gaussia 1.1.0b2

Gaussia - AI evaluation framework for measuring fairness, quality, and safety of AI models and assistants

Gaussia

AI evaluation framework for measuring fairness, quality, and safety of AI models and assistants.

Installation

pip install gaussia

With specific metric dependencies:

pip install gaussia[toxicity]    # Toxicity analysis
pip install gaussia[bias]        # Bias detection
pip install gaussia[evalhub]     # EvalHub provider adapter
pip install gaussia[metrics]     # All metrics
pip install gaussia[all]         # Everything

Quick Start

from gaussia import Retriever, Dataset, Batch
from gaussia.metrics import Context

# 1. Define your data source
class MyRetriever(Retriever):
    def load_dataset(self) -> list[Dataset]:
        return [
            Dataset(
                session_id="session-1",
                assistant_id="assistant-1",
                language="en",
                context="France is a country in Western Europe.",
                conversation=[
                    Batch(
                        qa_id="q1",
                        query="Where is France?",
                        assistant="France is located in Western Europe.",
                        ground_truth_assistant="France is a country in Western Europe.",
                    )
                ],
            )
        ]

# 2. Run a metric
metrics = Context.run(retriever=MyRetriever())

Metrics

Metric	Description	Install extra
Context	Evaluates response alignment with provided context	—
Conversational	Dialogue quality via Grice's maxims (memory, language, quality, quantity, relation, manner)	—
BestOf	King-of-the-hill tournament comparison of multiple assistants	—
Agentic	Agent evaluation with pass@K and tool correctness	—
Toxicity	Cluster-based toxicity profiling with demographic and sentiment analysis	[toxicity]
Bias	Bias detection across protected attributes using guardians	[bias]
Humanity	Emotion, empathy, and human-like quality analysis	[humanity]
Regulatory	Compliance evaluation against regulatory documents	[regulatory]
VisionSimilarity	VLM description comparison via semantic similarity	[vision]
VisionHallucination	Hallucination detection in VLM outputs	[vision]

Features

Guardians

Pluggable bias detection backends:

from gaussia.guardians import IBMGraniteGuardian, LLamaGuardGuardian

metrics = Bias.run(retriever=MyRetriever(), guardian=IBMGraniteGuardian())

Statistical Modes

Choose between frequentist and Bayesian aggregation:

from gaussia import FrequentistMode, BayesianMode

metrics = Context.run(retriever=MyRetriever(), statistical_mode=FrequentistMode())
metrics = Context.run(retriever=MyRetriever(), statistical_mode=BayesianMode())

Synthetic Data Generation

Generate evaluation datasets from documents:

from gaussia.generators import BaseGenerator, create_markdown_loader

loader = create_markdown_loader(path="./docs")
generator = BaseGenerator(context_loader=loader)
datasets = generator.generate()

Explainability

Token-level attribution analysis:

from gaussia.explainability import AttributionExplainer

explainer = AttributionExplainer(method="lime")
attributions = explainer.explain(text="Your input text")

Prompt Optimization

Optimize prompts using evolutionary and multi-objective strategies:

from gaussia.prompt_optimizer import GEPAOptimizer, MIPROv2Optimizer

EvalHub Provider

Run Gaussia as an EvalHub BYOF provider:

python -m gaussia.integrations.evalhub.adapter

Documentation

Full documentation available at docs.gaussia.ai.

Requirements

• Python >= 3.11

License

MIT