Function Calling with LLMs
Getting Started with Function Calling
Function calling is the ability to reliably connect LLMs to external tools to enable effective tool usage and interaction with external APIs.
LLMs like GPT-4 and GPT-3.5 have been fine-tuned to detect when a function needs to be called and then output JSON containing arguments to call the function. The functions that are being called by function calling will act as tools in your AI application and you can define more than one in a single request.
Function calling is an important ability for building LLM-powered chatbots or agents that need to retrieve context for an LLM or interact with external tools by converting natural language into API calls.
Functional calling enables developers to create:
conversational agents that can efficiently use external tools to answer questions. For example, the query "What is the weather like in Belize?" will be converted to a function call such as get_current_weather(location: string, unit: 'celsius' | 'fahrenheit')
LLM-powered solutions for extracting and tagging data (e.g., extracting people names from a Wikipedia article)
applications that can help convert natural language to API calls or valid database queries
conversational knowledge retrieval engines that interact with a knowledge base
In this guide, we demonstrate how to prompt models like GPT-4 and open-source models to perform function calling for different use cases.https://www.promptingguide.ai/applications/function_calling
Getting Started with Function Calling
Function calling is the ability to reliably connect LLMs to external tools to enable effective tool usage and interaction with external APIs.
LLMs like GPT-4 and GPT-3.5 have been fine-tuned to detect when a function needs to be called and then output JSON containing arguments to call the function. The functions that are being called by function calling will act as tools in your AI application and you can define more than one in a single request.
Function calling is an important ability for building LLM-powered chatbots or agents that need to retrieve context for an LLM or interact with external tools by converting natural language into API calls.
Functional calling enables developers to create:
conversational agents that can efficiently use external tools to answer questions. For example, the query "What is the weather like in Belize?" will be converted to a function call such as get_current_weather(location: string, unit: 'celsius' | 'fahrenheit')
LLM-powered solutions for extracting and tagging data (e.g., extracting people names from a Wikipedia article)
applications that can help convert natural language to API calls or valid database queries
conversational knowledge retrieval engines that interact with a knowledge base
In this guide, we demonstrate how to prompt models like GPT-4 and open-source models to perform function calling for different use cases.https://www.promptingguide.ai/applications/function_calling