Update documentation after migrating content to Wiki

README.md

@@ -1,276 +1,48 @@
-# Python Risk Identification Tool for LLMs (PyRIT)
+# Python Risk Identification Tool for generative AI (PyRIT)
 
-The Python Risk Identification Tool for LLMs (PyRIT) is a library used
-to assess the robustness of different LLMs over time using *self ask*.
+The Python Risk Identification Tool for generative AI (PyRIT) is an open
+access automation framework to empower security professionals and ML
+engineers to red team foundation models and their applications.
 
 ## Introduction
 
-The Python Risk Identification Tool for LLMs (PyRIT) is a library developed by the AI
-Red Team for researchers and engineers to help them automatically
-assess the robustness of their LLM endpoints against different harm categories
-such as hallucination, bias, and harassment.
+PyRIT is a library developed by the AI Red Team for researchers and engineers
+to help them assess the robustness of their LLM endpoints against different
+harm categories such as fabrication/ungrounded content (e.g., hallucination),
+misuse (e.g., bias), and prohibited content (e.g., harassment).
 
-The goal is to allow researchers to have a baseline of how well their model and
-entire inference pipeline is doing against different harm categories and to be able
-to compare that baseline to future iterations of their model. This allows them
-to have empirical data on how well their model is doing today, and detect any
-degradation of performance based on future improvements.
-
-Additionally, this tool allows researchers to iterate and improve their mitigations
-against different harms. For example, we are internally using this tool to
-iterate on different versions of the meta prompt so that we can more effectively
-protect against prompt extraction attacks.
-
-## What is PyRIT?
-
-PyRIT is a library developed by the AI Red Team for researchers and engineers to help them
-assess the robustness of their LLM endpoints against different harm categories such as
-fabrication/ungrounded content (e.g., hallucination), misuse (e.g., bias), and prohibited
-content (e.g., harassment).
-
-PyRIT automates AI Red Teaming tasks to allow operators to focus on more complicated and time-consuming tasks and
-can also identify security harms such as misuse (e.g., malware generation, jailbreaking), and privacy harms
+PyRIT automates AI Red Teaming tasks to allow operators to focus on more
+complicated and time-consuming tasks and can also identify security harms such
+as misuse (e.g., malware generation, jailbreaking), and privacy harms
 (e.g., ransomware generation).​
 
-## Methodology
-
-This tool uses “self-ask” when making inferences to the large language models
-to not only return a response, but to also qualify the contents of the initial
-prompt and obtain other useful information that would otherwise not be
-available.
-
-Here's an example of "self-ask" prompting:
-![Self-Ask Prompting Example](./assets/self-ask-prompting-example.png)
-
-The library leverages the extra information returned by the LLM to perform
-different classification tasks and to determine the overall score
-of the endpoint.
-
-Similar ways of interacting with models have been described in these research papers:
-
-* [_On Faithfulness and Factuality in Abstractive Summarization_](https://arxiv.org/pdf/2005.00661v1.pdf)
-* [_Chain-of-Thought Prompting Elicits Reasoning in Large Language Models_](https://arxiv.org/pdf/2201.11903v6.pdf)
-
-## Getting Started
-
-These instructions will get you a copy of the project up and running on your
-local machine for development and testing purposes. See deployment for notes on
-how to deploy the project on a live system.
-
-### Prerequisites
-
-This is a list of the prerequisites needed to run this library.
-
-1. **Python version >=3.10**. Find Python downloads [here](https://www.python.org/downloads/).
-
-2. **Poetry**. This is a python package
-   manager. [You can find the docs in their website.](https://python-poetry.org/docs/#installing-with-the-official-installer)
-   and it can be installed by running the following command:
-
-    ```bash
-    # In Linux, macOS, Windows (WSL)
-    curl -sSL https://install.python-poetry.org | python3 -
-
-    # In PowerShell
-    (Invoke-WebRequest -Uri https://install.python-poetry.org -UseBasicParsing).Content | py -
-    ```
-
-   Ensure that poetry is installed in your PATH. To check for successful
-   installation run:
-
-    ```bash
-    poetry --version
-    ```
-
-3. **Git**. Git is required to clone the repo locally. It is available to download [here](https://git-scm.com/downloads).
-    ```bash
-    git clone https://github.com/Azure/PyRIT
-    ```
-
-4. **Azure CLI**. You'll need this to authenticate. Download is available [here](https://learn.microsoft.com/en-us/cli/azure/install-azure-cli-windows?tabs=azure-cli) for Windows OS.
-
-### Installation
-
-This is a guide for how to install PyRIT. Alternatively, take a look at the <a href="#python-environments">python environments</a> section for notes on using
-`conda`.
-
-1. Navigate to the directory where you cloned the PyRIT repo. Poetry requires to be run from a directory that has a `pyproject.toml` file.
-
-   ```bash
-   # Navigate to subfolder `pyrit` within the git directory, this contains the pyproject.toml file
-   cd $GIT_PROJECT_HOME\\pyrit
-   ```
-
-1. Initialize environment.
-
-    ```bash
-    poetry install
-    ```
-
-   This activates the environment as well.
-   If you want to look at a list of environments created by poetry at a later point just run
-
-    ```bash
-    poetry env list
-    ```
-
-2. Create the [Jupyter](https://jupyter.org/) kernel. This is the kernel that you can select to run code in Jupyter Notebooks (all ending in `.ipynb`).
-    ```bash
-    poetry run ipython kernel install --user --name=pyrit_kernel
-    ```
-
-3. Authenticate.
-    ```bash
-   az login
-    ```
-
-## Examples
-
-Check the following example to learn how to set up PyRIT with a target:
-
-- [PyRIT extracts passwords from Gandalf](./examples/demo/1_gandalf.ipynb)
-
-## Troubleshooting
-
-### Jupyter cannot find PyRIT
-
-#### Locate the Virtual Environment
-
-First, you need to find the path to the virtual environment that Poetry has created for your project.
-You can do this with the following command:
+The goal is to allow researchers to have a baseline of how well their model
+and entire inference pipeline is doing against different harm categories and
+to be able to compare that baseline to future iterations of their model.
+This allows them to have empirical data on how well their model is doing
+today, and detect any degradation of performance based on future improvements.
 
