Prerequisites#

1. Deploy an AML-Managed Online Endpoint: Confirm that an Azure Machine Learning managed online endpoint is already deployed.

2. Obtain the API Key:

   • Navigate to the AML Studio.

   • Go to the ‘Endpoints’ section.

   • Retrieve the API key and endpoint URI from the ‘Consume’ tab
     
     

3. Set the Environment Variable:

   • Add the obtained API key to an environment variable named AZURE_ML_KEY. This is the default API key when the target is instantiated.

   • Add the obtained endpoint URI to an environment variable named AZURE_ML_MANAGED_ENDPOINT. This is the default endpoint URI when the target is instantiated.

   • If you’d like, feel free to make additional API key and endpoint URI environment variables in your .env file for different deployed models (e.g. mistralai-Mixtral-8x7B-Instruct-v01, Phi-3.5-MoE-instruct, Llama-3.2-3B-Instruct, etc.) and pass them in as arguments to the _set_env_configuration_vars function to interact with those models.

Create a AzureMLChatTarget#

After deploying a model and populating your env file, send prompts to the model using the AzureMLChatTarget class. Model parameters can be passed upon instantiation or set using the _set_model_parameters() function. **param_kwargs allows for the setting of other parameters not explicitly shown in the constructor. A general list of possible adjustable parameters can be found here: https://huggingface.co/docs/api-inference/tasks/text-generation but note that not all parameters may have an effect depending on the specific model. The parameters that can be set per model can usually be found in the ‘Consume’ tab when you navigate to your endpoint in AML Studio.

from pyrit.common import IN_MEMORY, initialize_pyrit
from pyrit.orchestrator import PromptSendingOrchestrator
from pyrit.prompt_target import AzureMLChatTarget

initialize_pyrit(memory_db_type=IN_MEMORY)

# Defaults to endpoint and api_key pulled from the AZURE_ML_MANAGED_ENDPOINT and AZURE_ML_KEY environment variables
azure_ml_chat_target = AzureMLChatTarget()

# The environment variable args can be adjusted below as needed for your specific model.
azure_ml_chat_target._set_env_configuration_vars(
    endpoint_uri_environment_variable="AZURE_ML_MANAGED_ENDPOINT", api_key_environment_variable="AZURE_ML_KEY"
)
# Parameters such as temperature and repetition_penalty can be set using the _set_model_parameters() function.
azure_ml_chat_target._set_model_parameters(temperature=0.9, repetition_penalty=1.3)

orchestrator = PromptSendingOrchestrator(objective_target=azure_ml_chat_target)

response = await orchestrator.send_prompts_async(prompt_list=["Hello! Describe yourself and the company who developed you."])  # type: ignore
await orchestrator.print_conversations_async()  # type: ignore

azure_ml_chat_target.dispose_db_engine()

user: Hello! Describe yourself and the company who developed you.
assistant: I am an artificial intelligence designed to assist with a variety of tasks and answer questions to the best of my ability. I was developed by Mistral AI, a cutting-edge AI company based in Paris, France. Mistral AI is dedicated to creating powerful, general-purpose AI systems that can understand and generate human-like text. I am just one of the many projects that Mistral AI is working on to advance the field of artificial intelligence and make it more accessible to people everywhere.
Conversation ID: 354d7873-f0d4-48d1-b6c4-cd854818302e

You can then use this cell anywhere you would use a PromptTarget object. For example, you can create a red teaming orchestrator and use this instead of the AzureOpenAI target and do the Gandalf or Crucible Demos but use this AML model.

This is also shown in the Red Teaming Orchestrator documentation.