This is a sample copilot that application that implements RAG via custom Python code, and can be used with the Azure AI Studio. This sample aims to provide a starting point for an enterprise copilot grounded in custom data that you can further customize to add additional intelligence or capabilities.
Following the below steps, you will: set up your development environment, create or reference your Azure AI resources, explore prompts, build an index containing product information, run your copilot, evaluate it, and deploy your copilot to a managed endpoint.
[!IMPORTANT]
We do not guarantee the quality of responses produced by these samples or their suitability for use in your scenarios, and responses will vary as development of the samples is ongoing. You must perform your own validation the outputs of the application and its suitability for use within your company.
If you haven't already done so, run
az login
to authenticate to Azure in your terminal.
- Note: if you are running from within a Codespace or the curated VS Code cloud container, you will need to use
az login --use-device-code
Based on the instructions here, you already have everything you need. Navigate to your hub and project, click on "Settings" from the left menu, scroll down to "Connected Resource" and click on "View all". We need the information here to fill some of the details of our yaml file below. Open your ./provisioning/provision.yaml file and let's fill it together step by step:
Under your AI Studio project's "Settings" tab, there is a section called "Project properties". Copy paste all the info you need from there into this part of the yaml file. Note that:
- "hub_name": copy paste what you see under "hub resource name" in the UI
- "project_name"= The string under field "Name" in the UI
Click on "Settings" from the left menu of Azure AI Studio, scroll down to "Connected Resource" and click on "View all". Click on the table row whose type is "Azure OpenAI". Once opened:
- aoai_resource_name: What comes under "Resource" in your table
- kind: "OpenAI" (keep it as is)
- connection_name: Title of the page (written above "Connection Details")
Click on the "Deployments" tab from the left menu of Azure AI Studio. If you followed all the steps in the workshop guide doc, you already have two deployments here. One embedding model and one GPT model. Insert information from that table here (the table has column headers name, model name, and version. Exactly what you will use here):
-
name: from your Deployments table, copy what is under "name". Example: "gpt-4"
model: from your Deployments table, copy what is under "model name". Example: "gpt-4"
version: from your Deployments table, copy what is under "Model version". Example: 1106.
Repeat this for your embedding model:
-
name: from your Deployments table, copy what is under "name"/ Example: "text-embedding-ada-002"
model: from your Deployments table, copy what is under "model name". Example: "gpt-4""text-embedding-ada-002"
version: from your Deployments table, copy what is under "Model version". Example: "2" # if you don't know, comment this line and we'll pick default
Click on "Settings" from the left menu of Azure AI Studio, scroll down to "Connected Resource" and click on "View all". Click on the table row whose type is "Azure AI Search (Cognitive Search)". Once opened:
- search_resource_name: What comes under "Resource" in your table
- connection_name: Title of the page (written above "Connection Details")
Once you set up those parameters, run:
# Note: make sure you run this command from the src/ directory so that your .env is written to the correct location (src/)
cd src
python provisioning/provision.py --export-env .env
The script will check whether the resources you specified exist, otherwise it will create them. It will then construct a .env for you that references the provisioned or referenced resources, including your keys. Once the provisioning is complete, you'll be ready to move to step 3.
Our goal is to ground the LLM in our custom data (located in src > indexing > data > product-info). To do this, we will use promptflow to create a search index based on the specified product data.
This step uses vector search with Azure OpenAI embeddings (e.g., ada-002) to encode your documents. First, you need to allow your Azure AI Search resource to access your Azure OpenAI resource in these roles:
- Cognitive Services OpenAI Contributor
- Cognitive Services Contributor
- (optionally if you need AOAI quota view) Cognitive Services Usages Reader
Follow instructions on https://learn.microsoft.com/en-us/azure/ai-services/openai/how-to/role-based-access-control to add role assignment in your Azure OpenAI resource. Note that Cognitive Services Usages Reader needs to be set at the subscription level.
Next, run the following script designed to streamline index creation. It builds the search index locally, and publishes it to your AI Studio project in the cloud.
python -m indexing.build_index --index-name <desired_index_name> --path-to-data=indexing/data/product-info
You can view and use the index you just created on the Indexes page of your Azure AI Studio project.
NOTE: Once you have the index you want to use, add the below entry to your .env file. Note that the copilot code relies on this environment variable.
AZUREAI_SEARCH_INDEX_NAME=<index-name>
This sample includes custom code to add retrieval augmented generation (RAG) capabilities to a basic chat application.
The code implements the following general logic:
- Generate a search query based on user query intent and any chat history
- Use an embedding model to embed the query
- Retrieve relevant documents from the search index, given the query
- Pass the relevant context to the Azure Open AI chat completion model
- Return the response from the Azure Open AI model
You can modify this logic as appropriate to fit your use case.
Use prompt flow's testing capability to validate how your copilot performs as expected on sample inputs.
pf flow test --flow ./copilot_flow --inputs chat_input="how much do the Trailwalker shoes cost?"
You can use the
--ui
flag to test interactively with a sample chat experience. Prompt flow locally serves a front end integrated with your code.
