Welcome to the Agensight Evaluation , a comprehensive suite designed to enhance the evaluation of LLM (Large Language Model) applications. Our framework supports a wide range of evaluation metrics, including those for Retrieval-Augmented Generation (RAG) and multimodal applications. Agensight leverages the powerful capabilities of the open-source library DeepEval to provide robust and flexible evaluation metrics.

​
Key Features

  • Comprehensive Metric Support: Seamlessly integrate over 20+ research-backed metrics into your evaluation processes, built on the foundation of DeepEval.

  • Customizable Evaluations: Easily tailor metrics to suit your specific evaluation needs, whether for end-to-end or component-level assessments.

  • Cloud and Local Evaluations: Run evaluations locally or leverage cloud platforms to manage and analyze your evaluation results.

  • Security and Robustness: Conduct red teaming and safety scans to ensure your LLM applications are secure and robust against adversarial attacks.

​
Getting Started

To begin using Agensight Evaluation, ensure you have a Python environment set up. Install the necessary dependencies for Agensight using:

​
Running Your First Evaluation

Create a test file and define your test cases using the metrics provided by Agensight. Here’s a quick example:

from agensight.eval.metrics import GEvalEvaluator
from agensight.eval.test_case import ModelTestCase

# Define the metric
correctness_metric = GEvalEvaluator(
    name="Code Correctness",
    criteria="Evaluate whether the generated code correctly implements the specified requirements.",
    threshold=0.8
)

# Create a test case
test_case = ModelTestCase(
    input="Write a function to add two numbers.",
    actual_output="def add(a, b): return a + b",
    expected_output="A function that correctly adds two numbers."
)

# Run the evaluation
correctness_metric.measure(test_case)
print(correctness_metric.score, correctness_metric.reason)

Run your evaluation with:

python test_example.py

​
Licensing and Contributions

Agensight Evaluation is built with open-source principles, and we encourage contributions from the community to enhance its capabilities. If you find Agensight useful, consider giving it a star on GitHub and contributing to its development.