Quelmap

Abstract

1. Introduction

16GB RAM is enough for practical performance using Lightning-4b

This command sets up 3 containers(Application, Python Sandbox, PostgresDB). Wait for starting up all containers, Access "http://localhost:3030". You can use any LLM provider’s model by setting the base_url and api_key from the settings icon in the top right (by default, Ollama is used).

2. Objective

Open Source Local Data Analysis Assistant.

This project demonstrates how TypeScript can be applied to a real-world Data Science problem.

3. Key Features / Modules

• Data visualization
• Table joins
• Run statistical tests directly on your dataset
• Unlimited rows, 30+ tables analyzed simultaneously
• Built-in python sandbox
• Ollama integration

4. Technology Stack

5. System Requirements

General requirements for this technology stack — check the README for exact versions.

• Node.js (LTS) and npm / yarn / pnpm
• VS Code or any code editor
• Git (to clone the repository)

6. Installation & Setup

git clone https://github.com/quelmap-inc/quelmap.git
cd quelmap

1. Ensure Docker is installed and running on your system.
2. Clone the quelmap repository:
3. After cloning, navigate to the directory containing the project files.
4. Start the application
5. ollama (default)
6. llama.cpp
7. LM Studio
8. Anthropic

git clone https://github.com/quelmap-inc/quelmap.git

cd quelmap

docker compose up --build -d

ollama pull hf.co/quelmap/Lightning-4b-GGUF-short-ctx:Q4_K_M

Full setup instructions are in the project README.

7. Future Enhancements

Suggested extensions you can add to make this your own project.

• Turn the analysis into an interactive dashboard
• Automate data refresh with a scheduled job
• Add a predictive model on top of the analysis

8. Viva / Review Questions

Common questions examiners ask for projects in this domain.

1. What is the source of the dataset and how was missing data handled?
2. Which exploratory analysis steps revealed the most useful insight?
3. Why were these particular charts chosen to present the data?
4. Which statistical or ML technique supports the conclusions?
5. How could the analysis be automated or refreshed with new data?

9. Source Code & License

This project is developed by quelmap-inc and published on GitHub under the Apache License 2.0. Please follow the license terms and credit the original author when you use or modify this code.