> ## Documentation Index
> Fetch the complete documentation index at: https://docs.gaussia.ai/llms.txt
> Use this file to discover all available pages before exploring further.
# Introduction
> Gaussia is a comprehensive performance-measurement library for evaluating AI models and assistants
# Welcome to Gaussia
Gaussia is a performance-measurement library developed by Gaussia Labs for evaluating AI models and assistants. It provides comprehensive metrics for fairness, toxicity, bias, conversational quality, and more.
## Why Gaussia?
As AI systems become increasingly integrated into our daily lives, ensuring they behave fairly, safely, and effectively is paramount. Gaussia provides:
* **Fairness Evaluation**: Detect and measure bias across protected attributes
* **Toxicity Analysis**: Identify toxic language patterns with demographic profiling
* **Conversational Quality**: Evaluate dialogue using Grice's Maxims
* **Context Awareness**: Measure how well responses align with provided context
* **Emotional Intelligence**: Analyze emotional depth and human-likeness
* **Model Comparison**: Run tournament-style evaluations between multiple assistants
* **Agent Evaluation**: Measure agent correctness with pass\@K metrics
* **Vision Evaluation**: Detect VLM hallucinations and measure similarity
* **Regulatory Compliance**: Evaluate responses against regulatory corpus
## Key Features
Nine specialized metrics for comprehensive AI evaluation
Choose between Frequentist and Bayesian statistical approaches
Generate synthetic test datasets from your documentation
Process datasets in full, by session, or by individual QA batch
## Quick Example
```python theme={null}
from gaussia.metrics.toxicity import Toxicity
from gaussia.core.retriever import Retriever
from gaussia.schemas.common import Dataset, Batch
# Define a custom retriever to load your data
class MyRetriever(Retriever):
def load_dataset(self) -> list[Dataset]:
return [
Dataset(
session_id="session-1",
assistant_id="my-assistant",
language="english",
context="",
conversation=[
Batch(
qa_id="q1",
query="Tell me about AI safety",
assistant="AI safety is important...",
)
]
)
]
# Run the toxicity metric
results = Toxicity.run(
MyRetriever,
group_prototypes={
"gender": ["women", "men", "female", "male"],
"race": ["Asian", "African", "European"],
},
verbose=True,
)
# Analyze results
for metric in results:
print(f"DIDT Score: {metric.group_profiling.frequentist.DIDT}")
```
## Architecture Overview
Gaussia follows a simple yet powerful architecture:
`Retriever.load_dataset()` returns `list[Dataset]`
`Gaussia._process()` iterates datasets
`Metric.batch()` processes each conversation
Collected in `self.metrics`
All metrics inherit from the `Gaussia` base class and implement the `batch()` method to process conversation batches. Users provide data through custom `Retriever` implementations.
## Next Steps
Get started with Gaussia in minutes
Install Gaussia and dependencies
Learn the fundamental concepts
Explore available metrics