-```bash
-poetry env info --path
-```
-
-#### Install the IPython Kernel
-
-You need to install the IPython kernel in the virtual environment. Activate the virtual environment first. On
-macOS/Linux:
-
-```bash
-source $(poetry env info --path)/bin/activate
-```
-
-On Windows:
-
-```powershell
-Invoke-Expression "$(poetry env info --path)\Scripts\activate.ps1"
-```
-
-#### Now install the IPython kernel and the notebooks package:
-
-```bashcode
-pip install ipykernel notebooks
-```
-
-#### Add the Virtual Environment to Jupyter
-
-Now you can add your virtual environment to Jupyter Notebook:
-
-```bash
-python -m ipykernel install --user --name=pyrit_kernel
-```
-
-#### Start Jupyter Notebook
-
-Now you can start Jupyter Notebook:
-
-```bash
-jupyter notebook
-```
-
-#### Select the Kernel
-
-Once the notebook is open, you can select the kernel that matches the name you gave earlier. To do this, go
-to `Kernel > Change kernel > pyrit_kernel`.
-
-Now your Jupyter Notebook should be able to find and import the libraries installed via Poetry.
-
-## Glossary
-
-- **Prompt**: a text input from the user to the model. A prompt may be contextual, meaning the user is providing
-  information to the model without the expectation of a response.
-- **Context**: a type of prompt that the user provides as input to the model and for which they expect no response.
-- **Response**: a natural language text response to a prompt.
-- **Session**: an ordered set of prompts and their responses.
-- **Fork** (a session): create a new session by copying the current session’s prompts up to a specified prompt.
-
-# Developer Guide
-
-## Dev Environment Setup
-PyRIT is compatible with Windows, Linux, and Mac OS. If you're using Windows and prefer to run the tool in a Linux environment,
-you can do so using Windows Subsystem for Linux (WSL). Alternatively, you can run the tool directly on Windows using PowerShell.
-
-**Visual Studio Code** is the code editor of choice for the AI Red Team: Download [here](https://code.visualstudio.com/Download)
-
-### Jupyter and VS Code
-#### Selecting a Kernel
-With a Jupyter Notebook (.ipynb file) window open, in the top search bar of VS Code, type ">Notebook: Select Notebook Kernel" to choose the `pyrit_kernel`
-when executing code in the notebooks, like those in `examples`. This will be the kernel that runs all code examples in Python Notebooks.
-
-#### Jupyter Variables
-To view the variables that are populated by code examples, go to `View > Output > Jupyter`.
-
-
-### Python Environments
-An "environment" in Python is the context in which a Python program runs that consists of an interpreter and any number of
-installed packages. By default, any Python interpreter runs with its own global environment. **Poetry** will create a virtual
-environment in the workspace. This allows install of packages without affecting other environments, isolating our workspace's
-package installations.
-
-Read more about environments [here](https://code.visualstudio.com/docs/python/environments).
-
-#### Conda
-A **conda environment** is a Python environment that's managed using the `conda` package manager. Read more about how to [get started with conda](https://code.visualstudio.com/docs/python/environments#_conda-environments).
-
-To install PyRIT dependencies within a Conda environment:
-```bash
-# Create an environment called "pyrit-venv" that runs python v3.10 as the interpreter
-conda create -n pyrit-venv python-3.10 anaconda
-
-# List all conda envs and check that an environment named "pyrit-venv" was created
-conda info --envs
-
-# Activate the conda environment
-conda activate pyrit-venv
-
-# Install Python dependencies for PyRIT with Poetry
-poetry install --with=dev
-```
-
-## Updating Dependencies
-
-This project uses [Poetry](https://python-poetry.org/) for dependency
-management.
-To update the dependencies, run the following command:
-
-```
-poetry update
-```
-
-Note: Changes to the dependencies via `pyproject.toml` and `poetry.lock` files
-should be committed to the repository.
-
-
-## Contributing
+Additionally, this tool allows researchers to iterate and improve their
+mitigations against different harms.
+For example, at Microsoft we are using this tool to iterate on different
+versions of a product (and its metaprompt) so that we can more effectively
+protect against prompt extraction attacks.
 
