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Add tutorials section and architecture explanation
Introduces a new 'Tutorials' section with beginner, intermediate, and advanced guides, including task manager, micro-CRM, and federation examples. Adds an architecture explanation ('Why ObjectQL?'), updates navigation and sidebar in VitePress config, and enhances the homepage with protocol examples. These changes restructure documentation for better onboarding and clarity following the Diataxis framework.
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‎DOCUMENTATION_PLAN.md‎

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# Documentation Optimization Plan
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## 🎯 Goal
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Restructure the documentation to follow the **Diataxis Framework** (Tutorials, Guides, Reference, Explanation) to make it more accessible and attractive to new developers.
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## 🏗 Structural Changes
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### 1. New Top-Level Section: `Tutorials` (Learning-oriented)
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Essential for onboarding. `Quick Start` is good, but developers need a "Step-by-Step" journey.
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- **Location**: `docs/tutorials/`
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- **Content**:
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- `build-your-first-app.md`: A complete Todo List or Blog implementation.
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- `building-crm.md`: Handling relationships and permissions.
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### 2. Refine `Guide` -> `How-to Guides` (Task-oriented)
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Focus on solving specific problems.
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- Move "Core Fundamentals" to focus on *tasks* (e.g., "How to define a schema", "How to query specific fields").
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- Rename broad titles to actionable ones.
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### 3. Highlight `Concepts` (Understanding-oriented)
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Create a clear distinction between "How do I do X?" and "How does X work?".
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- **New Section in Guide**: `Architecture & Concepts`
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- `why-objectql.md`: Deep dive into the "Why".
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- `federation-architecture.md`: Visual diagrams of how federation works.
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### 4. Optimize Homepage (`docs/index.md`)
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- Add a "Code Block" Hero section: Show YAML Input -> SQL/API Output immediately.
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- Add "Use Cases" section.
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## 📅 Execution Steps
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### Step 1: Add Tutorials Section
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- [x] Create directory `docs/tutorials`
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- [x] Create `docs/tutorials/index.md` placeholder
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- [x] Update `docs/.vitepress/config.mts` to include "Tutorials" in Nav and Sidebar.
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### Step 2: Content Injection
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- [ ] Write `build-your-first-app.md` content.
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### Step 3: Visual Polish
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- [ ] Add diagrams to `guide/architecture.md`.

‎docs/.vitepress/config.mts‎

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logo: '/logo.svg',
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// Top Navigation
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nav: [
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{ text: 'Tutorials', link: '/tutorials/' },
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{ text: 'Guide', link: '/guide/' },
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{ text: 'AI-Native', link: '/ai/' },
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{ text: 'Protocol', link: '/spec/' },
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// Sidebar Configuration
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sidebar: {
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// Sidebar for Tutorials
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'/tutorials/': [
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{
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text: 'Tutorials',
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items: [
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{ text: 'Overview', link: '/tutorials/' },
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{ text: '1. Task Manager (Beginner)', link: '/tutorials/task-manager' },
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{ text: '2. Micro-CRM (Intermediate)', link: '/tutorials/crm-system' },
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{ text: '3. Federated Data (Advanced)', link: '/tutorials/federation' },
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]
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}
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],
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// Sidebar for API section
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'/api/': [
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{
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text: 'Start Here',
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items: [
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{ text: 'Introduction', link: '/guide/' },
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{ text: 'Why ObjectQL?', link: '/guide/architecture/why-objectql' },
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{ text: 'Quick Start', link: '/guide/getting-started' },
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{ text: '🤖 AI Coding Setup', link: '/ai/coding-assistant' },
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]

‎docs/guide/architecture/why-objectql.md‎

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# Why ObjectQL?
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In the era of AI automation, the requirements for backend infrastructure have shifted. We are no longer just building for human users on web forms; we are building systems that **AI Agents** need to read, understand, and manipulate.
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## The Problem with Traditional ORMs
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Tools like TypeORM, Prisma, or Sequelize are fantastic for human developers. They rely heavily on:
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1. **Complex TypeScript Types**: Great for IDE autocomplete, but invisible to an LLM running in a production execution environment.
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2. **Chained Method Calls**: `db.users.where(...).include(...)`. This requires the AI to synthesize valid executable code, which is prone to syntax errors and hallucinations.
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3. **Code-First Schema**: The schema is buried in class definitions, making it hard to extract a simple "map" of the data for the AI context window.
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## The ObjectQL Solution: Protocol-First
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ObjectQL treats **Logic as Data**.
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### 1. Schema is JSON (The Context)
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Instead of parsing TypeScript files, an AI Agent can simply read a JSON definition. This is the native tongue of LLMs.
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```json
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{
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"name": "contact",
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"fields": { "email": { "type": "text", "required": true } }
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}
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```
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This compact format fits perfectly in RAG (Retrieval-Augmented Generation) contexts.
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### 2. Queries are ASTs (The Action)
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To fetch data, the AI doesn't need to write SQL or function code. It generates a JSON object (Abstract Syntax Tree).
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```json
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{
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"op": "find",
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"from": "contact",
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"filters": [["email", "contains", "@gmail.com"]]
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}
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```
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* **Safe**: No SQL Injection possible. The Driver handles escaping.
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* **Deterministic**: It either parses or it fails. No subtle logic bugs from misplaced parentheses in code.
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* **Portability**: The same JSON runs on Mongo, Postgres, or a REST API.
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## Comparison
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| Feature | Traditional ORM | ObjectQL |
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| :--- | :--- | :--- |
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| **Schema Definition** | TypeScript Classes / Migration Files | JSON / YAML Metadata |
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| **Query Language** | Fluent API / Raw SQL | JSON AST |
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| **Runtime** | Node.js / Python Runtime | Universal Protocol (Any Language) |
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| **AI Friendliness** | Low (Requires Code Gen) | High (Requires JSON Gen) |
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| **Dynamic Fields** | Hard (Migration required) | Native (Metadata-driven) |
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## Conclusion
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If you are building a Copilot, an Autonomous Agent, or a Low-Code platform, ObjectQL provides the structured, safe, and descriptive layer that connects your LLM to your database.

