fix(dashboard): remove st.secrets dependency, add .streamlit/secrets.toml, fix ChromaDB metadata types, v0.3.2

.streamlit/secrets.toml

@@ -0,0 +1 @@
+[default]

README.md

@@ -1,88 +1,14 @@
-# 🧠 Second Brain
+# Second Brain
 
-Zweites Gehirn für OpenClaw - Langzeit- und Kurzzeitgedächtnis mit Bewertung, Proaktivität und Selbstheilung.
+An embeddable, offline-first memory system for AI agents with correctness tracking, neural scoring, and semantic retrieval.
 
-## Features
+## What's New (Phase 2-5)
 
-- **Engramme** - Gedächtniseinheiten mit Confidence, Korrektheit, Verknüpfungen
+- **Sentence-Transformer Embeddings** (`src/embedder.py`) — Cached, offline, 384-Dim
-- **SQLite + FTS5** - Lokaler Speicher ohne externe Abhängigkeiten
+- **ChromaDB Vector Store** (`src/chroma_store.py`) — Semantic similarity search
-- **Hybrid-Retrieval** - Keyword-Suche + Reranking (später + Embeddings)
+- **Neural Confidence Scorer** (`src/neural_scorer.py`) — PyTorch RL net, trains on confirm/reject feedback
-- **Correctness-Tracking** - Richtig/Falsch-Feedback mit Lern-Loop
+- **Hybrid Retrieval** (`src/retriever.py`) — Keyword + Semantic + Neural fusion
-- **Proaktivität** - Heartbeat + Cron für selbständige Checks
+- **Streamlit Dashboard** (`src/app_dashboard.py`) — Search, confirm/reject, neural training UI
-- **Fehlerheilung** - Fehler als Engramme, Mustererkennung, Auto-Fix
+- **Graph Visualization** (`src/graph_view.py`) — Interactive Cytoscape.js graph with confidence colors
-- **Dashboard** - HTML-Visualisierung, kein Framework nötig
-- **OpenClaw-Bridge** - Direkte Integration in Agent-Sessions
 
-## Schnellstart
+## Architecture
-
-```bash
-cd /root/.openclaw/workspace/second-brain
-
-# Engramm hinzufügen
-python3 -m src.cli add "Das ist wichtig" --tag wichtig --source user
-
-# Suchen
-python3 -m src.cli search "wichtig"
-
-# Feedback geben
-python3 -m src.cli confirm <id>
-python3 -m src.cli reject <id>
-
-# Dashboard öffnen
-python3 -m src.dashboard
-
-# Stats
-python3 -m src.cli stats
-
-# Backup
-python3 -m src.openclaw_bridge backup
-
-# Tests
-python3 -m tests.test_core
-```
-
-## Architektur
-
-```
-┌─────────────────┐     ┌──────────────┐     ┌────────────────┐
-│   OpenClaw      │────▶│   Bridge     │────▶│  Engram Store  │
-│   Agent         │     │  (Session)   │     │   (SQLite)     │
-└─────────────────┘     └──────────────┘     └────────────────┘
-       │                                               │
-       ▼                                               ▼
-┌─────────────────┐                           ┌──────────────┐
-│   Heartbeat     │                           │  Retriever   │
-│   (Cron/Check)  │                           │  (FTS + RR)  │
-└─────────────────┘                           └──────────────┘
-                                                      │
-                                                      ▼
-                                              ┌──────────────┐
-                                              │   Dashboard  │
-                                              │   (HTML)     │
-                                              └──────────────┘
-```
-
-## Module
-
-| Datei | Zweck |
-|-------|-------|
-| `src/engram.py` | Engramm-Modell, Confidence, Correctness |
-| `src/store.py` | SQLite-CRUD, FTS5-Index, Backup/Export |
-| `src/retriever.py` | Suche, Reranking, Verknüpfungen |
-| `src/cli.py` | Kommandozeilen-Interface |
-| `src/openclaw_bridge.py` | OpenClaw-Integration, Heartbeat, Fehler-Handling |
-| `src/dashboard.py` | HTML-Dashboard-Generator |
-
-## CI/CD
-
-- **Repo**: http://192.168.6.31:3000/Otto/second-brain
-- **Issues**: 8 offen (Features, Bugs)
-- **Cron**: Täglich 2 Uhr Backup
-
-## Nächste Schritte (Phase 2)
-
-1. Vektor-Embeddings via sentence-transformers
-2. ChromaDB-Store als Alternative zu SQLite
-3. PyTorch Neural Scorer
-4. Streamlit-Dashboard
-5. Graph-Visualisierung (cytoscape.js)

src/app_dashboard.py

@@ -1,174 +1,210 @@
 """
 app_dashboard.py - Streamlit-Dashboard für Second Brain.
-Seiten: Übersicht, Engramme, Suche, Graph, Stats.
+Seiten: Übersicht, Engramme, Suche, Graph, Heal-Log, Neural Scorer.
 """
 
 import json
 import sys
+import os
 from pathlib import Path
 
 import streamlit as st
 
-sys.path.insert(0, str(Path(__file__).resolve().parent))
+_root = Path(__file__).resolve().parent.parent
+sys.path.insert(0, str(_root))
 
 from src.engram import Engram
 from src.store import EngramStore
 from src.chroma_store import ChromaStore
 from src.retriever import Retriever
 from src.neural_scorer import NeuralScorer
+from src.graph_view import generate_graph_html
+from src.loop_detector import LoopDetector
+from src.error_healer import ErrorHealer
 
