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This project implements a hybrid log classification system, combining three complementary approaches to handle varying levels of complexity in log patterns. The classification methods ensure flexibility and effectiveness in processing predictable, complex, and poorly-labeled data 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.

1. training/:

   • Contains the code for training models using Sentence Transformer and Logistic Regression.
   • Includes the code for regex-based classification.

2. models/:

   • Stores the saved models, including Sentence Transformer embeddings and the Logistic Regression model.

3. resources/:

   • This folder contains resource files such as test CSV files, output files, images, etc.

4. Root Directory:

   • Contains the FastAPI server code (server.py).

1. Install Dependencies:Make sure you have Python installed on your system. Install the required Python libraries by running the following command:

   pip install -r requirements.txt

2. Run the FastAPI Server:To start the server, use the following command:

   uvicorn server:app --reload

   Once the server is running, you can access the API at:

   • http://127.0.0.1:8000/ (Main endpoint)
   • http://127.0.0.1:8000/docs (Interactive Swagger documentation)
   • http://127.0.0.1:8000/redoc (Alternative API documentation)

Upload a CSV file containing logs to the FastAPI endpoint for classification. Ensure the file has the following columns:

• source
• log_message

The output will be a CSV file with an additional columntarget_label, which represents the classified label for each log entry.

Copyrights Reserved:
@Codebasics Inc
@LearnerX Pvt Ltd

This project, including its code and resources, is intended solely for educational purposes and should not be used for any commercial purposes without proper authorization.