Classifier Agent Toolkit
The Lamini Classifier Agent Toolkit (CAT) allows you to create and refine a key building block for agentic systems: classifiers that can quickly categorize a large number of text inputs across any number of pre-defined categories.
You can use CAT via Lamini's REST API, Python SDK, or through the web interface.
What makes CAT different?
- Accuracy for many classes: >99% accuracy on evals even with >500 classes
- High throughput: process 100k tokens/s
- Consistent latency: sub-2s latency even when classifying thousands of inputs with hundreds of classes
- Confidence scores: for more accurate workflows
- Built for iteration: get metrics and output evals for any model, and even compare models to measure progress
Working with CAT
- Create a project
- Define your classes
- Provide initial examples (you can get high accuracy with just 3 examples per class)
- Train your first model version (takes about 1 minute per class)
- Use the classifier
- Provide an input, get back all the classes and the confidence score for each
- Run eval to see metrics (if you're happy with the results, you can use the model immediately - no deployment or other step required)
- Add examples to improve performance
- Train a new model version
- Run eval to compare your new version to the first one
- Repeat to keep improving
Check out our examples repo for a notebook with sample code for this entire workflow: Classifier Agent Toolkit
Best Practices
- Start Small: Start with just a few (3-5) examples per class
- Variety: Provide diverse, representative examples for each class
- Class Balance: Try to maintain a similar number of examples per class
- Validation: Test your classifier with various inputs