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Merge pull request 'WP19' (#10) from WP19 into main
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Reviewed-on: #10
Merge feature/wp19-graph-viz into main

WP-19: Frontend Modularization & Advanced Graph Visualization

Dieses Update transformiert die Frontend-Architektur umfassend:
- **Refactoring:** Die monolithische `ui.py` wurde in eine modulare Struktur (`ui_graph_service.py`, `ui_editor.py`, `ui_cytoscape.py` etc.) zerlegt.
- **Feature (Graph):** Integration von `st-cytoscape` mit COSE-Layout für stabile, überlappungsfreie Visualisierung ("Active Inspector, Passive Graph" Pattern).
- **Feature (Editor):** Implementierung der "Single Source of Truth" Logik. Der Editor lädt Inhalte nun direkt vom Dateisystem statt aus Datenbank-Payloads.
- **Feature (UX):** Layout-Einstellungen und Graph-Tiefe werden nun via URL-Parameter persistiert (Deep Linking).
- **Documentation:** Erstellung der `03_tech_frontend.md` und Aktualisierung aller relevanten Guides.
- **Dependencies:** Wechsel von `streamlit-cytoscapejs` zu `st-cytoscape`.
This commit is contained in:
Lars 2025-12-14 20:50:03 +01:00
commit 84e319a42f
19 changed files with 1732 additions and 575 deletions
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551
app/frontend/ui.py
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@ -1,533 +1,50 @@
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
# --- 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")
# 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 v2.5", page_icon="🧠", layout="wide")
# --- CSS STYLING ---
# --- CONFIG & STYLING ---
st.set_page_config(page_title="mindnet v2.6", page_icon="🧠", layout="wide")
st.markdown("""
<style>
.block-container { padding-top: 2rem; max_width: 1000px; margin: auto; }
.intent-badge {
background-color: #e8f0fe; color: #1a73e8;
padding: 4px 10px; border-radius: 12px;
font-size: 0.8rem; font-weight: 600;
border: 1px solid #d2e3fc; display: inline-block; margin-bottom: 0.5rem;
}
.draft-box {
border: 1px solid #d0d7de;
border-radius: 6px;
padding: 16px;
background-color: #f6f8fa;
margin-top: 10px;
margin-bottom: 10px;
}
.preview-box {
border: 1px solid #e0e0e0;
border-radius: 6px;
padding: 24px;
background-color: white;
font-family: -apple-system,BlinkMacSystemFont,"Segoe UI",Helvetica,Arial,sans-serif;
}
.block-container { padding-top: 2rem; max_width: 1200px; margin: auto; }
.intent-badge { background-color: #e8f0fe; color: #1a73e8; padding: 4px 10px; border-radius: 12px; font-size: 0.8rem; font-weight: 600; border: 1px solid #d2e3fc; display: inline-block; margin-bottom: 0.5rem; }
.draft-box { border: 1px solid #d0d7de; border-radius: 6px; padding: 16px; background-color: #f6f8fa; margin: 10px 0; }
.preview-box { border: 1px solid #e0e0e0; border-radius: 6px; padding: 24px; background-color: white; }
</style>
""", unsafe_allow_html=True)
# --- MODULE IMPORTS ---
try:
from ui_config import QDRANT_URL, QDRANT_KEY, COLLECTION_PREFIX
from ui_graph_service import GraphExplorerService
# Komponenten
from ui_sidebar import render_sidebar
from ui_chat import render_chat_interface
from ui_editor import render_manual_editor
# Die beiden Graph-Engines
from ui_graph import render_graph_explorer as render_graph_agraph
from ui_graph_cytoscape import render_graph_explorer_cytoscape # <-- Import
except ImportError as e:
st.error(f"Import Error: {e}. Bitte stelle sicher, dass alle UI-Dateien im Ordner liegen und 'streamlit-cytoscapejs' installiert ist.")
st.stop()
# --- 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())
# --- HELPER FUNCTIONS ---
# --- SERVICE INIT ---
graph_service = GraphExplorerService(QDRANT_URL, QDRANT_KEY, COLLECTION_PREFIX)
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):
"""
HEALING PARSER: Repariert kaputten LLM Output (z.B. fehlendes schließendes '---').
"""
clean_text = full_text.strip()
# 1. Code-Block Wrapper entfernen
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 = ""
# 2. Versuch A: Standard Split (Idealfall)
parts = re.split(r"^---+\s*$", clean_text, maxsplit=2, flags=re.MULTILINE)
if len(parts) >= 3:
yaml_str = parts[1]
body = parts[2]
# 3. Versuch B: Healing (Wenn LLM das schließende --- vergessen hat)
elif clean_text.startswith("---"):
# Wir suchen die erste Überschrift '#', da Frontmatter davor sein muss
# Pattern: Suche --- am Anfang, dann nimm alles bis zum ersten # am Zeilenanfang
fallback_match = re.search(r"^---\s*(.*?)(?=\n#)", clean_text, re.DOTALL | re.MULTILINE)
if fallback_match:
yaml_str = fallback_match.group(1)
# Der Body ist alles NACH dem YAML String (inklusive dem #)
body = clean_text.replace(f"---{yaml_str}", "", 1).strip()
# 4. YAML Parsing
if yaml_str:
yaml_str_clean = yaml_str.replace("#", "") # Tags cleanen
try:
parsed = yaml.safe_load(yaml_str_clean)
if isinstance(parsed, dict):
meta = parsed
except Exception as e:
print(f"YAML Parsing Warning: {e}")
# Fallback: Titel aus H1
if not meta.get("title"):
h1_match = re.search(r"^#\s+(.*)$", body, re.MULTILINE)
if h1_match:
meta["title"] = h1_match.group(1).strip()
# Correction: type/status swap
if meta.get("type") == "draft":
meta["status"] = "draft"
meta["type"] = "experience"
return normalize_meta_and_body(meta, body)
def build_markdown_doc(meta, body):
"""Baut das finale Dokument zusammen."""
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
# --- 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("v2.5 | Healing Parser")
mode = st.radio("Modus", ["💬 Chat", "📝 Manueller Editor"], 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"
# --- 1. 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)
# Persistent Data
st.session_state[data_meta_key] = meta
st.session_state[data_sugg_key] = []
st.session_state[data_body_key] = body.strip()
# Init Widgets Keys
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
# --- 2. 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'<div class="draft-box">', 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"])
# --- TAB 1: EDITOR ---
with tab_edit:
st.text_area(
"Body",
key=widget_body_key,
height=500,
on_change=_sync_body,
label_visibility="collapsed"
)
# --- TAB 2: INTELLIGENCE ---
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"""
<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);">
<b>{sugg.get('target_title')}</b> <small>({sugg.get('type')})</small><br>
<i>{sugg.get('reason')}</i><br>
<code>{link_text}</code>
</div>
""", 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,))
# --- TAB 3: SAVE ---
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('<div class="preview-box">', unsafe_allow_html=True)
st.markdown(final_doc)
st.markdown('</div>', 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("</div>", 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'<div class="intent-badge">{icon} Intent: {intent} <span style="opacity:0.6; font-size:0.8em">({src})</span></div>', 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)
# --- MAIN ---
# --- MAIN ROUTING ---
mode, top_k, explain = render_sidebar()
if mode == "💬 Chat":
render_chat_interface(top_k, explain)
else:
render_manual_editor()
elif mode == "📝 Manueller Editor":
render_manual_editor()
elif mode == "🕸️ Graph (Agraph)":
render_graph_agraph(graph_service)
elif mode == "🕸️ Graph (Cytoscape)":
render_graph_explorer_cytoscape(graph_service)

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app/frontend/ui_api.py Normal file
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import requests
import streamlit as st
from ui_config import CHAT_ENDPOINT, INGEST_ANALYZE_ENDPOINT, INGEST_SAVE_ENDPOINT, FEEDBACK_ENDPOINT, API_TIMEOUT
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

