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Inputs and Outputs

🔨 In Development — This section is still being developed and may change.
Learn how to structure inputs and understand outputs when working with model responses.

Overview

Freddy accepts flexible input formats ranging from simple text strings to complex multi-modal arrays with roles, images, audio, and files. The API automatically handles conversation context, content type detection, and proper formatting.

Input Format

Inputs are always provided as an array of structured items:

{
  "model": "gpt-4.1",
  "inputs": [
    {
      "role": "system",
      "texts": [{ "text": "You are a helpful geography expert" }]
    },
    {
      "role": "user",
      "texts": [{ "text": "What is the capital of France?" }]
    }
  ]
}

Roles

System Role

High-priority instructions that guide the model's behavior throughout the conversation.

When to use:

  • Set behavior guidelines
  • Define expertise or persona
  • Provide important constraints
  • Give formatting instructions

Example:

{
  "role": "system",
  "texts": [{
    "text": "You are a Python expert. Always include code examples. Be concise."
  }]
}

User Role

End-user messages, questions, and instructions.

When to use:

  • User questions
  • Task descriptions
  • Follow-up messages
  • Most conversation inputs

Example:

{
  "role": "user",
  "texts": [{
    "text": "How do I sort a list in Python?"
  }]
}

Assistant Role

Previous model responses, used for conversation history or few-shot examples.

When to use:

  • Conversation history
  • Few-shot examples
  • Guiding response style

Example:

{
  "role": "assistant",
  "texts": [{
    "text": "You can use the sorted() function or .sort() method."
  }]
}

Content Types

Text Content

{
  "role": "user",
  "texts": [
    { "text": "First paragraph" },
    { "text": "Second paragraph" }
  ]
}

Image Content

By file ID:

{
  "role": "user",
  "texts": [{ "text": "What's in this image?" }],
  "images": [
    { "fileId": "file_abc123" }
  ]
}

By URL:

{
  "role": "user",
  "texts": [{ "text": "Describe this image" }],
  "images": [
    { "url": "https://example.com/image.jpg" }
  ]
}

Audio Content

{
  "role": "user",
  "texts": [{ "text": "Transcribe this audio" }],
  "audio": [
    { "fileId": "file_audio123" }
  ]
}

File Attachments

{
  "role": "user",
  "texts": [{ "text": "Summarize this PDF" }],
  "files": [
    { "fileId": "file_doc123" }
  ]
}

Multi-modal Content

Combine multiple content types:

{
  "role": "user",
  "texts": [
    { "text": "Compare these two images" }
  ],
  "images": [
    { "fileId": "file_img1" },
    { "fileId": "file_img2" }
  ]
}

Common Patterns

System Prompt + User Question

const response = await fetch('/v1/model/response', {
  method: 'POST',
  body: JSON.stringify({
    model: 'gpt-4.1',
    inputs: [
      {
        role: 'system',
        texts: [{ text: 'You are a helpful coding assistant' }]
      },
      {
        role: 'user',
        texts: [{ text: 'Write a function to reverse a string' }]
      }
    ]
  })
});

Conversation History

conversation = [
    {'role': 'user', 'texts': [{'text': 'Hello'}]},
    {'role': 'assistant', 'texts': [{'text': 'Hi! How can I help?'}]},
    {'role': 'user', 'texts': [{'text': 'Tell me about Python'}]}
]

response = requests.post(
    'https://api.freddy.aitronos.com/v1/model/response',
    json={'model': 'gpt-4.1', 'inputs': conversation}
)

Few-Shot Examples

{
  "model": "gpt-4.1",
  "inputs": [
    {
      "role": "system",
      "texts": [{ "text": "Classify sentiment as positive, negative, or neutral" }]
    },
    {
      "role": "user",
      "texts": [{ "text": "I love this product!" }]
    },
    {
      "role": "assistant",
      "texts": [{ "text": "Sentiment: positive" }]
    },
    {
      "role": "user",
      "texts": [{ "text": "This is terrible." }]
    },
    {
      "role": "assistant",
      "texts": [{ "text": "Sentiment: negative" }]
    },
    {
      "role": "user",
      "texts": [{ "text": "The package arrived today." }]
    }
  ]
}

Image Analysis

{
  "model": "gpt-4.1-vision",
  "inputs": [
    {
      "role": "user",
      "texts": [{ "text": "Describe this image in detail" }],
      "images": [{ "fileId": "file_abc123" }]
    }
  ]
}

Output Structure

Standard Output

{
  "id": "resp_67ccd2bed1ec8190",
  "object": "response",
  "created_at": 1741476542,
  "status": "completed",
  "model": "gpt-4.1",
  "output": [
    {
      "type": "message",
      "id": "msg_67ccd2bf17f0",
      "role": "assistant",
      "content": [
        {
          "type": "output_text",
          "text": "The capital of France is Paris.",
          "annotations": []
        }
      ]
    }
  ],
  "usage": {
    "input_tokens": 15,
    "output_tokens": 8,
    "total_tokens": 23
  }
}

Tool Call Output

{
  "output": [
    {
      "type": "tool_call",
      "tool_name": "web_search",
      "query": "latest AI news",
      "result": "Recent developments include..."
    },
    {
      "type": "message",
      "role": "assistant",
      "content": [
        {
          "type": "output_text",
          "text": "Based on recent news, here's what's happening in AI..."
        }
      ]
    }
  ]
}

Best Practices

Separate System Instructions

# ✅ Good - clear separation
inputs = [
    {'role': 'system', 'texts': [{'text': 'Be concise'}]},
    {'role': 'user', 'texts': [{'text': 'Explain AI'}]}
]

# ❌ Bad - mixing in user message
inputs = [{
    'role': 'user',
    'texts': [{'text': 'Be concise. Explain AI'}]
}]

Maintain Conversation Context

// ✅ Good - preserves context
const messages = [...previousMessages, newMessage];
const response = await sendMessage(messages);

// ❌ Bad - loses context
const response = await sendMessage(newMessage);
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