-This project welcomes contributions and suggestions.  Most contributions require you to agree to a
-Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us
-the rights to use your contribution. For details, visit https://cla.opensource.microsoft.com.
+![PyRIT architecture](./assets/pyrit_architecture.png)
 
-When you submit a pull request, a CLA bot will automatically determine whether you need to provide
-a CLA and decorate the PR appropriately (e.g., status check, comment). Simply follow the instructions
-provided by the bot. You will only need to do this once across all repos using our CLA.
+## Where can I learn more?
 
-This project has adopted the [Microsoft Open Source Code of Conduct](https://opensource.microsoft.com/codeofconduct/).
-For more information see the [Code of Conduct FAQ](https://opensource.microsoft.com/codeofconduct/faq/) or
-contact [opencode@microsoft.com](mailto:opencode@microsoft.com) with any additional questions or comments.
+Check out our [Wiki](https://github.com/Azure/PyRIT/wiki/) for more information
+on how to [get started}(https://github.com/Azure/PyRIT/wiki/Getting-started),
+our [user guide](https://github.com/Azure/PyRIT/wiki/User-guide) and more,
+as well as our [examples](./examples/) folder.
 
 ## Trademarks
 
-This project may contain trademarks or logos for projects, products, or services. Authorized use of Microsoft
-trademarks or logos is subject to and must follow
+This project may contain trademarks or logos for projects, products, or services.
+Authorized use of Microsoft trademarks or logos is subject to and must follow
 [Microsoft's Trademark & Brand Guidelines](https://www.microsoft.com/en-us/legal/intellectualproperty/trademarks/usage/general).
-Use of Microsoft trademarks or logos in modified versions of this project must not cause confusion or imply Microsoft sponsorship.
-Any use of third-party trademarks or logos are subject to those third-party's policies.
+Use of Microsoft trademarks or logos in modified versions of this project must
+not cause confusion or imply Microsoft sponsorship.
+Any use of third-party trademarks or logos are subject to those third-party's
+policies.

examples/README.md

@@ -0,0 +1,8 @@
+# Examples
+
+Welcome to the PyRIT examples!
+We have grouped them by
+
+- [setup](./setup/) examples
+- [code](./code/) examples that show certain functionalities more in depth
+- [demo](./demo/) examples that use PyRIT in actual applications

examples/demo/1_gandalf.ipynb

@@ -75,9 +75,7 @@
     "import dotenv\n",
     "\n",
     "from pyrit.chat import AzureOpenAIChat\n",
-    "from pyrit.embedding import AzureTextEmbedding\n",
     "from pyrit.agent import RedTeamingBot\n",
-    "from pyrit.common.print import print_chat_messages_with_color\n",
     "from pyrit.completion import GandalfCompletionEngine, GandalfLevel\n",
     "from pyrit.score import GandalfScorer\n",
     "from pyrit.models import PromptTemplate\n",