promptfoo/examples/openai-realtime

openai-realtime (OpenAI Realtime API Example)

This example demonstrates how to use promptfoo to test OpenAI's Realtime API capabilities. The Realtime API allows for real-time communication with GPT-4o class models using WebSockets, supporting both text and audio inputs/outputs.

Quick Start

You can run this example with:

npx promptfoo@latest init --example openai-realtime

This will create all necessary files and folder structure to get started quickly.

Setup

1. Set your OpenAI API key as an environment variable:

export OPENAI_API_KEY=your-api-key-here

2. Ensure you have access to the OpenAI Realtime API (Beta), which may require specific permissions from OpenAI.

Files

• promptfooconfig.yaml: Configuration file defining the providers and tests
• realtime-input.json: JSON template for the realtime input prompt
• promptfooconfig-conversation.yaml: Configuration for multi-turn conversation tests
• realtime-conversation.js: JavaScript prompt function for multi-turn conversations
• realtime-conversation-input.json: JSON template for multi-turn conversations (alternative approach)

Multi-Turn Conversations

The Realtime API supports maintaining conversation history across multiple turns. This example includes a multi-turn conversation configuration that demonstrates how to:

1. Maintain Conversation Context: Keep track of previous exchanges
2. Utilize Previous Responses: Reference information from earlier in the conversation
3. Create Independent Conversation Threads: Run multiple separate conversations in parallel

To run the multi-turn conversation example:

npx promptfoo eval -c examples/openai-realtime/promptfooconfig-conversation.yaml

How Multi-Turn Conversations Work

The multi-turn conversation example demonstrates how the OpenAI Realtime API can maintain context across multiple exchanges. This is implemented using promptfoo's built-in support for conversation history through the _conversation variable and metadata.

Key Components

1. Special Variable: The _conversation variable contains all previous turns in the conversation
2. JavaScript Prompt Function: The main approach uses a JavaScript function to properly format conversations
3. Conversation IDs: Each test with the same conversationId metadata value is part of the same conversation thread

When using conversationId in the metadata of tests, promptfoo automatically:

• Groups tests with the same ID into a conversation thread
• Makes previous exchanges available in each subsequent test
• Builds a complete conversation history the model can use for context

How Conversation State is Maintained

For each conversation turn:

1. The _conversation variable is automatically populated with all previous prompts and outputs
2. Messages are properly formatted for the Realtime API WebSocket protocol
3. The model responds with contextually relevant answers based on the conversation history

Example Conversation Flow

User: What are some popular tourist destinations in Japan?
AI: Some popular tourist destinations in Japan include Tokyo, Kyoto, Osaka, Hiroshima, and Hokkaido...

User: Which of those places is best to visit in autumn?
AI: Kyoto is particularly beautiful in autumn with its colorful maple leaves...

User: What traditional foods should I try there?
AI: In Kyoto during autumn, you should try momiji manju (maple leaf-shaped cakes), kyo-kaiseki (traditional multi-course meal)...

The API maintains context throughout this exchange, understanding that follow-up questions refer to Japan and then to the specific autumn locations.

JavaScript Prompt Function

This example uses a JavaScript function (realtime-conversation.js) to properly format the conversation for the OpenAI Realtime API:

module.exports = async function ({ vars, provider }) {
  // Create the messages array starting with system message
  const messages = [
    {
      role: 'system',
      content: [
        {
          type: 'input_text',
          text: vars.system_message || 'You are a helpful AI assistant.',
        },
      ],
    },
  ];

  // Add previous conversation turns if they exist
  if (vars._conversation && Array.isArray(vars._conversation)) {
    for (const completion of vars._conversation) {
      // Add user message
      messages.push({
        role: 'user',
        content: [
          {
            type: 'input_text',
            text: completion.input,
          },
        ],
      });

      // Add assistant message
      messages.push({
        role: 'assistant',
        content: [
          {
            type: 'text',
            text: completion.output,
          },
        ],
      });
    }
  }

  // Add the current question as the final user message
  messages.push({
    role: 'user',
    content: [
      {
        type: 'input_text',
        text: vars.question || '',
      },
    ],
  });

  return messages;
};

This approach provides better flexibility and error handling than using JSON templates with Nunjucks.

Alternative: JSON Template with Nunjucks

We also provide a JSON template approach for reference:

[
  {
    "role": "system",
    "content": [
      {
        "type": "input_text",
        "text": "{{ system_message }}"
      }
    ]
  }{% for completion in _conversation %},
  {
    "role": "user",
    "content": [
      {
        "type": "input_text",
        "text": "{{ completion.input }}"
      }
    ]
  },
  {
    "role": "assistant",
    "content": [
      {
        "type": "text",
        "text": "{{ completion.output }}"
      }
    ]
  }{% endfor %},
  {
    "role": "user",
    "content": [
      {
        "type": "input_text",
        "text": "{{ question }}"
      }
    ]
  }
]

Note: JSON validators may show errors for this template because of the Nunjucks expressions, but promptfoo will correctly process this file at runtime. This approach uses the _conversation variable to maintain conversation history in a way that works with the Realtime API.

