph_center_id" not in st.session_state:
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st.session_state.graph_center_id = None
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col_ctrl, col_graph = st.columns([1, 4]) # Graph bekommt mehr Platz
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with col_ctrl:
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st.subheader("Fokus setzen")
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search_term = st.text_input("Suche Notiz (Titel)", placeholder="z.B. Project Alpha")
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selected_note_id = None
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st.subheader("Fokus")
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# 1. Suchfeld mit Autocomplete-Logik
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search_term = st.text_input("Suche Notiz", placeholder="Titel eingeben...")
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options = {}
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if search_term:
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hits, _ = graph_service.client.scroll(
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collection_name=f"{COLLECTION_PREFIX}_notes",
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@ -263,43 +314,69 @@ def render_graph_explorer(graph_service):
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limit=10
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)
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options = {h.payload['title']: h.payload['note_id'] for h in hits}
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if options:
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selected_title = st.selectbox("Wähle Notiz:", list(options.keys()))
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selected_note_id = options[selected_title]
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else:
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st.warning("Keine Notiz gefunden.")
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st.markdown("---")
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st.markdown("**Legende:**")
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st.markdown(f"🔴 **Blocker** (Risk/Block)")
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st.markdown(f"🔵 **Konzept/Struktur**")
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st.markdown(f"🟣 **Entscheidung**")
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st.markdown(f"🟢 **Beitrag**")
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st.markdown(f"--- **Solid**: Explicit Link")
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st.markdown(f"- - **Dashed**: Smart/AI Link")
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# 2. Auswahlliste
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if options:
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selected_title = st.selectbox("Ergebnisse:", list(options.keys()))
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if st.button("Laden", use_container_width=True):
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st.session_state.graph_center_id = options[selected_title]
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st.rerun()
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st.divider()
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# 3. Legende (Top Typen)
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st.caption("Legende (Wichtigste Typen)")
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# Wir zeigen nur die ersten 6 Farben an, um die UI nicht zu sprengen
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for k, v in list(GRAPH_COLORS.items())[:8]:
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st.markdown(f"<span style='color:{v}'>●</span> {k}", unsafe_allow_html=True)
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st.caption("Weitere Farben siehe `ui_config.py`")
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st.divider()
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# 4. Tiefe Steuerung
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depth_val = st.slider("Tiefe (Tier)", 1, 3, 2, help="Level 1 = Nachbarn, Level 2 = Nachbarn der Nachbarn")
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with col_graph:
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if selected_note_id:
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with st.spinner(f"Lade Graph für {selected_note_id}..."):
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nodes, edges = graph_service.get_ego_graph(selected_note_id)
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center_id = st.session_state.graph_center_id
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if center_id:
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with st.spinner(f"Lade Graph für {center_id} (Tiefe {depth_val})..."):
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# Daten laden (mit Tiefe)
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nodes, edges = graph_service.get_ego_graph(center_id, depth=depth_val)
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if not nodes:
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st.error("Knoten konnte nicht geladen werden.")
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st.warning("Keine Daten gefunden. Vielleicht existiert die Notiz nicht mehr?")
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else:
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# CONFIG: Abstand und Physik optimiert für Lesbarkeit
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config = Config(
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width=900,
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height=700,
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width=1000,
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height=750,
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directed=True,
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physics=True,
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hierarchical=False,
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# Erweiterte Physik-Einstellungen
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key="graph_view",
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nodeHighlightBehavior=True,
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highlightColor="#F7A7A6",
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collapsible=False
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collapsible=False,
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# Tuning für Abstand:
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gravity=-4000, # Starke Abstoßung (Minus-Wert)
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central_gravity=0.3,# Zieht Nodes leicht zur Mitte
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spring_length=250, # Längere Kanten für bessere Lesbarkeit
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spring_strength=0.05,
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damping=0.09
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)
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st.caption(f"Graph zeigt {len(nodes)} Knoten und {len(edges)} Kanten.")
