This repo contains sample code for a simple chat webapp that integrates with Azure OpenAI. Note: some portions of the app use preview APIs.

• An existing Azure OpenAI resource and model deployment of a chat model (e.g. gpt-35-turbo-16k, gpt-4)
• To use Azure OpenAI on your data, one of the following data sources:
  • Azure AI Search Index
  • Azure CosmosDB Mongo vCore vector index
  • Elasticsearch index (preview)
  • Pinecone index (private preview)
  • Azure SQL Server (private preview)
  • Mongo DB (preview)

Follow instructions below in the app configuration section to create a .env file for local development of your app. This file can be used as a reference to populate the app settings for your Azure App Service deployed webapp.

After creating your .env file, run one of the following commands in your preferred shell to create a JSON representation of your environment which is recognized by Azure App Service.

Get-Content .env | ForEach-Object {   
     if ($_ -match "(?<name>[A-Z_]+)=(?<value>.*)") {   
         [PSCustomObject]@{   
             name = $matches["name"]   
             value = $matches["value"]   
             slotSetting = $false  
         }  
    }  
} | ConvertTo-Json | Out-File -FilePath env.json

cat .env | jq -R '. | capture("(?<name>[A-Z_]+)=(?<value>.*)")' | jq -s '.[].slotSetting=false' > env.json

Please see README_azd.md for detailed instructions.

Click on the Deploy to Azure button and configure your settings in the Azure Portal as described in the Environment variables section.

Please see the section below for important information about adding authentication to your app.

1. Follow the steps below in the app configuration section to construct your .env file with the appropriate variables for your use case.

2. Start the app with start.cmd. This will build the frontend, install backend dependencies, and then start the app. Or, just run the backend in debug mode using the VSCode debug configuration in .vscode/launch.json.

3. You can see the local running app at http://127.0.0.1:50505.

NOTE: If you've made code changes, be sure to build the app code with start.cmd or start.sh before you deploy, otherwise your changes will not be picked up. If you've updated any files in the frontend folder, make sure you see updates to the files in the static folder before you deploy.

You can use the Azure CLI to deploy the app from your local machine. Make sure you have version 2.48.1 or later.

If this is your first time deploying the app, you can use az webapp up. Run the following command from the root folder of the repo, updating the placeholder values to your desired app name, resource group, location, and subscription. You can also change the SKU if desired.

az webapp up --runtime PYTHON:3.11 --sku B1 --name <new-app-name> --resource-group <resource-group-name> --location <azure-region> --subscription <subscription-name>

Note: if using the Azure CLI version 2.62 or greater, you may also want to add the flag --track-status False to prevent the command from failing due to startup errors. Startup errors can be solved by following the instructions in the next section about updating app configuration.

After creating your Azure App Service, follow these steps to update the configuration to allow your application to properly start up.

1. Set the app startup command

az webapp config set --startup-file "python3 -m gunicorn app:app" --name <new-app-name>

2. Set WEBSITE_WEBDEPLOY_USE_SCM=false to allow local code deployment.

az webapp config appsettings set -g <resource-group-name> -n <existing-app-name> --settings WEBSITE_WEBDEPLOY_USE_SCM=false

3. Set all of your app settings in your local .env file at once by creating a JSON representation of the .env file, and then run the following command.

az webapp config appsettings set -g <resource-group-name> -n <existing-app-name> --settings "@env.json"

Check the runtime stack for your app by viewing the app service resource in the Azure Portal. If it shows "Python - 3.10", use PYTHON:3.10 in the runtime argument below. If it shows "Python - 3.11", use PYTHON:3.11 in the runtime argument below.

Check the SKU in the same way. Use the abbreviated SKU name in the argument below, e.g. for "Basic (B1)" the SKU is B1.

Then, use these commands to deploy your local code to the existing app:

1. az webapp up --runtime <runtime-stack> --sku <sku> --name <existing-app-name> --resource-group <resource-group-name>
2. az webapp config set --startup-file "python3 -m gunicorn app:app" --name <existing-app-name>

Make sure that the app name and resource group match exactly for the app that was previously deployed.

Deployment will take several minutes. When it completes, you should be able to navigate to your app at {app-name}.azurewebsites.net.

After deployment, you will need to add an identity provider to provide authentication support in your app. See this tutorial for more information.

If you don't add an identity provider, the chat functionality of your app will be blocked to prevent unauthorized access to your resources and data.

