Harnessing Multimodal Function Calling with GPT-4 Turbo

Function Calling in Multimodal Applications

Function calling is instrumental in developing AI systems that require interactions with external tools or APIs. With GPT-4 Turbo, this feature is further enhanced with vision capabilities, enabling the model to analyze images and invoke appropriate functions based on visual inputs.

Real-World Use Cases

One practical application is simulating a customer service assistant that analyzes package images to determine the next steps, such as issuing refunds or replacements. For example, if a package appears damaged, the model can automatically initiate a refund process.

Another use case is extracting employee data from organizational charts. The model can analyze a chart, recognize hierarchical relationships, and output structured information such as names, roles, and reporting lines.

Implementing Function Calling with GPT-4 Turbo

Here’s an example of how to implement function calling using the new GPT-4 Turbo model. Suppose a user needs to process an image of a package to determine if it's damaged:

1. Prepare the Image Data

   Encode the image in base64 format to pass it as input to the model:

   def encode_image(image_path: str):
       with open(image_path, "rb") as image_file:
           return base64.b64encode(image_file.read()).decode('utf-8')

2. Define the Actions

   Set up the actions the model can take, such as issuing a refund or escalating to an agent:

   def refund_order(order):
       return f"Order {order.order_id} has been refunded successfully."
   
   def escalate_to_agent(order, message):
       return f"Order {order.order_id} has been escalated to an agent with message: {message}"

3. Process the Image

   Pass the encoded image to the model, which will analyze it and call the appropriate function:

   def process_package_image(image_data):
       # Simulate function calling
       action = "refund_order" if "damaged" in image_data else "escalate_to_agent"
       return action

Conclusion

The introduction of multimodal function calling in GPT-4 Turbo unlocks new possibilities for AI applications, particularly in tasks that require both visual and textual analysis. By integrating these capabilities, developers can create more sophisticated and responsive AI systems.