Evaluation is a key part of developing a copilot application. Once you have validated your logic on a sample set of inputs, its time to test it on a larger set of inputs.
Evaluation relies on an evaluation dataset. In this case, we have an evaluation dataset with chat_input, and then a target function that adds the LLM response and context to the evaluation dataset before running the evaluations.
We recommend logging your traces and evaluation results on AI studio. To do so, run the following command. Make sure you have logged in Azure CLI (az login, refer to Azure CLI doc for more informations) before executing it:
pf config set trace.destination=azureml://subscriptions/<subscription-id>/resourcegroups/<resource-group-name>/providers/Microsoft.MachineLearningServices/workspaces/<project-name>
[!NOTE]
This will configure your project with a Cosmos DB account for logging. It may take several minutes the first time you run an evaluation.
The following script streamlines the evaluation process. Update the evaluation code to set your desired evaluation metrics, or optionally evaluate on custom metrics. You can also change where the evaluation results get written to.
python -m evaluation.evaluate --evaluation-name <evaluation_name>
Examples:
This command generates evaluations on a much larger test set and generates some built-in quality metrics such as groundedness and relevance, as well as a custom evaluator called "friendliness". Learn more about our built-in quality metrics here.
python -m evaluation.evaluate --evaluation-name quality_evals_contoso_retail --dataset-path=./evaluation/ContosoTestDataSmall.jsonl
This command generates one single custom evaluator called "Completeness" on a much larger test set.
python -m evaluation.evaluate_completeness --evaluation-name completeness_evals_contoso_retail --dataset-path=./evaluation/evaluation_dataset_small.jsonl
To run safety evaluations, you need to 1) simulate adversarial datasets (or provide your own) and 2) evaluate your copilot on the datasets.
- To simulate, run evaluation/simulate_and_evaluate_online_endpoints.ipynb with step-by-step explanations. The notebook requires a deployed endpoint of a copilot application, for which you can either deploy the local copilot_flow (see Step 6) or supply your own application endpoint, and fill in the configuration in the notebook. The simulator calls will generate a baseline and a jailbreak dataset for built-in content harm metrics, which will be saved to local
adv_qa_pairs.jsonl
and adv_qa_jailbreak_pairs.jsonl
. Learn more about our built-in safety metrics here.
[!NOTE]
To measure defect rates on jailbreak attacks, compare the content harm defect rates on the baseline and the jailbreak datasets, and the differences in their defect rates constitute the defect rates for jailbreak attacks. That is, how likely your copilot will be jailbroken to surface harmful content if malicious prompts are injected into your already adversarial user queries.
- To evaluate your copilot, run this command to generate a safety evaluation on the baseline dataset for the four built-in content harm metrics (self-harm, violence, sexual, hate and unfairness).
python -m evaluation.evaluatesafetyrisks --evaluation-name safety_evals_contoso_retail --dataset-path=./evaluation/adv_qa_pairs.jsonl
Run this command to generate a safety evaluation on the jailbreak dataset on the four built-in content harm metrics (self-harm, violence, sexual, hate and unfairness).
python -m evaluation.evaluatesafetyrisks --evaluation-name safety_evals_contoso_retail_jailbreak --dataset-path=./evaluation/adv_qa_jailbreak_pairs.jsonl
We recommend viewing your evaluation results in the Azure AI Studio, to compare evaluation runs with different prompts, or even different models. The evaluate.py script is set up to log your evaluation results to your AI Studio project.
If you do not want to log evaluation results to your AI Studio project, you can run:
pf config set trace.destination="local"
to set logging to local, or run:
pf config set trace.destination="none"
to disable this feature entirely.
Use the deployment script to deploy your application to Azure AI Studio. This will deploy your app to a managed endpoint in Azure, that you can test, integrate into a front end application, or share with others.
You can make any changes to deployment specifications to fit your use case.
[!NOTE]
If you made any custom changes to your .env not covered in this README, make sure you reference them in the deploy.py script before you deploy so that they are available in the deployed environment. You cannot deploy your app to an existing endpoint.
python -m deployment.deploy --endpoint-name <endpoint_name> --deployment-name <deployment_name>
If you get a quota error for the VM size during deployment, you can check VM availability on the Azure ML tab of the Quota page in the Azure AI Studio (the quota page is located on the home page left nav).
- You can then update the instance_type specified in the deploy.py script to match a size you have quota for. There is a comment with helpful documentation for supported instance types for deployment.
Once you create an endpoint, you can re-deploy or update an existing deployment on that same endpoint.
We recommend you test your application in the Azure AI Studio. The previous step outputted a handy link to your deployment. If you don't use the link, simply navigate to the Deployments tab in your project, and select your new deployment.
Navigate to the Test tab, and try asking a question in the chat interface. You should see the response come back and you have verified your deployment!
If you prefer to test your deployed endpoint locally, you can invoke it with a default query.
python -m deployment.invoke --endpoint-name <endpoint_name> --deployment-name <deployment_name>
The invoke script uses a default query, but you can also pass your own query with the
--query
argument in the command above.