To remove this restriction, you can add AUTH_ENABLED=False to the environment variables. This will disable authentication and allow anyone to access the chat functionality of your app. This is not recommended for production apps.

To add further access controls, update the logic in getUserInfoList in frontend/src/pages/chat/Chat.tsx.

To enable Microsoft Entra ID for intra-service authentication:

1. Enable managed identity on Azure OpenAI
2. Configure AI search to allow access from Azure OpenAI
   1. Enable Role Based Access control on the used AI search instance (see documentation)
   2. Assign Search Index Data Reader and Search Service Contributor to the identity of the Azure OpenAI instance
3. Do not configure AZURE_SEARCH_KEY and AZURE_OPENAI_KEY to use Entra ID authentication.
4. Configure the webapp identity
   1. Enable managed identity in the app service that hosts the webapp
   2. Go to the Azure OpenAI instance and assign the role Cognitive Services OpenAI User to the identity of the webapp

Note: RBAC assignments can take a few minutes before becoming effective.

1. Copy .env.sample to a new file called .env and configure the settings as described in the table below.

   App Setting	Required?	Default Value	Note
   AZURE_OPENAI_RESOURCE	Only if AZURE_OPENAI_ENDPOINT is not set		The name of your Azure OpenAI resource (only one of AZURE_OPENAI_RESOURCE/AZURE_OPENAI_ENDPOINT is required)
   AZURE_OPENAI_ENDPOINT	Only if AZURE_OPENAI_RESOURCE is not set		The endpoint of your Azure OpenAI resource (only one of AZURE_OPENAI_RESOURCE/AZURE_OPENAI_ENDPOINT is required)
   AZURE_OPENAI_MODEL	Yes		The name of your model deployment
   AZURE_OPENAI_KEY	Optional if using Microsoft Entra ID -- see our documentation on the required resource setup for identity-based authentication.		One of the API keys of your Azure OpenAI resource
   AZURE_OPENAI_TEMPERATURE	No	0	What sampling temperature to use, between 0 and 2. Higher values like 0.8 will make the output more random, while lower values like 0.2 will make it more focused and deterministic. A value of 0 is recommended when using your data.
   AZURE_OPENAI_TOP_P	No	1.0	An alternative to sampling with temperature, called nucleus sampling, where the model considers the results of the tokens with top_p probability mass. We recommend setting this to 1.0 when using your data.
   AZURE_OPENAI_MAX_TOKENS	No	1000	The maximum number of tokens allowed for the generated answer.
   AZURE_OPENAI_STOP_SEQUENCE	No		Up to 4 sequences where the API will stop generating further tokens. Represent these as a string joined with "
   AZURE_OPENAI_SYSTEM_MESSAGE	No	You are an AI assistant that helps people find information.	A brief description of the role and tone the model should use
   AZURE_OPENAI_STREAM	No	True	Whether or not to use streaming for the response. Note: Setting this to true prevents the use of prompt flow.
   AZURE_OPENAI_EMBEDDING_NAME	Only if using vector search using an Azure OpenAI embedding model		The name of your embedding model deployment if using vector search.

   See the documentation for more information on these parameters.

More information about Azure OpenAI on your data

1. Update the AZURE_OPENAI_* environment variables as described in the basic chat experience above.

2. To connect to your data, you need to specify an Azure Cognitive Search index to use. You can create this index yourself or use the Azure AI Studio to create the index for you.