-_DEFAULT_DB = Path(__file__).resolve().parent.parent / "data" / "brain.sqlite"
+_DEFAULT_DB = _root / "data" / "brain.sqlite"
-_DB_PATH = str(st.secrets.get("db_path", _DEFAULT_DB) if hasattr(st, "secrets") else _DEFAULT_DB)
 
 
+@st.cache_resource
 def _store():
-    return EngramStore(_DB_PATH)
+    return EngramStore(str(_DEFAULT_DB))
 
 
+@st.cache_resource
 def _chroma():
-    p = Path(_DB_PATH).parent / "chroma"
+    p = Path(str(_DEFAULT_DB)).parent / "chroma"
     return ChromaStore(str(p))
 
 
+_retriever_cache = None
 def _retriever():
-    return Retriever(_store(), _chroma())
+    global _retriever_cache
+    if _retriever_cache is None:
+        _retriever_cache = Retriever(_store(), _chroma())
+    return _retriever_cache
 
 
+@st.cache_resource
 def _scorer():
     return NeuralScorer()
 
 
-st.set_page_config(page_title="Second Brain Dashboard", layout="wide")
+@st.cache_resource
-st.title("🧠 Second Brain Dashboard")
+def _healer():
+    return ErrorHealer(_store())
 
-page = st.sidebar.radio("Seite", ["Übersicht", "Engramme", "Suche", "Graph", "Stats", "Neural Scorer"])
+
+st.set_page_config(page_title="Second Brain Dashboard", layout="wide")
+st.title("🧠 2.Brain v0.3.1")
+
+page = st.sidebar.radio("Seite", ["Übersicht", "Engramme", "Suche", "Graph", "Heal-Log", "Neural Scorer"])
 
 
 if page == "Übersicht":
     store = _store()
-    engrams = store.get_all()
+    engrams = store.get_all(limit=10000)
     confirmed = sum(1 for e in engrams if e.correctness.confirmed)
     unconfirmed = len(engrams) - confirmed
     avg_conf = sum(e.compute_confidence() for e in engrams) / max(1, len(engrams))
+    errors = [e for e in engrams if "error" in e.metadata.get("tags", [])]
 
-    c1, c2, c3, c4 = st.columns(4)
+    c1, c2, c3, c4, c5 = st.columns(5)
     c1.metric("Total", len(engrams))
     c2.metric("Confirmed", confirmed)
     c3.metric("Pending", unconfirmed)
     c4.metric("Avg Confidence", f"{avg_conf:.2f}")
+    c5.metric("Errors", len(errors))
 
     st.subheader("Recent Engramme")
     for eg in sorted(engrams, key=lambda e: e.metadata.get("modified", ""), reverse=True)[:5]:
-        with st.expander(f"{eg.content[:80]}..."):
+        valid = eg.validate_grounding()
+        marker = "✅" if valid["valid"] else "⚠️"
+        with st.expander(f"{marker} {eg.content[:80]}..."):
+            st.write(f"ID: `{eg.id}`")
             st.write(f"Source: {eg.metadata.get('source')}")
             st.write(f"Confidence: {eg.compute_confidence():.2f}")
             st.write(f"Confirmed: {'✅' if eg.correctness.confirmed else '❓'}")
             st.write("Tags:", ", ".join(eg.metadata.get("tags", [])))
+            if not valid["valid"]:
+                st.warning(f"Grounding: {valid['issue']}")
+                if st.button("Auto-Fix", key=f"af_{eg.id}"):
+                    eg.auto_fix_grounding()
+                    store.save(eg)
+                    st.experimental_rerun()
 
 
 elif page == "Engramme":
     store = _store()
-    st.subheader("Alle Engramme")
+    st.subheader("Alle Engramme (max 1000)")
     tag_filter = st.text_input("Filter tags")
     source_filter = st.selectbox("Source", ["alle", "user", "agent", "web", "file", "system"])
-    for eg in store.get_all():
+    for eg in store.get_all(limit=1000):
         tags = eg.metadata.get("tags", [])
         src = eg.metadata.get("source", "")
         if tag_filter and tag_filter not in tags:
             continue
         if source_filter != "alle" and source_filter != src:
             continue
-        with st.expander(f"{eg.content[:100]}"):
+        col1, col2 = st.columns([4, 1])
-            st.write("Confidence:", f"{eg.compute_confidence():.2f}")
+        with col1:
-            st.write("Tags:", ", ".join(tags))
+            conf = eg.compute_confidence()
-            st.write("Source:", src)
+            marker = "✅" if conf > 0.7 else "⚠️"
-            c1, c2 = st.columns(2)
+            st.markdown(f"{marker} **{eg.content[:100]}**")
-            if c1.button("✅ Confirm", key=f"conf_{eg.id}"):
+            st.caption(f"Conf: {conf:.2f} | Tags: {', '.join(tags)} | Source: {src}")
+        with col2:
+            if st.button("✅ Confirm", key=f"conf_{eg.id}"):
                 eg.correctness.confirm("user")
                 store.save(eg)
-                st.success("Confirmed!")
+                st.success("Confirmed")
-            if c2.button("❌ Reject", key=f"rej_{eg.id}"):
+            if st.button("❌ Reject", key=f"rej_{eg.id}"):
                 eg.correctness.reject("user")
                 store.save(eg)
-                st.warning("Rejected.")
+                st.warning("Rejected")
+        st.divider()
 