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import streamlit as st
import os
from ui_utils import build_markdown_doc
def switch_to_editor_callback(note_payload):
"""
Callback für den 'Bearbeiten'-Button im Graphen.
Versucht, die Datei direkt aus dem Vault (Dateisystem) zu lesen.
Das garantiert, dass Frontmatter und Inhalt vollständig sind (Single Source of Truth).
"""
# 1. Pfad ermitteln (Priorität auf 'path' aus Qdrant)
origin_fname = note_payload.get('path')
# Fallback für Legacy-Datenfelder
if not origin_fname:
origin_fname = note_payload.get('file_path') or note_payload.get('filename')
content = ""
file_loaded = False
# 2. Versuch: Direkt von der Festplatte lesen
# Wir prüfen, ob der Pfad existiert und lesen den aktuellen Stand der Datei.
if origin_fname and os.path.exists(origin_fname):
try:
with open(origin_fname, "r", encoding="utf-8") as f:
content = f.read()
file_loaded = True
except Exception as e:
# Fehler im Terminal loggen, aber UI nicht crashen lassen
print(f"Fehler beim Lesen von {origin_fname}: {e}")
# 3. Fallback: Inhalt aus Qdrant nehmen (wenn Datei nicht zugreifbar)
if not file_loaded:
# Wir nehmen 'fulltext' aus dem Payload
content = note_payload.get('fulltext', '')
if not content:
# Letzter Ausweg: Metadaten nehmen und Dummy-Content bauen
content = build_markdown_doc(note_payload, "Inhalt konnte nicht geladen werden (Datei nicht gefunden).")
else:
# Check: Hat der Text ein Frontmatter? Wenn nein, rekonstruieren wir es.
if not content.strip().startswith("---"):
content = build_markdown_doc(note_payload, content)
# Notfall-Pfad Konstruktion (falls gar kein Pfad im System ist)
if not origin_fname and 'note_id' in note_payload:
origin_fname = f"{note_payload['note_id']}.md"
# 4. Daten an den Editor übergeben
# Wir nutzen den Chat-Verlauf als Transportmittel für den State
st.session_state.messages.append({
"role": "assistant",
"intent": "INTERVIEW",
"content": content,
"query_id": f"edit_{note_payload.get('note_id', 'unknown')}", # Trigger für den Editor
"origin_filename": origin_fname,
"origin_note_id": note_payload.get('note_id')
})
# 5. Modus umschalten (wechselt den Tab beim nächsten Rerun)
st.session_state["sidebar_mode_selection"] = "📝 Manueller Editor"

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import streamlit as st
from ui_api import send_chat_message, submit_feedback
from ui_editor import render_draft_editor
def render_chat_interface(top_k, explain):
"""
Rendert das Chat-Interface.
Zeigt Nachrichten an und behandelt User-Input.
"""
# 1. Verlauf anzeigen
for idx, msg in enumerate(st.session_state.messages):
with st.chat_message(msg["role"]):
if msg["role"] == "assistant":
# Intent Badge
intent = msg.get("intent", "UNKNOWN")
st.markdown(f'<div class="intent-badge">Intent: {intent}</div>', unsafe_allow_html=True)
# Debugging (optional, gut für Entwicklung)
with st.expander("🐞 Payload", expanded=False):
st.json(msg)
# Unterscheidung: Normaler Text oder Editor-Modus (Interview)
if intent == "INTERVIEW":
render_draft_editor(msg)
else:
st.markdown(msg["content"])
# Quellen anzeigen
if "sources" in msg and msg["sources"]:
for hit in msg["sources"]:
score = hit.get('total_score', 0)
# Wenn score None ist, 0.0 annehmen
if score is None: score = 0.0
with st.expander(f"📄 {hit.get('note_id', '?')} ({score:.2f})"):
st.markdown(f"_{hit.get('source', {}).get('text', '')[:300]}..._")
# Explanation Layer
if hit.get('explanation'):
st.caption(f"Grund: {hit['explanation']['reasons'][0]['message']}")
# Feedback Buttons pro Source
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)
# Globales Feedback für die Antwort
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:
# User Nachricht
st.markdown(msg["content"])
# 2. Input Feld
if prompt := st.chat_input("Frage Mindnet..."):
st.session_state.messages.append({"role": "user", "content": prompt})
st.rerun()
# 3. Antwort generieren (wenn letzte Nachricht vom User ist)
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"),
"sources": resp.get("sources", []),
"query_id": resp.get("query_id")
})
st.rerun()

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import os
import hashlib
from dotenv import load_dotenv
from pathlib import Path
load_dotenv()
# --- API & PORTS ---
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"
# --- QDRANT ---
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")
# --- FILES & TIMEOUTS ---
HISTORY_FILE = Path("data/logs/search_history.jsonl")
timeout_setting = os.getenv("MINDNET_API_TIMEOUT") or os.getenv("MINDNET_LLM_TIMEOUT")
API_TIMEOUT = float(timeout_setting) if timeout_setting else 300.0
# --- STYLING CONSTANTS ---
# Basierend auf types.yaml
GRAPH_COLORS = {
# Kerntypen
"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
}
# 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%)"

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import streamlit as st
import uuid
import re
from datetime import datetime
from ui_utils import parse_markdown_draft, build_markdown_doc, slugify
from ui_api import save_draft_to_vault, analyze_draft_text
def render_draft_editor(msg):
"""
Rendert den Markdown-Editor.
Nutzt 'origin_filename' aus der Message, um zwischen Update und Neu zu unterscheiden.
"""
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"]
# Pfad übernehmen (Source of Truth)
st.session_state[f"{key_base}_origin_filename"] = msg.get("origin_filename")
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]
# --- SYNC HELPER ---
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(t):
st.session_state[widget_body_key] = f"{st.session_state.get(widget_body_key, '')}\n\n{t}"
st.session_state[data_body_key] = st.session_state[widget_body_key]
def _remove_text(t):
st.session_state[widget_body_key] = st.session_state.get(widget_body_key, '').replace(t, "").strip()
st.session_state[data_body_key] = st.session_state[widget_body_key]
# --- UI LAYOUT ---
# Header Info (Debug Pfad anzeigen, damit wir sicher sind)
origin_fname = st.session_state.get(f"{key_base}_origin_filename")
if origin_fname:
# Dateiname extrahieren für saubere Anzeige
display_name = str(origin_fname).split("/")[-1]
st.success(f"📂 **Update-Modus**: `{display_name}`")
# Debugging: Zeige vollen Pfad im Expander
with st.expander("Dateipfad Details", expanded=False):
st.code(origin_fname)
st.markdown(f'<div class="draft-box" style="border-left: 5px solid #ff9f43;">', unsafe_allow_html=True)
else:
st.info("✨ **Erstell-Modus**: Neue Datei wird angelegt.")
st.markdown(f'<div class="draft-box">', unsafe_allow_html=True)
st.markdown("### Editor")
# Meta Felder
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)
# Tabs
tab_edit, tab_intel, tab_view = st.tabs(["✏️ Inhalt", "🧠 Intelligence", "👁️ Vorschau"])
with tab_edit:
st.text_area("Body", key=widget_body_key, height=600, on_change=_sync_body, label_visibility="collapsed")
with tab_intel:
st.info("Analysiert den Text auf Verknüpfungsmöglichkeiten.")
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, ""))
with st.spinner("Analysiere..."):
analysis = analyze_draft_text(text_to_analyze, st.session_state.get(f"{key_base}_wdg_type", "concept"))
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.")
else: st.success(f"{len(suggestions)} Vorschläge gefunden.")
suggestions = st.session_state[data_sugg_key]
if suggestions:
current_text = 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
bg_color = "#e6fffa" if is_inserted else "#ffffff"
border = "3px solid #28a745" if is_inserted else "3px solid #1a73e8"
st.markdown(f"<div style='border-left: {border}; background-color: {bg_color}; padding: 10px; margin-bottom: 8px;'><b>{sugg.get('target_title')}</b> <small>({sugg.get('type')})</small><br><i>{sugg.get('reason')}</i><br><code>{link_text}</code></div>", 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,))
# Save Logic Preparation
final_tags = [t.strip() for t in st.session_state.get(f"{key_base}_wdg_tags", "").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
}
if "origin_note_id" in msg:
final_meta["id"] = msg["origin_note_id"]
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('<div class="preview-box">', unsafe_allow_html=True)
st.markdown(final_doc)
st.markdown('</div>', unsafe_allow_html=True)
st.markdown("---")
# Save Actions
b1, b2 = st.columns([1, 1])
with b1:
save_label = "💾 Update speichern" if origin_fname else "💾 Neu anlegen & Indizieren"
if st.button(save_label, type="primary", key=f"{key_base}_save"):
with st.spinner("Speichere im Vault..."):
if origin_fname:
# UPDATE: Ziel ist der exakte Pfad
target_file = origin_fname
else:
# CREATE: Neuer Dateiname
raw_title = final_meta.get("title", "draft")
target_file = f"{datetime.now().strftime('%Y%m%d')}-{slugify(raw_title)[:60]}.md"
result = save_draft_to_vault(final_doc, filename=target_file)
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("</div>", unsafe_allow_html=True)
def render_manual_editor():
"""
Rendert den manuellen Editor.
PRÜFT, ob eine Edit-Anfrage aus dem Graphen vorliegt!
"""
target_msg = None
# 1. Prüfen: Gibt es Nachrichten im Verlauf?
if st.session_state.messages:
last_msg = st.session_state.messages[-1]
# 2. Ist die letzte Nachricht eine Edit-Anfrage? (Erkennbar am query_id prefix 'edit_')
qid = str(last_msg.get("query_id", ""))
if qid.startswith("edit_"):
target_msg = last_msg
# 3. Fallback: Leeres Template, falls keine Edit-Anfrage vorliegt
if not target_msg:
target_msg = {
"content": "---\ntype: concept\ntitle: Neue Notiz\nstatus: draft\ntags: []\n---\n# Titel\n",
"query_id": f"manual_{uuid.uuid4()}" # Eigene ID, damit neuer State entsteht
}
render_draft_editor(target_msg)

