Files
taskpile/docs/guide/getting-started.md
Alvis 9b77d6ea67 Add MLOps feature store, fix UI layout, add docs and Gitea remote
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>
2026-04-10 06:16:28 +00:00

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1.4 KiB
Markdown

# 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
```bash
# 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
```bash
# 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
```bash
# Backend (Rust)
cd backend && cargo test
# Frontend (Jest)
cd frontend && npx jest
```