 
 elif page == "Suche":
-    st.subheader("Semantic + Keyword Suche")
+    st.subheader("Hybrid Search (Semantic + Keyword)")
-    query = st.text_input("Query")
+    query = st.text_input("Query", placeholder="Suchbegriff eingeben...")
-    mode = st.radio("Modus", ["Hybrid", "Keyword", "Semantic"])
+    mode = st.radio("Modus", ["Hybrid", "Keyword", "Semantic"], horizontal=True)
     if st.button("Suchen") and query:
         ret = _retriever()
-        if mode == "Hybrid":
+        results = ret.hybrid_retrieve(query, limit=10) if mode == "Hybrid" else \
-            results = ret.hybrid_retrieve(query, limit=10)
+                  ret.semantic_retrieve(query, limit=10) if mode == "Semantic" else \
-        elif mode == "Semantic":
+                  ret.retrieve(query, limit=10)
-            results = ret.semantic_retrieve(query, limit=10)
+        if not results:
-        else:
+            st.info("Keine Ergebnisse gefunden.")
-            results = ret.retrieve(query, limit=10)
         for r in results:
             eg = r["engram"]
             with st.container():
                 st.markdown(f"**{eg.content[:200]}...**")
-                st.write(f"Score: {r['score']:.3f} | Match: {r['match_type']} | Conf: {eg.compute_confidence():.2f}")
+                st.write(f"Score: `{r['score']:.3f}` | Match: `{r['match_type']}` | Conf: `{eg.compute_confidence():.2f}`")
                 c1, c2 = st.columns(2)
                 if c1.button("✅ Confirm", key=f"sc_{eg.id}"):
                     eg.correctness.confirm("user")
-                    store = _store()
+                    _store().save(eg)
-                    store.save(eg)
                     st.success("Confirmed")
                 if c2.button("❌ Reject", key=f"sr_{eg.id}"):
                     eg.correctness.reject("user")
-                    store = _store()
+                    _store().save(eg)
-                    store.save(eg)
                     st.warning("Rejected")
 
 
 elif page == "Graph":
     st.subheader("Graph-Visualisierung")
-    graph_html_path = Path(_DB_PATH).parent / "graph_view.html"
+    graph_html_path = Path(str(_DEFAULT_DB)).parent / "graph_view.html"
+    if st.button("Graph neu generieren"):
+        with st.spinner("Generiere Graph..."):
+            path = generate_graph_html(_store(), str(graph_html_path))
+        st.success(f"Graph generiert: {path}")
     if graph_html_path.exists():
         with open(graph_html_path, "r", encoding="utf-8") as f:
             html = f.read()
-        # iframe
+        st.components.v1.html(html, height=800)
-        st.components.v1.html(html, height=800, scrolling=True)
     else:
-        st.info("Graph nicht generiert. Führe `python -m src.cli graph` aus.")
+        st.info("Graph noch nicht generiert. Klicke oben.")
-        if st.button("Graph generieren"):
-            from src.graph_view import generate_graph_html
-            store = _store()
-            path = generate_graph_html(store, str(Path(_DB_PATH).parent / "graph_view.html"))
-            st.success(f"Graph generiert: {path}")
 
 
-elif page == "Stats":
+elif page == "Heal-Log":
-    store = _store()
+    st.subheader("Error Healing & Loop Detection")
-    engrams = store.get_all()
+    healer = _healer()
-    st.json({
+    stats = healer.get_error_stats()
-        "total": len(engrams),
+    c1, c2, c3 = st.columns(3)
-        "confirmed": sum(1 for e in engrams if e.correctness.confirmed),
+    c1.metric("Total Errors", stats["total_errors"])
-        "pending": sum(1 for e in engrams if not e.correctness.confirmed),
+    c2.metric("Repeated", stats["repeated_errors"])
-        "sources": {s: sum(1 for e in engrams if e.metadata.get("source") == s) for s in {e.metadata.get("source") for e in engrams}},
+    c3.metric("Error Types", len(stats.get("error_types", {})))
-        "tags": {t: sum(1 for e in engrams for t2 in e.metadata.get("tags", []) if t2 == t) for t in {t for e in engrams for t in e.metadata.get("tags", [])}},
+
-        "avg_confidence": sum(e.compute_confidence() for e in engrams) / max(1, len(engrams)),
+    st.subheader("Error Types")
-    })
+    for etype, count in stats.get("error_types", {}).items():
+        st.write(f"- **{etype}**: {count}")
+
+    st.subheader("Loop-Checker")
+    q = st.text_input("Query")
+    r = st.text_input("Response")
+    if st.button("Check Loop") and q and r:
+        detector = LoopDetector()
+        result = detector.check(q, r)
+        st.json(result)
+        if result["loop_detected"]:
+            st.error(result["suggestion"])
 
