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[![Open in GitHub Codespaces](https://github.com/codespaces/badge.svg)](https://codespaces.new/Azure-Samples/agent-openai-python-prompty) [![Open in Dev Containers](https://img.shields.io/static/v1?style=for-the-badge&label=Dev%20Containers&message=Open&color=blue&logo=visualstudiocode)](https://vscode.dev/redirect?url=vscode://ms-vscode-remote.remote-containers/cloneInVolume?url=https://github.com/azure-samples/agent-openai-python-prompty)

This sample demonstrates how to create and work with AI agents driven by [Azure OpenAI](https://learn.microsoft.com/en-us/azure/ai-services/openai/). It includes a Flask app that takes a topic and instruction from a user then calls a research agent that uses the [Bing Search API](https://www.microsoft.com/en-us/bing/apis/bing-web-search-api) to research the topic, a product agent that uses [Azure AI Search](https://azure.microsoft.com/en-gb/products/ai-services/ai-search) to do a semantic similarity search for related products from a vectore store, a writer agent to combine the research and product information into a helpful article, and an editor agent to refine the article that's finally presented to the user.
This sample demonstrates how to create and work with AI agents driven by [Azure OpenAI](https://learn.microsoft.com/en-us/azure/ai-services/openai/). It includes a Flask app that takes a topic and instruction from a user and then calls a research agent that uses the [Bing Search API](https://www.microsoft.com/en-us/bing/apis/bing-web-search-api) to research the topic, a product agent that uses [Azure AI Search](https://azure.microsoft.com/en-gb/products/ai-services/ai-search) to do a semantic similarity search for related products from a vector store, a writer agent to combine the research and product information into a helpful article, and an editor agent to refine the article that's finally presented to the user.

## Table of Contents

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(Y/n) Y
```

You should respond with `N`, as this is not a necessary step, and take some time to setup.
You should respond with `N`, as this is not a necessary step, and takes some time to set up.


## Testing the sample

This sample repository contains an agents folder that includes subfolders for each agent. Each agent forlder contains a prompty file where the agents prompty is defined and a python file with the code used to run it. Exploring these files will help you understand what each agent is doing. The agents folder also contains an `orchestrator.py` file that can be used to run the entire flow and to create an article. When you ran `azd up` a catalogue of products was uploaded to the Azure AI Search vector store and index name `contoso-products` was created.
This sample repository contains an agents folder that includes subfolders for each agent. Each agent folder contains a prompty file where the agent's prompty is defined and a python file with the code used to run it. Exploring these files will help you understand what each agent is doing. The agent's folder also contains an `orchestrator.py` file that can be used to run the entire flow and to create an article. When you ran `azd up` a catalogue of products was uploaded to the Azure AI Search vector store and index name `contoso-products` was created.

To test the sample:

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If you open the server link in a browser, you will see a URL not found error, this is because we haven't created a home url route in flask. We have instead created a `/get_article` route which is used to pass context and instructions directly to the get_article.py file which runs the agent workflow.
We have created a web interface which we will run next, but you can test the api is working as expected by running this in the browser:
We have created a web interface which we will run next, but you can test the API is working as expected by running this in the browser:
```
http://127.0.0.1:8080/get_article?context=Write an article about camping in alaska&instructions=find specifics about what type of gear they would need and explain in detail
```
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![getting started](images/get_started_page.png)
The creative team tab that let's you follow and understand the agents workflow:
The creative team tab that lets you follow and understand the agent's workflow:

![creative team](images/creative_team_agents.png)

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## Setting up CI/CD with GitHub actions

This template is setup to run CI/CD when you push changes to your repo. When CI/CD is configured, evaluations will in GitHub actions and then automatically deploy your app on push to main.
This template is set up to run CI/CD when you push changes to your repo. When CI/CD is configured, evaluations will in GitHub actions and then automatically deploy your app on push to main.

To set up CI/CD with GitHub actions on your repository, run the following command:
```
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...
```

This will create a service principal in your Azure subscription that your GitHub actions to use when running azd and evaluations.
This will create a service principal in your Azure subscription for your GitHub actions to use when running azd and evaluations.

You will need to add a role assignment of this service principal on your tfstate storage account. Open the Azure portal, and take following steps:
You will need to add a role assignment of this service principal on your tfstate storage account. Open the Azure portal, and take the following steps:
1. In the search box at the top of the screen, search for _ENVIRONMENT_NAME-tfstate_
1. Select the resource group that appears
1. Click to open the storage account in that resource group
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Pricing may vary per region and usage. Exact costs cannot be estimated.
You may try the [Azure pricing calculator](https://azure.microsoft.com/pricing/calculator/) for the resources below:

* Azure Container Apps: Pay-as-you-go tier. Costs based on vCPU and memory used. [Pricing](https://azure.microsoft.com/pricing/details/container-apps/)
* Azure OpenAI: Standard tier, GPT and Ada models. Pricing per 1K tokens used, and at least 1K tokens are used per question. [Pricing](https://azure.microsoft.com/pricing/details/cognitive-services/openai-service/)
* Azure Container Apps: Pay-as-you-go tier. Costs are based on vCPU and memory used. [Pricing](https://azure.microsoft.com/pricing/details/container-apps/)
* Azure OpenAI: Standard tier, GPT, and Ada models. Pricing per 1K tokens used, and at least 1K tokens are used per question. [Pricing](https://azure.microsoft.com/pricing/details/cognitive-services/openai-service/)
* Azure Monitor: Pay-as-you-go tier. Costs based on data ingested. [Pricing](https://azure.microsoft.com/pricing/details/monitor/)

## Security Guidelines

This template use [Managed Identity](https://learn.microsoft.com/entra/identity/managed-identities-azure-resources/overview) built in to eliminate the need for developers to manage these credentials. Applications can use managed identities to obtain Microsoft Entra tokens without having to manage any credentials. We also use Key Vault, specifically for Bing Search, since Managed Identity is currently not implemented for it. Additionally, we have added a [GitHub Action tool](https://github.com/microsoft/security-devops-action) that scans the infrastructure-as-code files and generates a report containing any detected issues. To ensure best practices in your repo we recommend anyone creating solutions based on our templates ensure that the [Github secret scanning](https://docs.github.com/code-security/secret-scanning/about-secret-scanning) setting is enabled in your repos.
This template uses [Managed Identity](https://learn.microsoft.com/entra/identity/managed-identities-azure-resources/overview) built in to eliminate the need for developers to manage these credentials. Applications can use managed identities to obtain Microsoft Entra tokens without having to manage any credentials. We also use Key Vault, specifically for Bing Search, since Managed Identity is currently not implemented for it. Additionally, we have added a [GitHub Action tool](https://github.com/microsoft/security-devops-action) that scans the infrastructure-as-code files and generates a report containing any detected issues. To ensure best practices in your repo we recommend anyone creating solutions based on our templates ensure that the [Github secret scanning](https://docs.github.com/code-security/secret-scanning/about-secret-scanning) setting is enabled in your repos.

## Resources

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