name	description	languages	products	page_type	urlFragment
Cosmic Food with Azure OpenAI and Azure Cosmos DB for MongoDB vCore	A Demo application for a streamlined ordering system tailored for various food categories. It allows users to request customized meals, such as "high protein dishes," with recommendations provided from our database. Users can further customize their choices before sending their orders from the app to the restaurant, including delivery details.	python typescript bicep azdeveloper	azure azure-app-service azure-openai cosmos-db mongodb-vcore	sample	cosmic-food-rag-app

Cosmic Food with Azure OpenAI and Azure Cosmos DB for MongoDB vCore

A Demo application for a streamlined ordering system tailored for various food categories. It allows users to request customized meals, such as "high protein dishes," with recommendations provided from our database. Users can further customize their choices before sending their orders from the app to the restaurant, including delivery details. A unique feature of our system is its ability to remember user preferences for future orders, using vCore to store that data. With the help of Langchain, this setup can be easily adapted by ISVs with minimal modifications needed for other food chains.

How to use?

1. Create the following resources on Microsoft Azure:

   • Azure Cosmos DB for MongoDB vCore cluster. See the Quick Start guide here.
   • Azure OpenAI resource with:
     • Embedding model deployment. (ex. text-embedding-ada-002) See the guide here.
     • Chat model deployment. (ex. gpt-35-turbo)

2. Open the repository in GitHub Codespaces:

   

3. 📝 Start here 👉 CBD_Mongo_vCore.ipynb

Local development

1. Download the project starter code locally

   git clone https://github.com/Azure-Samples/Cosmic-Food-RAG-app.git
   cd Cosmic-Food-RAG-app

2. Initialize and activate a virtualenv using:

   python3 -m venv .venv
   source .venv/bin/activate

   Note - In Windows, the .venv does not have a bin directory. Therefore, you'd use the analogous command shown below:

   source .venv/Scripts/activate

3. Install the development dependencies as an editable package:

   python3 -m pip install -e 'src[dev]'

4. Run the notebook to generate the .env file and test out everything

Running the website locally

1. Execute the following command to build the website inside the frontend/ folder and return to the root folder

   cd ./frontend
   npm install && npm run build
   cd ../

2. Execute the following command in your terminal to start the quart app

   export QUART_APP=src.quartapp
   export QUART_ENV=development
   export QUART_DEBUG=true
   quart run -h localhost -p 50505

   For Windows, use setx command shown below:

    setx QUART_APP src.quartapp
    setx QUART_ENV development
    setx QUART_DEBUG true
    quart run -h localhost -p 50505

3. Verify on the Browser

Navigate to project homepage http://127.0.0.1:50505/ or http://localhost:50505

azd Deployment

This repository is set up for deployment on Azure App Service (w/Azure Cosmos DB for MongoDB vCore) using the configuration files in the infra folder.

To deploy your own instance, follow these steps:

1. Sign up for a free Azure account

2. Install the Azure Dev CLI.

3. Login to your Azure account:

   azd auth login

4. Initialize a new azd environment:

   azd init

   It will prompt you to provide a name (like "quart-app") that will later be used in the name of the deployed resources.

5. Provision and deploy all the resources:

   azd up

   It will prompt you to login, pick a subscription, and provide a location (like "eastus"). Then it will provision the resources in your account and deploy the latest code. If you get an error with deployment, changing the location (like to "centralus") can help, as there may be availability constraints for some of the resources.

When azd has finished deploying, you'll see an endpoint URI in the command output. Visit that URI to browse the app! 🎉

Note

If you make any changes to the app code, you can just run this command to redeploy it:

azd deploy

Add the Data

1. Open the Azure portal and sign in.

2. Navigate to your App Service page.

   

3. Select SSH from the left menu then, select Go.

4. In the SSH terminal, run python ./scripts/add_data.py --file="./data/food_items.json".