 
 elif page == "Neural Scorer":
     st.subheader("Neural Scorer Training")
     scorer = _scorer()
     store = _store()
-    engrams = store.get_all()
+    engrams = store.get_all(limit=10000)
     labeled = [e for e in engrams if e.correctness.confirmed or e.correctness.rejections > 0]
-    st.write(f"Labelled Engramme: {len(labeled)}")
+    st.write(f"Labelled Engramme: **{len(labeled)}**")
     if st.button("Train Neural Scorer"):
         if len(labeled) < 2:
             st.error("Mindestens 2 labelierte Engramme nötig (confirm + reject).")
         else:
-            result = scorer.train(labeled, epochs=30)
+            with st.spinner("Training läuft..."):
+                result = scorer.train(labeled, epochs=30)
             st.json(result)
             st.success("Training abgeschlossen!")
-
     if st.button("Predict All"):
-        for eg in engrams[:10]:
+        for eg in engrams[:20]:
             pred = scorer.predict(eg)
-            st.write(f"{eg.content[:60]}... → {pred:.3f}")
+            st.write(f"{eg.content[:50]}... → **{pred:.3f}**")

src/chroma_store.py

@@ -31,19 +31,21 @@ class ChromaStore:
         )
 
     def _build_metadata(self, engram: Engram) -> Dict[str, Any]:
-        """Serialisierte Metadaten für ChromaDB (nur primitives)."""
+        """Serialisierte Metadaten für ChromaDB (nur primitiv/scalar/Str)."""
-        meta = engram.metadata.copy()
+        m = engram.metadata
-        # ChromaDB akzeptiert nur Listen/Strings/Numbers/Bools
+        safe: Dict[str, Any] = {}
-        tags = meta.pop("tags", [])
+        # Nur explizit erlaubte Felder übernehmen
-        if isinstance(tags, list):
+        safe["source"] = str(m.get("source", "agent"))
-            meta["tags"] = ",".join(str(t) for t in tags)
+        safe["confidence"] = float(m.get("confidence", 0.5))
-        meta.setdefault("source", "agent")
+        safe["grounding"] = int(m.get("grounding", 1))
-        meta.setdefault("confidence", 0.5)
+        tags = m.get("tags", [])
-        meta.setdefault("correctness", "unconfirmed")
+        safe["tags"] = ",".join(str(t) for t in tags) if isinstance(tags, list) else str(tags)
-        # Hierarchy als JSON-String
+        safe["created"] = str(m.get("created", ""))
-        if "hierarchy" in meta:
+        safe["modified"] = str(m.get("modified", ""))
-            meta["hierarchy"] = json.dumps(meta["hierarchy"])
+        safe["access_count"] = int(m.get("access_count", 0))
-        return meta
+        safe["correctness"] = "confirmed" if engram.correctness.confirmed else "unconfirmed"
+        safe["content"] = str(engram.content)[:500]  # Chroma akzeptiert kurze Strings besser
+        return safe
 
     def add(self, engram: Engram, embedding: Optional[List[float]] = None) -> None:
         """Engramm mit Embedding zur Vektor-DB hinzufügen."""

src/cli.py

@@ -3,7 +3,7 @@
 Second Brain CLI - direkte Nutzung ohne externe Abhängigkeiten.
 
 Usage:
-    python -m src.cli add "Das ist ein Faktum" --tag wichtig --source user
+    python -m src.cli add "Faktum" --tag wichtig --source user
     python -m src.cli search "Faktum"
     python -m src.cli show <id>
     python -m src.cli confirm <id>
@@ -11,18 +11,31 @@ Usage:
     python -m src.cli list
     python -m src.cli stats
     python -m src.cli export backup.jsonl
+    python -m src.cli graph
+    python -m src.cli heal
+    python -m src.cli neural-train
+    python -m src.cli loop-check "query" "response"
+    python -m src.cli dashboard
 """
 
-import sys
-import json
 import argparse
+import json
+import os
+import subprocess
+import sys
 from pathlib import Path
 
 from .store import EngramStore
 from .engram import Engram, Grounding
 from .retriever import Retriever
+from .chroma_store import ChromaStore
+from .graph_view import generate_graph_html
+from .neural_scorer import NeuralScorer
+from .loop_detector import LoopDetector
+from .error_healer import ErrorHealer
 
 DB_PATH = Path(__file__).parent.parent / "data" / "brain.sqlite"
+CHROMA_PATH = Path(__file__).parent.parent / "data" / "chroma"
 
 
 def get_store():
@@ -30,6 +43,10 @@ def get_store():
     return EngramStore(str(DB_PATH))
 
 
+def get_chroma():
+    return ChromaStore(str(CHROMA_PATH))
+
+
 def cmd_add(args):
     store = get_store()
     eg = Engram.create(
@@ -38,20 +55,46 @@ def cmd_add(args):
         tags=args.tag,
         grounding=Grounding[args.grounding] if args.grounding else Grounding.ASSUMPTION,
     )
+    # Grounding-Regel prüfen (Issue #8)
+    validation = eg.validate_grounding()
+    if not validation["valid"] and args.auto_fix:
+        eg.auto_fix_grounding()
+        print(f"🔧 Auto-Fix: {validation['suggestion']}")
+    elif not validation["valid"]:
+        print(f"⚠️  Warnung: {validation['issue']}")
+        print(f"   Suggestion: {validation['suggestion']}")
+
     store.save(eg)
     print(f"Created: {eg.id}\n  Content: {eg.content[:100]}\n  Confidence: {eg.compute_confidence():.2f}")
 
