Overview - Redash-LLM-Chatbot-Add-on

A Redash add-on which integrates PostgreSQL, and OpenAI's GPT with natural language processing and SQL generation to democratize SQL for a non-technical user to seamlessly fetch and visualize data using LLM and query generation.

Navigation

• Project Title
• Description
• Business Need
• Installation Instructions
• Usage Guidelines
• Contributing
• License
• Authors and Acknowledgment
• Changelog
• Project Tasks

Project Title

Redash-LLM-Chatbot-Add-on for Advanced Data Analytics and Visualization

Description

This project focuses on developing a Redash chat add-on designed to enhance data analytics by enabling seamless extraction of insights from multiple Redash dashboards and connected databases using natural language queries. It provides a user-friendly interface for non-technical team members to interact with complex SQL queries autonomously, facilitating deeper, actionable insights from business intelligence platforms. This add-on is crucial for transforming data from platforms like YouTube, Slack, and GMeet into strategic decisions.

Business Need

Organizations require robust tools to navigate vast amounts of data for competitive advantages in digital content trends. This project addresses the need by enabling

• Simplified data querying through natural language.
• Autonomous knowledge discovery from visualized data on dashboards.
• Enhanced decision-making capabilities through easy access to complex data insights.

Installation Instructions

1. Prerequisites

• Docker and docker-compose installed.
• Node.js and npm for running the frontend.
• Python environment for backend setup.

2. Fork the Repository

   git clone https://github.com/10Accademy-InsightStreamInc/Redash-LLM-Chatbot-Add-on

3. Navigate to the project directory.

   • Clone the Forked Repository and navigate to the directory

4. Set Up Python Environment:

   python -m venv your_env_name

   Replace your_env_name with the desired name for your environment.

5. Activate the Environment:

   • On Windows:

   .\your_env_name\scripts\activate

   • On macOS/Linux:

   source your_env_name/bin/activate

Install dependencies

npm install

pip install -r requirements.txt

Start the application using Docker

docker-compose up --build

Usage Guidelines

To interact with the Redash Chat Add-on

• Login to Redash Dashboard:
• Navigate to http://localhost:5005 and log in.

Engage with the Chat Interface

Input queries like, "What are the peak viewership times for our latest videos?" The chat add-on processes the question and either displays results from existing SQL queries or generates new SQL queries to fetch the required information.

Contributing

To contribute to this project:

Fork the main repository.

• Create your feature branch

git checkout -b feature/your-feature 

• Commit your changes

git commit -am 'Add some feature'

• Push to the branch

git push origin feature/your-feature

• Submit a pull request.

Code of Conduct

Please adhere to this project's code of conduct, ensure code quality with comments, and include tests for new features.

License

This project is released under the MIT License. For more details, see the LICENSE.md file.

Authors and Acknowledgment

Developers: Contributors: Full list in CONTRIBUTORS.md. Acknowledgments: Thanks to 10 Academy for support and resources.

Changelog (Future Development)

v1.0.0 - Initial release with EDA, enviroment setup and core functionality for querying via natural language integrated within Redash.

Project Tasks

• Pre Tasks
  • Project Understanding
  • Environment setup
  • EDA analysis
• Task 1: Schema Design - Develop a database schema suitable for handling complex YouTube data analytics.
• Task 2: Backend API Development - Build the backend to process natural language queries and convert them into SQL commands.
• Task 3: Frontend Integration - Implement a user-friendly interface within Redash to facilitate interaction with the chat add-on.
• Task 4: Testing and Validation - Rigorously test the add-on to ensure reliability and performance.
• Task 5: Documentation and Deployment - Complete comprehensive documentation and prepare the project for deployment using Docker.