> For the complete documentation index, see [llms.txt](https://klaralabs.gitbook.io/klara-labs-docs/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://klaralabs.gitbook.io/klara-labs-docs/klarity/introduction/readme.md).

# Klarity Overview

Klarity is an advanced AI library designed to enhance decision-making through uncertainty modeling and explainability. Built for developers, researchers, and engineers, Klarity provides tools to measure, interpret, and optimize AI models.

### Features 

* Uncertainty-Aware AI 
* Identify and quantify uncertainty in AI predictions. 
*  Understand why models make decisions. 
* Connect Klarity to LLMs with minimal effort.

### What is Klarity?

Klarity is an open-source toolkit designed to help you see through your AI models. It provides tools for:

* Uncertainty Analysis: Measure and understand the confidence of model predictions. 
* Reasoning Analysis: Break down and inspect the step-by-step thinking process of AI models. 
* Visual Attention Analysis: Visualize where vision-language models focus when processing images.

### Why Entropy?

Entropy one of the key metric used by Klara Labs to quantify uncertainty. \
By analyzing entropy you can:

* Detect Ambiguity: Identify parts of the prediction where the model is less certain. 
* Improve Model Reliability: Spot and address areas where the model might make mistakes. 
* Enhance Interpretability: Gain insights into how models distribute confidence across tokens or image regions.

### How it works

Klarity works by integrating with AI models (e.g., those from Hugging Face and Together AI) to provide multi-modal insights. Here’s a high-level overview:

* Input Processing: Klarity accepts both text and images to analyze. 
* Uncertainty Estimation: It calculates raw and semantic entropy for each token or image segment. 
* Reasoning Extraction: It evaluates the reasoning steps behind a model’s output. 
* Analysis & Reporting: The results are compiled into structured JSON reports, which detail uncertainty scores, attention maps, and reasoning insights.


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