Streamlit Starter Kit for data.world's AI Context Engine

This starter kit will let you quickly get started with data.world's AI Context Engine™. It is a simple Streamlit app that demonstrates how to use the data.world API to send a question to the AI Context Engine and display its response.

Getting started

Pre-requisites

• Python v3.12 or later
• Poetry v1.8.3 or later
• A data.world account, with an AI Context Engine™ License and available Knowledge Tokens

Installation

First, fetch this repo however you like:

• Clone it using the web URL

  git clone https://github.com/datadotworld/ddw-streamlit-starter-kit.git

or

• Use the GitHub CLI

  gh repo clone datadotworld/ddw-streamlit-starter-kit

or

• Download it via this link ddw-streamlit-starter-kit.zip and unzip it.

Then, navigate to the project directory:

$ cd ddw-streamlit-starter-kit

Get a data.world token (either as an individual user for development, or from a service account for deployment), copy secrets.toml.example to .streamlit/secrets.toml, replacing the placeholder token:

[ddw]
api_endpoint = "https://api.data.world"
token = "apikey123"

In the vast majority of most cases, you'll be able to leave the default api_endpoint value as is.

Running the app

There are a multitude of ways to set up a Python virtual environment. Here, we will use Poetry. From the root of the project you cloned or downloaded:

$ poetry --version # Ensure you have a recent version of Poetry installed
$ poetry install   # Installs both production dependencies and development dependencies
$ poetry shell     # Activates the virtual environment

Then, you may start the app:

$ streamlit run app.py

This will open a new tab in your default browser with the app running. You'll see two form fields for specifying your organization ID, project ID, and a text field for entering a question. After entering the required information, click the "Get Answer" button to send the question to the AI Context Engine. The response will be displayed below the form. All of this is driven by Streamlit and the app.py script.

Developing the app

We make use of standard tools in the Python ecosystem for development. We use black for code formatting, flake8 for linting, and isort for organizing imports:

# Activate the virtual environment if you haven't already: poetry shell
$ black .
$ flake8 .
$ isort .

Testing

After setting up a secrets.toml file as described above, you can run the tests:

# Activate the virtual environment if you haven't already: poetry shell
$ pytest

Deploying the app

Since this is Streamlit, the easiest way to deploy the app is to use Streamlit's own hosting service. You can find more information in their documentation, "Deploy your app".

Contributing

We welcome contributions to this project. Please see our CONTRIBUTING.md file for more information.

License

This project is licensed under the Apache License 2.0. See the LICENSE file for more information.