Baserow

# MISSION: Scaffold "Baserow" Production Architecture
You are an expert Senior Staff Software Architect and Full-Stack Engineer. Your mission is to scaffold and implement a production-grade, highly reliable, and modular codebase following the proven architecture of **Baserow**.
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## 1. PROJECT SPECIFICATIONS & BENCHMARK
- **Reference Architecture**: Baserow
- **What It Does**: Baserow is an open-source no-code database and Airtable alternative for building relational databases, automations, and AI agents, offered as a hosted cloud product or self-hosted deployment.
- **Domain & Category**: No-Code Database Platform
- **Production Scale**: 6-20 people
- **Development Mode**: HYBRID
- **Architectural Rationale**: A Django/PostgreSQL core with Nuxt/Vue frontend layers lets Baserow add premium and enterprise features as separate installable packages on top of one open-source base, while Channels and Celery keep realtime sync and background work inside the same deployable unit.
- **Live Website Reference**: https://baserow.io
- **Source Repository**: https://github.com/bram2w/baserow
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## 2. PRODUCTION TECH STACK
- **Full Stack Array**: Python, Django, Django REST Framework, PostgreSQL, Vue, Nuxt, TypeScript, Celery, Redis, Django Channels, pgvector, Docker
- **Primary Language(s)**: Python, JavaScript, Vue
- **License of the reference repo**: NOASSERTION
- **Frontend**: Nuxt — Vue 4 meta-framework building the main web-frontend app, run through nuxt dev/build.; Vue — Component framework for the grid, form, and automation UI, shared by core, premium, and enterprise modules.; Tiptap — Rich text editor used for long-text fields and comments in the frontend.
- **Backend & APIs**: Django — Core web framework serving the backend/baserow app and its REST endpoints.; Django REST Framework — Builds the versioned REST API that the Nuxt frontend and public API clients consume.; Celery — Runs background jobs, exports, and scheduled tasks via django-celery-beat and celery-redbeat.; Django Channels — Serves websocket connections (with Daphne) for realtime table and automation updates.
- **Data & persistence**: PostgreSQL — Primary relational datastore for user data and Baserow application tables.; pgvector — Postgres extension used for embeddings in the AI agent and semantic search features.; Redis — Backs Celery broker/result storage and Django caching (django-redis, django-cachalot).
- **Infrastructure & deploy**: Docker — Ships as multi-stage images for backend, frontend, and an all-in-one Caddy-fronted bundle.; Caddy — Reverse proxy routing requests between backend and web-frontend in the standard compose setup.; Helm — Kubernetes chart config for deploying Baserow, listed alongside Heroku, Render, and Cloudron targets.
- **Tooling, testing & ops**: MCP — Model Context Protocol server dependency exposing Baserow data to AI agents.
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## 3. KEY ARCHITECTURAL DECISIONS (audited from https://github.com/bram2w/baserow @ 479e616)
1. **Backend is a Django monolith split into core, premium, and enterprise apps**: backend/pyproject.toml holds the open-source core, while premium/backend/pyproject.toml and enterprise/backend/pyproject.toml add paid features as separate installable Python packages layered on top of the same Django project.
2. **Frontend mirrors the backend split as three Nuxt/Vue workspaces**: web-frontend/package.json is the core Nuxt app; premium/web-frontend and enterprise/web-frontend hold matching Vue modules with their own vitest configs (test:premium, test:enterprise scripts), so paid UI ships as additive layers rather than forks.
3. **Realtime updates go through Django Channels and Daphne, not a separate service**: The channels[daphne] and channels-redis dependencies in backend/pyproject.toml let a single Django process serve both HTTP and websocket traffic, with Redis as the channel layer.
4. **Untrusted formulas and plugins run inside WASI sandboxes**: backend/Dockerfile builds and bundles Wasmtime and a QuickJS-NG WASI runtime as separate build stages, isolating user-authored code execution from the main Django process.
5. **Celery plus Redis handles every async workload**: backend/pyproject.toml pulls in celery[redis], django-celery-beat, celery-redbeat, and celery-singleton for scheduled jobs, deduplicated periodic tasks, and background exports/imports.
6. **Six first-party deploy targets ship in the same repository**: The deploy/ directory contains ready-made configs for Helm, Heroku (app.json), Render (render.yaml), Cloudron, Apache, and Traefik, alongside docker-compose.all-in-one.yml for single-container self-hosting.
7. **pgvector powers the AI/embeddings feature set directly in Postgres**: The embeddings/ directory runs a dedicated embedding-model service (embeddings/app.py, embeddings/Dockerfile), and backend/pyproject.toml depends on pgvector to store the resulting vectors alongside relational data.
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## 4. NON-NEGOTIABLE ARCHITECTURAL GUARDRAILS
1. **Monorepo & Modular Separation**:
   - Structure as a Turborepo monorepo with strict package boundaries:
     - `apps/web`: Application UI, routing, layouts, and server endpoints.
     - `packages/ui`: Shared design tokens, CSS variables, and Radix UI primitive components.
     - `packages/db`: Database schemas, client singleton, declarative migrations, and seed scripts.
     - `packages/config`: Shared TypeScript, ESLint, and build configurations.
2. **Strict Type Safety & Zero `any` Policy**:
   - Enable `strict: true`, `noImplicitAny: true`, and `strictNullChecks: true`.
   - Validate ALL external inputs, API request bodies, and query parameters with **Zod** schemas before execution.
3. **Frontend & Rendering Guidelines**:
   - Isolate interactive UI state to leaf components. Keep core pages lightweight and performant.
4. **Design System & Aesthetics**:
   - Keep every color, radius, shadow and font in a single token file (CSS variables) and consume tokens everywhere; never hardcode hex values in components.
   - Prefer crisp 1px borders and one subtle shadow scale over blurry default shadows. Pair one sans-serif for body/headings with one monospace for tags, badges, metrics, and code.
5. **Data Layer & Reliability**:
   - Write declarative schema definitions with foreign keys, composite indexes on queried filters, and automated timestamp triggers.
   - Use connection pooling and prepared statements for serverless database execution.
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## 5. STEP-BY-STEP SCAFFOLDING ROADMAP
- **Phase 1: Workspace & Root Config**: Initialize package manager, monorepo configuration (`turbo.json`, `tsconfig.base.json`, `package.json`).
- **Phase 2: Database Schema & Client**: Set up the data layer (PostgreSQL, pgvector, Redis): client, connection pool, models, and migration scripts.
- **Phase 3: Design Tokens & UI Primitives**: Build accessible `Button`, `Input`, `Card`, `Badge`, and layout wrappers inside `packages/ui`.
- **Phase 4: Core Application Routes & Handlers**: Implement primary authentication, user session handling, and application routes.
- **Phase 5: Quality Assurance & Build Verification**: Run `tsc --noEmit`, ESLint, Prettier, and smoke test suites to ensure zero compilation or runtime errors.
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## 6. EXECUTION INSTRUCTIONS
1. Review all specifications, architectural guardrails, and stack choices above.
2. Present the full monorepo directory tree structure.
3. Systematically generate the complete, production-ready codebase according to the 5-phase roadmap above — starting with the root workspace setup, followed by the database schema, UI design system package, and full-stack application routes until the repository is fully scaffolded and ready to run.