Gemini: This video is about Function Calling in Gemini, a framework for connecting large language models (LLMs) to real-time data and APIs. The video starts with an overview of generative AI in Google Cloud. Generative AI has been rapidly advancing, and Google Cloud is at the forefront of this innovation. Google Cloud offers a variety of generative AI models, including the latest Gemini series. Gemini models are not only powerful, but also easy to integrate with other Google Cloud services like Vertex AI. Then, the video dives into the challenges of using LLMs and how Function Calling in Gemini can address them. LLMs can produce inconsistent outputs, lack access to information after their training date, and are disconnected from the world. Function Calling in Gemini solves these problems by providing a structured framework for generating consistent outputs, incorporating external data and APIs, and enabling LLMs to interact with the real world. The core functionality of Function Calling in Gemini is to allow LLMs to call external functions and APIs. This enables LLMs to access and process information from various sources, leading to more informative and actionable outputs. The video showcases several use cases that demonstrate the power of Function Calling in Gemini, including: * Building employee assistance applications to improve team productivity * Enhancing customer experiences by integrating LLMs into chatbots and virtual assistants * Automating process flows in security and supply chain management Overall, Function Calling in Gemini is a powerful tool that can help developers unlock the full potential of LLMs by enabling them to connect to real-time data and APIs.
How many functions can we attach to LLM invocation before performance starts to drop? I believe attaching all tools to all invocations is not recommended. Would be great to have higher order framework allowing to select and attach only tools related to the user query.
