bvandeusen
a7002a89a0
Backend half of the conversation+curator architecture (Fable #172). Decouples the journal chat surface from tool calling: the chat model now sees `tools=[]` and just talks, while a separate curator pass extracts beats and fires the tool calls. services/generation_task.py: - When conversation_type == "journal", pass `tools=[]` to Ollama regardless of what the journal tool set would normally provide. The chat model literally cannot fire record_moment / create_task / etc., so it cannot lie about firing them — the primary failure mode this architecture removes. services/curator.py (new): - `run_curator_for_conversation(conv_id, since=None)` loads recent messages, builds a curator-specific system prompt (extract beats, emit tool calls, optionally a one-line summary), and iterates the Ollama tool-call loop using the user's background_model so the chat model's KV cache survives. - Same tool registry as a normal journal conversation (record_moment, search_notes, update_task, create_task, save_person, save_place, etc.). The curator chooses naturally among them; no need for a separate curator-specific filter. - Returns CuratorRunResult with per-call status + a summary line. - Caps at 4 tool-call rounds — bounded task (extract beats from a fixed transcript), shouldn't need more. - Errors land in result.error rather than raising; the manual trigger surface (and later the scheduler) want a structured result, not exceptions. routes/journal.py: - New POST /api/journal/curator/run/<conv_id> for manual triggers. Validates conv ownership before running. Returns the CuratorRunResult dict so the UI can show what was captured. What's not in this commit (deferred to later phases): - The scheduler that auto-runs the curator (phase 2 — adds the `conversations.last_curator_run_at` column + APScheduler job). - Curator → chat feedback loop (phase 3 — summary gets injected into subsequent chat system prompts). - Right-rail captures panel in JournalView (phase 1b — pure frontend work, separate commit for clean review). - Research surface separation (phase 4). 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.