AI

Both OpenAI programing guide and Anyscale Endpoints blog [7] distill down to simple steps: Call the model with the user query and a list of functions defined in the Chat Completions API parameter as tools. The model can choose to call one or more functions; if so, the content will be a stringified JSON object adhering to your custom schema. Parse the string into JSON in your code, and call your function with the provided arguments if they exist. Call the model again by appending the function response as a new message, and let the model summarize the results back to the user. Following the above simple steps, our user_content to the LLM generates three required parameters (location, latitude, longitude) as a JSON object in its response.

Examples and Use Cases of Function Calling in LLM

Apart from the above use cases mentioned in the Open AI programming guide [10], Ben Lorica visually and comprehensively captures use cases of general function calling in LLMs, including the OpenAI Assistant Tools API [11]. Lorica succinctly states that early use cases include applications such as customer service chatbots, data analysis assistants, and code generation tools. Other examples extend to creative, logistical, and operational domains: writing assistants, scheduling agents, summarizing news., etc.