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st.caption(f"Zentrum: **{center_id}** | Knoten: {len(nodes)} | Kanten: {len(edges)}")
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# Interaktion: Agraph gibt die ID des geklickten Nodes zurück
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return_value = agraph(nodes=nodes, edges=edges, config=config)
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if return_value:
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st.info(f"Auswahl: {return_value}")
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# NAVIGATION LOGIK
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# Wenn ein Node geklickt wurde UND es nicht der aktuelle Center ist -> Navigation
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if return_value and return_value != center_id:
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st.session_state.graph_center_id = return_value
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st.rerun() # Refresh mit neuem Center
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else:
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st.info("👈 Bitte wähle links eine Notiz aus, um den Graphen zu starten.")
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st.info("👈 Wähle links eine Notiz, um den Graphen zu starten.")
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@ -1,4 +1,5 @@
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import os
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import hashlib
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from dotenv import load_dotenv
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from pathlib import Path
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@ -23,27 +24,56 @@ timeout_setting = os.getenv("MINDNET_API_TIMEOUT") or os.getenv("MINDNET_LLM_TIM
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API_TIMEOUT = float(timeout_setting) if timeout_setting else 300.0
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# --- STYLING CONSTANTS ---
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# Basierend auf types.yaml
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GRAPH_COLORS = {
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"project": "#ff9f43", # Orange
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"concept": "#54a0ff", # Blau
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"decision": "#5f27cd", # Lila
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"risk": "#ff6b6b", # Rot
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"person": "#1dd1a1", # Grün
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"experience": "#feca57",# Gelb
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"value": "#00d2d3", # Cyan
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"goal": "#ff9ff3", # Pink
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"default": "#8395a7" # Grau
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# Kerntypen
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"experience": "#feca57", # Gelb/Orange
|
||||
"project": "#ff9f43", # Dunkleres Orange
|
||||
"decision": "#5f27cd", # Lila
|
||||
|
||||
# Persönlichkeit
|
||||
"value": "#00d2d3", # Cyan
|
||||
"principle": "#0abde3", # Dunkles Cyan
|
||||
"belief": "#48dbfb", # Helles Blau
|
||||
"profile": "#1dd1a1", # Grün
|
||||
|
||||
# Strategie & Risiko
|
||||
"goal": "#ff9ff3", # Pink
|
||||
"risk": "#ff6b6b", # Rot
|
||||
|
||||
# Basis
|
||||
"concept": "#54a0ff", # Blau
|
||||
"task": "#8395a7", # Grau-Blau
|
||||
"journal": "#c8d6e5", # Hellgrau
|
||||
"source": "#576574", # Dunkelgrau
|
||||
"glossary": "#222f3e", # Sehr dunkel
|
||||
|
||||
"default": "#8395a7" # Fallback
|
||||
}
|
||||
|
||||
EDGE_COLORS = {
|
||||
"depends_on": "#ff6b6b", # Rot (Blocker)
|
||||
"blocks": "#ee5253", # Dunkelrot
|
||||
"caused_by": "#ff9ff3", # Pink
|
||||
"related_to": "#c8d6e5", # Hellgrau
|
||||
"similar_to": "#c8d6e5", # Hellgrau
|
||||
"next": "#54a0ff", # Blau
|
||||
"derived_from": "#ff9ff3", # Pink
|
||||
"references": "#bdc3c7", # Grau
|
||||
"belongs_to": "#2e86de", # Dunkelblau
|
||||
"contributes_to": "#1dd1a1" # Grün (Neu!)
|
||||
}
|
||||
# System-Kanten, die wir NICHT im Graphen sehen wollen, um Rauschen zu reduzieren
|
||||
SYSTEM_EDGES = ["prev", "next", "belongs_to"]
|
||||
|
||||
def get_edge_color(kind: str) -> str:
|
||||
"""Generiert eine deterministische Farbe basierend auf dem Edge-Typ."""
