bvandeusen
322cbc3b5e
#755 Phase 5. create_note / create_task now BLOCK a near-duplicate instead of silently inserting: they return {"duplicate": true, "existing_id", message} pointing at the record to UPDATE. Fights store bloat and stale competing copies that semantic search (RAG) would otherwise resurface for reconciliation. A force=true override creates anyway for genuinely-distinct records. - services/dedup.py: find_duplicate_note — two signals, scoped to owner + same project + same kind: (1) normalized-title exact match (cheap, always); (2) semantic cosine ≥ 0.90 but ONLY when body ≥ 200 chars (short/title-only embeddings false-positive — the pre-pivot lesson). Project-less (orphan) records compare only to other orphans on BOTH signals (orphan_only on the semantic call) — they're not matched across every project. - Gate wired into the MCP create_note/create_task tools (the LLM write path) with force override; _INSTRUCTIONS documents the duplicate response + force. - Opt-in by design: the service helper is only called from the interactive create tools. Internal/programmatic creates (recurrence spawn, imports) go straight through services.create_note and are NOT gated — a recurring task spawning its next same-titled instance must not be blocked. - Scope v1: MCP tools only. REST/web (human CRUD, needs a UI affordance) and create_rule (not a RAG surface; _INSTRUCTIONS already steer it) are follow-ups. - tests: dedup service (title/semantic/body-gate/type-filter) + tool gate (blocks, force bypasses) for notes and tasks. Co-Authored-By: Claude Opus 4.8 (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, and an MCP server for external AI clients.
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 |
License
This project is privately maintained.