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import streamlit as st
from streamlit_agraph import agraph, Config
from qdrant_client import models
from ui_config import COLLECTION_PREFIX, GRAPH_COLORS
from ui_callbacks import switch_to_editor_callback
def render_graph_explorer(graph_service):
st.header("🕸️ Graph Explorer")
# Session State initialisieren
if "graph_center_id" not in st.session_state:
st.session_state.graph_center_id = None
# Defaults für View & Physik setzen
st.session_state.setdefault("graph_depth", 2)
st.session_state.setdefault("graph_show_labels", True)
# Höhere Default-Werte für Abstand
st.session_state.setdefault("graph_spacing", 250)
st.session_state.setdefault("graph_gravity", -4000)
col_ctrl, col_graph = st.columns([1, 4])
# --- LINKE SPALTE: CONTROLS ---
with col_ctrl:
st.subheader("Fokus")
# Sucheingabe
search_term = st.text_input("Suche Notiz", placeholder="Titel eingeben...")
# Suchlogik Qdrant
if search_term:
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("Ergebnisse:", list(options.keys()))
if st.button("Laden", use_container_width=True):
st.session_state.graph_center_id = options[selected_title]
st.rerun()
st.divider()
# Layout & Physik Einstellungen
with st.expander("👁️ Ansicht & Layout", expanded=True):
st.session_state.graph_depth = st.slider("Tiefe (Tier)", 1, 3, st.session_state.graph_depth)
st.session_state.graph_show_labels = st.checkbox("Kanten-Beschriftung", st.session_state.graph_show_labels)
st.markdown("**Physik (BarnesHut)**")
st.session_state.graph_spacing = st.slider("Federlänge (Abstand)", 50, 800, st.session_state.graph_spacing)
st.session_state.graph_gravity = st.slider("Abstoßung (Gravity)", -20000, -500, st.session_state.graph_gravity)
if st.button("Reset Layout"):
st.session_state.graph_spacing = 250
st.session_state.graph_gravity = -4000
st.rerun()
st.divider()
st.caption("Legende (Top Typen)")
for k, v in list(GRAPH_COLORS.items())[:8]:
st.markdown(f"<span style='color:{v}'>●</span> {k}", unsafe_allow_html=True)
# --- RECHTE SPALTE: GRAPH & ACTION BAR ---
with col_graph:
center_id = st.session_state.graph_center_id
if center_id:
# Action Container oben fixieren (Layout Fix)
action_container = st.container()
# Graph und Daten laden
with st.spinner(f"Lade Graph..."):
nodes, edges = graph_service.get_ego_graph(
center_id,
depth=st.session_state.graph_depth,
show_labels=st.session_state.graph_show_labels
)
# WICHTIG: Daten für Editor holen (inkl. Pfad)
note_data = graph_service.get_note_with_full_content(center_id)
# Action Bar rendern
with action_container:
c1, c2 = st.columns([3, 1])
with c1:
st.caption(f"Aktives Zentrum: **{center_id}**")
with c2:
if note_data:
st.button("📝 Bearbeiten",
use_container_width=True,
on_click=switch_to_editor_callback,
args=(note_data,))
else:
st.error("Datenfehler: Note nicht gefunden")
# Debug Inspector
with st.expander("🕵️ Data Inspector", expanded=False):
if note_data:
st.json(note_data)
if 'path' in note_data:
st.success(f"Pfad OK: {note_data['path']}")
else:
st.error("Pfad fehlt!")
else:
st.info("Leer.")
if not nodes:
st.warning("Keine Daten gefunden.")
else:
# --- CONFIGURATION (BarnesHut) ---
# Height-Trick für Re-Render (da key-Parameter manchmal crasht)
dyn_height = 800 + (abs(st.session_state.graph_gravity) % 5)
config = Config(
width=1000,
height=dyn_height,
directed=True,
physics={
"enabled": True,
"solver": "barnesHut",
"barnesHut": {
"gravitationalConstant": st.session_state.graph_gravity,
"centralGravity": 0.005, # Extrem wichtig für die Ausbreitung!
"springLength": st.session_state.graph_spacing,
"springConstant": 0.04,
"damping": 0.09,
"avoidOverlap": 0.1
},
"stabilization": {"enabled": True, "iterations": 600}
},
hierarchical=False,
nodeHighlightBehavior=True,
highlightColor="#F7A7A6",
collapsible=False
)
return_value = agraph(nodes=nodes, edges=edges, config=config)
# Interaktions-Logik (Klick auf Node)
if return_value:
if return_value != center_id:
# Navigation: Neues Zentrum setzen
st.session_state.graph_center_id = return_value
st.rerun()
else:
# Klick auf das Zentrum selbst
st.toast(f"Zentrum: {return_value}")
else:
st.info("👈 Bitte wähle links eine Notiz aus, um den Graphen zu starten.")

