Building AI-Native Apps

ObjectQL is engineered to be the ideal data layer for AI Agents and LLMs. By providing a Structure-First protocol (JSON AST) instead of raw strings (SQL), it drastically reduces hallucinations and injection risks.

1. Why ObjectQL for AI?

Feature	SQL / Traditional ORM	ObjectQL
Output	Unstructured String	Strict JSON
Safety	Injection Vulnerable	Injection Safe
Context	Heavy DDL dumps	Lightweight Scoped Schema

LLMs excel at generating JSON. ObjectQL lets the LLM speak its native language.

2. Semantic Search (RAG)

ObjectQL has first-class support for Vector Search. You don't need a separate vector database (like Pinecone) or generic ORM hacks.

Configuration

Enable search in your *.object.yml.

# knowledge.object.yml
name: knowledge
fields:
  title: { type: text }
  content: { type: textarea }

# Enable AI capabilities
ai:
  search:
    enabled: true
    fields: [title, content] # Fields to embed
    model: text-embedding-3-small

Usage

When enabled, the driver manages the embeddings automatically. You can then search using natural language.

// Search for "How to reset password"
const results = await objectql.search('knowledge', 'How to reset password');

// returns: [{ id: 1, title: 'Reset Config', _score: 0.89 }, ...]

3. Explicit Vector Columns

For advanced use cases (e.g., Image Search or Multi-modal embeddings), you can define raw vector columns.

fields:
  image_url:
    type: url
  
  clip_embedding:
    type: vector
    dimension: 512
    index: true # Create IVFFlat/HNSW index

4. LLM to Query (Text-to-SQL alternative)

Instead of asking an LLM to write SQL, ask it to write ObjectQL JSON.

Prompt Pattern:

You are a data assistant.
Schema:
- Object: Task (fields: title, status, priority)

User: "Find my high priority tasks"

Output JSON in ObjectQL format:
{
  "entity": "task",
  "filters": [["priority", "=", "High"]]
}

This output can be safely executed by the ObjectQL engine without fear of DROP TABLE injections.