Otto
0ff6db73ea
- FastAPI: parse_engram now includes link_suggestions and predictive_links from metadata - FastAPI: add POST /api/links/accept to create links from suggestions - Dashboard: new renderCardsWithSuggestions() displays suggestions in each card - Dashboard: acceptLink() function handles click-to-link - Dashboard: loadCards() calls renderCardsWithSuggestions() - Systemd: remove DirectoryNotEmpty from ingest path unit (already present) Refs: #25 #27
104 lines
3.2 KiB
Python
104 lines
3.2 KiB
Python
#!/usr/bin/env python3
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"""
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Importiert abgeschlossene Topics aus context-buffer/ als Engramme.
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Ein Topic gilt als abgeschlossen, wenn es den Status 'done' oder 'completed' hat.
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"""
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from __future__ import annotations
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import json
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import sqlite3
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import subprocess
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import sys
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from datetime import datetime, timezone
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from pathlib import Path
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BRAIN_DIR = Path("/root/.openclaw/workspace/second-brain")
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WORKSPACE = Path("/root/.openclaw/workspace")
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CURRENT_DIR = WORKSPACE / "context-buffer" / "current"
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def run():
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# Lese context-buffer index.json direkt
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index_path = WORKSPACE / "context-buffer" / "index.json"
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try:
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with open(index_path) as f:
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idx = json.load(f)
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topics = []
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for tid, t in idx.get("topics", {}).items():
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status = t.get("status", "active")
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if status in ("done", "completed"):
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# Lade den vollen Inhalt aus der topic-Datei
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topic_file = CURRENT_DIR / f"topic-{tid}.md"
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if topic_file.exists():
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content = topic_file.read_text(encoding="utf-8")
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# Entferne Frontmatter für reinen Content
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if content.startswith("---"):
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parts = content.split("---", 2)
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if len(parts) >= 3:
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content = parts[2].strip()
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t["content"] = content
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else:
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t["content"] = ""
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topics.append(t)
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except Exception as e:
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print(json.dumps({"success": False, "error": str(e)}, indent=2, ensure_ascii=False))
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return
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if not topics:
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print(json.dumps({"success": True, "imported": 0, "message": "No completed topics found"}, indent=2, ensure_ascii=False))
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return
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# Import in Second Brain
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DB_PATH = BRAIN_DIR / "data" / "brain.sqlite"
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conn = sqlite3.connect(str(DB_PATH))
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conn.row_factory = sqlite3.Row
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c = conn.cursor()
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sys.path.insert(0, str(BRAIN_DIR))
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from src.store import EngramStore
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from src.engram import Engram, Grounding
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store = EngramStore(str(DB_PATH))
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imported = 0
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for topic in topics:
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topic_id = topic.get("id")
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title = topic.get("title", "Untitled Topic")
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content = topic.get("content", "")
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if not content.strip():
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continue
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# Tags aus topic-type und status
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tags = ["context-buffer", topic.get("status", "unknown")]
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if topic.get("type"):
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tags.append(topic["type"])
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eg = Engram.create(
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content=content,
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source="context-buffer",
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tags=tags,
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grounding=Grounding.ASSUMPTION,
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)
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eg.metadata.update({
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"title": title,
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"context_buffer_id": topic_id,
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"imported_from": "context-buffer",
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"original_status": topic.get("status"),
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})
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store.save(eg)
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imported += 1
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conn.close()
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print(json.dumps({
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"success": True,
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"time": datetime.now(timezone.utc).isoformat(),
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"topics_found": len(topics),
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"imported": imported,
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}, indent=2, ensure_ascii=False))
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if __name__ == "__main__":
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import sys
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sys.path.insert(0, str(BRAIN_DIR))
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run()
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