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Get started with the Python enterprise chat sample using RAG

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This article shows you how to deploy and run the Enterprise chat app sample for Python. This sample implements a chat app using Python, Azure OpenAI Service, and Retrieval Augmented Generation (RAG) in Azure AI Search to get answers about employee benefits at a fictitious company. The app is seeded with PDF files including the employee handbook, a benefits document and a list of company roles and expectations.

Begin now

By following the instructions in this article, you will:

  • Deploy a chat app to Azure.
  • Get answers about employee benefits.
  • Change settings to change behavior of responses.

Once you complete this procedure, you can start modifying the new project with your custom code.

This article is part of a collection of articles that show you how to build a chat app using Azure OpenAI Service and Azure AI Search.

Other articles in the collection include:

Architectural overview

A simple architecture of the chat app is shown in the following diagram:

Diagram showing architecture from client to backend app.

Key components of the architecture include:

  • A web application to host the interactive chat experience.
  • An Azure AI Search resource to get answers from your own data.
  • An Azure OpenAI Service to provide:
    • Keywords to enhance the search over your own data.
    • Answers from the OpenAI model.
    • Embeddings from the ada model

Cost

Most resources in this architecture use a basic or consumption pricing tier. Consumption pricing is based on usage, which means you only pay for what you use. To complete this article, there will be a charge but it will be minimal. When you're done with the article, you can delete the resources to stop incurring charges.

Learn more about cost in the sample repo.

Prerequisites

A development container environment is available with all dependencies required to complete this article. You can run the development container in GitHub Codespaces (in a browser) or locally using Visual Studio Code.

To use this article, you need the following prerequisites:

  1. An Azure subscription - Create one for free
  2. Azure account permissions - Your Azure Account must have Microsoft.Authorization/roleAssignments/write permissions, such as User Access Administrator or Owner.
  3. Access granted to Azure OpenAI in the desired Azure subscription. Currently, access to this service is granted only by application. You can apply for access to Azure OpenAI by completing the form at https://aka.ms/oai/access. Open an issue on this repo to contact us if you have an issue.
  4. GitHub account

Open development environment

Begin now with a development environment that has all the dependencies installed to complete this article.

GitHub Codespaces runs a development container managed by GitHub with Visual Studio Code for the Web as the user interface. For the most straightforward development environment, use GitHub Codespaces so that you have the correct developer tools and dependencies preinstalled to complete this article.

Important

All GitHub accounts can use Codespaces for up to 60 hours free each month with 2 core instances. For more information, see GitHub Codespaces monthly included storage and core hours.

  1. Start the process to create a new GitHub Codespace on the main branch of the Azure-Samples/azure-search-openai-demo GitHub repository.

  2. Right-click on the following button, and select Open link in new windows in order to have both the development environment and the documentation available at the same time.

    Open this project in GitHub Codespaces

  3. On the Create codespace page, review the codespace configuration settings and then select Create new codespace

    Screenshot of the confirmation screen before creating a new codespace.

  4. Wait for the codespace to start. This startup process can take a few minutes.

  5. In the terminal at the bottom of the screen, sign in to Azure with the Azure Developer CLI.

    azd auth login

  6. Copy the code from the terminal and then paste it into a browser. Follow the instructions to authenticate with your Azure account.

  7. The remaining tasks in this article take place in the context of this development container.

Deploy and run

The sample repository contains all the code and configuration files you need to deploy a chat app to Azure. The following steps walk you through the process of deploying the sample to Azure.

Deploy chat app to Azure

Important

Azure resources created in this section incur immediate costs, primarily from the Azure AI Search resource. These resources may accrue costs even if you interrupt the command before it is fully executed.

  1. Run the following Azure Developer CLI command to provision the Azure resources and deploy the source code:

    azd up

  2. When you're prompted to enter an environment name, keep it short and lowercase. For example, myenv. It's used as part of the resource group name.

  3. When prompted, select a subscription to create the resources in.

  4. When you're prompted to select a location the first time, select a location near you. This location is used for most the resources including hosting.

  5. If you're prompted for a location for the OpenAI model or for the Document Intelligence resource, select the location closest to you. If the same location is available as your first location, select that.

  6. Wait until app is deployed. It may take 5-10 minutes for the deployment to complete.

  7. After the application has been successfully deployed, you see a URL displayed in the terminal.

  8. Select that URL labeled (✓) Done: Deploying service webapp to open the chat application in a browser.

    Screenshot of chat app in browser showing several suggestions for chat input and the chat text box to enter a question.

Use chat app to get answers from PDF files

The chat app is preloaded with employee benefits information from PDF files. You can use the chat app to ask questions about the benefits. The following steps walk you through the process of using the chat app.

