bvandeusen 7861607fb8
CI & Build / Python lint (push) Successful in 3s
CI & Build / TypeScript typecheck (push) Successful in 39s
CI & Build / Python tests (push) Successful in 43s
CI & Build / Build & push image (push) Successful in 1m18s
feat(rules): project rule + topic suppressions
Lets a project mute individual rules or whole topics from rulebooks it
subscribes to, without unsubscribing the rulebook. Two new association
tables (migration 0060), 4 MCP tools (suppress/unsuppress × rule/topic),
4 REST endpoints, and an inline "× skip" affordance plus collapsed
"Suppressed (N)" section in the project's Rules tab.

get_applicable_rules now emits suppressed_rules and suppressed_topics
(detail objects with rulebook/topic context, not just IDs) so the UI
can render the suppressed list without a follow-up lookup. The main
rules projection grew topic_id and rulebook_id columns for the per-row
suppress affordance.

Project deletion cascades the suppression rows via hard DELETE — they
are pure associations with no soft-delete column, and restoring a
deleted project should start fresh, not inherit stale mutes.

Project-scoped rules (Rule.project_id) are deliberately not suppressible
— delete them with delete_rule instead.

Implements plan-task #187.

Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
2026-06-01 02:26:20 -04:00

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, and an MCP server for external AI clients.

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.yml to 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

License

This project is privately maintained.

S
Description
No description provided
Readme 14 MiB
v26.06.03 Latest
2026-06-03 12:51:04 -04:00
Languages
Python 53.7%
Vue 38.1%
TypeScript 5.8%
CSS 1.4%
Shell 0.8%