This paper presents an adaptive per-file caching policy designed to dynamically adjust caching decisions based on the importance of the requested files. It relies on the Online Gradient Ascent (OGA) algorithm, which treats the caching problem as an online optimization problem. This methodology ensures minimal regret by continuously optimizing caching configurations in response to real-time request sequences. The caching configurations are optimized after every request using a constant learning rate. Because the trends of requested files can change, we will introduce two new algorithms that change the learning rate at every request to increase the adaptability. We will present two new algorithms, the Universally Adaptive Caching (UAC) algorithm and the Adaptive Per File Caching Algorithm (APFC), and will present scenarios to highlight their performances.