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import streamlit as st
from st_cytoscape import cytoscape
from qdrant_client import models
from ui_config import COLLECTION_PREFIX, GRAPH_COLORS
from ui_callbacks import switch_to_editor_callback
def update_url_params():
"""Callback: Schreibt Slider-Werte in die URL und synchronisiert den State."""
# Werte aus den Slider-Keys in die Logik-Variablen übertragen
if "cy_depth_slider" in st.session_state:
st.session_state.cy_depth = st.session_state.cy_depth_slider
if "cy_len_slider" in st.session_state:
st.session_state.cy_ideal_edge_len = st.session_state.cy_len_slider
if "cy_rep_slider" in st.session_state:
st.session_state.cy_node_repulsion = st.session_state.cy_rep_slider
# In URL schreiben
st.query_params["depth"] = st.session_state.cy_depth
st.query_params["len"] = st.session_state.cy_ideal_edge_len
st.query_params["rep"] = st.session_state.cy_node_repulsion
def render_graph_explorer_cytoscape(graph_service):
st.header("🕸️ Graph Explorer (Cytoscape)")
# ---------------------------------------------------------
# 1. STATE & PERSISTENZ
# ---------------------------------------------------------
if "graph_center_id" not in st.session_state:
st.session_state.graph_center_id = None
if "graph_inspected_id" not in st.session_state:
st.session_state.graph_inspected_id = None
# Lade Einstellungen aus der URL (falls vorhanden), sonst Defaults
params = st.query_params
# Helper um sicher int zu parsen
def get_param(key, default):
try: return int(params.get(key, default))
except: return default
# Initialisiere Session State Variablen, falls noch nicht vorhanden
if "cy_depth" not in st.session_state:
st.session_state.cy_depth = get_param("depth", 2)
if "cy_ideal_edge_len" not in st.session_state:
st.session_state.cy_ideal_edge_len = get_param("len", 150)
if "cy_node_repulsion" not in st.session_state:
st.session_state.cy_node_repulsion = get_param("rep", 1000000)
col_ctrl, col_graph = st.columns([1, 4])
# ---------------------------------------------------------
# 2. LINKES PANEL (Steuerung)
# ---------------------------------------------------------
with col_ctrl:
st.subheader("Fokus")
search_term = st.text_input("Suche Notiz", placeholder="Titel eingeben...", key="cy_search")
if search_term:
hits, _ = graph_service.client.scroll(
collection_name=f"{COLLECTION_PREFIX}_notes",
limit=10,
scroll_filter=models.Filter(must=[models.FieldCondition(key="title", match=models.MatchText(text=search_term))])
)
options = {h.payload['title']: h.payload['note_id'] for h in hits}
if options:
selected_title = st.selectbox("Ergebnisse:", list(options.keys()), key="cy_select")
if st.button("Laden", use_container_width=True, key="cy_load"):
new_id = options[selected_title]
st.session_state.graph_center_id = new_id
st.session_state.graph_inspected_id = new_id
st.rerun()
st.divider()
# LAYOUT EINSTELLUNGEN (Mit URL Sync)
with st.expander("👁️ Layout Einstellungen", expanded=True):
st.slider("Tiefe (Tier)", 1, 3,
value=st.session_state.cy_depth,
key="cy_depth_slider",
on_change=update_url_params)
st.markdown("**COSE Layout**")
st.slider("Kantenlänge", 50, 600,
value=st.session_state.cy_ideal_edge_len,
key="cy_len_slider",
on_change=update_url_params)
st.slider("Knoten-Abstoßung", 100000, 5000000, step=100000,
value=st.session_state.cy_node_repulsion,
key="cy_rep_slider",
on_change=update_url_params)
if st.button("Neu berechnen", key="cy_rerun"):
st.rerun()
st.divider()
st.caption("Legende")
for k, v in list(GRAPH_COLORS.items())[:8]:
st.markdown(f"<span style='color:{v}'>●</span> {k}", unsafe_allow_html=True)
# ---------------------------------------------------------
# 3. RECHTES PANEL (GRAPH & INSPECTOR)
# ---------------------------------------------------------
with col_graph:
center_id = st.session_state.graph_center_id
# Fallback Init
if not center_id and st.session_state.graph_inspected_id:
center_id = st.session_state.graph_inspected_id
st.session_state.graph_center_id = center_id
if center_id:
# Sync Inspection
if not st.session_state.graph_inspected_id:
st.session_state.graph_inspected_id = center_id
inspected_id = st.session_state.graph_inspected_id
# --- DATEN LADEN ---
with st.spinner(f"Lade Graph (Tiefe {st.session_state.cy_depth})..."):
# 1. Graph Daten
nodes_data, edges_data = graph_service.get_ego_graph(
center_id,
depth=st.session_state.cy_depth
)
# 2. Detail Daten (Inspector)
inspected_data = graph_service.get_note_with_full_content(inspected_id)
# --- ACTION BAR ---
action_container = st.container()
with action_container:
c1, c2, c3 = st.columns([2, 1, 1])
with c1:
title_show = inspected_data.get('title', inspected_id) if inspected_data else inspected_id
st.info(f"**Ausgewählt:** {title_show}")
with c2:
# NAVIGATION
if inspected_id != center_id:
if st.button("🎯 Als Zentrum setzen", use_container_width=True, key="cy_nav_btn"):
st.session_state.graph_center_id = inspected_id
st.rerun()
else:
st.caption("_(Ist aktuelles Zentrum)_")
with c3:
# EDITIEREN
if inspected_data:
st.button("📝 Bearbeiten",
use_container_width=True,
on_click=switch_to_editor_callback,
args=(inspected_data,),
key="cy_edit_btn")
# --- DATA INSPECTOR ---
with st.expander("🕵️ Data Inspector (Details)", expanded=False):
if inspected_data:
col_i1, col_i2 = st.columns(2)
with col_i1:
st.markdown(f"**ID:** `{inspected_data.get('note_id')}`")
st.markdown(f"**Typ:** `{inspected_data.get('type')}`")
with col_i2:
st.markdown(f"**Tags:** {', '.join(inspected_data.get('tags', []))}")
path_check = "✅" if inspected_data.get('path') else "❌"
st.markdown(f"**Pfad:** {path_check}")
st.caption("Inhalt (Vorschau):")
st.text_area("Content Preview", inspected_data.get('fulltext', '')[:1000], height=200, disabled=True, label_visibility="collapsed")
with st.expander("📄 Raw JSON anzeigen"):
st.json(inspected_data)
else:
st.warning("Keine Daten geladen.")
# --- GRAPH ELEMENTS ---
cy_elements = []
for n in nodes_data:
is_center = (n.id == center_id)
is_inspected = (n.id == inspected_id)
tooltip_text = n.title if n.title else n.label
display_label = n.label
if len(display_label) > 15 and " " in display_label:
display_label = display_label.replace(" ", "\n", 1)
cy_node = {
"data": {
"id": n.id,
"label": display_label,
"bg_color": n.color,
"tooltip": tooltip_text
},
# Wir steuern das Aussehen rein über Klassen (.inspected / .center)
"classes": " ".join([c for c in ["center" if is_center else "", "inspected" if is_inspected else ""] if c]),
"selected": False
}
cy_elements.append(cy_node)
for e in edges_data:
target_id = getattr(e, "to", getattr(e, "target", None))
if target_id:
cy_edge = {
"data": {
"source": e.source,
"target": target_id,
"label": e.label,
"line_color": e.color
}
}
cy_elements.append(cy_edge)
# --- STYLESHEET ---
stylesheet = [
{
"selector": "node",
"style": {
"label": "data(label)",
"width": "30px", "height": "30px",
"background-color": "data(bg_color)",
"color": "#333", "font-size": "12px",
"text-valign": "center", "text-halign": "center",
"text-wrap": "wrap", "text-max-width": "90px",
"border-width": 2, "border-color": "#fff",
"title": "data(tooltip)"
}
},
# Inspiziert (Gelber Rahmen)
{
"selector": ".inspected",
"style": {
"border-width": 6,
"border-color": "#FFC300",
"width": "50px", "height": "50px",
"font-weight": "bold",
"z-index": 999
}
},
# Zentrum (Roter Rahmen)
{
"selector": ".center",
"style": {
"border-width": 4,
"border-color": "#FF5733",
"width": "40px", "height": "40px"
}
},
# Mix
{
"selector": ".center.inspected",
"style": {
"border-width": 6,
"border-color": "#FF5733",
"width": "55px", "height": "55px"
}
},
# Default Selection unterdrücken
{
"selector": "node:selected",
"style": {
"border-width": 0,
"overlay-opacity": 0
}
},
{
"selector": "edge",
"style": {
"width": 2,
"line-color": "data(line_color)",
"target-arrow-color": "data(line_color)",
"target-arrow-shape": "triangle",
"curve-style": "bezier",
"label": "data(label)",
"font-size": "10px", "color": "#666",
"text-background-opacity": 0.8, "text-background-color": "#fff"
}
}
]
# --- RENDER ---
graph_key = f"cy_{center_id}_{st.session_state.cy_depth}_{st.session_state.cy_ideal_edge_len}"
clicked_elements = cytoscape(
elements=cy_elements,
stylesheet=stylesheet,
layout={
"name": "cose",
"idealEdgeLength": st.session_state.cy_ideal_edge_len,
"nodeOverlap": 20,
"refresh": 20,
"fit": True,
"padding": 50,
"randomize": False,
"componentSpacing": 100,
"nodeRepulsion": st.session_state.cy_node_repulsion,
"edgeElasticity": 100,
"nestingFactor": 5,
"gravity": 80,
"numIter": 1000,
"initialTemp": 200,
"coolingFactor": 0.95,
"minTemp": 1.0,
"animate": False
},
key=graph_key,
height="700px"
)
# --- EVENT HANDLING ---
if clicked_elements:
clicked_nodes = clicked_elements.get("nodes", [])
if clicked_nodes:
clicked_id = clicked_nodes[0]
if clicked_id != st.session_state.graph_inspected_id:
st.session_state.graph_inspected_id = clicked_id
st.rerun()
else:
st.info("👈 Bitte wähle links eine Notiz aus.")