3. Configure data source settings as described in the table below.

   App Setting	Required?	Default Value	Note
   DATASOURCE_TYPE	Yes		Must be set to AzureCognitiveSearch
   AZURE_SEARCH_SERVICE	Yes		The name of your Azure AI Search resource
   AZURE_SEARCH_INDEX	Yes		The name of your Azure AI Search Index
   AZURE_SEARCH_KEY	Optional if using Microsoft Entra ID -- see our documentation on the required resource setup for identity-based authentication.		An admin key for your Azure AI Search resource.
   AZURE_SEARCH_USE_SEMANTIC_SEARCH	No	False	Whether or not to use semantic search
   AZURE_SEARCH_QUERY_TYPE	No	simple	Query type: simple, semantic, vector, vectorSimpleHybrid, or vectorSemanticHybrid. Takes precedence over AZURE_SEARCH_USE_SEMANTIC_SEARCH
   AZURE_SEARCH_SEMANTIC_SEARCH_CONFIG	No		The name of the semantic search configuration to use if using semantic search.
   AZURE_SEARCH_TOP_K	No	5	The number of documents to retrieve when querying your search index.
   AZURE_SEARCH_ENABLE_IN_DOMAIN	No	True	Limits responses to only queries relating to your data.
   AZURE_SEARCH_STRICTNESS	No	3	Integer from 1 to 5 specifying the strictness for the model limiting responses to your data.
   AZURE_SEARCH_CONTENT_COLUMNS	No		List of fields in your search index that contains the text content of your documents to use when formulating a bot response. Represent these as a string joined with "
   AZURE_SEARCH_FILENAME_COLUMN	No		Field from your search index that gives a unique identifier of the source of your data to display in the UI.
   AZURE_SEARCH_TITLE_COLUMN	No		Field from your search index that gives a relevant title or header for your data content to display in the UI.
   AZURE_SEARCH_URL_COLUMN	No		Field from your search index that contains a URL for the document, e.g. an Azure Blob Storage URI. This value is not currently used.
   AZURE_SEARCH_VECTOR_COLUMNS	No		List of fields in your search index that contain vector embeddings of your documents to use when formulating a bot response. Represent these as a string joined with "
   AZURE_SEARCH_PERMITTED_GROUPS_COLUMN	No		Field from your Azure AI Search index that contains AAD group IDs that determine document-level access control.

   When using your own data with a vector index, ensure these settings are configured on your app:

   • AZURE_SEARCH_QUERY_TYPE: can be vector, vectorSimpleHybrid, or vectorSemanticHybrid,
   • AZURE_OPENAI_EMBEDDING_NAME: the name of your Ada (text-embedding-ada-002) model deployment on your Azure OpenAI resource.
   • AZURE_SEARCH_VECTOR_COLUMNS: the vector columns in your index to use when searching. Join them with | like contentVector|titleVector.

1. Update the AZURE_OPENAI_* environment variables as described in the basic chat experience above.

2. To connect to your data, you need to specify an Azure Cosmos DB database configuration. Learn more about creating an Azure Cosmos DB resource.

3. Configure data source settings as described in the table below.

   App Setting	Required?	Default Value	Note
   DATASOURCE_TYPE	Yes		Must be set to AzureCosmosDB
   AZURE_COSMOSDB_MONGO_VCORE_CONNECTION_STRING	Yes		The connection string used to connect to your Azure Cosmos DB instance
   AZURE_COSMOSDB_MONGO_VCORE_INDEX	Yes		The name of your Azure Cosmos DB vector index
   AZURE_COSMOSDB_MONGO_VCORE_DATABASE	Yes		The name of your Azure Cosmos DB database
   AZURE_COSMOSDB_MONGO_VCORE_CONTAINER	Yes		The name of your Azure Cosmos DB container
   AZURE_COSMOSDB_MONGO_VCORE_TOP_K	No	5	The number of documents to retrieve when querying your search index.
   AZURE_COSMOSDB_MONGO_VCORE_ENABLE_IN_DOMAIN	No	True	Limits responses to only queries relating to your data.
   AZURE_COSMOSDB_MONGO_VCORE_STRICTNESS	No	3	Integer from 1 to 5 specifying the strictness for the model limiting responses to your data.
   AZURE_COSMOSDB_MONGO_VCORE_CONTENT_COLUMNS	No		List of fields in your search index that contains the text content of your documents to use when formulating a bot response. Represent these as a string joined with "
   AZURE_COSMOSDB_MONGO_VCORE_FILENAME_COLUMN	No		Field from your search index that gives a unique identifier of the source of your data to display in the UI.
   AZURE_COSMOSDB_MONGO_VCORE_TITLE_COLUMN	No		Field from your search index that gives a relevant title or header for your data content to display in the UI.
   AZURE_COSMOSDB_MONGO_VCORE_URL_COLUMN	No		Field from your search index that contains a URL for the document, e.g. an Azure Blob Storage URI. This value is not currently used.
   AZURE_COSMOSDB_MONGO_VCORE_VECTOR_COLUMNS	No		List of fields in your search index that contain vector embeddings of your documents to use when formulating a bot response. Represent these as a string joined with "

   Azure Cosmos DB uses vector search by default, so ensure these settings are configured on your app:

   • AZURE_OPENAI_EMBEDDING_NAME: the name of your Ada (text-embedding-ada-002) model deployment on your Azure OpenAI resource.
   • AZURE_COSMOSDB_MONGO_VCORE_VECTOR_COLUMNS: the vector columns in your index to use when searching. Join them with | like contentVector|titleVector.

