bvandeusen
bf7a29e8a0
Adds an empirical surface for evaluating model swaps. One row per assistant turn captures: model, think_enabled, tools_available, tools_attempted, tools_succeeded, tools_failed (with error details as JSONB). Without this, judging whether a new model "actually fires record_moment when it should" relies on anecdote across user-reported sessions. With it, the data is queryable directly. Pieces: - Migration 0046: generation_tool_log table with user_created and per-conversation indexes. - Model: SQLAlchemy GenerationToolLog with to_dict() for plain-dict consumption outside session scope. - Service: log_tool_outcomes() normalizes the in-app tool-call shape (function/result/status) into the split buckets and persists. It catches its own exceptions — telemetry failure must NEVER affect the user-facing generation flow. recent_logs() helper for read. - Integration in run_generation: called once per turn right after log_generation, fire-and-forget. - Tests: pure-normalization unit tests using a stub session — no DB needed in CI. Cover the success/error split, the empty-tool-calls case, the exception-swallowing contract, and the success=False edge case where status incorrectly says "success". No UI for the telemetry yet — internal infrastructure (the operator is the consumer, not the journal user), which the FabledRulebook "no UI no ship" explicitly excepts. Query via psql or extend the Fable MCP later if direct shell access gets tiresome. 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.