/**
 * Cloudflare Workers AI - Wrangler Configuration
 *
 * This configuration file sets up Workers AI binding for your Worker.
 * Place this in your project root as wrangler.jsonc
 */

{
  "name": "my-ai-worker",
  "main": "src/index.ts",
  "compatibility_date": "2025-10-21",

  /**
   * AI Binding
   * Provides access to Workers AI models via env.AI
   */
  "ai": {
    "binding": "AI" // Available in your Worker as env.AI
  },

  /**
   * Optional: AI Gateway Integration
   * Provides caching, logging, and analytics for AI requests
   * Create a gateway at: https://dash.cloudflare.com/ai/ai-gateway
   */
  // Note: AI Gateway is configured per-request, not in wrangler.jsonc
  // Use the gateway option in env.AI.run():
  // env.AI.run(model, inputs, { gateway: { id: 'my-gateway' } })

  /**
   * Optional: Vectorize Binding (for RAG patterns)
   * Store and search vector embeddings
   */
  "vectorize": [
    {
      "binding": "VECTORIZE",
      "index_name": "my-embeddings-index"
    }
  ],

  /**
   * Optional: D1 Database (for RAG document storage)
   */
  "d1_databases": [
    {
      "binding": "DB",
      "database_name": "my-database",
      "database_id": "YOUR_DATABASE_ID"
    }
  ],

  /**
   * Optional: R2 Bucket (for image storage)
   */
  "r2_buckets": [
    {
      "binding": "BUCKET",
      "bucket_name": "ai-generated-images"
    }
  ],

  /**
   * Optional: KV Namespace (for caching AI responses)
   */
  "kv_namespaces": [
    {
      "binding": "CACHE",
      "id": "YOUR_KV_NAMESPACE_ID"
    }
  ],

  /**
   * Environment Variables
   * Store API keys and configuration
   */
  "vars": {
    "ENVIRONMENT": "production"
  },

  /**
   * Secrets (use: wrangler secret put SECRET_NAME)
   * - CLOUDFLARE_API_KEY
   * - CLOUDFLARE_ACCOUNT_ID
   */

  /**
   * Workers Configuration
   */
  "limits": {
    "cpu_ms": 30000 // 30 seconds (increase for long AI operations)
  },

  /**
   * Local Development
   * Run: npx wrangler dev
   */
  "dev": {
    "port": 8787
  }
}

/**
 * TypeScript Types
 *
 * Add to your src/index.ts:
 *
 * export interface Env {
 *   AI: Ai;
 *   VECTORIZE?: Vectorize;
 *   DB?: D1Database;
 *   BUCKET?: R2Bucket;
 *   CACHE?: KVNamespace;
 *   CLOUDFLARE_API_KEY?: string;
 *   CLOUDFLARE_ACCOUNT_ID?: string;
 * }
 */

/**
 * Usage Examples
 *
 * Basic AI inference:
 * const response = await env.AI.run('@cf/meta/llama-3.1-8b-instruct', {
 *   prompt: 'Hello!',
 * });
 *
 * With AI Gateway:
 * const response = await env.AI.run(
 *   '@cf/meta/llama-3.1-8b-instruct',
 *   { prompt: 'Hello!' },
 *   { gateway: { id: 'my-gateway' } }
 * );
 *
 * Streaming:
 * const stream = await env.AI.run('@cf/meta/llama-3.1-8b-instruct', {
 *   messages: [{ role: 'user', content: 'Hello!' }],
 *   stream: true,
 * });
 */