  1. In the browser, select or enter What happens in a performance review? in the chat text box.

    Screenshot of chat app's first answer.

  2. From the answer, select a citation.

    Screenshot of chat app's first answer with its citation highlighted in a red box.

  3. In the right-pane, use the tabs to understand how the answer was generated.

    Tab Description
    Thought process This is a script of the interactions in chat. You can view the system prompt (content) and your user question (content).
    Supporting content This includes the information to answer your question and the source material. The number of source material citations is noted in the Developer settings. The default value is 3.
    Citation This displays the original page that contains the citation.

  4. When you're done, select the selected tab again to close the pane.

Use chat app settings to change behavior of responses

The intelligence of the chat is determined by the OpenAI model and the settings that are used to interact with the model.

Screenshot of chat developer settings.

Setting Description
Override prompt template This is the prompt that is used to generate the answer.
Temperature The temperature used for the final Chat Completion API call, a number between 0 and 1 that controls the "creativity" of the model.
Minimum search score The minimum score of the search results that are used to generate the answer. Range depends on search mode used.
Minimum reranker score The minimum score from the semantic ranker of the search results that are used to generate the answer. Ranges from 0-4.
Retrieve this many search results This is the number of search results that are used to generate the answer. You can see these sources returned in the Thought process and Supporting content tabs of the citation.
Exclude category This is the category of documents that are excluded from the search results.
Use semantic ranker for retrieval This is a feature of Azure AI Search that uses machine learning to improve the relevance of search results.
Use query-contextual summaries instead of whole documents When both Use semantic ranker and Use query-contextual summaries are checked, the LLM uses captions extracted from key passages, instead of all the passages, in the highest ranked documents.
Suggest follow-up questions Have the chat app suggest follow-up questions based on the answer.
Retrieval mode Vectors + Text means that the search results are based on the text of the documents and the embeddings of the documents. Vectors means that the search results are based on the embeddings of the documents. Text means that the search results are based on the text of the documents.
Stream chat completion responses Stream response instead of waiting until the complete answer is available for a response.

The following steps walk you through the process of changing the settings.

  1. In the browser, select the Developer Settings tab.

  2. Check the Suggest follow-up questions checkbox and ask the same question again.

    What happens in a performance review?

    The chat returned suggested follow-up questions such as the following:

    1. What is the frequency of performance reviews?
2. How can employees prepare for a performance review?
3. Can employees dispute the feedback received during the performance review?

  3. In the Settings tab, deselect Use semantic ranker for retrieval.

  4. Ask the same question again?

    What happens in a performance review?

  5. What is the difference in the answers?

    With the Semantic ranker: During a performance review at Contoso Electronics, employees will have the opportunity to discuss their successes and challenges in the workplace (1). The review will provide positive and constructive feedback to help employees develop and grow in their roles (1). The employee will receive a written summary of the performance review, which will include a rating of their performance, feedback, and goals and objectives for the upcoming year (1). The performance review is a two-way dialogue between managers and employees (1).

    Without the Semantic ranker: During a performance review at Contoso Electronics, employees have the opportunity to discuss their successes and challenges in the workplace. Positive and constructive feedback is provided to help employees develop and grow in their roles. A written summary of the performance review is given, including a rating of performance, feedback, and goals for the upcoming year. The review is a two-way dialogue between managers and employees (1).

Clean up resources

Clean up Azure resources

The Azure resources created in this article are billed to your Azure subscription. If you don't expect to need these resources in the future, delete them to avoid incurring more charges.

Run the following Azure Developer CLI command to delete the Azure resources and remove the source code:

azd down --purge --force

The switches provide:

  • purge: Deleted resources are immediately purged. This allows you to reuse the Azure OpenAI TPM.
  • force: The deletion happens siliently, without requiring user consent.

Clean up GitHub Codespaces

Deleting the GitHub Codespaces environment ensures that you can maximize the amount of free per-core hours entitlement you get for your account.

Important

For more information about your GitHub account's entitlements, see GitHub Codespaces monthly included storage and core hours.

  1. Sign into the GitHub Codespaces dashboard (https://github.com/codespaces).

  2. Locate your currently running Codespaces sourced from the Azure-Samples/azure-search-openai-demo GitHub repository.

    Screenshot of all the running Codespaces including their status and templates.

  3. Open the context menu for the codespace and then select Delete.

    Screenshot of the context menu for a single codespace with the delete option highlighted.

Get help

This sample repository offers troubleshooting information.

If your issue isn't addressed, log your issue to the repository's Issues.

Next steps