AI Assistant on CSV Files

Supports importing multiple CSV files, each with their own schema.

Pre-requisites

You’ll need the Hasura CLI (authenticated via a Hasura account) and Docker installed on your local machine. Links to these steps are below:

• Sign up for a Hasura account (if you haven’t)
• Hasura CLI installed and authenticated
• Docker installed on your local machine

Import your CSV Files

Step 1: Clone the project

git clone [email protected]:hasura/csv-promptql.git
cd csv-promptql

Step 2: Configure CSV file(s)

Place your CSV file(s) inside app/connector/csv/csv_files directory.

cd app/connector/csv/csv_files

Add your CSV files here.

Step 3: Introspect the Connector

ddn connector introspect csv

Note: Depending on how big the dataset is, it should take sometime to fully import the data. The schema will be initialized quickly and the data import happens in the background, so you can proceed to follow the steps below.

Step 4: Add Models

Based on the dataset imported, a SQL schema would be generated. Let’s track all the models to get started quickly.

ddn model add csv "*"

Build your PromptQL app

Now, let’s set up the Hasura DDN project with PromptQL to start exploring the data in natural language!

• Set up the Hasura DDN project already scaffolded in the repo:

In the root directory of the repo, run the following commands:

ddn supergraph build local
ddn project init

• Start the DDN project

Let’s start the DDN project by executing the following command:

ddn run docker-start

• Open the local DDN Console to start exploring:

ddn console --local

This should open up your browser (or print a browser URL) for displaying the Hasura Console. It’ll typically be something like: https://console.hasura.io/local?engine=localhost:3280&promptql=localhost:3282

Ask questions about your dataset

The app will have metadata about the dataset that you just imported above. You should be able to ask domain specific questions and play around with the data.

Here’s a sample of what you can ask to get started.

• Hi, what can you do?

Depending on the dataset schema, PromptQL will tell you what it can answer and you can go from there.

Clean up and restart your app

If you want to reset the data and start from scratch:

Modify or delete the csv files in the app/connector/csv/csv_files directory

You can stop the ddn run docker-start command, whereever it is running and you can execute the following in the root directory of the repo:

docker compose down -v && ddn run docker-start