 
 def cmd_search(args):
     store = get_store()
-    ret = Retriever(store)
+    chroma = get_chroma()
-    results = ret.retrieve(
+    ret = Retriever(store, chroma)
-        " ".join(args.query),
+
-        limit=args.limit,
+    mode = args.mode
-        min_confidence=args.min_confidence,
+    if mode == "hybrid":
-        tag_filter=args.tag,
+        results = ret.hybrid_retrieve(
-    )
+            " ".join(args.query),
-    print(f"\n=== {len(results)} Results ===")
+            limit=args.limit,
+            min_confidence=args.min_confidence,
+        )
+    elif mode == "semantic":
+        results = ret.semantic_retrieve(
+            " ".join(args.query),
+            limit=args.limit,
+            min_confidence=args.min_confidence,
+        )
+    else:
+        results = ret.retrieve(
+            " ".join(args.query),
+            limit=args.limit,
+            min_confidence=args.min_confidence,
+            tag_filter=args.tag,
+        )
+
+    print(f"\n=== {len(results)} Results ({mode}) ===")
     for r in results:
         eg = r["engram"]
         conf = eg.compute_confidence()
@@ -106,7 +149,17 @@ def cmd_list(args):
 def cmd_stats(args):
     store = get_store()
     ret = Retriever(store)
-    s = ret.stats()
+    try:
+        s = ret.stats()
+    except AttributeError:
+        egs = store.get_all(limit=10000)
+        s = {
+            "total_engrams": len(egs),
+            "confirmed": sum(1 for e in egs if e.correctness.confirmed),
+            "unconfirmed": sum(1 for e in egs if not e.correctness.confirmed),
+            "sources": {src: sum(1 for e in egs if e.metadata.get("source") == src) for src in {e.metadata.get("source") for e in egs}},
+            "db_size_bytes": os.path.getsize(str(DB_PATH)) if os.path.exists(str(DB_PATH)) else 0,
+        }
     print("\n=== Second Brain Stats ===")
     print(f"  Total Engrams: {s['total_engrams']}")
     print(f"  Confirmed: {s['confirmed']}")
@@ -123,6 +176,67 @@ def cmd_export(args):
     print(f"Exported {count} engrams to {args.path}")
 
 
+def cmd_graph(args):
+    store = get_store()
+    path = args.output or str(DB_PATH.parent / "graph_view.html")
+    result = generate_graph_html(store, path)
+    print(f"✅ Graph generiert: {result}")
+
+
+def cmd_heal(args):
+    store = get_store()
+    healer = ErrorHealer(store)
+    stats = healer.get_error_stats()
+    print("\n=== Error Heal Stats ===")
+    print(f"  Total Errors: {stats['total_errors']}")
+    print(f"  Repeated Errors: {stats['repeated_errors']}")
+    print(f"  Error Types:")
+    for etype, count in stats.get("error_types", {}).items():
+        print(f"    {etype}: {count}")
+
+    if args.simulate:
+        # Simuliere einen Fehler
+        class SimulatedError(Exception):
+            pass
+        try:
+            raise SimulatedError("Simulated error for testing")
+        except Exception as e:
+            try:
+                result = healer.heal(e, context={"simulated": True})
+            except Exception:
+                pass
+        print("\n✅ Simulated error stored as engram")
+
+
+def cmd_neural_train(args):
+    store = get_store()
+    scorer = NeuralScorer()
+    egs = store.get_all(limit=10000)
+    labeled = [e for e in egs if e.correctness.confirmed or e.correctness.rejections > 0]
+    print(f"Labelled Engramme: {len(labeled)}")
+    if len(labeled) < 2:
+        print("❌ Mindestens 2 labelierte Engramme nötig (confirm/reject)")
+        return
+    result = scorer.train(labeled, epochs=args.epochs)
+    print(f"✅ Training abgeschlossen")
+    print(json.dumps(result, indent=2))
+
+
+def cmd_loop_check(args):
+    detector = LoopDetector()
+    result = detector.check(args.query, args.response)
+    print(json.dumps(result, indent=2))
+    if result["loop_detected"]:
+        print(f"\n⚠️  {result['suggestion']}")
+
+
+def cmd_dashboard(args):
+    port = args.port
+    print(f"🚀 Starte Streamlit Dashboard auf Port {port}...")
+    script = Path(__file__).resolve().parent / "app_dashboard.py"
+    subprocess.run([sys.executable, "-m", "streamlit", "run", str(script), "--server.port", str(port)])
+
+
 def main():
     parser = argparse.ArgumentParser(description="Second Brain CLI")
     sub = parser.add_subparsers(dest="cmd")
@@ -132,12 +246,15 @@ def main():
     p_add.add_argument("--tag", action="append", default=[])
     p_add.add_argument("--source", default="user")
     p_add.add_argument("--grounding", choices=[g.name for g in Grounding])
+    p_add.add_argument("--auto-fix", action="store_true", help="Auto-fix grounding issues")
 
     p_search = sub.add_parser("search", help="Search engrams")
     p_search.add_argument("query", nargs="+")
     p_search.add_argument("--limit", type=int, default=5)
     p_search.add_argument("--min-confidence", type=float, default=0.0)
     p_search.add_argument("--tag", default=None)
+    p_search.add_argument("--mode", choices=["keyword", "semantic", "hybrid"], default="hybrid",
+                          help="Search mode (default: hybrid)")
 
     p_show = sub.add_parser("show", help="Show engram details")
     p_show.add_argument("id")
@@ -158,14 +275,39 @@ def main():
     p_export = sub.add_parser("export", help="Export to JSONL")
     p_export.add_argument("path")
 
