Retrieval Augmented Generation Text-to-SQL Application To Analyze US Government Contract Data

This python application uses Retrieval Augmented Generation(RAG) to ask the data questions directly using plain English. The application then uses OPENAI's GPT 4.o-mini model to convert this question i.e prompt into SQL, which then queries the DuckDb database that stores the data and returns the solution, in addition to the SQL statement that generates this data.

The simplicity of testing the correctness of the answers makes this application a powerful, and useful use of Large Language Models(LLMs) in Data Science that can directly provide values to Business Users who are unfamiliar with SQL by allowing them to directly use Business Questions to answer Data Questions in seconds with a Gradio Application.

Demo

Customize this Application with your own Data

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Clone this Repository

git clone https://github.com/LNshuti/usgov-contracts-rag.git

Setup your Environment

conda env create --file=environment.yaml

Activate your Environment

conda activate gov-data

Install Dependencies

pip install -r requirements.txt

Add your data in the data folder

cd data

cp <your_data> .

Update path in the connect_db.py file to load your Excel/CSV data into the database

CSV_FILE_PATH = 'data/your_data.csv'
DB_FILE_PATH = 'gov-contracts.db'
TABLE_NAME = 'your_table_name'

Run the connect_db.py python helper file to load your data into the database

python connect_db.py

Examine the data with Datasette

datasette serve gov-contracts.db

Run the app.py python file to start the Gradio Application

python run app/app.py

Update: 03/28/2025

I just published a medium article proving more details on this app, and the plan for keeping the underlying data up to date to keep the application useful. Please read on!

References.

1. Harshad Suryawanshi. From Natural Language to SQL(Na2SQL): Extracting Insights from Databases using OPENAI GPT3.5 and LlamaIndex. https://github.com/AI-ANK/Na2SQL

2. Ravi Theja. Evaluate RAG with Llamaindex. https://cookbook.openai.com/examples/evaluation/evaluate_rag_with_llamaindex

3. Mostafa Ibrahim. A Gentle Introduction to Advanced RAG. https://wandb.ai/mostafaibrahim17/ml-articles/reports/A-Gentle-Introduction-to-Advanced-RAG--Vmlldzo2NjIyNTQw

4. Adam Obeng; J.C. Zhong; Charlie Gu. How we built Text-to-SQL at Pinterest. https://medium.com/pinterest-engineering/how-we-built-text-to-sql-at-pinterest-30bad30dabff