Add comprehensive AI capabilities planning and documentation

[Copilot]

Establishes complete technical foundation for AI integration in ObjectQL: architecture, security framework, implementation roadmap, and task breakdown.

Documentation Added (4,100+ lines)

Core Technical Guides

• AI Integration Guide (585 lines) - LLM integration patterns, prompt engineering, validation pipeline, caching strategies
• AI Safety Guidelines (801 lines) - Multi-layer validation (structural/semantic/complexity), permission enforcement, rate limiting, sandboxing, audit logging
• Quick Start Guide (208 lines) - 5-minute setup with common patterns

Implementation Planning

• 6-Phase Roadmap (684 lines) - 16-22 week plan from SDK development through production templates

  • Phase 1: Foundation & docs (2-3w)
  • Phase 2: AI SDK core (3-4w) - Query generator, schema analyzer, safety layer
  • Phase 3: Advanced features (4-5w) - NL API, analytics, automation
  • Phase 4: Governance (2-3w) - Monitoring, audit, cost controls
  • Phase 5: Dev tools (3-4w) - Playground, VS Code extension, CLI
  • Phase 6: Templates (2-3w) - Production-ready AI apps

• 20+ GitHub Issue Templates (797 lines) - Detailed tasks with acceptance criteria, estimates, dependencies

Supporting Materials

• Documentation Index (277 lines) - Navigation hub with learning paths
• Changelog (300+ lines) - Version tracking and release plan
• Chinese Summary (264 lines) - Stakeholder briefing (中文)

Key Capabilities Designed

Natural Language Query Engine

const query = await aiQuery.generate("Find high-priority tasks due this week", "tasks");
// Generates validated ObjectQL query with permission checks

Schema Generation

await generateSchema("CRM with customers, deals, contacts");
// Auto-generates object definitions with inferred relationships

Safety Architecture

• Query validation: Zod schemas, complexity limits, semantic checks
• Permission layer: Object/field/filter-level enforcement
• Rate limiting: Token bucket with per-user quotas
• Sandboxing: VM2 isolation for AI-generated code
• Audit trail: Complete operation logging with anomaly detection

Tech Stack

• LLM providers: OpenAI, Anthropic Claude, Ollama (local)
• Validation: Zod, custom semantic analyzers
• Security: VM2, DOMPurify, rate-limit-redis
• Orchestration: LangChain

README Updates

Added AI capabilities section highlighting JSON-based query protocol optimized for LLM generation vs. SQL string injection risks.

Original prompt

帮我规划如何增加AI能力，编写文档和安排任务

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