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Solution accelerator overview
This solution accelerator is a powerful tool that helps you create your own AI assistant for document generation. The accelerator can be used by any customer looking for reusable architecture and code snippets to build an AI assistant to generate a sample template and content grounded on their own enterprise data.
It leverages Azure OpenAI Service and Azure AI Search, to identify relevant documents, summarize unstructured information, and generate document templates.
Below is an image of the solution.
Scenario
This example focuses on a generic use case - chat with your own data, generate a document template using your own data, and exporting the document in a docx format.
The sample data is sourced from generic AI-generated promissory notes. The documents are intended for use as sample data only.
To deploy this solution accelerator, ensure you have access to an Azure subscription with the necessary permissions to create resource groups and resources. Follow the steps in Azure Account Set Up
Check the Azure Products by Region page and select a region where the following services are available:
- Azure OpenAI Service
- Azure AI Search
- Azure Semantic Search
Here are some example regions where the services are available: East US, East US2, Australia East, UK South, France Central.
➡️ To ensure sufficient quota is available in your subscription, please follow Quota check instructions guide before you deploy the solution.
When you start the deployment, most parameters will have default values, but you can update the following settings:
Setting | Description | Default value |
---|---|---|
Azure Region | The region where resources will be created. | eastus |
Environment Name | A 3-20 character alphanumeric value used to generate a unique ID to prefix the resources. | byctemplate |
Secondary Location | A less busy region for CosmosDB, useful in case of availability constraints. | eastus2 |
Deployment Type | Select from a drop-down list. | Global Standard |
GPT Model | Choose from gpt-4, gpt-4o | gpt-4o |
GPT Model Deployment Capacity | Configure capacity for GPT models. | 30k |
Embedding Model | Default: text-embedding-ada-002. | text-embedding-ada-002 |
Embedding Model Capacity | Set the capacity for embedding models. | 80k |
By default, the Gpt-4o model capacity in deployment is set to 30k tokens, so we recommend
For Global Standard | GPT-4o - the capacity to at least 150k tokens post-deployment for optimal performance.
For Standard | GPT-4 - ensure a minimum of 30k–40k tokens for best results.
To adjust quota settings, follow these steps
Pick from the options below to see step-by-step instructions for: GitHub Codespaces, VS Code Dev Containers, Local Environments, and Bicep deployments.
Deploy in GitHub Codespaces
You can run this solution using GitHub Codespaces. The button will open a web-based VS Code instance in your browser:
-
Open the solution accelerator (this may take several minutes):
-
Accept the default values on the create Codespaces page
-
Open a terminal window if it is not already open
-
Continue with the deploying steps
Deploy in VS Code
You can run this solution in VS Code Dev Containers, which will open the project in your local VS Code using the Dev Containers extension:
-
Start Docker Desktop (install it if not already installed)
-
Open the project:
-
In the VS Code window that opens, once the project files show up (this may take several minutes), open a terminal window.
-
Continue with the deploying steps
Deploy in your local environment
If you're not using one of the above options for opening the project, then you'll need to:
-
Make sure the following tools are installed:
-
Download the project code:
azd init -t microsoft/document-generation-solution-accelerator/
-
Open the project folder in your terminal or editor.
-
Continue with the deploying steps.
Once you've opened the project in Codespaces or in Dev Containers or locally, you can deploy it to Azure following the following steps.
To change the azd parameters from the default values, follow the steps here.
-
Login to Azure:
azd auth login
azd auth login --tenant-id <tenant-id>
-
Provision and deploy all the resources:
azd up
-
Provide an
azd
environment name (like "bycapp") -
Select a subscription from your Azure account, and select a location which has quota for all the resources.
- This deployment will take 7-10 minutes to provision the resources in your account and set up the solution with sample data.
- If you get an error or timeout with deployment, changing the location can help, as there may be availability constraints for the resources.
-
Once the deployment has completed successfully, open the Azure Portal, go to the deployed resource group, find the App Service and get the app URL from
Default domain
. -
You can now delete the resources by running
azd down
, if you are done trying out the application.
-
Add App Authentication
Follow steps in App Authentication to configure authenitcation in app service.
Note: Authentication changes can take up to 10 minutes
-
Deleting Resources After a Failed Deployment
Follow steps in Delete Resource Group If your deployment fails and you need to clean up the resources.
To help you get started, here are some Sample Questions you can follow once your application is up and running.
Please refer to Transparency FAQ for responsible AI transparency details of this solution accelerator.
Pricing varies per region and usage, so it isn't possible to predict exact costs for your usage. The majority of the Azure resources used in this infrastructure are on usage-based pricing tiers. However, Azure Container Registry has a fixed cost per registry per day.
You can try the Azure pricing calculator for the resources:
- Azure AI Foundry: Free tier. Pricing
- Azure AI Search: Standard tier, S1. Pricing is based on the number of documents and operations. Pricing
- Azure Storage Account: Standard tier, LRS. Pricing is based on storage and operations. Pricing
- Azure Key Vault: Standard tier. Pricing is based on the number of operations. Pricing
- Azure AI Services: S0 tier, defaults to gpt-4o and text-embedding-ada-002 models. Pricing is based on token count. Pricing
- Azure Container App: Consumption tier with 0.5 CPU, 1GiB memory/storage. Pricing is based on resource allocation, and each month allows for a certain amount of free usage. Pricing
- Azure Container Registry: Basic tier. Pricing
- Log analytics: Pay-as-you-go tier. Costs based on data ingested. Pricing
- Azure Cosmos DB: Pricing
azd down
.
This template uses Azure Key Vault to store all connections to communicate between resources.
This template also uses Managed Identity for local development and deployment.
To ensure continued best practices in your own repository, we recommend that anyone creating solutions based on our templates ensure that the Github secret scanning setting is enabled.
You may want to consider additional security measures, such as:
- Enabling Microsoft Defender for Cloud to secure your Azure resources.
- Protecting the Azure Container Apps instance with a firewall and/or Virtual Network.
This release is an artificial intelligence (AI) system that generates text based on user input. The text generated by this system may include ungrounded content, meaning that it is not verified by any reliable source or based on any factual data. The data included in this release is synthetic, meaning that it is artificially created by the system and may contain factual errors or inconsistencies. Users of this release are responsible for determining the accuracy, validity, and suitability of any content generated by the system for their intended purposes. Users should not rely on the system output as a source of truth or as a substitute for human judgment or expertise.
This release only supports English language input and output. Users should not attempt to use the system with any other language or format. The system output may not be compatible with any translation tools or services, and may lose its meaning or coherence if translated.
This release does not reflect the opinions, views, or values of Microsoft Corporation or any of its affiliates, subsidiaries, or partners. The system output is solely based on the system's own logic and algorithms, and does not represent any endorsement, recommendation, or advice from Microsoft or any other entity. Microsoft disclaims any liability or responsibility for any damages, losses, or harms arising from the use of this release or its output by any user or third party.
This release does not provide any financial advice, and is not designed to replace the role of qualified client advisors in appropriately advising clients. Users should not use the system output for any financial decisions or transactions, and should consult with a professional financial advisor before taking any action based on the system output. Microsoft is not a financial institution or a fiduciary, and does not offer any financial products or services through this release or its output.
This release is intended as a proof of concept only, and is not a finished or polished product. It is not intended for commercial use or distribution, and is subject to change or discontinuation without notice. Any planned deployment of this release or its output should include comprehensive testing and evaluation to ensure it is fit for purpose and meets the user's requirements and expectations. Microsoft does not guarantee the quality, performance, reliability, or availability of this release or its output, and does not provide any warranty or support for it.
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