AI & 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 updated hourly.

GET https://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://lyovson.com/api/posts/123"
    }
  }]
}

2. GraphQL API

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

POST https://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://lyovson.com/api/posts?limit=10&sort=-publishedAt&where[_status][equals]=published

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

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

# Get specific post with full depth
GET https://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.

# Get embedding for a specific post (pre-computed, ~50ms)
GET https://lyovson.com/api/embeddings/posts/123

# Get embeddings for all posts (bulk access)
GET https://lyovson.com/api/embeddings?type=posts&limit=50

# Get embedding for a text query (real-time generation)
GET https://lyovson.com/api/embeddings?q=programming tutorials

# Get system status and coverage
GET https://lyovson.com/api/embeddings/status

# Response includes:
{
  "id": 123,
  "embedding": [0.1, -0.2, 0.3, ...], // 1536-dimensional vector
  "dimensions": 1536,
  "metadata": {
    "title": "Post Title",
    "url": "https://lyovson.com/project/post-slug",
    "wordCount": 1200,
    "readingTime": 6,
    "topics": ["programming", "javascript"],
    "isPrecomputed": true
  },
  "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: