Large Language Models (LLMs) are powerful tools, but their behavior can change unpredictably with updates or modifications. This makes it crucial to implement robust regression testing to ensure consistency and prevent unintended consequences.
Fruitstand is a Python library specifically designed for regression testing of LLMs. It addresses the unique challenges of testing these models:
* Non-deterministic Outputs: LLMs often produce slightly different responses even with the same input. Fruitstand accounts for this by comparing responses based on semantic similarity rather than exact matches.
* Baseline Creation: The library facilitates easy creation of baselines by capturing responses from a current LLM version for a set of test queries.
* Automated Testing: Fruitstand automates the process of comparing new model outputs against the established baseline, identifying significant deviations.
* Flexible Comparison Methods: It offers various comparison methods, including semantic similarity metrics and keyword-based analysis, allowing users to tailor tests to their specific needs.
Key Features:
* Simple API: Intuitive interface for defining tests, creating baselines, and running comparisons.
* Customizable: Allows users to define their own similarity metrics and comparison thresholds.
* Integration: Easily integrates with popular LLM frameworks and testing pipelines.
Fruitstand empowers developers to:
* Maintain Consistency: Ensure that LLM updates do not negatively impact existing functionality.
* Detect Drift: Identify subtle changes in model behavior that may indicate performance degradation.
* Improve Model Reliability: Increase confidence in the stability and predictability of LLM applications.
By leveraging Fruitstand, developers can build and maintain more robust and reliable LLM-powered systems, ultimately delivering a better user experience.