bvandeusen
dbd9f00061
Removes the entire RSS feature surface — feeds, items, embeddings, reactions, discussion-note flow, briefing news context, settings, env-vars, and DB tables. Keeps the URL-generic article-reader (the read_article LLM tool) under a clean module so the LLM can still fetch arbitrary article content from URLs the user provides. Backend: - New services/article_fetcher.py — single source of trafilatura URL→text - New services/tools/article.py — read_article tool (was nested under tools/rss) - Delete services/rss.py, rss_classifier.py, rss_filtering.py, article_context.py - Delete services/tools/rss.py - Delete models/rss_feed.py (RssFeed, RssItem), models/rss_item_embedding.py - services/embeddings.py: drop upsert/semantic_search/backfill RSS helpers - services/llm.py: remove _build_briefing_article_context, briefing-conv branch, ARTICLE_DISCUSS_SEED skip-RAG branch; drop get_rss_items / add_rss_feed from the actions list - services/generation_task.py: drop _maybe_save_article_discussion_note + caller - routes/chat.py: drop /api/chat/from-article/<id> endpoint - routes/journal.py: re-import via web.py refactor (article_fetcher path) - services/tools/__init__.py: register `article`, drop `rss` - services/tools/_registry.py: drop the requires=='rss' check - app.py: drop backfill_rss_item_embeddings + backfill_rss_article_content tasks - config.py: prose-only edit (no env var change — RSS env vars were never first-class) Frontend: - stores/settings.ts: drop rssEnabled - SettingsView.vue: drop the RSS-classification mention - api/client.ts: drop openArticleInChat (the from-article endpoint is gone) Tests: - Delete tests/test_rss_service.py, test_news_api.py, test_article_reading.py Migration: - 0042_drop_rss: DROP TABLE rss_item_embeddings, rss_item_reactions, rss_items, rss_feeds; DELETE settings rows for rss_enabled / briefing_*_topics 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.