9b77d6ea67
Backend: - Replace on-the-fly Ollama calls with versioned feature store (task_features, task_edges) - Background Tokio worker drains pending rows; write path returns immediately - MLConfig versioning: changing model IDs triggers automatic backfill via next_stale() - AppState with FromRef; new GET /api/ml/status observability endpoint - Idempotent mark_pending (content hash guards), retry failed rows after 30s - Remove tracked build artifacts (backend/target/, frontend/.next/, node_modules/) Frontend: - TaskItem: items-center alignment (fixes checkbox/text offset), break-words for overflow - TaskDetailPanel: fix invisible AI context (text-gray-700→text-gray-400), show all fields - TaskDetailPanel: pending placeholder when latent_desc not yet computed, show task ID - GraphView: surface pending_count as amber pulsing "analyzing N tasks…" hint in legend - Fix Task.created_at type (number/Unix seconds, not string) - Auth gate: LoginPage + sessionStorage; fix e2e tests to bypass gate in jsdom - Fix deleteTask test assertion and '1 remaining'→'1 left' stale text Docs: - VitePress docs in docs/ with guide, MLOps pipeline, and API reference Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
1.4 KiB
1.4 KiB
Getting Started
Prerequisites
| Tool | Version | Notes |
|---|---|---|
| Rust | ≥ 1.78 | rustup update stable |
| Node.js | ≥ 20 | For the frontend |
| Ollama | any | ollama pull nomic-embed-text && ollama pull qwen2.5:1.5b |
Port note — Port 3000 is used by Gitea on this machine. The frontend runs on 3003; the backend on 3001.
Running locally
# 1. Backend (Rust + SQLite)
cd backend
cargo run
# → Listening on http://0.0.0.0:3001
# 2. Frontend (Next.js)
cd frontend
npm install
npm run dev -- -p 3003
# → http://localhost:3003
The backend auto-creates taskpile.db and runs schema migrations on startup. It also seeds task_features pending rows for any existing task that doesn't have embeddings yet, then wakes the ML worker to process them.
First login
The default credentials are admin / VQ7q1CzFe3Y (configured via ValidateRequestHeaderLayer::basic in backend/src/main.rs).
Verifying the ML pipeline
# Check ML status (requires auth)
curl -u admin:VQ7q1CzFe3Y --noproxy '*' http://localhost:3001/api/ml/status | jq
You should see pending ticking down toward 0 as the worker processes tasks. Once ready matches your task count, edges will appear in the graph.
Running tests
# Backend (Rust)
cd backend && cargo test
# Frontend (Jest)
cd frontend && npx jest