bvandeusen
83f1676d72
The curator now runs automatically every 15 minutes against any journal conversation that has user messages newer than its last curator run. Manual triggers from Phase 1b still work and now also stamp the timestamp so the scheduler doesn't double-process. Migration 0048: - conversations.last_curator_run_at (timestamptz, nullable). - Partial index ix_conversations_journal_last_curator on the column filtered to conversation_type='journal'. The scheduler's candidate query is "journal AND (NULL OR stale)" so an index narrowed to journal rows is the right shape — index size stays small even on instances with many non-journal conversations. models/conversation.py: - New `last_curator_run_at` column on Conversation. DateTime imported. services/curator_scheduler.py (new): - IntervalTrigger every 15 min via BackgroundScheduler (same pattern as journal_scheduler.py). - _candidate_conversations(): SELECT journal conversations where the newest user message is newer than last_curator_run_at (or NULL). Capped at 20 per sweep so a backlog after downtime doesn't stall the scheduler. - _sweep() processes candidates sequentially under an asyncio.Lock so overlapping ticks can't double-fire on the same conversation. Failed runs leave the timestamp alone — natural retry on next sweep. - start_/stop_curator_scheduler() wired into app.py boot/shutdown. routes/journal.py: - Manual /api/journal/curator/run/<conv_id> stamps last_curator_run_at on success. Errors don't stamp so the scheduler retries. What's still pending: - Phase 3: feedback loop (curator summary into chat context). Currently the curator's summary lives in the run result but doesn't reach the chat model. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Fabled Scribe
A self-hosted second brain and project management application with integrated LLM capabilities. Write, organise, and act on your notes and tasks with the help of a local AI assistant — all running on your own hardware.
Features
Notes and tasks with a Markdown editor, sub-tasks, milestones, and kanban project workspaces. AI chat with streaming responses, RAG over your notes, and tool use (web search, calendar, weather). A daily briefing that digests your tasks, RSS feeds, and weather on a schedule. Knowledge graph, per-user/group sharing, PWA with push notifications, an MCP server for external AI clients, and an Android companion app.
Quick Start
Prerequisites: Docker and Docker Compose. 8 GB+ RAM recommended for LLM inference.
Download docker-compose.quickstart.yml from this repo, then:
# Optional but recommended — set a secret key
export SECRET_KEY=your-random-secret-here
docker compose -f docker-compose.quickstart.yml up -d
Open http://localhost:5000. The first user to register becomes admin. Go to Settings → General to pull an LLM model — qwen3:8b or llama3.1:8b are good starting points.
GPU: Ollama runs CPU-only by default. See the comments in
docker-compose.quickstart.ymlto enable NVIDIA GPU passthrough.
Development: To build from source, see Development.
Documentation
| Doc | Contents |
|---|---|
| Architecture | Stack, design decisions, data models, key services |
| Configuration | Environment variables, Docker Compose, production setup, security |
| Features | Detailed feature breakdown and keyboard shortcuts |
| Development | Dev workflow, CI/CD, migrations, release process |
| API Keys & MCP | API key management and Fable MCP install guide |
| SSO / OAuth | OIDC setup for Authentik, Keycloak, and other providers |
| API Reference | All REST API endpoints |
| Android App | Flutter companion app architecture and feature status |
License
This project is privately maintained.