‎docs/index.md‎

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details: Seamlessly aggregate data from local databases and remote microservices into a single unified graph. The "GraphQL Federation" for backend logic.
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---
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## Protocol by Example
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See how ObjectQL transforms abstract definitions into working software.
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### 1. Logic & Filtering
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::: code-group
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```yaml [1. Input: account.object.yml]
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name: account
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fields:
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name: text
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industry: select
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revenue: currency
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status: select
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```
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```bash [2. Request: Complex Query]
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{
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"fields": ["name", "revenue"],
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"filters": [
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["industry", "=", "tech"],
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"and",
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[["revenue", ">", 1000000], "or", ["status", "=", "vip"]]
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]
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}
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```
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```sql [3. Output: Optimized SQL]
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SELECT name, revenue
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FROM account
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WHERE industry = 'tech'
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AND (revenue > 1000000 OR status = 'vip')
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LIMIT 20
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:::
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### 2. Auto-Joins & Aggregations
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ObjectQL automatically handles `JOIN` operations when you group by related fields.
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::: code-group
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```yaml [1. Input: payment.object.yml]
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name: payment
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fields:
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amount: currency
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account:
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type: lookup
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reference_to: account
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```
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```json [2. Request: JSON Protocol]
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{
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"op": "aggregate",
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"from": "payment",
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"group": ["account.industry"],
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"sum": ["amount"]
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}
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```
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```sql [3. Output: SQL Generation]
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SELECT t2.industry, SUM(t1.amount)
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FROM payment AS t1
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LEFT JOIN account AS t2 ON t1.account = t2.id
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GROUP BY t2.industry
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```
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:::
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## The Shift to AI Programming
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## The Shift to AI Programming
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We believe the future of software development isn't about humans writing better code—it's about **Humans architecting systems that AI can implement**.

‎docs/tutorials/crm-system.md‎

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# Building a Micro-CRMSystem
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> **Prerequisites**: Completed [Build Your First App](./task-manager).
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In this tutorial, we will explore **Relationships**, **Validation**, and **Hooks** by building a minimal CRM (Customer Relationship Management) system.
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## 1. Define the Objects
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We need two objects: `account` (Companies) and `contact` (People).
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Create `account.object.yml`:
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```yaml
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name: account
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label: Account
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fields:
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name:
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type: text
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required: true
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searchable: true
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industry:
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type: select
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options:
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- technology
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- finance
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- retail
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annual_revenue:
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type: currency
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owner:
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type: lookup
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reference_to: users
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```
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Create `contact.object.yml`:
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```yaml
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name: contact
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label: Contact
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fields:
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first_name:
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type: text
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required: true
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last_name:
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type: text
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required: true
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email:
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type: text
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required: true
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account:
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type: lookup
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reference_to: account # <--- Relationship
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required: true
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```
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## 2. Add Validation
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We want to ensure that `annual_revenue` is never negative.
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In `account.object.yml`, add a validation rule:
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```yaml
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validation_rules:
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positive_revenue:
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expression: "annual_revenue >= 0"
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message: "Annual revenue cannot be negative"
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```
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> **Note**: ObjectQL supports expression-based validation directly in the metadata.
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## 3. Implement Business Logic (Hooks)
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We want to automatically set the `full_name` of a contact whenever it is created or updated.
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Create or update your server entry point (e.g., `index.ts`):
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```typescript
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// ... app initialization ...
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// Register a trigger
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app.on('beforeCreate', 'contact', async ({ data }) => {
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if (data) {
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data.full_name = `${data.first_name} ${data.last_name}`;
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}
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});
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app.on('beforeUpdate', 'contact', async ({ data }) => {
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if (data && (data.first_name || data.last_name)) {
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// Note: In a real app, you might need to fetch the existing first/last name
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// if only one is being updated, but for simplicity:
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data.full_name = `${data.first_name} ${data.last_name}`;
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}
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});
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```
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Don't forget to add the `full_name` field to `contact.object.yml`:
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```yaml
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full_name:
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type: text
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readonly: true # Prevent manual edits via API
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```
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## 4. Query with Relationships
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Now start your server and try querying contacts with their account details included.
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**Request:**
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```bash
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curl -G http://localhost:3000/api/data/contact \
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--data-urlencode 'filters=[["email", "contains", "@example.com"]]' \
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--data-urlencode 'expand=["account"]'
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```
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**ObjectQL automatically resolves the lookup:**
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```json
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{
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"value": [
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{
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"_id": "contact_123",
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"first_name": "Alice",
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"full_name": "Alice Smith",
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"account": {
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"_id": "acc_456",
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"name": "Tech Corp",
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"industry": "technology"
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}
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}
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]
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}
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```
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## Summary
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You have just built a relational data system with business logic in minutes.
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- **Relationships**: Defined via `reference_to`.
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- **Validation**: Declarative rules in YAML.
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- **Logic**: TypeScript hooks for dynamic behavior.

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