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import re
from qdrant_client import QdrantClient, models
from streamlit_agraph import Node, Edge
from ui_config import GRAPH_COLORS, get_edge_color, SYSTEM_EDGES
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_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 (Wir überschreiben das 'fulltext' Feld mit dem frischen Stitching)
# Wir geben eine Kopie zurück, um den Cache nicht zu verfälschen
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)
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])
# Wir packen den Text in den Tooltip (title attribute)
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 Logik
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 im Browser."""
if not text: return ""
# Entferne Header Marker (## )
text = re.sub(r'#+\s', '', text)
# Entferne Bold/Italic (** oder *)
text = re.sub(r'\*\*|__|\*|_', '', text)
# Entferne Links [Text](Url) -> Text
text = re.sub(r'\[([^\]]+)\]\([^\)]+\)', r'\1', text)
# Entferne Wikilinks [[Link]] -> Link
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))]
)
# Limit hoch genug setzen
chunks, _ = self.client.scroll(self.chunks_col, scroll_filter=scroll_filter, limit=100, with_payload=True)
# Sortieren nach 'ord' (Reihenfolge im Dokument)
chunks.sort(key=lambda x: x.payload.get('ord', 999))
full_text = []
for c in chunks:
# 'text' ist der reine Inhalt ohne Overlap
txt = c.payload.get('text', '')
if txt: full_text.append(txt)
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 Chunk für eine Liste von Nodes."""
if not node_ids: return {}
previews = {}
try:
scroll_filter = models.Filter(must=[models.FieldCondition(key="note_id", match=models.MatchAny(any=node_ids))])
# Limit = Anzahl Nodes * 3 (Puffer)
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")
# Nur den ersten gefundenen Chunk pro Note nehmen
if nid and nid not in previews:
previews[nid] = c.payload.get("window") or c.payload.get("text") or ""
except: pass
return previews
def _find_connected_edges(self, note_ids, note_title=None):
"""Findet eingehende und ausgehende Kanten."""
results = []
# 1. OUTGOING EDGES (Der "Owner"-Fix)
# Wir suchen Kanten, die im Feld 'note_id' (Owner) eine unserer Notizen haben.
# Das findet ALLE ausgehenden Kanten, egal ob sie an einem Chunk oder der Note hängen.
if note_ids:
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}))
])
# Limit hoch, um alles zu finden
res_out, _ = self.client.scroll(self.edges_col, scroll_filter=out_filter, limit=500, with_payload=True)
results.extend(res_out)
# 2. INCOMING EDGES (Ziel = Chunk ID oder Titel oder Note ID)
# Hier müssen wir Chunks auflösen, um Treffer auf Chunks zu finden.
# Chunk IDs der aktuellen Notes holen
chunk_ids = []
if note_ids:
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=300)
chunk_ids = [c.id for c in chunks]
shoulds = []
# Case A: Edge zeigt auf einen unserer Chunks
if chunk_ids:
shoulds.append(models.FieldCondition(key="target_id", match=models.MatchAny(any=chunk_ids)))
# Case B: Edge zeigt direkt auf unsere Note ID
if note_ids:
shoulds.append(models.FieldCondition(key="target_id", match=models.MatchAny(any=note_ids)))
# Case C: Edge zeigt auf unseren Titel (Wikilinks)
if note_title:
shoulds.append(models.FieldCondition(key="target_id", match=models.MatchValue(value=note_title)))
if shoulds:
in_filter = 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_filter, limit=500, with_payload=True)
results.extend(res_in)
return results
def _find_connected_edges_batch(self, note_ids):
# Wrapper für Level 2 Suche
return self._find_connected_edges(note_ids)
def _process_edge(self, record, nodes_dict, unique_edges, current_depth):
"""Verarbeitet eine rohe Edge, löst IDs auf und fügt sie den Dictionaries hinzu."""
payload = record.payload
src_ref = payload.get("source_id")
tgt_ref = payload.get("target_id")
kind = payload.get("kind")
provenance = payload.get("provenance", "explicit")
# IDs zu Notes auflösen
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:
# Nodes 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 hinzufügen (mit Deduplizierung)
key = (src_id, tgt_id)
existing = unique_edges.get(key)
should_update = True
# Bevorzuge explizite Kanten vor Smart Kanten
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))]),
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):
"""Löst eine ID (Chunk, Note oder Titel) zu einer Note Payload auf."""
if not ref_str: return None
# Fall A: Chunk ID (enthält #)
if "#" in ref_str:
try:
# Versuch 1: Chunk ID direkt
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
# Versuch 2: NoteID#Section (Hash abtrennen)
possible_note_id = ref_str.split("#")[0]
if self._fetch_note_cached(possible_note_id): return self._fetch_note_cached(possible_note_id)
# Fall B: Note ID direkt
if self._fetch_note_cached(ref_str): return self._fetch_note_cached(ref_str)
# 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:
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, level=1):
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"])
# Basis-Tooltip (wird später erweitert)
tooltip = f"Titel: {note_payload.get('title')}\nTyp: {ntype}"
if level == 0: size = 45
elif level == 1: size = 25
else: size = 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}
)

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import streamlit as st
from ui_utils import load_history_from_logs
from ui_config import HISTORY_FILE
def render_sidebar():
with st.sidebar:
st.title("🧠 mindnet")
st.caption("v2.6 | WP-19 Graph View")
if "sidebar_mode_selection" not in st.session_state:
st.session_state["sidebar_mode_selection"] = "💬 Chat"
mode = st.radio(
"Modus",
[
"💬 Chat",
"📝 Manueller Editor",
"🕸️ Graph (Agraph)",
"🕸️ Graph (Cytoscape)" # <-- Neuer Punkt
],
key="sidebar_mode_selection"
)
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(HISTORY_FILE, 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

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import re
import yaml
import unicodedata
import json
from datetime import datetime
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(filepath, limit=10):
queries = []
if filepath.exists():
try:
with open(filepath, "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

2
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View File

@ -44,6 +44,7 @@ Das Repository ist in **logische Domänen** unterteilt.
| `03_tech_ingestion_pipeline.md`| **Import.** Ablauflogik (13 Schritte), Chunker-Profile, Smart Edge Allocation. |
| `03_tech_retrieval_scoring.md` | **Suche.** Die mathematischen Formeln für Scoring, Hybrid Search und Explanation Layer. |
| `03_tech_chat_backend.md` | **API & LLM.** Implementation des Routers, Traffic Control (Semaphore) und Feedback-Traceability. |
| `03_tech_frontend.md` | **UI & Graph.** Architektur des Streamlit-Frontends, State-Management, Cytoscape-Integration und Editor-Logik. |
| `03_tech_configuration.md` | **Config.** Referenztabellen für `.env`, `types.yaml` und `retriever.yaml`. |
### 📂 04_Operations (Betrieb)
@ -77,6 +78,7 @@ Nutze diese Matrix, wenn du ein Workpackage bearbeitest, um die Dokumentation ko
| **Importer / Parsing** | `03_tech_ingestion_pipeline.md` |
| **Datenbank-Schema** | `03_tech_data_model.md` (Payloads anpassen) |
| **Retrieval / Scoring** | `03_tech_retrieval_scoring.md` (Formeln anpassen) |
| **Frontend / Visualisierung** | 1. `03_tech_frontend.md` (Technische Details)<br>2. `01_chat_usage_guide.md` (Bedienung) |
| **Chat-Logik / Prompts**| 1. `02_concept_ai_personality.md` (Konzept)<br>2. `03_tech_chat_backend.md` (Tech)<br>3. `01_chat_usage_guide.md` (User-Sicht) |
| **Deployment / Server** | `04_admin_operations.md` |
| **Neuen Features (Allg.)**| `06_active_roadmap.md` (Status Update) |

29
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@ -1,13 +1,13 @@
---
doc_type: user_manual
audience: user, mindmaster
scope: chat, ui, feedback
scope: chat, ui, feedback, graph
status: active
version: 2.6
context: "Anleitung zur Nutzung der Web-Oberfläche, der Chat-Personas und des Feedbacks."
context: "Anleitung zur Nutzung der Web-Oberfläche, der Chat-Personas und des Graph Explorers."
---
# Chat Usage Guide
# Chat & Graph Usage Guide
**Quellen:** `user_guide.md`
@ -36,10 +36,25 @@ Seit Version 2.3.1 bedienst du Mindnet über eine grafische Oberfläche im Brows
* **Grüner Punkt:** Hohe Relevanz (Score > 0.8).
* **Klick darauf:** Zeigt den Textauszug und die **Begründung** (Explanation Layer).
### 2.2 Die Sidebar
* **Modus-Wahl:** Umschalten zwischen "💬 Chat" und "📝 Manueller Editor".
* *Feature:* Der Editor nutzt ein "Resurrection Pattern" – deine Eingaben bleiben erhalten, auch wenn du den Tab wechselst.
* **Settings:** Hier kannst du `Top-K` (Anzahl der Quellen) und `Explanation Layer` steuern.
### 2.2 Modus: 🕸️ Graph Explorer (Cytoscape)
*Neu in v2.6*: Eine interaktive Karte deines Wissens.
**Die Farb-Logik:**
* 🔴 **Roter Rahmen:** Das aktuelle **Zentrum** (Ego). Alle geladenen Knoten sind Nachbarn dieses Knotens.
* 🟡 **Gelber Rahmen:** Der **inspizierte** Knoten. Dessen Details siehst du im "Data Inspector" und in der Action-Bar.
**Bedienung:**
1. **Klick auf einen Knoten:** Wählt ihn aus (Gelb). Der Graph bleibt stabil, aber die Info-Leiste oben aktualisiert sich.
2. **Button "🎯 Als Zentrum setzen":** Lädt den Graphen neu und macht den ausgewählten Knoten zum roten Zentrum.
3. **Button "📝 Bearbeiten":** Springt mit dem Inhalt dieses Knotens direkt in den Editor.
**Layout & Persistenz:**
Du kannst in der linken Spalte die Physik (Abstoßung, Kantenlänge) einstellen. Diese Einstellungen werden in der **URL gespeichert**. Du kannst den Link als Lesezeichen speichern, um genau diese Ansicht wiederzufinden.
### 2.3 Modus: 📝 Manueller Editor
Ein Editor mit **"File System First"** Garantie.
* Wenn du eine Datei bearbeitest, liest Mindnet sie direkt von der Festplatte.
* **Resurrection:** Wenn du zwischendurch in den Graph wechselst und zurückkommst, ist dein getippter Text noch da.
---

