AI-powered backend that converts natural language queries into executable SQL, enabling users to analyze datasets without writing queries manually.
Built during a hackathon — Top 2 project
This system processes user-uploaded datasets and allows querying using plain English.
It is designed to be dataset-agnostic, dynamically adapting to different data structures without requiring predefined schemas.
It dynamically:
- Interprets intent using LLMs
- Converts intent → SQL queries
- Executes queries on structured data
- Returns analytical results
- Natural language → SQL query conversion
- Automatic dataset ingestion (CSV → SQLite)
- Dataset-agnostic querying (works across arbitrary datasets)
- Modular backend design for flexible query handling
- LLM-assisted data retrieval pipeline
- Support for dynamic analytical queries
User Query
↓
Intent Extraction (LLM)
↓
SQL Generation
↓
Database Execution
↓
Result Output
Clone the repository
git clone <repo-url>
cd HACK-A-DON/backend
Install dependencies
pip install pandas requests chardet numpy
Simply upload a dataset (CSV), and the system will automatically:
- Process and clean the data
- Convert it into a database
- Enable natural language querying
Run:
python run_query.py
Example queries:
Average views in US
Likes by region
Top performing categories
- CSV data is automatically processed and stored internally
- Database setup is handled dynamically
- Dataset-agnostic design enables reuse across different datasets
- Developed as a hackathon prototype