moteva/README.md

# img-infinite-canvas

Moteva 风格的 AI 设计工作台复刻 MVP：主页 Prompt 创建项目，项目页提供无限画布、节点拖拽、缩放平移、右侧设计对话、历史回放、图片与 DWG/DXF 图纸导入、项目保存与再生成。

## Stack

- Backend: go-zero, DDD layering, sqlc, pgx, PostgreSQL optional persistence
- Cache: memory or Redis, default memory
- Agent: sky-valley/pi Go agent runtime adapter, local desktop agent disabled
- Image storage: memory, MinIO, AWS S3, Cloudflare R2, Aliyun OSS
- Frontend: Next.js App Router, pnpm, Vite artifact build, TypeScript, gzip, i18n
- Deploy: local binary, Docker Compose, k3s manifests

## Run Locally

Backend:

```bash
cd server
go mod tidy
go run . -f etc/config.yaml
```

Frontend:

```bash
cd frontend
pnpm install
pnpm dev
```

Open `http://localhost:5173`. Next.js rewrites `/api` to `http://localhost:8888` in development. `pnpm build` runs `next build` and `vite build`, producing gzip assets in `frontend/dist`.

## CAD Import

The canvas asset picker and drag-and-drop surface accept DWG and DXF files up to 24 MB. Conversion runs locally in a browser worker, uploads a sanitized derived SVG, and leaves the source file unchanged. DXF supports ASCII and binary drawings; DWG supports AutoCAD R14 through AutoCAD 2018 format families.

Derived CAD SVGs retain bounded semantic metadata for drawing units, layers, reusable blocks, entity types, visible labels, and crop-indexed bounds. Agent Chat sends this metadata as hidden reference context while rasterizing only the model-bound image input to transparent WebP. Vector crop keeps the original SVG and creates a smaller transparent WebP sibling beside it with crop-specific semantic context, ready for follow-up questions in Agent Chat.

## Cache Config

Default in-memory cache:

```yaml
Cache:
  Driver: memory
  TTLSeconds: 300
```

Redis cache:

```yaml
Cache:
  Driver: redis
  Addr: localhost:6379
  Password: ""
  DB: 0
  TTLSeconds: 300
```

## Storage Config

Default memory storage is useful for local UI work:

```yaml
Storage:
  Driver: memory
```

## Image Object Storage

Default memory mode is useful for local prototyping. Production can use MinIO, AWS S3, Cloudflare R2, or Aliyun OSS:

```yaml
ObjectStorage:
  Driver: minio # memory|minio|s3|aws|r2|aliyun
  Bucket: canvas-assets
  Endpoint: localhost:9000
  Region: us-east-1
  AccessKey: canvas
  SecretKey: canvas-secret
  PublicBase: http://localhost:9000/canvas-assets
  UseSSL: false
  ExpiresIn: 900
```

PostgreSQL storage:

```yaml
Storage:
  Driver: postgres
  DataSource: postgres://canvas:canvas@localhost:5432/canvas?sslmode=disable
```

## Docker Compose

```bash
cd deploy
docker compose up --build
```

Open `http://localhost:3000`.

## k3s

Build and load/push these images first:

```bash
docker build -t img-infinite-canvas-api:latest server
docker build -t img-infinite-canvas-web:latest frontend
kubectl apply -f deploy/k3s/img-infinite-canvas.yaml
```

The sample ingress host is `canvas.local`.

## API

See [server/API.md](server/API.md).