|
||||
if not kind: return "#bdc3c7"
|
||||
|
||||
# Einige feste Farben für wichtige semantische Typen
|
||||
fixed_colors = {
|
||||
"depends_on": "#ff6b6b", # Rot (Blocker/Abhängigkeit)
|
||||
"blocks": "#ee5253", # Dunkelrot
|
||||
"caused_by": "#ff9ff3", # Pink
|
||||
"related_to": "#c8d6e5", # Hellgrau (Hintergrund)
|
||||
"references": "#bdc3c7", # Grau
|
||||
"derived_from": "#1dd1a1" # Grün
|
||||
}
|
||||
|
||||
if kind in fixed_colors:
|
||||
return fixed_colors[kind]
|
||||
|
||||
# Fallback: Hash-basierte Farbe für dynamische Typen
|
||||
# Wir nutzen einen Pastell-Generator, damit es nicht zu grell wird
|
||||
hash_obj = hashlib.md5(kind.encode())
|
||||
hue = int(hash_obj.hexdigest(), 16) % 360
|
||||
return f"hsl({hue}, 60%, 50%)"
|
||||
|
|
@ -1,6 +1,6 @@
|
|||
from qdrant_client import QdrantClient, models
|
||||
from streamlit_agraph import Node, Edge
|
||||
from ui_config import GRAPH_COLORS, EDGE_COLORS
|
||||
from ui_config import GRAPH_COLORS, get_edge_color, SYSTEM_EDGES
|
||||
|
||||
class GraphExplorerService:
|
||||
def __init__(self, url, api_key=None, prefix="mindnet"):
|
||||
|
|
@ -11,100 +11,53 @@ class GraphExplorerService:
|
|||
self.edges_col = f"{prefix}_edges"
|
||||
self._note_cache = {}
|
||||
|
||||
def get_ego_graph(self, center_note_id: str):
|
||||
def get_ego_graph(self, center_note_id: str, depth=2):
|
||||
nodes_dict = {}
|
||||
unique_edges = {}
|
||||
|
||||
# 1. Center Note laden
|
||||
# --- LEVEL 1: Center & direkte Nachbarn ---
|
||||
|
||||
# 1. Center Note
|
||||
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)
|
||||
self._add_node_to_dict(nodes_dict, center_note, level=0)
|
||||
|
||||
center_title = center_note.get("title")
|
||||
|
||||
# 2. Chunks der Center Note finden
|
||||
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]
|
||||
# Wir sammeln IDs für Level 2 Suche
|
||||
level_1_ids = {center_note_id}
|
||||
|
||||
raw_edges = []
|
||||
|
||||
# 3. OUTGOING EDGES: Source = einer meiner 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)
|
||||
|
||||
# 4. INCOMING EDGES: Target = Chunk, Titel oder Note-ID
|
||||
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)))
|
||||
# Suche Kanten für Center
|
||||
l1_edges = self._find_connected_edges([center_note_id], center_note.get("title"))
|
||||
|
||||
# FIX: Auch exakte Note-ID als Target prüfen
|
||||
must_conditions.append(models.FieldCondition(key="target_id", match=models.MatchValue(value=center_note_id)))
|
||||
# Verarbeite L1 Kanten
|
||||
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)
|
||||
|
||||
if must_conditions:
|
||||
in_filter = models.Filter(should=must_conditions) # 'should' = 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)
|
||||
# --- LEVEL 2: Nachbarn der Nachbarn ---
|
||||
if depth > 1 and level_1_ids:
