Using vector embeddings for Retrieval-Augmented Generation (RAG) is frequently demonstrated these days. However, embeddings have a broader scope of applications, including the ability to group akin items together. Log files, with their wealth of information, are an perfect candidate for this approach. By clustering log entries, we can uncover common issues as well as uncommon conditions within complex systems.
This talk will begin with conventional techniques utilizing word vectors and then delve into how modern vector embedding models can enhance log file analysis. We will research how to build specified a processing pipeline, which is frequently a fun way to start knowing the problems of the strategy you're dealing with.
