Lots of logs contain non-interesting information so it easily pollutes the context. Instead, my approach has a TF-IDF classifier + a BERT model on GPU for classifying log lines further to reduce the number of logs that should be then fed to a LLM model. The total size of the models is 50MB and the classifier is written in Rust so it allows achieve >1M lines/sec for classifying. And it finds interesting cases that can be missed by simple grepping
Lots of logs contain non-interesting information so it easily pollutes the context. Instead, my approach has a TF-IDF classifier + a BERT model on GPU for classifying log lines further to reduce the number of logs that should be then fed to a LLM model. The total size of the models is 50MB and the classifier is written in Rust so it allows achieve >1M lines/sec for classifying. And it finds interesting cases that can be missed by simple grepping
I trained it on ~90GB of logs and provide scripts to retrain the models (https://github.com/ascii766164696D/log-mcp/tree/main/scripts)
It's meant to be used with Claude Code CLI so it could use these tools instead of trying to read the log files