|
||||
# Wir suchen Kanten, bei denen Source oder Target einer der L1 Nodes ist
|
||||
# Wichtig: Wir filtern System-Edges schon in der Query oder Python, um Traffic zu sparen
|
||||
|
||||
# Um die Performance zu wahren, limitieren wir die L2 Suche auf die IDs, die wir schon haben (als Source)
|
||||
# Das ist ein "Ego-Network" Ansatz.
|
||||
|
||||
# Wir nehmen alle IDs aus Level 1 (außer Center, das haben wir schon)
|
||||
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)
|
||||
|
||||
# 5. Verarbeitung & Auflösung
|
||||
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']
|
||||
|
||||
# Keine Self-Loops und valide Verbindung
|
||||
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)
|
||||
|
||||
# Deduplizierung: Explizite Kanten überschreiben Smart Edges
|
||||
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
|
||||
}
|
||||
|
||||
# 6. Agraph Objekte bauen
|
||||
# --- GRAPH CONSTRUCTION ---
|
||||
final_edges = []
|
||||
for (src, tgt), data in unique_edges.items():
|
||||
kind = data['kind']
|
||||
prov = data['provenance']
|
||||
|
||||
color = EDGE_COLORS.get(kind, "#bdc3c7")
|
||||
# Dynamische Farbe holen
|
||||
color = get_edge_color(kind)
|
||||
is_smart = (prov != "explicit" and prov != "rule")
|
||||
|
||||
final_edges.append(Edge(
|
||||
|
|
@ -114,9 +67,92 @@ class GraphExplorerService:
|
|||
|
||||
return list(nodes_dict.values()), final_edges
|
||||
|
||||
def _find_connected_edges(self, note_ids, note_title=None):
|
||||
"""Findet In- und Outgoing Edges für eine Liste von Note-IDs."""
|
||||
# 1. Chunks zu diesen Notes finden
|
||||
scroll_filter = models.Filter(
|
||||
must=[models.FieldCondition(key="note_id", match=models.MatchAny(any=note_ids))]
|
||||
)
|
||||
chunks, _ = self.client.scroll(
|
||||
collection_name=self.chunks_col, scroll_filter=scroll_filter, limit=200
|
||||
)
|
||||
chunk_ids = [c.id for c in chunks]
|
||||
|
||||
results = []
|
||||
|
||||
# Outgoing (Source is Chunk)
|
||||
if chunk_ids:
|
||||
out_f = models.Filter(must=[
|
||||
models.FieldCondition(key="source_id", match=models.MatchAny(any=chunk_ids)),
|
||||
# Filter System Edges
|
||||
models.FieldCondition(key="kind", match=models.MatchExcept(except_=SYSTEM_EDGES))
|
||||
])
|
||||
res_out, _ = self.client.scroll(self.edges_col, scroll_filter=out_f, limit=100, with_payload=True)
|
||||
results.extend(res_out)
|
||||
|
||||
# Incoming (Target is Chunk OR Title OR NoteID)
|
||||
shoulds = []
|
||||
if chunk_ids: shoulds.append(models.FieldCondition(key="target_id", match=models.MatchAny(any=chunk_ids)))
|
||||
if note_title: shoulds.append(models.FieldCondition(key="target_id", match=models.MatchValue(value=note_title)))
|
||||
shoulds.append(models.FieldCondition(key="target_id", match=models.MatchAny(any=note_ids)))
|
||||
|
||||
if shoulds:
|
||||
in_f = models.Filter(
|
||||
must=[models.FieldCondition(key="kind", match=models.MatchExcept(except_=SYSTEM_EDGES))],
|
||||
should=shoulds
|
||||
)
|
||||
res_in, _ = self.client.scroll(self.edges_col, scroll_filter=in_f, limit=100, with_payload=True)
|
||||
results.extend(res_in)
|
||||
|
||||
return results
|
||||
|
||||
def _find_connected_edges_batch(self, note_ids):
|
||||
"""Batch-Suche für Level 2 (nur ausgehend und eingehend auf Note-Ebene, keine Title-Suche für Performance)."""
