DATAKI

Dataki is an app that allows you to generate analytics reports for your data. It is a tool that allows you to connect to your data sources, and generate reports with the help of AI.

You can find the live demo at https://app.dataki.ai.

Running it locally

To run the project locally you need to run at least the frontend (and connect to the deployed backend).

Or you can also run the frontend and the backend locally.

We want to make it as easy as possible for you to run the project, so we have included the instructions to run both the frontend and the backend in this README.

This repository should have all the config files correctly set up, so you should be able to run the project without any additional configuration. However we include the steps to configure the project with a different Google Cloud project, in case you want to use your own project.

Frontend

This is the frontend for the Dataki project. It is a React application that uses some of the components from the FireCMS project.

IMPORTANT: By default the project will use the Firebase project datatalk-443fb, and connect to it's deployed Cloud functions and Firestore. If you run the project locally, or use a different project you can change the API endpoint in the .env file. If you want to use a different project, make sure to change the Firebase config in src/firebase_config.ts.

It is connecting by default to the backend of this demo project, which is hosted at https://datakiapi-4mgflsd2ha-ey.a.run.app.

Running the project

Change directory to the app folder:

cd app

Install the dependencies (you need to force the install because of the 19-rc react version):

npm i --force

And run the project locally:

npm run dev

(alternatively you can use yarn and yarn dev)

Building the project

Make sure you update your package.json build script with the correct project name. Then run:

npm run build

Changing the API endpoint

You can change the API endpoint in the .env file. By default it is set to the deployed backend of this project.

Backend

Steps to run the backend

Note: these are instructions to setup a completely new project. You can use our demo project too, for which you will need a SA key. Feel free to reach us at [email protected] if you need it!

You need a Google Cloud project with the following services enabled:

• Firebase
• Firestore
• Cloud Functions
• Vertex AI
• Oauth2 API

1. Change directory to the functions folder:

   cd functions

2. Run

   npm install

3. Create a service account to run locally.

   To run, create a service account for the App Engine default service account and download the JSON file. Then, set the environment variable GOOGLE_APPLICATION_CREDENTIALS to the path of the JSON file.

   Example: export GOOGLE_APPLICATION_CREDENTIALS=/Users/developer/dataki-hackathon/sample-service-account.json

4. Go to .firebaserc and change the default project to your project id

5. Go to functions/ and copy .env.template to .env and set up the GEMINI API KEY

Steps to configure the Google Cloud Project

1. You need a firebase project
2. Enable and configure firebase auth
3. Enable and configure firestore
4. Enable and configure IAM Service Account Credentials API, https://console.developers.google.com/apis/api/iamcredentials.googleapis.com/overview?project=PROJECT_ID
5. Enable Vertex AI Studio and Vertex AI API, https://console.cloud.google.com/vertex-ai/generative?referrer=search&project=PROJECT_ID and https://console.cloud.google.com/apis/library/aiplatform.googleapis.com?project=PROJECT_ID
6. Add Vertex AI permissions to default service account, https://console.cloud.google.com/iam-admin/iam?project=PROJECT_ID

Running the backend

Run the backend locally with

npm run serve

Deploying the backend

Deploy the backend with

npm run deploy