1. Update the AZURE_OPENAI_* environment variables as described in the basic chat experience above.

2. To connect to your data, you need to specify an Elasticsearch cluster configuration. Learn more about Elasticsearch.

3. Configure data source settings as described in the table below.

   App Setting	Required?	Default Value	Note
   DATASOURCE_TYPE	Yes		Must be set to Elasticsearch
   ELASTICSEARCH_ENDPOINT	Yes		The base URL of your Elasticsearch cluster API
   ELASTICSEARCH_ENCODED_API_KEY	Yes		The encoded API key for your user identity on your Elasticsearch cluster
   ELASTICSEARCH_INDEX	Yes		The name of your Elasticsearch index
   ELASTICSEARCH_QUERY_TYPE	No	simple	Can be one of simple or vector
   ELASTICSEARCH_EMBEDDING_MODEL_ID	Only if using vector search with an Elasticsearch embedding model		The name of the embedding model deployed to your Elasticsearch cluster which was used to produce embeddings for your index
   ELASTICSEARCH_TOP_K	No	5	The number of documents to retrieve when querying your search index.
   ELASTICSEARCH_ENABLE_IN_DOMAIN	No	True	Limits responses to only queries relating to your data.
   ELASTICSEARCH_STRICTNESS	No	3	Integer from 1 to 5 specifying the strictness for the model limiting responses to your data.
   ELASTICSEARCH_CONTENT_COLUMNS	No		List of fields in your search index that contains the text content of your documents to use when formulating a bot response. Represent these as a string joined with "
   ELASTICSEARCH_FILENAME_COLUMN	No		Field from your search index that gives a unique identifier of the source of your data to display in the UI.
   ELASTICSEARCH_TITLE_COLUMN	No		Field from your search index that gives a relevant title or header for your data content to display in the UI.
   ELASTICSEARCH_URL_COLUMN	No		Field from your search index that contains a URL for the document, e.g. an Azure Blob Storage URI. This value is not currently used.
   ELASTICSEARCH_VECTOR_COLUMNS	No		List of fields in your search index that contain vector embeddings of your documents to use when formulating a bot response. Represent these as a string joined with "

   To use vector search with Elasticsearch, there are two options:

   1. To use Azure OpenAI embeddings, ensure that your index contains Azure OpenAI embeddings, and that the following variables are set:
   • AZURE_OPENAI_EMBEDDING_NAME: the name of your Ada (text-embedding-ada-002) model deployment on your Azure OpenAI resource, which was also used to create the embeddings in your index.
   • ELASTICSEARCH_VECTOR_COLUMNS: the vector columns in your index to use when searching. Join them with | like contentVector|titleVector.
   2. Use Elasticsearch embeddings, ensure that your index contains embeddings produced from a trained model on your Elasticsearch cluster, and that the following variables are set:
   • ELASTICSEARCH_EMBEDDING_MODEL_ID: the ID of the trained model used to produce embeddings on your index.
   • ELASTICSEARCH_VECTOR_COLUMNS: the vector columns in your index to use when searching. Join them with | like contentVector|titleVector.

1. Update the AZURE_OPENAI_* environment variables as described in the basic chat experience above.

2. To connect to your data, you need to specify an Pinecone vector database configuration. Learn more about Pinecone.

3. Configure data source settings as described in the table below.

   App Setting	Required?	Default Value	Note
   DATASOURCE_TYPE	Yes		Must be set to Pinecone
   PINECONE_ENVIRONMENT	Yes		The name of your Pinecone environment
   PINECONE_INDEX_NAME	Yes		The name of your Pinecone index
   PINECONE_API_KEY	Yes		The API key used to connect to your Pinecone instance
   PINECONE_TOP_K	No	5	The number of documents to retrieve when querying your search index.
   PINECONE_ENABLE_IN_DOMAIN	No	True	Limits responses to only queries relating to your data.
   PINECONE_STRICTNESS	No	3	Integer from 1 to 5 specifying the strictness for the model limiting responses to your data.
   PINECONE_CONTENT_COLUMNS	No		List of fields in your search index that contains the text content of your documents to use when formulating a bot response. Represent these as a string joined with "
   PINECONE_FILENAME_COLUMN	No		Field from your search index that gives a unique identifier of the source of your data to display in the UI.
   PINECONE_TITLE_COLUMN	No		Field from your search index that gives a relevant title or header for your data content to display in the UI.
   PINECONE_URL_COLUMN	No		Field from your search index that contains a URL for the document, e.g. an Azure Blob Storage URI. This value is not currently used.
   PINECONE_VECTOR_COLUMNS	No		List of fields in your search index that contain vector embeddings of your documents to use when formulating a bot response. Represent these as a string joined with "

