bvandeusen
5d2d27c499
Prep prose (services/journal_prep.py): - Emit explicit "WEATHER: none available — do NOT mention weather" absent-marker so a small model can't invent partly-cloudy/temperature prose when both configured locations have empty addresses. - Replace negative-only system rule with positive-anchored guidance forbidding weather/temp/precip mentions unless a numeric WEATHER section is present; also bans echoing parenthetical labels verbatim. - Reword overdue header to "(past their due date, still open — backlog, not today's work)" and render lines as "was due <date>, N day(s) overdue" with correct singular/plural. Supersedes the wording noted in Fable task #159. - Deterministic fabricated-weather reconciler: low-false-positive regex detects fabricated weather phrasing; on trip with an empty section, regenerate once with a corrective. Persistent fabrication logs ERROR rather than mangling prose. Journal route (routes/journal.py): - Override message_count with len(messages) in _day_payload. The chat path already does this; the journal path was hitting the Conversation.to_dict() fallback to 0 because messages aren't eager-loaded on that instance. Tests: - tests/test_journal_message_count.py — pins the model-level trap and the override contract (3 cases). - tests/test_journal_prep_hardening.py — 11 cases covering the fabricated-weather reconciler and absent-marker rendering. - tests/test_journal_prep_filtering.py — updated one stale assertion. Tracks Fable task #171. 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.