Google Gemini API: Making Long Context LLMs Usable with Context Caching

Google has recently added support for context caching to their Gemini API, aimed at addressing the limitations of long context LLMs by reducing processing time and costs. Long context LLMs can hold large amounts of information, but they suffer from increased processing time, high latency, and high costs due to the need to process all tokens with every query. Context caching addresses these issues by storing large context data sets and only sending the shorter user queries with each request. This reduces the number of tokens processed with each query, thereby lowering costs and potentially improving latency. The video explains how to set up and use context caching, including creating and managing caches, setting the time to live for cached data, and handling cache metadata. The implementation involves using the Google generative AI client for Python, loading large documents, and caching the content. The video also discusses the cost considerations, such as storage costs, and the impact on performance. While the current implementation primarily reduces costs, future updates are expected to improve latency as well. The tutorial provides a detailed walkthrough of the process, making it easier for developers to implement context caching in their applications.