   Pinecone uses vector search by default, so ensure these settings are configured on your app:

   • AZURE_OPENAI_EMBEDDING_NAME: the name of your Ada (text-embedding-ada-002) model deployment on your Azure OpenAI resource.
   • PINECONE_VECTOR_COLUMNS: the vector columns in your index to use when searching. Join them with | like contentVector|titleVector.

1. Update the AZURE_OPENAI_* environment variables as described in the basic chat experience above.

2. To connect to your data, you need to specify an Mongo DB database configuration. Learn more about MongoDB.

3. Configure data source settings as described in the table below.

   App Setting	Required?	Default Value	Note
   DATASOURCE_TYPE	Yes		Must be set to MongoDB
   MONGODB_CONNECTION_STRING	Yes		The connection string used to connect to your Mongo DB instance
   MONGODB_VECTOR_INDEX	Yes		The name of your Mongo DB vector index
   MONGODB_DATABASE_NAME	Yes		The name of your Mongo DB database
   MONGODB_CONTAINER_NAME	Yes		The name of your Mongo DB container
   MONGODB_TOP_K	No	5	The number of documents to retrieve when querying your search index.
   MONGODB_ENABLE_IN_DOMAIN	No	True	Limits responses to only queries relating to your data.
   MONGODB_STRICTNESS	No	3	Integer from 1 to 5 specifying the strictness for the model limiting responses to your data.
   MONGODB_CONTENT_COLUMNS	No		List of fields in your search index that contains the text content of your documents to use when formulating a bot response. Represent these as a string joined with "
   MONGODB_FILENAME_COLUMN	No		Field from your search index that gives a unique identifier of the source of your data to display in the UI.
   MONGODB_TITLE_COLUMN	No		Field from your search index that gives a relevant title or header for your data content to display in the UI.
   MONGODB_URL_COLUMN	No		Field from your search index that contains a URL for the document, e.g. an Azure Blob Storage URI. This value is not currently used.
   MONGODB_VECTOR_COLUMNS	No		List of fields in your search index that contain vector embeddings of your documents to use when formulating a bot response. Represent these as a string joined with "

   MongoDB uses vector search by default, so ensure these settings are configured on your app:

   • AZURE_OPENAI_EMBEDDING_NAME: the name of your Ada (text-embedding-ada-002) model deployment on your Azure OpenAI resource.
   • MONGODB_VECTOR_COLUMNS: the vector columns in your index to use when searching. Join them with | like contentVector|titleVector.

1. Update the AZURE_OPENAI_* environment variables as described in the basic chat experience above.

2. To enable Azure SQL Server, you will need to set up Azure SQL Server resources. Refer to this instruction guide to create an Azure SQL database.

3. Configure data source settings as described in the table below.

   App Setting	Required?	Default Value	Note
   DATASOURCE_TYPE	Yes		Must be set to AzureSqlServer
   AZURE_SQL_SERVER_CONNECTION_STRING	Yes		The connection string to use to connect to your Azure SQL Server instance
   AZURE_SQL_SERVER_TABLE_SCHEMA	Yes		The table schema for your Azure SQL Server table. Must be surrounded by double quotes (").
   AZURE_SQL_SERVER_PORT		Not publicly available at this time.	The port to use to connect to your Azure SQL Server instance.
   AZURE_SQL_SERVER_DATABASE_NAME		Not publicly available at this time.
   AZURE_SQL_SERVER_DATABASE_SERVER		Not publicly available at this time.

Configure your settings using the table below.

1. Update the AZURE_OPENAI_* environment variables as described in the basic chat experience above.

2. Add any additional configuration (described in previous sections) needed for chatting with data, if required.

3. To enable chat history, you will need to set up CosmosDB resources. The ARM template in the infrastructure folder can be used to deploy an app service and a CosmosDB with the database and container configured.