Conversation Threads

The configuration includes two separate conversation threads:

1. Japan Travel Thread: Questions about traveling in Japan, with follow-up questions
2. Technology Thread: Questions about real-time AI technology

Each thread maintains its own independent context while tests are evaluated.

About the Realtime API Implementation

The provider implementation in promptfoo creates a direct WebSocket connection with the OpenAI Realtime API, following the official protocol:

1. WebSocket Connection: Establishes a secure WebSocket connection to wss://api.openai.com/v1/realtime?model=MODEL_ID
2. Authentication: Authenticates using the API key in the request headers, including the required beta header
3. Conversation Management: Implements the full conversation protocol:
   • Creates user messages
   • Processes model responses
   • Handles text deltas in real-time
   • Processes function calls from the model
4. Error Handling: Implements robust error handling and timeout management

Connection Details

The WebSocket connection follows the official OpenAI documentation:

const wsUrl = `wss://api.openai.com/v1/realtime?model=${modelName}`;
const ws = new WebSocket(wsUrl, {
  headers: {
    Authorization: `Bearer ${apiKey}`,
    'OpenAI-Beta': 'realtime=v1',
    // Other headers...
  },
});

Message Format Requirements

When sending messages to the OpenAI Realtime API, you must use the correct content type format:

// When sending user or system messages, use the 'input_text' content type
ws.send(
  JSON.stringify({
    type: 'conversation.item.create',
    item: {
      type: 'message',
      role: 'user',
      content: [
        {
          type: 'input_text', // Must be 'input_text' for user/system inputs
          text: 'Your message here',
        },
      ],
    },
  }),
);

// When configuring modalities for response settings, use 'text' and 'audio'
const config = {
  modalities: ['text', 'audio'],
};

Important Note: The OpenAI Realtime API is in beta and the format requirements may change. The implementation in promptfoo follows the current requirements as of the latest API version.

Function Calling Support

The implementation supports the Realtime API's function calling capabilities:

1. Tool Definition: You can define tools (functions) in the configuration
2. Function Arguments: When the model decides to use a function, the implementation captures the arguments
3. Function Result Handling: Results from function calls are sent back to the model for further processing

In this example, we've configured a simple weather function to demonstrate the capability. In a real implementation, you would connect this to actual weather data.

Implementing a Custom Function Handler

To use function calling in your application, you would implement a function call handler. Here's an example of how you might implement a weather function handler in JavaScript:

// In your application code
const functionCallHandler = async (name, args) => {
  // Parse the function arguments
  const parsedArgs = JSON.parse(args);

  if (name === 'get_weather') {
    const { location } = parsedArgs;

    // In a real implementation, you would call a weather API here
    // This is just a mock example
    return JSON.stringify({
      location,
      temperature: '72°F',
      condition: 'Sunny',
      humidity: '45%',
      forecast: 'Clear skies for the next 24 hours',
    });
  }

  // Handle unknown function
  return JSON.stringify({ error: `Unknown function: ${name}` });
};

// You can then pass this handler in your prompt configuration
const config = {
  functionCallHandler,
};

Audio Support

While the Realtime API supports audio input and output, the current implementation focuses on text interactions for simplicity. Future enhancements could include support for:

• Audio input processing
• Receiving and handling audio output
• Turn detection and interruption capabilities

Running the Example

From the root directory of promptfoo, run:

npx promptfoo eval -c examples/openai-realtime/promptfooconfig.yaml

Troubleshooting WebSocket Connection Issues

If you encounter a "WebSocket error: Unexpected server response: 403" error, this typically indicates one of these issues:

1. Network/Firewall Restrictions: WebSocket connections may be blocked by your network or firewall.

   • Try running the example from a different network (e.g., mobile hotspot)
   • Check if your company network blocks WebSocket connections

2. API Access: Your OpenAI API key may not have access to the Realtime API beta.

   • Verify that you have been granted access to the Realtime API beta
   • Check your OpenAI dashboard for any access restrictions

3. Rate Limits: You may have hit rate limits or quotas for the Realtime API.

   • Check your OpenAI usage dashboard for any quota limitations

Alternative API Usage

If you're unable to use the Realtime API due to WebSocket connection issues, you can still use the regular OpenAI chat API for most use cases. The configuration includes both providers, so you'll see results from the regular chat API even if the Realtime API fails to connect.