160
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@ -0,0 +1,160 @@
---
doc_type: technical_reference
audience: developer, frontend_architect
scope: architecture, graph_viz, state_management
status: active
version: 2.6
context: "Technische Dokumentation des modularen Streamlit-Frontends, der Graph-Engines und des Editors."
---
# Technical Reference: Frontend & Visualization
**Kontext:** Mindnet nutzt Streamlit nicht nur als einfaches UI, sondern als komplexe Applikation mit eigenem State-Management, Routing und Persistenz.
---
## 1. Architektur & Modularisierung (WP19)
Seit Version 2.6 ist das Frontend (`app/frontend/`) kein Monolith mehr, sondern in funktionale Module unterteilt.
### 1.1 Dateistruktur & Aufgaben
| Modul | Funktion |
| :--- | :--- |
| `ui.py` | **Router.** Der Entrypoint. Initialisiert Session-State und entscheidet anhand der Sidebar-Auswahl, welche View gerendert wird. |
| `ui_config.py` | **Konstanten.** Zentraler Ort für Farben (`GRAPH_COLORS`), API-Endpunkte und Timeouts. |
| `ui_api.py` | **Backend-Bridge.** Kapselt alle `requests`-Aufrufe an die FastAPI. |
| `ui_callbacks.py` | **State Transitions.** Logik für View-Wechsel (z.B. Sprung vom Graph in den Editor). |
| `ui_utils.py` | **Helper.** Markdown-Parsing (`parse_markdown_draft`) und String-Normalisierung. |
| `ui_graph_service.py`| **Data Logic.** Holt Daten aus Qdrant und bereitet Nodes/Edges auf (unabhängig von der Vis-Library). |
| `ui_graph_cytoscape.py`| **View: Graph.** Implementierung mit `st-cytoscape` (COSE Layout). |
| `ui_editor.py` | **View: Editor.** Logik für Drafts und manuelles Editieren. |
### 1.2 Konfiguration (`ui_config.py`)
Zentrale Steuerung der visuellen Semantik.
# Mapping von Node-Typen zu Farben (Hex)
GRAPH_COLORS = {
"project": "#ff9f43", # Orange
"decision": "#5f27cd", # Lila
"experience": "#feca57", # Gelb (Empathie)
"concept": "#54a0ff", # Blau
"risk": "#ff6b6b" # Rot
}
---
## 2. Graph Visualisierung
Mindnet nutzt primär **Cytoscape** für Stabilität bei großen Graphen.
### 2.1 Engine: Cytoscape (`st-cytoscape`)
* **Algorithmus:** `COSE` (Compound Spring Embedder).
* **Vorteil:** Verhindert Überlappungen aktiv (`nodeRepulsion`).
### 2.2 Architektur-Pattern: "Active Inspector, Passive Graph"
Ein häufiges Problem bei Streamlit-Graphen ist das "Flackern" (Re-Render) bei Klicks. Wir lösen das durch Entkopplung:
1. **Stable Key:** Der React-Key der Komponente hängt *nicht* von der Selektion ab, sondern nur vom Zentrum (`center_id`) und Layout-Settings.
2. **CSS-Selektion:** Wir nutzen **nicht** den nativen `:selected` State (buggy bei Single-Select), sondern injizieren eine CSS-Klasse `.inspected`.
**Stylesheet Implementierung (`ui_graph_cytoscape.py`):**
stylesheet = [
{
"selector": "node",
"style": { "background-color": "data(bg_color)" }
},
# Wir steuern das Highlight manuell über eine Klasse
{
"selector": ".inspected",
"style": {
"border-width": 6,
"border-color": "#FFC300", # Gelb/Gold
"z-index": 999
}
},
# Native Selektion wird unterdrückt/unsichtbar gemacht
{
"selector": "node:selected",
"style": { "overlay-opacity": 0, "border-width": 0 }
}
]
---
## 3. Editor & Single Source of Truth
Ein kritisches Design-Pattern ist der Umgang mit Datenkonsistenz beim Editieren ("File System First").
### 3.1 Das Problem
Qdrant speichert Metadaten und Chunks, aber das Feld `fulltext` im Payload kann veraltet sein oder Formatierungen verlieren.
### 3.2 Die Lösung (Logic Flow)
Der `switch_to_editor_callback` in `ui_callbacks.py` implementiert folgende Kaskade:
def switch_to_editor_callback(note_payload):
# 1. Pfad aus Qdrant Payload lesen
origin_fname = note_payload.get('path')
content = ""
# 2. Versuch: Hard Read von der Festplatte (Source of Truth)
if origin_fname and os.path.exists(origin_fname):
with open(origin_fname, "r", encoding="utf-8") as f:
content = f.read()
else:
# 3. Fallback: Rekonstruktion aus der DB ("Stitching")
# Nur Notfall, falls Docker-Volume fehlt
content = note_payload.get('fulltext', '')
# State setzen (Transport via Message-Bus)
st.session_state.messages.append({
"role": "assistant",
"intent": "INTERVIEW",
"content": content,
"origin_filename": origin_fname
})
st.session_state["sidebar_mode_selection"] = "📝 Manueller Editor"
Dies garantiert, dass der Editor immer den **echten, aktuellen Stand** der Markdown-Datei anzeigt.
---
## 4. State Management Patterns
### 4.1 URL Persistenz (Deep Linking)
Layout-Einstellungen werden in der URL gespeichert, damit sie einen Page-Refresh (F5) überleben.
# ui_graph_cytoscape.py
def update_url_params():
st.query_params["depth"] = st.session_state.cy_depth
st.query_params["rep"] = st.session_state.cy_node_repulsion
# Init
if "cy_depth" not in st.session_state:
st.session_state.cy_depth = int(st.query_params.get("depth", 2))
### 4.2 Resurrection Pattern
Verhindert Datenverlust, wenn der Nutzer während des Tippens den Tab wechselt. Der Editor-Inhalt wird bei jedem Keystroke (`on_change`) in `st.session_state` gespiegelt und beim Neuladen der Komponente von dort wiederhergestellt.
### 4.3 Healing Parser (`ui_utils.py`)
Das LLM liefert oft invalides YAML oder Markdown. Der Parser (`parse_markdown_draft`):
* Repariert fehlende Frontmatter-Trenner (`---`).
* Extrahiert JSON/YAML aus Code-Blöcken.
* Normalisiert Tags (entfernt `#`).
---
## 5. Constraints & Security (Known Limitations)
### 5.1 File System Security
Der Editor ("File System First") vertraut dem Pfad im Qdrant-Feld `path`.
* **Risiko:** Path Traversal (z.B. `../../etc/passwd`).
* **Mitigation:** Aktuell findet keine strikte Prüfung statt, ob der Pfad innerhalb des `./vault` Ordners liegt. Das System setzt voraus, dass die Vektor-Datenbank eine **Trusted Source** ist und nur vom internen Importer befüllt wird.
* **ToDo:** Bei Öffnung der API für Dritte muss hier eine `Path.resolve().is_relative_to(VAULT_ROOT)` Prüfung implementiert werden.
### 5.2 Browser Performance
Die Graph-Visualisierung (`st-cytoscape`) rendert Client-seitig im Browser.
* **Limit:** Ab ca. **500 Knoten/Kanten** kann das Rendering träge werden.
* **Design-Entscheidung:** Das UI ist auf **Ego-Graphen** (Nachbarn eines Knotens) und gefilterte Ansichten ausgelegt, nicht auf die Darstellung des gesamten Knowledge-Graphs ("Whole Brain Visualization").

