I was very intrigued and excited by the recent announcement from OpenAI about their function calling feature. This upgrade promised a significant boost in the performance of AI agents, intriguing me enough to test this new feature in my AI agents.
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What are OpenAI`s Function Calling?
Before we dive into the complexities of OpenAI’s function calling, it’s important to simplify this concept for those who might be unfamiliar with it.
Imagine being at a restaurant and ordering a specific dish from the menu. Here, the chef acts as the function, the restaurant represents the program, and the dish symbolizes the task you want to accomplish. Function calling operates on a similar principle. It simplifies tasks that are external to their main program, like APIs.
This again just shows how powerful it is to combine LLMs like GPT-4 with Python Code.
Incorporating Function Calling in My AI Agents
To demonstrate how function calling can be practically applied, let’s examine my Python code where I implemented this feature.
In my AI agent’s code, I defined several functions such as fetching organic traffic from Google, scraping websites, and sending emails.
Every function was assigned a distinct name and description. For example, ‘get organic results’ was designed to fetch organic search results from a specified query. In a similar manner, functions for scraping websites, saving files, opening files, and sending emails were defined.
The real game-changer here is OpenAI’s function calling ability that gives the AI Agents an option to whether or not to invoke these functions. This autonomy facilitates consistency and significantly boosts overall efficiency.
Harnessing The Power of Function Calling with OpenAI
With the OpenAI function calling integrated into my Python code, I began setting various goals for my AI agents to achieve. These included finding contact information for Kris from YouTube channel All About AI and drafting an interview request email.
The new function calling from OpenAI feature expedited this process tremendously, and is a great new tool for AI Engineers. My AI agent swiftly executed a Google search for contact information and returned relevant results within moments. It then used the ‘scrape website’ function to retrieve necessary data from Kris’s website.
Once obtained, it suggested using the ‘save file’ function to save this data. Just like that, within moments, I had a well-drafted email ready.
But merely having a well-drafted email wasn’t enough; I yearned for more sophisticated tasks that would push the boundaries of what my AI agent could achieve with OpenAI’s function calling.
Pushing The Boundaries with OpenAI Function Calling
Eager to test my AI agent’s limits further with different tasks such as writing Python code for a simple chatbot using OpenAI API and saving it into a .py file.
The outcome surpassed all expectations. The agents successfully wrote practical Python code that worked perfectly when tested—a testament to OpenAI’s function calling prowess.
Not stopping at that, I set another task for my AI agents—finding a sushi restaurant in San Francisco and gathering safety information about its neighborhood. Once again, my AI agents did not disappoint me. They quickly found highly recommended sushi restaurants by gathering reviews from various sites.
Their ability to call on functions when required made the entire process seamless—something that has been made possible due to OpenAI’s function calling feature.
Concluding Thoughts
OpenAI’s introduction of function calling has indeed revolutionized our interaction with AI agents. Its efficiency and consistency upgrades have transformed tasks like finding contact information, writing code or conducting research from being merely feasible to seamlessly executed actions.
As we venture further into this thrilling era of artificial intelligence with larger context windows and memory capabilities at our disposal, one can’t help but wonder— is this just the tip of an iceberg when it comes to what AI agents are capable of doing? Only time will tell.
