Otto/second-brain
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

feat(complete): Phase 6 - Loop-Detector, Error-Healer, Grounding-Regel, erweiterte CLI

Browse Source
This commit is contained in:
Otto
2026-05-25 10:26:53 +02:00
parent d38f564445
commit 687f1df818
4 changed files with 520 additions and 16 deletions
Download Patch File Download Diff File Expand all files Collapse all files

172
src/cli.py
View File

@@ -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)
results = ret.retrieve(
" ".join(args.query),
limit=args.limit,
min_confidence=args.min_confidence,
tag_filter=args.tag,
)
print(f"\n=== {len(results)} Results ===")
chroma = get_chroma()
ret = Retriever(store, chroma)
mode = args.mode
if mode == "hybrid":
results = ret.hybrid_retrieve(
" ".join(args.query),
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,
"confirm": cmd_confirm, "reject": cmd_reject, "list": cmd_list,
"stats": cmd_stats, "export": cmd_export}[args.cmd](args)
handlers = {
"add": cmd_add, "search": cmd_search, "show": cmd_show,
"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__":