18
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@ -85,6 +85,7 @@ Environment="STREAMLIT_SERVER_PORT=8501"
Environment="STREAMLIT_SERVER_ADDRESS=0.0.0.0"
Environment="STREAMLIT_SERVER_HEADLESS=true"
# WICHTIG: Pfad zur neuen Router-Datei (ui.py)
ExecStart=/home/llmadmin/mindnet/.venv/bin/streamlit run app/frontend/ui.py
Restart=always
RestartSec=5
@ -107,21 +108,28 @@ Führt den Sync stündlich durch. Nutzt `--purge-before-upsert` für Sauberkeit.
### 3.2 Troubleshooting Guide
**Fehler: "ModuleNotFoundError: No module named 'st_cytoscape'"**
* Ursache: Alte Dependencies oder falsches Paket installiert.
* Lösung: Environment aktualisieren.
```bash
source .venv/bin/activate
pip uninstall streamlit-cytoscapejs
pip install st-cytoscape
# Oder sicherheitshalber:
pip install -r requirements.txt
```
**Fehler: "Vector dimension error: expected 768, got 384"**
* Ursache: Alte DB (v2.2), neues Modell (v2.4).
* Lösung: **Full Reset** (siehe Kap. 4.2).
**Fehler: "500 Internal Server Error" (Ollama)**
* Ursache: Timeout bei Cold-Start des Modells.
* Lösung: `MINDNET_LLM_TIMEOUT=300.0` in `.env` setzen.
**Fehler: Import sehr langsam**
* Ursache: Smart Edges sind aktiv und analysieren jeden Chunk.
* Lösung: `MINDNET_LLM_BACKGROUND_LIMIT` prüfen oder Feature in `types.yaml` deaktivieren.
**Fehler: UI "Read timed out"**
* Ursache: Backend braucht für Smart Edges länger als 60s.
* Lösung: `MINDNET_API_TIMEOUT=300.0` setzen.
* Lösung: `MINDNET_API_TIMEOUT=300.0` in `.env` setzen (oder im Systemd Service).
---

42
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@ -32,6 +32,7 @@ Mindnet läuft in einer verteilten Umgebung (Post-WP15 Setup).
Der Code ist modular in `app` (Logik), `scripts` (CLI) und `config` (Steuerung) getrennt.
### 2.1 Verzeichnisbaum
```text
mindnet/
├── app/
@ -50,8 +51,15 @@ mindnet/
│ │ ├── semantic_analyzer.py# LLM-Filter für Edges (WP15)
│ │ ├── embeddings_client.py# Async Embeddings (HTTPX)
│ │ └── discovery.py # Intelligence Logic (WP11)
│ ├── frontend/
│ │ └── ui.py # Streamlit App inkl. Healing Parser
│ ├── frontend/ # UI Logic (WP19 Modularisierung)
│ │ ├── ui.py # Main Entrypoint & Routing
│ │ ├── ui_config.py # Styles & Constants
│ │ ├── ui_api.py # Backend Connector
│ │ ├── ui_callbacks.py # State Transitions
│ │ ├── ui_utils.py # Helper & Parsing
│ │ ├── ui_graph_service.py # Graph Data Logic
│ │ ├── ui_graph_cytoscape.py # Modern Graph View (St-Cytoscape)
│ │ └── ui_editor.py # Editor View
│ └── main.py # Entrypoint der API
├── config/ # YAML-Konfigurationen (Single Source of Truth)
├── scripts/ # CLI-Tools (Import, Diagnose, Reset)
@ -61,6 +69,12 @@ mindnet/
### 2.2 Modul-Details (Wie es funktioniert)
**Das Frontend (`app.frontend`) - *Neu in v2.6***
* **Router (`ui.py`):** Entscheidet, welche View geladen wird.
* **Service-Layer (`ui_graph_service.py`):** Kapselt die Qdrant-Abfragen. Liefert rohe Nodes/Edges, die dann von den Views visualisiert werden.
* **Graph Engine:** Wir nutzen `st-cytoscape` für das Layout. Die Logik zur Vermeidung von Re-Renders (Stable Keys, CSS-Selektion) ist essentiell.
* **Data Consistency:** Der Editor (`ui_editor.py`) liest Dateien bevorzugt direkt vom Dateisystem ("File System First"), um Datenverlust durch veraltete DB-Einträge zu vermeiden.
**Der Importer (`scripts.import_markdown`)**
* Das komplexeste Modul.
* Nutzt `app.core.chunker` und `app.services.semantic_analyzer` (Smart Edges).
@ -76,10 +90,6 @@ mindnet/
* Implementiert die Scoring-Formel (`Semantik + Graph + Typ`).
* **Hybrid Search:** Lädt dynamisch den Subgraphen (`graph_adapter.expand`).
**Das Frontend (`app.frontend.ui`)**
* **Resurrection Pattern:** Nutzt `st.session_state`, um Eingaben bei Tab-Wechseln zu erhalten.
* **Healing Parser:** Die Funktion `parse_markdown_draft` repariert defekte YAML-Frontmatter vom LLM automatisch.
**Traffic Control (`app.services.llm_service`)**
* Stellt sicher, dass der Chat responsive bleibt, auch wenn ein Import läuft.
* Nutzt `asyncio.Semaphore` (`MINDNET_LLM_BACKGROUND_LIMIT`), um Hintergrund-Jobs zu drosseln.
@ -91,6 +101,7 @@ mindnet/
**Voraussetzungen:** Python 3.10+, Docker, Ollama.
**Installation:**
```bash
# 1. Repo & Venv
git clone <repo> mindnet
@ -98,7 +109,7 @@ cd mindnet
python3 -m venv .venv
source .venv/bin/activate
# 2. Dependencies
# 2. Dependencies (inkl. st-cytoscape)
pip install -r requirements.txt
# 3. Ollama (Nomic ist Pflicht!)
@ -107,6 +118,7 @@ ollama pull nomic-embed-text
```
**Konfiguration (.env):**
```ini
QDRANT_URL="http://localhost:6333"
COLLECTION_PREFIX="mindnet_dev"
@ -164,7 +176,11 @@ Mindnet lernt durch Konfiguration, nicht durch Training.
DECISION:
inject_types: ["value", "risk"] # Füge 'risk' hinzu
```
3. **Kognition (`prompts.yaml`):** (Optional) Passe den System-Prompt an, falls nötig.
3. **Visualisierung (`ui_config.py`):**
Füge dem `GRAPH_COLORS` Dictionary einen Eintrag hinzu:
```python
"risk": "#ff6b6b"
```
### Workflow B: Interview-Schema anpassen (WP07)
Wenn Mindnet neue Fragen stellen soll (z.B. "Budget" bei Projekten):
@ -183,12 +199,14 @@ Wenn Mindnet neue Fragen stellen soll (z.B. "Budget" bei Projekten):
## 6. Tests & Debugging
**Unit Tests:**
```bash
pytest tests/test_retriever_basic.py
pytest tests/test_chunking.py
```
**Pipeline Tests:**
```bash
# JSON-Schema prüfen
python3 -m scripts.payload_dryrun --vault ./test_vault
@ -198,6 +216,7 @@ python3 -m scripts.edges_full_check
```
**E2E Smoke Tests:**
```bash
# Decision Engine
python tests/test_wp06_decision.py -p 8002 -e DECISION -q "Soll ich X tun?"
@ -211,21 +230,24 @@ python tests/test_feedback_smoke.py --url http://localhost:8002/query
## 7. Troubleshooting & One-Liners
**DB komplett zurücksetzen (Vorsicht!):**
```bash
python3 -m scripts.reset_qdrant --mode wipe --prefix "mindnet_dev" --yes
```
**Einen einzelnen File inspizieren (Parser-Sicht):**
```bash
python3 tests/inspect_one_note.py --file ./vault/MeinFile.md
```
**Live-Logs sehen:**
```bash
journalctl -u mindnet-dev -f
journalctl -u mindnet-ui-dev -f
```
**"Read timed out" im Frontend:**
* Ursache: Smart Edges brauchen länger als 60s.
* Lösung: `MINDNET_API_TIMEOUT=300.0` in `.env`.
* Ursache: Backend braucht für Smart Edges länger als 60s.
* Lösung: `MINDNET_API_TIMEOUT=300.0` in `.env` setzen.

