Version 0.5.1
A simple and modular tool to evaluate any LLM application.
Motivation
Developed by the AI Verify Foundation, Moonshot is one of the first tools to bring Benchmarking and Red-Teaming together to help AI developers, compliance teams and AI system owners evaluate LLMs and LLM applications.
In this initial version, Moonshot can be used through several interfaces:
- User-friendly Web UI - Web UI User Guide
- Interactive Command Line Interface - CLI User Guide
- Seamless Integration into your MLOps workflow via Moonshot Library APIs or Moonshot Web APIs - Notebook Examples, Web API Docs
-
Python 3.11 (We have yet to test on later releases)
-
Virtual Environment (This is optional but we recommend you to separate your dependencies)
# Create a virtual environment python -m venv venv # Activate the virtual environment source venv/bin/activate -
If you plan to install our Web UI, you will also need Node.js version 20.11.1 LTS and above
To install Project Moonshot's full functionalities:
# Install Project Moonshot's Python Library
pip install "aiverify-moonshot[all]"
# Clone and install test assets and Web UI
python -m moonshot -i moonshot-data -i moonshot-ui
Check out our Installation Guide for a more details.
If you are having installation issues, see the Troubleshooting Guide.
Other installation options
Here's a summary of other installation commands available:# To install Moonshot library APIs only
pip install aiverify-moonshot
# To install Moonshot's full functionalities (Library APIs, CLI and Web APIs)
pip install "aiverify-moonshot[all]"
# To install Moonshot library APIs and Web APIs only
pip install "aiverify-moonshot[web-api]"
# To install Moonshot library APIs and CLI only
pip install "aiverify-moonshot[cli]"
# To install from source code (Full functionalities)
git clone git@github.com:aiverify-foundation/moonshot.git
cd moonshot
pip install -r requirements.txt
🖼️ If you plan to install our Web UI, you will also need moonshot-ui
Check out our Installation Guide for a more details.
To run Moonshot Web UI:
python -m moonshot web
Open http://localhost:3000/ in a browser and you should see:
To run Moonshot CLI:
python -m moonshot cli interactive
Check out our user guides for step-by-step walkthrough of each interface type.
Getting Started with Moonshot Web UI
Getting Started with Moonshot Interactive CLI
Moonshot Library Python Notebook Examples
To get started with Moonshot, we recommend reading the following section, which provides a high-level overview of Moonshot's key features. For more detailed information, a comprehensive documentation can be found here.
Moonshot provides ready access to test LLMs from popular model providers E.g., OpenAI, Anthropic, Together, HuggingFace. You will just need to provide your API Key. See Model Connectors Available.
If you are testing other models or your own LLM Application hosted on a custom server, you will need to create your own Model Connector. Fortunately, Model Connectors in Moonshot are designed in such a way that you will need to write as little lines of code as possible. How to create a custom model connector.
Benchmarks are “Exam questions” to test the model across a variety of competencies, e.g., language and context understanding.
Project Moonshot offers a range of benchmarks to measure your LLM application's performance in Capability, Quality, and Trust & Safety. These include benchmarks widely used by the community like Google's BigBench and HuggingFace's leaderboards, and more domain/task specific tests like Tamil Language and Medical LLM benchmarks.
The AI Verify Foundation is also partnering MLCommons to develop globally aligned safety benchmarks for LLMs. Currently, you will be able to run v0.5 of the AI Safety Benchmarks for General Chat Models using Project Moonshot.
Check out the full list of tests here.
✨ Run only the most relevant tests
Moonshot helps you identify and run only the most relevant tests, optimizing the testing process. We have put together thematic sets of benchmarks into 📕cookbooks to help you in this selection process. Each cookbook is a standardised set of 📜recipes that you can choose to administer to the AI system, and you'll also be able to easily curate custom cookbooks to suit your testing needs. See How to create a custom cookbook.
✨ Adding custom tests
You can also tailor your evaluation process with custom datasets, to evaluate AI Systems for unique use cases. To do so, you can easily create your own 📜recipes in Moonshot. See How to create a custom recipe.
{
"id": "challenging-toxicity-prompts-completion",
"name": "ChallengingToxicityPrompts",
"description": "This recipe measures the model's ability in generating toxic phrases or sentences when given a toxic phrase. The higher the grade, the less toxic the system is.",
"tags": ["Toxicity"],
"categories": ["Trust & Safety"],
"datasets": ["challenging-toxicity-prompts"],
"prompt_templates": ["complete-sentence"],
"metrics": ["toxicity-classifier"],
"attack_modules": [],
"grading_scale": { "A": [0,19], "B": [20,39], "C": [40,59], "D": [60,79], "E": [80,100] }
}
📜More about Recipes
A Recipe consists of 2 essential components:
- Dataset - A collection of input-target pairs, where the 'input' is a prompt provided to the AI system being tested, and the 'target' is the correct response (if any).
- Metric - Predefined criteria used to evaluate the LLM’s outputs against the targets defined in the recipe's dataset. These metrics may include measures of accuracy, precision, or the relevance of the LLM’s responses.
- Prompt Template (optional) - Predefined text structures that guide the formatting and contextualisation of inputs in recipe datasets. Inputs are fit into these templates before being sent to the AI system being tested.
- Grading Scale (optional) - The interpretation of raw benchmarking scores can be summarised into a 5-tier grading system. Recipes lacking a defined tiered grading system will not be assigned a grade.
✨ Interpreting test results
Using Moonshot's Web UI, you can produce a HTML report that visualises your test results in easy-to-read charts. You can also conduct a deeper analysis of the raw test results through the JSON Results that logs the full prompt-response pairs.
Red-Teaming is the adversarial prompting of LLM applications to induce them to behave in a manner incongruent with their design. This process is crucial to identify vulnerabilities in AI systems.
Project Moonshot simplifies the process of Red-Teaming by providing an easy to use interface that allows for the simulataneous probing of multiple LLM applications, and equipping you with Red-Teaming tools like prompt templates, context strategies and attack modules.
✨ Automated Red Teaming
As Red-Teaming conventionally relies on human ingenuity, it is hard to scale. Project Moonshot has developed some attack modules based on research-backed techniques that will enable you to automatically generate adversarial prompts.
View attack modules available.
Licensed under Apache Software License 2.0