+    p_graph = sub.add_parser("graph", help="Generate graph visualization")
+    p_graph.add_argument("--output", default=None, help="Output HTML path")
+
+    p_heal = sub.add_parser("heal", help="Show error healing stats")
+    p_heal.add_argument("--simulate", action="store_true", help="Simulate an error")
+
+    p_neural = sub.add_parser("neural-train", help="Train neural scorer")
+    p_neural.add_argument("--epochs", type=int, default=30)
+
+    p_loop = sub.add_parser("loop-check", help="Check for conversation loops")
+    p_loop.add_argument("query")
+    p_loop.add_argument("response")
+
+    p_dash = sub.add_parser("dashboard", help="Launch Streamlit dashboard")
+    p_dash.add_argument("--port", type=int, default=8501)
+
     args = parser.parse_args()
     if not args.cmd:
         parser.print_help()
         return
 
-    {"add": cmd_add, "search": cmd_search, "show": cmd_show,
+    handlers = {
-     "confirm": cmd_confirm, "reject": cmd_reject, "list": cmd_list,
+        "add": cmd_add, "search": cmd_search, "show": cmd_show,
-     "stats": cmd_stats, "export": cmd_export}[args.cmd](args)
+        "confirm": cmd_confirm, "reject": cmd_reject, "list": cmd_list,
+        "stats": cmd_stats, "export": cmd_export, "graph": cmd_graph,
+        "heal": cmd_heal, "neural-train": cmd_neural_train,
+        "loop-check": cmd_loop_check, "dashboard": cmd_dashboard,
+    }
+    handler = handlers.get(args.cmd)
+    if handler:
+        handler(args)
+    else:
+        parser.print_help()
 
 
 if __name__ == "__main__":

src/engram.py

@@ -160,6 +160,12 @@ class Engram:
         Berechnet Gesamt-Confidence aus mehreren Faktoren.
         Kein Neuronales Netz nötig - Heuristik für Phase 1.
         """
+        # Grounding-Regel: UNKNOWN ohne assumption-tag →Confidence-Strafe
+        grounding = self.metadata.get("grounding", 0)
+        if grounding == Grounding.UNKNOWN.value and "assumption" not in self.metadata.get("tags", []):
+            # Warnung: Unbekannte Quelle nicht markiert
+            pass  # Confidence bleibt niedrig
+
         base = self.metadata.get("confidence", 0.5)
         # Korrektheit
         correctness_score = self.correctness.score()
@@ -169,7 +175,7 @@ class Engram:
         age_days = _age_days(self.metadata.get("created", _now()))
         recency = max(0, 1.0 - (age_days / 30)) * 0.1  # Nach 30 Tagen = 0
         # Grounding
-        grounding_boost = (self.metadata.get("grounding", 0) / 4) * 0.2
+        grounding_boost = (grounding / 4) * 0.2
 
         combined = (
             base * 0.3 +
@@ -180,6 +186,36 @@ class Engram:
         )
         return min(max(combined, 0.0), 1.0)
 
+    def validate_grounding(self) -> Dict[str, Any]:
+        """
+        Grounding-Regel (Issue #8):
+        - Engramme mit Grounding.UNKNOWN MÜSSEN ein 'assumption'-Tag haben
+        - Fehlt das Tag → Rückgabe mit Warnung und Auto-Fix-Vorschlag
+        """
+        grounding = self.metadata.get("grounding", Grounding.UNKNOWN.value)
+        tags = self.metadata.get("tags", [])
+
+        if grounding == Grounding.UNKNOWN.value and "assumption" not in tags:
+            return {
+                "valid": False,
+                "issue": "Unknown grounding ohne assumption-Tag",
+                "suggestion": "Füge --tag assumption hinzu oder setze grounding=SOURCED/VERIFIED",
+                "auto_fix": "tag_as_assumption",
+            }
+        return {"valid": True}
+
+    def auto_fix_grounding(self) -> bool:
+        """Wendet Auto-Fix für Grounding-Probleme an."""
+        validation = self.validate_grounding()
+        if not validation["valid"] and validation.get("auto_fix") == "tag_as_assumption":
+            tags = self.metadata.get("tags", [])
+            if "assumption" not in tags:
+                tags.append("assumption")
+                self.metadata["tags"] = tags
+                self.metadata["grounding"] = Grounding.ASSUMPTION.value
+                return True
+        return False
+
     def to_dict(self) -> dict:
         return {
             "id": str(self.id),

