/**
 * Anthropic Claude Messages API via Lava
 *
 * Key Differences from OpenAI:
 * - Uses `x-api-key` header (NOT `Authorization: Bearer`)
 * - Requires `anthropic-version: 2023-06-01` header
 * - `max_tokens` is REQUIRED (not optional)
 * - `system` prompt is separate field (not in messages array)
 * - Usage fields: `input_tokens` and `output_tokens`
 */

// Load environment variables
require('dotenv').config({ path: '.env.local' });

async function callClaudeViaLava() {
  // 1. Define the provider endpoint
  const PROVIDER_ENDPOINT = 'https://api.anthropic.com/v1/messages';

  // 2. Build the Lava forward proxy URL
  const url = `${process.env.LAVA_BASE_URL}/forward?u=${PROVIDER_ENDPOINT}`;

  // 3. Set up authentication headers (Anthropic-specific)
  const headers = {
    'Content-Type': 'application/json',
    'x-api-key': process.env.LAVA_FORWARD_TOKEN,  // NOTE: x-api-key, not Authorization
    'anthropic-version': '2023-06-01'  // Required by Anthropic
  };

  // 4. Define the request body (Anthropic format)
  const requestBody = {
    model: 'claude-sonnet-4-5',
    max_tokens: 1024,  // REQUIRED (not optional like OpenAI)
    messages: [
      { role: 'user', content: 'Say hello in one sentence.' }
    ],
    system: 'You are a helpful assistant.'  // Separate from messages array
  };

  // 5. Make the request
  try {
    const response = await fetch(url, {
      method: 'POST',
      headers: headers,
      body: JSON.stringify(requestBody)
    });

    // 6. Parse the response
    const data = await response.json();

    // 7. Extract usage data (Anthropic-specific field names)
    const usage = data.usage;
    console.log('\nUsage Tracking:');
    console.log(`  Input tokens: ${usage.input_tokens}`);
    console.log(`  Output tokens: ${usage.output_tokens}`);
    console.log(`  Total tokens: ${usage.input_tokens + usage.output_tokens}`);

    // Check for prompt caching
    if (usage.cache_read_input_tokens) {
      console.log(`  Cache read tokens: ${usage.cache_read_input_tokens}`);
    }
    if (usage.cache_creation_input_tokens) {
      console.log(`  Cache creation tokens: ${usage.cache_creation_input_tokens}`);
    }

    // 8. Extract request ID (from response header)
    const requestId = response.headers.get('x-lava-request-id');
    console.log(`\nLava Request ID: ${requestId}`);

    // 9. Display the AI response (Anthropic content structure)
    console.log('\nAI Response:');
    console.log(data.content[0].text);

    return data;
  } catch (error) {
    console.error('Error calling Claude via Lava:', error.message);
    throw error;
  }
}

// Run the example
callClaudeViaLava();

{
  "model": "claude-sonnet-4-5",
  "max_tokens": 1024,
  "messages": [
    {"role": "user", "content": "Hello, Claude"}
  ],
  "system": "You are a helpful assistant",
  "temperature": 0.7
}

{
  "id": "msg_01HCDu5LRGeP2o7s2xGmxyx8",
  "type": "message",
  "role": "assistant",
  "content": [{
    "type": "text",
    "text": "Hello! How can I help you today?"
  }],
  "model": "claude-sonnet-4-5",
  "stop_reason": "end_turn",
  "usage": {
    "input_tokens": 10,
    "output_tokens": 25
  }
}

{
  "model": "claude-sonnet-4-5",
  "max_tokens": 1024,
  "system": [
    {
      "type": "text",
      "text": "Large system prompt or document...",
      "cache_control": {"type": "ephemeral"}
    }
  ],
  "messages": [...]
}

{
  "model": "claude-sonnet-4-5",
  "max_tokens": 1024,
  "messages": [{
    "role": "user",
    "content": [
      {
        "type": "image",
        "source": {
          "type": "base64",
          "media_type": "image/jpeg",
          "data": "iVBORw0KGgoAAAANS..."
        }
      },
      {
        "type": "text",
        "text": "What's in this image?"
      }
    ]
  }]
}

POST /v1/messages/batches