AI and Bot Access Documentation

Quick Access

Content Access Methods

1. RSS/JSON/Atom Feeds (Recommended)

For bulk content consumption, use our syndication feeds. They include full article content, metadata, and are refreshed multiple times per day (typically every 6-12 hours).

GET https://www.lyovson.com/feed.json

// Returns JSON with full content + AI-friendly metadata:
{
  "items": [{
    "title": "Article Title",
    "content_text": "Full article content...",
    "_lyovson_metadata": {
      "wordCount": 1200,
      "readingTime": 6,
      "contentType": "article",
      "language": "en",
      "projectSlug": "next",
      "apiUrl": "https://www.lyovson.com/api/posts/123"
    }
  }]
}

2. GraphQL API

For structured queries and real-time data access. Supports filtering, sorting, and relationship traversal.

POST https://www.lyovson.com/api/graphql

query LatestPosts {
  Posts(limit: 10, sort: "-publishedAt", where: { _status: { equals: "published" } }) {
    docs {
      title
      slug
      content
      publishedAt
      populatedAuthors {
        name
        username
      }
      project {
        name
        slug
      }
      topics {
        name
        slug
      }
      meta {
        title
        description
      }
    }
  }
}

3. REST API

Standard REST endpoints for all content types. Supports pagination, filtering, and depth control.

# Get latest posts
GET https://www.lyovson.com/api/posts?limit=10&sort=-publishedAt&where[_status][equals]=published

# Get all projects with related posts
GET https://www.lyovson.com/api/projects?depth=1

# Search content
GET https://www.lyovson.com/api/search?q=programming&limit=20

# Search within one person scope
GET https://www.lyovson.com/api/search?q=programming&limit=20&scope=rafa

# Get specific post with full depth
GET https://www.lyovson.com/api/posts/[id]?depth=2

4. Vector Embeddings API

Get vector embeddings for semantic search, content similarity, and AI applications. Supports both OpenAI embeddings and fallback hash-based vectors.

# Collection-specific endpoints (pre-computed, ~50ms)
GET https://www.lyovson.com/api/embeddings/posts/123      # Articles & blog posts
GET https://www.lyovson.com/api/embeddings/activities/456 # Reading/watching/listening logs
GET https://www.lyovson.com/api/embeddings/notes/789      # Personal notes

# Bulk access for training/analysis
GET https://www.lyovson.com/api/embeddings?type=posts&limit=50

# Real-time query embedding
GET https://www.lyovson.com/api/embeddings?q=programming tutorials

# System health across all collections
GET https://www.lyovson.com/api/embeddings/status

# Advanced options
GET https://www.lyovson.com/api/embeddings/posts/123?content=true&format=full
GET https://www.lyovson.com/api/embeddings/activities/456?regenerate=true

# Response structure:
{
  "id": 123,
  "embedding": [0.1, -0.2, 0.3, ...], // 1536-dimensional vector
  "dimensions": 1536,
  "metadata": {
    "type": "post", // or "activity", "note"
    "title": "Post Title",
    "url": "https://www.lyovson.com/posts/post-slug",
    "wordCount": 1200,
    "readingTime": 6,
    "topics": ["programming", "javascript"]
  },
  "model": "text-embedding-3-small"
}

🧠 Advanced Vector Embeddings System

Best Practices for AI Systems

Structured Data & Metadata

All pages include comprehensive structured data following Schema.org standards:

Contact & Support

Need custom access patterns, partnership support, or have questions about using our content?

Last updated: April 22, 2026 • Machine-readable version ↗