Interact with your SQL database, Natural Language to SQL using LLMs

Query your relational data in natural language.

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Dataherald is a natural language-to-SQL engine built for enterprise-level question answering over relational data. It allows you to set up an API from your database that can answer questions in plain English. You can use Dataherald to:

• Allow business users to get insights from the data warehouse without going through a data analyst
• Enable Q+A from your production DBs inside your SaaS application
• Create a ChatGPT plug-in from your proprietary data

This repository contains four components under /services which can be used together to set up an end-to-end Dataherald deployment:

1. Engine: The core natural language-to-SQL engine. If you would like to use the dataherald API without users or authentication, running the engine will suffice.
2. Enterprise: The application API layer which adds authentication, organizations and users, and other business logic to Dataherald.
3. Admin-console: The front-end component of Dataherald which allows a GUI for configuration and observability. You will need to run both engine and enterprise for the admin-console to work.
4. Slackbot: A slackbot which allows users from a slack channel to interact with dataherald. Requires both engine and enterprise to run.

For more information on each component, please take a look at their README.md files.

Each component in the /services directory has its own docker-compose.yml file. To set up the environment, follow these steps:

1. Set Environment Variables: Each service requires specific environment variables. Refer to the .env.example file in each service directory and create a .env file with the necessary values.

   For the Next.js front-end app is .env.local

2. Run Services: You can run all the services using a single script located in the root directory. This script creates a common Docker network and runs each service in detached mode.

Run the script to start all services:

sh docker-run.sh

As an open-source project in a rapidly developing field, we are open to contributions, whether it be in the form of a new feature, improved infrastructure, or better documentation.

For detailed information on how to contribute, see here.