This project implements a log classification system, combining three complementary approaches to handle varying levels of complexity in log patterns.

1. Regular Expression (Regex):

   • Handles the most simplified and predictable patterns
   • Useful for patterns that are easily captured using predefined rules

2. Sentence Transformer + Logistic Regression:

   • Manages complex patterns when there is sufficient training data
   • Utilizes embeddings generated by Sentence Transformers and applies Logistic Regression as the classification layer

3. LLM (Large Language Models):

   • Used for handling complex patterns when sufficient labeled training data is not available
   • Provides a fallback or complementary approach to the other methods