4. Configure data source settings as described in the table below.

   App Setting	Required?	Default Value	Note
   AZURE_COSMOSDB_ACCOUNT	Only if using chat history		The name of the Azure Cosmos DB account used for storing chat history
   AZURE_COSMOSDB_DATABASE	Only if using chat history		The name of the Azure Cosmos DB database used for storing chat history
   AZURE_COSMOSDB_CONVERSATIONS_CONTAINER	Only if using chat history		The name of the Azure Cosmos DB container used for storing chat history
   AZURE_COSMOSDB_ACCOUNT_KEY	Only if using chat history		The account key for the Azure Cosmos DB account used for storing chat history
   AZURE_COSMOSDB_ENABLE_FEEDBACK	No	False	Whether or not to enable message feedback on chat history messages

The interface allows for easy adaptation of the UI by modifying certain elements, such as the title and logo, through the use of the following environment variables.

Any custom images assigned to variables UI_LOGO, UI_CHAT_LOGO or UI_FAVICON should be added to the public folder before building the project. The Vite build process will automatically copy theses files to the static folder on each build of the frontend. The corresponding environment variables should then be set using a relative path such as static/<my image filename> to ensure that the frontend code can find them.

Feel free to fork this repository and make your own modifications to the UX or backend logic. You can modify the source (frontend/src). For example, you may want to change aspects of the chat display, or expose some of the settings in app.py in the UI for users to try out different behaviors. After your code changes, you will need to rebuild the front-end via start.sh or start.cmd.

You can configure the number of threads and workers in gunicorn.conf.py. After making a change, redeploy your app using the commands listed above.

See the Oryx documentation for more details on these settings.

First, add an environment variable on the app service resource called "DEBUG". Set this to "true".

Next, enable logging on the app service. Go to "App Service logs" under Monitoring, and change Application logging to File System. Save the change.

Now, you should be able to see logs from your app by viewing "Log stream" under Monitoring.

The Citation panel is defined at the end of frontend/src/pages/chat/Chat.tsx. The citations returned from Azure OpenAI On Your Data will include content, title, filepath, and in some cases url. You can customize the Citation section to use and display these as you like. For example, the title element is a clickable hyperlink if url is not a blob URL.

    <h5 
        className={styles.citationPanelTitle} 
        tabIndex={0} 
        title={activeCitation.url && !activeCitation.url.includes("blob.core") ? activeCitation.url : activeCitation.title ?? ""} 
        onClick={() => onViewSource(activeCitation)}
    >{activeCitation.title}</h5>

    const onViewSource = (citation: Citation) => {
        if (citation.url && !citation.url.includes("blob.core")) {
            window.open(citation.url, "_blank");
        }
    };

We recommend keeping these best practices in mind:

• Reset the chat session (clear chat) if the user changes any settings. Notify the user that their chat history will be lost.
• Clearly communicate to the user what impact each setting will have on their experience.
• When you rotate API keys for your AOAI or ACS resource, be sure to update the app settings for each of your deployed apps to use the new key.
• Pull in changes from main frequently to ensure you have the latest bug fixes and improvements, especially when using Azure OpenAI on your data.

A note on Azure OpenAI API versions: The application code in this repo will implement the request and response contracts for the most recent preview API version supported for Azure OpenAI. To keep your application up-to-date as the Azure OpenAI API evolves with time, be sure to merge the latest API version update into your own application code and redeploy using the methods described in this document.

This project welcomes contributions and suggestions. Most contributions require you to agree to a Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us the rights to use your contribution. For details, visit https://cla.opensource.microsoft.com.

When you submit a pull request, a CLA bot will automatically determine whether you need to provide a CLA and decorate the PR appropriately (e.g., status check, comment). Simply follow the instructions provided by the bot. You will only need to do this once across all repos using our CLA.

This project has adopted the Microsoft Open Source Code of Conduct. For more information see the Code of Conduct FAQ or contact [email protected] with any additional questions or comments.

When contributing to this repository, please help keep the codebase clean and maintainable by running the formatter and linter with npm run format this will run npx eslint --fix and npx prettier --write on the frontebnd codebase.

If you are using VSCode, you can add the following settings to your settings.json to format and lint on save:

{
    "editor.codeActionsOnSave": {
        "source.fixAll.eslint": "explicit"
    },
    "editor.formatOnSave": true,
    "prettier.requireConfig": true,
}

This project may contain trademarks or logos for projects, products, or services. Authorized use of Microsoft trademarks or logos is subject to and must follow Microsoft's Trademark & Brand Guidelines. Use of Microsoft trademarks or logos in modified versions of this project must not cause confusion or imply Microsoft sponsorship. Any use of third-party trademarks or logos are subject to those third-party's policies.