50
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@ -8,20 +8,20 @@ context: "Aktuelle Planung für kommende Features (ab WP16), Release-Strategie u
# Mindnet Active Roadmap
**Aktueller Stand:** v2.6.0 (Post-WP15)
**Fokus:** Usability, Memory & Visualisierung.
**Aktueller Stand:** v2.6.0 (Post-WP19)
**Fokus:** Visualisierung, Exploration & Deep Search.
## 1. Programmstatus
Wir haben Phase D (Interaktion) weitgehend abgeschlossen. Das System ist stabil, verfügt über Traffic Control und Smart Edges. Der Fokus verschiebt sich nun auf **Phase E (Maintenance & Scaling)** sowie die Vertiefung der KI-Fähigkeiten.
Wir haben mit der Implementierung des Graph Explorers (WP19) einen Meilenstein in **Phase E (Maintenance & Scaling)** erreicht. Die Architektur ist nun modular. Der nächste Schritt (WP19a) vertieft die Analyse-Fähigkeiten.
| Phase | Fokus | Status |
| :--- | :--- | :--- |
| **Phase A** | Fundament & Import | ✅ Fertig |
| **Phase B** | Semantik & Graph | ✅ Fertig |
| **Phase C** | Persönlichkeit | ✅ Fertig |
| **Phase D** | Interaktion & Tools | 🟡 Abschlussphase |
| **Phase E** | Maintenance & Visualisierung | 🚀 Start |
| **Phase D** | Interaktion & Tools | ✅ Fertig |
| **Phase E** | Maintenance & Visualisierung | 🚀 Aktiv |
---
@ -44,6 +44,7 @@ Eine Übersicht der implementierten Features zum schnellen Auffinden von Funktio
| **WP-10a**| Draft Editor | GUI-Komponente zum Bearbeiten und Speichern generierter Notizen. |
| **WP-11** | Backend Intelligence | `nomic-embed-text` (768d) und Matrix-Logik für Kanten-Typisierung. |
| **WP-15** | Smart Edge Allocation | LLM-Filter für Kanten in Chunks + Traffic Control (Semaphore). |
| **WP-19** | Graph Visualisierung | **Frontend Modularisierung:** Umbau auf `ui_*.py`.<br>**Graph Engines:** Parallelbetrieb von Cytoscape (COSE) und Agraph.<br>**Tools:** "Single Source of Truth" Editor, Persistenz via URL. |
---
@ -51,19 +52,24 @@ Eine Übersicht der implementierten Features zum schnellen Auffinden von Funktio
Diese Features stehen als nächstes an.
### WP-19a – Graph Intelligence & Discovery (Sprint-Fokus)
**Status:** 🚀 Startklar
**Ziel:** Vom "Anschauen" zum "Verstehen". Deep-Dive Werkzeuge für den Graphen.
* **Discovery Screen:** Neuer Tab für semantische Suche ("Finde Notizen über Vaterschaft") und Wildcard-Filter.
* **Filter-Logik:** "Zeige nur Wege, die zu `type:decision` führen".
* **Chunk Inspection:** Umschaltbare Granularität (Notiz vs. Chunk) zur Validierung des Smart Chunkers.
### WP-16 – Auto-Discovery & Enrichment
**Status:** 🟡 Geplant
**Ziel:** Automatisches Erkennen von fehlenden Kanten in "dummem" Text *vor* der Speicherung.
* **Problem:** Nutzer vergessen Wikilinks.
* **Lösung:** Ein "Enricher" scannt Text vor dem Import, findet Keywords (z.B. "Mindnet") und schlägt Links vor (`[[Mindnet]]`).
* **Abgrenzung:** Anders als *Active Intelligence* (WP11, UI-basiert) läuft dies im Backend (Importer).
### WP-17 – Conversational Memory (Gedächtnis)
**Status:** 🟡 Geplant
**Ziel:** Echte Dialoge statt Request-Response.
* **Tech:** Erweiterung des `ChatRequest` DTO um `history`.
* **Logik:** Token-Management (Context Window Balancing zwischen RAG-Doks und Chat-Verlauf).
* **Nutzen:** Rückfragen ("Was meinst du damit?") werden möglich.
### WP-18 – Graph Health & Maintenance
**Status:** 🟡 Geplant (Prio 2)
@ -71,17 +77,10 @@ Diese Features stehen als nächstes an.
* **Feature:** Cronjob `check_graph_integrity.py`.
* **Funktion:** Findet "Dangling Edges" (Links auf gelöschte Notizen) und repariert/löscht sie.
### WP-19 – Graph Visualisierung & Explorer
**Status:** 🟡 Geplant (Prio 1)
**Ziel:** Vertrauen durch Transparenz.
* **UI:** Neuer Tab "🕸️ Graph" in Streamlit.
* **Tech:** `streamlit-agraph` oder `pyvis`.
* **Nutzen:** Visuelle Kontrolle der *Smart Edge Allocation* ("Hat das LLM die Kante wirklich hierhin gesetzt?").
### WP-20 – Cloud Hybrid Mode (Optional)
**Status:** ⚪ Optional
**Ziel:** "Turbo-Modus" für Massen-Imports.
* **Konzept:** Switch in `.env`, um statt Ollama (Lokal) auf Google Gemini (Cloud) umzuschalten, wenn Datenschutz-unkritische Daten verarbeitet werden.
* **Konzept:** Switch in `.env`, um statt Ollama (Lokal) auf Google Gemini (Cloud) umzuschalten.
---
@ -89,20 +88,7 @@ Diese Features stehen als nächstes an.
```mermaid
graph TD
WP15(Smart Edges) --> WP19(Visualisierung)
WP15 --> WP16(Auto-Discovery)
WP10(Chat UI) --> WP17(Memory)
WP03(Import) --> WP18(Health Check)
```
**Nächstes Release (v2.7):**
* Ziel: Visualisierung (WP19) + Conversational Memory (WP17).
* ETA: Q1 2026.
---
## 5. Governance
* **Versionierung:** Semantic Versioning via Gitea Tags.
* **Feature-Branches:** Jedes WP erhält einen Branch `feature/wpXX-name`.
* **Sync First:** Bevor ein neuer Branch erstellt wird, muss `main` gepullt werden.
WP19(Graph Viz) --> WP19a(Discovery)
WP19a --> WP17(Memory)
WP15(Smart Edges) --> WP16(Auto-Discovery)
WP03(Import) --> WP18(Health Check)

18
docs/99_Archive/99_legacy_workpackages.md
View File

@ -2,12 +2,12 @@
doc_type: archive
audience: historian, architect
status: archived
context: "Archivierte Details zu abgeschlossenen Workpackages (WP01-WP15). Referenz für historische Design-Entscheidungen."
context: "Archivierte Details zu abgeschlossenen Workpackages (WP01-WP19). Referenz für historische Design-Entscheidungen."
---
# Legacy Workpackages (Archiv)
**Quellen:** `Programmplan_V2.2.md`
**Quellen:** `Programmplan_V2.2.md`, `Active Roadmap`
**Status:** Abgeschlossen / Implementiert.
Dieses Dokument dient als Referenz für die Entstehungsgeschichte von Mindnet v2.6.
@ -79,4 +79,16 @@ Dieses Dokument dient als Referenz für die Entstehungsgeschichte von Mindnet v2
### WP-15 – Smart Edge Allocation (Meilenstein)
* **Problem:** "Broadcasting". Ein Chunk erbte alle Links der Note, auch irrelevante. Das verwässerte die Suchergebnisse.
* **Lösung:** LLM prüft jeden Chunk auf Link-Relevanz.
* **Tech:** Einführung von **Traffic Control** (Semaphore), um Import und Chat zu parallelisieren, ohne die Hardware zu überlasten.
* **Tech:** Einführung von **Traffic Control** (Semaphore), um Import und Chat zu parallelisieren, ohne die Hardware zu überlasten.
---
## Phase E: Visualisierung & Maintenance (WP19)
### WP-19 – Graph Visualisierung & Modularisierung
* **Ziel:** Transparenz über die Datenstruktur schaffen und technische Schulden (Monolith) abbauen.
* **Ergebnis:**
* **Modularisierung:** Aufsplittung der `ui.py` in Router, Services und Views (`ui_*.py`).
* **Graph Explorer:** Einführung von `st-cytoscape` für stabile, nicht-überlappende Layouts (COSE) als Ergänzung zur Legacy-Engine (Agraph).
* **Single Source of Truth:** Der Editor lädt Inhalte nun direkt vom Dateisystem statt aus (potenziell veralteten) Vektor-Payloads.
* **UX:** Einführung von URL-Persistenz für Layout-Settings und CSS-basiertes Highlighting zur Vermeidung von Re-Renders.

6
requirements.txt
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@ -30,4 +30,8 @@ tqdm>=4.67.1
pytest>=8.4.2
# --- Frontend (WP-10) ---
streamlit>=1.39.0
streamlit>=1.39.0
# Visualization (Parallelbetrieb)
streamlit-agraph>=0.0.45
st-cytoscape>=1.0.0