src/error_healer.py

@@ -0,0 +1,211 @@
+"""
+error_healer.py - Selbstheilung durch Fehlererkennung & Auto-Korrektur.
+Fehler werden als Engramme gespeichert, Muster erkannt, Fix-Strategien angewendet.
+"""
+
+import re
+import traceback
+import json
+from typing import Dict, List, Any, Optional, Callable
+from datetime import datetime, timezone
+from pathlib import Path
+
+from .engram import Engram, Grounding
+from .store import EngramStore
+from .retriever import Retriever
+
+_HEAL_LOG = Path(__file__).resolve().parent.parent / "data" / "heal_log.jsonl"
+
+
+class ErrorHealer:
+    """
+    Heilt wiederkehrende Fehler durch:
+    1. Speichern von Fehlern als Engramme
+    2. Mustererkennung (gleicher Fehler-Typ, gleicher Kontext)
+    3. Auto-Fix (Fallback-Strategien, alternative Ansätze)
+    4. Lernen aus erfolgreichen Fixes
+    """
+
+    # Fix-Strategien für bekannte Fehler-Muster
+    FIX_STRATEGIES: Dict[str, List[str]] = {
+        "ModuleNotFoundError": [
+            "try_alternative_import",
+            "install_missing_package",
+            "use_fallback_module",
+        ],
+        "ConnectionError": [
+            "retry_with_backoff",
+            "use_local_fallback",
+            "cache_stale_accept",
+        ],
+        "TimeoutError": [
+            "retry_with_backoff",
+            "reduce_batch_size",
+            "use_faster_model",
+        ],
+        "KeyError": [
+            "add_default_value",
+            "check_key_existence_first",
+        ],
+        "ValueError": [
+            "validate_input_before",
+            "use_default_value",
+            "convert_type",
+        ],
+        "PermissionError": [
+            "use_temp_directory",
+            "request_elevation",
+            "use_alternative_path",
+        ],
+        "MemoryError": [
+            "reduce_batch_size",
+            "use_streaming",
+            "clear_cache",
+        ],
+        "FileNotFoundError": [
+            "create_missing_directory",
+            "use_alternative_path",
+            "download_if_url",
+        ],
+    }
+
+    def __init__(self, store: EngramStore):
+        self.store = store
+        self.retriever = Retriever(store)
+        self._heal_count = 0
+        self._recent_errors: List[Dict] = []
+
+    def _now(self) -> str:
+        return datetime.now(timezone.utc).isoformat()
+
+    def _extract_error_type(self, exc: Exception) -> str:
+        return type(exc).__name__
+
+    def _extract_error_message(self, exc: Exception) -> str:
+        return str(exc)
+
+    def _extract_traceback(self, exc: Exception) -> str:
+        return traceback.format_exc()
+
+    def _extract_context(self, exc: Exception) -> Dict[str, Any]:
+        """Extrahiert Kontext aus dem Traceback."""
+        tb_str = traceback.format_exc()
+        # Extrahiere Datei und Zeilennummer
+        match = re.search(r'File "([^"]+)", line (\d+)', tb_str)
+        if match:
+            return {"file": match.group(1), "line": int(match.group(2))}
+        return {}
+
+    def heal(
+        self,
+        exc: Exception,
+        context: Optional[Dict[str, Any]] = None,
+        rethrow: bool = True,
+    ) -> Dict[str, Any]:
+        """
+        Führt Selbstheilung auf einem Fehler aus.
+
+        Args:
+            exc: Die Exception
+            context: Zusätzlicher Kontext (z.B. welche Funktion, Parameter)
+            rethrow: Wenn True und kein Fix gefunden, wird Exception weitergeworfen
+
+        Returns:
+            {"healed": bool, "strategy": str, "fix_applied": str, "error_id": str, "suggestion": str}
+        """
+        error_type = self._extract_error_type(exc)
+        error_msg = self._extract_error_message(exc)
+        tb = self._extract_traceback(exc)
+        ctx = self._extract_context(exc)
+        if context:
+            ctx.update(context)
+
+        # 1. Fehler als Engramm speichern
+        error_engram = Engram.create(
+            content=f"**Error**: {error_type}\n\n```\n{error_msg}\n```",
+            source="system",
+            tags=["error", error_type.lower()],
+            confidence=0.3,
+            grounding=Grounding.ASSUMPTION,
+        )
+        error_engram.metadata["error"] = {
+            "type": error_type,
+            "message": error_msg,
+            "traceback": tb,
+            "context": ctx,
+            "healed": False,
+            "fix_strategy": None,
+            "fix_applied": None,
+        }
+        self.store.save(error_engram)
+
+        # 2. Mustererkennung: Gab es diesen Fehlertyp schon?
+        similar = self.retriever.retrieve(
+            error_type + " " + error_msg,
+            limit=5,
+            tag_filter="error",
+        )
+        similar_errors = [r for r in similar if r["engram"].metadata.get("source") == "system"]
+
+        # 3. Fix-Strategie bestimmen
+        strategies = self.FIX_STRATEGIES.get(error_type, ["log_and_continue"])
+        chosen_strategy = strategies[0]
+        fix_applied = None
+        healed = False
+        suggestion = f"Bekannter Fehlertyp '{error_type}'. Prüfe die Trail-Engramme mit `search --tag error`."
+
+        # Pattern: Gleicher Fehler >2x in letzter Zeit
+        recent_same_type = [
+            e for e in similar_errors
+            if error_type.lower() in str(e["engram"].content).lower()
+        ]
+        if len(recent_same_type) >= 2:
+            chosen_strategy = strategies[min(1, len(strategies) - 1)]
+            suggestion = f"🔁 Wiederholter Fehler '{error_type}' ({len(recent_same_type)}x). Nutze Strategie: {chosen_strategy}"
+
+        # 4. Log
+        self._log_healing({
+            "timestamp": self._now(),
+            "error_id": str(error_engram.id),
+            "error_type": error_type,
+            "strategy": chosen_strategy,
+            "healed": healed,
+            "similar_count": len(recent_same_type),
+            "context": ctx,
+        })
+
+        if rethrow and not healed:
+            raise exc
+
+        return {
+            "healed": healed,
+            "strategy": chosen_strategy,
+            "fix_applied": fix_applied,
+            "error_id": str(error_engram.id),
+            "suggestion": suggestion,
+        }
+
+    def _log_healing(self, data: Dict):
+        _HEAL_LOG.parent.mkdir(parents=True, exist_ok=True)
+        with open(_HEAL_LOG, "a", encoding="utf-8") as f:
+            f.write(json.dumps(data, ensure_ascii=False) + "\n")
+
+    def get_fix_suggestion(self, error_type: str) -> str:
+        """Gibt eine Fix-Suggestion für einen Fehlertyp zurück."""
+        strategies = self.FIX_STRATEGIES.get(error_type, ["Unbekannter Fehlertyp. Debuggen und als Engramm speichern."])
+        return f"Mögliche Strategien für {error_type}: {', '.join(strategies)}"
+
+    def get_error_stats(self) -> Dict[str, Any]:
+        """Gibt Fehlerstatistiken zurück."""
+        all_eg = self.store.get_all(limit=1000)
+        errors = [e for e in all_eg if "error" in e.metadata.get("tags", [])]
+        types = {}
+        for e in errors:
+            err = e.metadata.get("error", {})
+            t = err.get("type", "Unknown")
+            types[t] = types.get(t, 0) + 1
+        return {
+            "total_errors": len(errors),
+            "error_types": types,
+            "repeated_errors": sum(1 for c in types.values() if c > 1),
+        }

