bvandeusen
80f30b705d
Data model: - Migration 0036: adds note_type TEXT (default 'note') and metadata JSONB to the notes table; index on note_type - Note model: entity_type property, note_type/metadata in to_dict() - create_note() accepts note_type and metadata params Backend: - /api/knowledge — unified paginated endpoint: type/tag/sort/q filters, semantic search via embeddings, excludes tasks - /api/knowledge/tags — distinct tags across knowledge objects - New LLM tools: create_person, create_place, create_list, add_to_list, clear_checked_items — all wired into execute_tool() - People and places auto-injected as compact summary into LLM system prompt Frontend: - KnowledgeView replaces HomeView at /; left filter panel (type+tag), toolbar (search, sort, graph toggle), card grid with type-aware cards (indigo=note, emerald=person, amber=place, sky=list), load-more pagination - Today bar: upcoming events, overdue task count, Briefing/Chat links - Floating mini-chat sticky to bottom: creates/continues a conversation inline, message history expands upward, close button ends session - Graph panel: toggles as a 420px right panel at full viewport width - AppHeader: Knowledge, Chat, Briefing, Calendar, Tasks, Projects - Router: / → KnowledgeView; /knowledge redirect; HomeView import removed Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Fabled Assistant
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.