|
||||
# Vereinfachte Suche: Wir suchen Kanten, die direkt mit den note_ids (oder deren Chunks) zu tun haben
|
||||
# Um Performance zu sparen, machen wir hier einen simpleren Lookup, wenn möglich.
|
||||
return self._find_connected_edges(note_ids)
|
||||
|
||||
def _process_edge(self, record, nodes_dict, unique_edges, current_depth):
|
||||
payload = record.payload
|
||||
src_ref = payload.get("source_id")
|
||||
tgt_ref = payload.get("target_id")
|
||||
kind = payload.get("kind")
|
||||
provenance = payload.get("provenance", "explicit")
|
||||
|
||||
# Resolve
|
||||
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:
|
||||
# Add Nodes
|
||||
self._add_node_to_dict(nodes_dict, src_note, level=current_depth)
|
||||
self._add_node_to_dict(nodes_dict, tgt_note, level=current_depth)
|
||||
|
||||
# Add Edge (Deduplication Logic)
|
||||
key = (src_id, tgt_id)
|
||||
existing = unique_edges.get(key)
|
||||
|
||||
# Update logic: Explicit > Smart
|
||||
should_update = True
|
||||
is_current_explicit = (provenance in ["explicit", "rule"])
|
||||
if existing:
|
||||
is_existing_explicit = (existing['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):
|
||||
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))]),
|
||||
|
|
@ -129,53 +165,57 @@ class GraphExplorerService:
|
|||
|
||||
def _resolve_note_from_ref(self, ref_str):
|
||||
if not ref_str: return None
|
||||
|
||||
# Fall A: Chunk ID (Format: note_id#cXX)
|
||||
# ... (Logik identisch zu vorher, hier gekürzt für Übersicht)
|
||||
# Fall A: Chunk ID / Section
|
||||
if "#" in ref_str:
|
||||
# Versuch 1: Echte Chunk ID in DB
|
||||
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
|
||||
|
||||
# Versuch 2: Section Link (note-id#Header) -> Hash abschneiden
|
||||
res = self.client.retrieve(self.chunks_col, ids=[ref_str], with_payload=True)
|
||||
if res: return self._fetch_note_cached(res[0].payload.get("note_id"))
|
||||
except: pass
|
||||
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
|
||||
if self._fetch_note_cached(possible_note_id): return self._fetch_note_cached(possible_note_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 B: Note ID
|
||||
if self._fetch_note_cached(ref_str): return self._fetch_note_cached(ref_str)
|
||||
|
||||
# Fall C: Es ist der Titel (Wikilink)
|
||||
# Fall C: Titel
|
||||
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
|
||||
|
||||
self._note_cache[res[0].payload['note_id']] = res[0].payload
|
||||
return res[0].payload
|
||||
return None
|
||||
|
||||
def _add_node_to_dict(self, node_dict, note_payload, is_center=False):
|
||||
def _add_node_to_dict(self, node_dict, note_payload, level=1):
|
||||
nid = note_payload.get("note_id")
|
||||
|
||||
# Wenn Node schon da ist, aber wir finden ihn auf einem "höheren" Level (näher am Zentrum), updaten wir ihn nicht zwingend,
|
||||
# außer wir wollen visuelle Eigenschaften ändern.
|
||||
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
|
||||
|
||||
# Größe & Label basierend auf Level
|
||||
if level == 0:
|
||||
size = 40
|
||||
label_prefix = ""
|
||||
elif level == 1:
|
||||
size = 25
|
||||
label_prefix = ""
|
||||
else:
|
||||
size = 15 # Level 2 kleiner
|
||||
label_prefix = ""
|
||||
|
||||
node_dict[nid] = Node(
|
||||
id=nid,
|
||||
label=note_payload.get("title", nid),
|
||||
label=f"{label_prefix}{note_payload.get('title', nid)}",
|
||||
size=size,
|
||||
color=color,
|
||||
shape="dot" if not is_center else "diamond",
|
||||
title=f"Type: {ntype}\nTags: {note_payload.get('tags')}",
|
||||
font={'color': 'black'}
|
||||
shape="dot" if level > 0 else "diamond",
|
||||
title=f"Type: {ntype}\nLevel: {level}\nTags: {note_payload.get('tags')}",
|
||||
font={'color': 'black', 'face': 'arial', 'size': 14 if level < 2 else 10}
|
||||
)
|
||||
Loading…
Reference in New Issue
Block a user