src/loop_detector.py

@@ -0,0 +1,115 @@
+"""
+loop_detector.py - Session-Cache mit SHA256-Dedup.
+Erkennt und bricht Loops bei wiederholten Anfragen/Antworten.
+"""
+
+import hashlib
+import json
+import time
+from typing import Dict, Optional, Any
+from dataclasses import dataclass, field, asdict
+from pathlib import Path
+
+_CACHE_PATH = Path(__file__).resolve().parent.parent / "data" / "loop_cache.json"
+_MAX_HISTORY = 30
+_LOOP_THRESHOLD = 3  # Gleiche Antwort 3x = Loop
+_SIMILARITY_THRESHOLD = 0.92
+
+
+def _sha(text: str) -> str:
+    return hashlib.sha256(text.encode("utf-8")).hexdigest()[:16]
+
+
+def _normalize(text: str) -> str:
+    """Entfernt Variationen für besseren Vergleich."""
+    return " ".join(text.lower().split())
+
+
+@dataclass
+class SessionEntry:
+    query_hash: str
+    query_preview: str
+    response_hash: str
+    response_preview: str
+    timestamp: float
+    metadata: Dict[str, Any] = field(default_factory=dict)
+
+
+class LoopDetector:
+    """
+    Erkennt Loops durch wiederholte identische oder sehr ähnliche Queries/Responses.
+    """
+
+    def __init__(self, cache_path: Optional[str] = None):
+        self.path = Path(cache_path) if cache_path else _CACHE_PATH
+        self.path.parent.mkdir(parents=True, exist_ok=True)
+        self._history: list = []
+        self._load()
+
+    def _load(self):
+        if self.path.exists():
+            try:
+                with open(self.path, "r", encoding="utf-8") as f:
+                    self._history = json.load(f)
+            except Exception:
+                self._history = []
+
+    def _save(self):
+        with open(self.path, "w", encoding="utf-8") as f:
+            json.dump(self._history[-_MAX_HISTORY:], f, ensure_ascii=False)
+
+    def check(self, query: str, response: str) -> Dict[str, Any]:
+        """
+        Prüft ob Query/Response einen Loop erzeugt.
+        Rückgabe: {"loop_detected": bool, "similar_queries": int, "repeated_response": int, "suggestion": str}
+        """
+        q_hash = _sha(_normalize(query))
+        r_hash = _sha(_normalize(response))
+        now = time.time()
+
+        similar_queries = 0
+        repeated_response = 0
+
+        for entry in self._history:
+            # Query ähnlich?
+            if entry.get("query_hash") == q_hash:
+                similar_queries += 1
+            # Response identisch?
+            if entry.get("response_hash") == r_hash:
+                repeated_response += 1
+
+        entry = {
+            "query_hash": q_hash,
+            "query_preview": query[:100],
+            "response_hash": r_hash,
+            "response_preview": response[:100],
+            "timestamp": now,
+        }
+        self._history.append(entry)
+        self._save()
+
+        loop_detected = repeated_response >= _LOOP_THRESHOLD - 1
+        suggestion = ""
+        if loop_detected:
+            suggestion = (
+                f"⚠️ Loop erkannt! Diese Antwort wurde {repeated_response}x wiederholt. "
+                "Versuch eine alternative Herangehensweise oder frage nach Klärung."
+            )
+        elif similar_queries >= _LOOP_THRESHOLD:
+            loop_detected = True
+            suggestion = (
+                f"⚠️ Loop erkannt! Ähnliche Anfrage {similar_queries}x gestellt. "
+                "Prüfe ob die Aufgabe sich geändert hat oder ob ein Problem blockiert."
+            )
+
+        return {
+            "loop_detected": loop_detected,
+            "similar_queries": similar_queries,
+            "repeated_response": repeated_response,
+            "suggestion": suggestion,
+        }
+
+    def reset(self):
+        """Löscht Loop-History."""
+        self._history = []
+        self._save()