bvandeusen
4f18023284
Belt-and-suspenders to the prompt-layer changes in 6c309f1. Even when
the model emits bogus task or place links, the server now refuses to
persist them.
## Task auto-linking guard
Reproducer (2026-04-27): a moment about restaging Docker on the swarm
ended up with `task_ids: [2]` (Weston's ADHD Evaluation) — the only
task in that day's prep. The model picked it up as filler.
`_filter_task_ids_by_keyword_overlap` now runs after id resolution: it
fetches each linked task's title, tokenizes both content and title
through `_content_keywords` (lowercased, stopwords stripped, <3-char
tokens dropped), and drops any link whose title shares no meaningful
keyword with the moment content. The drop is logged at INFO so we can
observe how often it fires post-deploy.
The guard runs against the merged id list, so it covers both the
preferred `task_titles` resolution path and the discouraged explicit
`task_ids` path.
## Place placeholder guard
Reproducer (2026-04-27): `place_names=["work"]` got passed to
`record_moment`. "work" / "home" / "office" aren't places — they're
role-labels for already-known geocoded locations.
`_filter_placeholder_places` drops a small set of generic single-word
labels before name resolution. Real user-named places that happen to
be one word (e.g. "Akron") pass through.
## Tests
9 new unit tests in `tests/test_record_moment_guards.py` cover:
- keyword tokenization & stopword stripping
- placeholder place filtering (generic, case-insensitive, real-place
pass-through)
- keyword-overlap filtering (the exact 4/27 reproducer, the genuine-
reference case, mixed/partial relevance, empty input)
13 tests pass; ruff clean.
Closes Fable task #158.
Co-Authored-By: Claude Opus 4.7 <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.