AI Banking App is an interactive web application designed to simulate a modern banking dashboard. Its primary purpose is to serve as an educational tool, demonstrating how SQL-based databases are leveraged across different types of workloads: OLTP, OLAP, and AI-driven analysis.

Through a hands-on interface, users can see the practical difference between writing a new transaction to the database, running complex analytical queries on historical data, and using natural language to ask an AI to query the database for them.

• Dashboard: A central hub to view account balance and navigate the application.
• Transactions (OLTP): View a real-time list of all past transactions. This demonstrates a typical high-volume, read-heavy OLTP workload.
• Money Transfer (OLTP): Perform transfers between accounts. This showcases a classic atomic, write-heavy OLTP operation that must be fast and reliable.
• Financial Analytics (OLAP): Explore an analytics dashboard with charts and summaries of spending habits. This represents an OLAP workload, running complex, aggregate queries over a large dataset.
• AI Agent (AI Workload):
  • Ask questions about your finances in plain English (e.g., "How much did I spend on groceries last month?"). An AI agent translates your question into a SQL query, executes it, and returns the answer.
  • Get customer support from using RAG over documents
  • Open new account, transfer money in plain English through an action oriented data agent

• Node.js (v18 or later)
• Python (v3.9 or later)
• An Azure SQL Database account with an Entra ID application registered
• An Azure OpenAI API Key
• ODBC Driver for SQL Server 18
• Recommend VSCode as tested in VS Code only

git clone https://github.com/arno756/Banking_App_SQL.git
cd Banking_App_SQL

a. Create a database called ##banking_app##.

b. The schema.sql file in the backend repository contains all the necessary T-SQL commands to create the required tables (users, accounts, transactions) and populate them with sample data.

You can execute this script against your Azure SQL Database using tools like VS Code Extension, SSMS, SQL Editor in Azure Portal or the sqlcmd command-line utility.

cd backend
python3 -m venv venv
source venv/bin/activate  # Or .\venv\Scripts\activate on Windows
pip install -r requirements.txt

Create a .env file inside the backend/ directory:

DB_SERVER="<YOUR_AZURE_SQL_CONNECTION_STRING_URI.database.windows.net>"
DB_DATABASE="banking_app"
DB_DRIVER="ODBC Driver 18 for SQL Server"

AZURE_CLIENT_ID="<YOUR_AZURE_CLIENT_ID>"
AZURE_CLIENT_SECRET="<YOUR_AZURE_CLIENT_SECRET>"
AZURE_TENANT_ID="<YOUR_AZURE_TENANT_ID>"

AZURE_OPENAI_KEY="<YOUR_OPENAI_API_KEY>"
AZURE_OPENAI_ENDPOINT="<YOUR_OPENAI_ENDPOINT.openai.azure.com/>"
AZURE_OPENAI_DEPLOYMENT=gpt-4.1-mini
AZURE_OPENAI_EMBEDDING_DEPLOYMENT=text-embedding-ada-002

From the root project directory:

npm install

You need to ingest embeddings from the PDF in the SQL Database

a. Copy the .env file in the folder Data_Ingest. b. From the folder Data_Ingest create a new virtual Python environment:

python3 -m venv new_env
source new_env/bin/activate  # Or .\new_env\Scripts\activate on Windows
pip install -r requirements.txt

c. Open the Jupyter Python Notebook in the path: Backend/Documentation ingestion_pdf_Bank_App.ipynb

d. Run all the cells in the notebook

cd backend
python3 app.py

Backend will run on: http://127.0.0.1:5001

Go to the root of your folder.

npm run dev

Frontend will run on: http://localhost:5173

Contributions are welcome!
If you have suggestions for improvements or find any bugs, feel free to open an issue or submit a pull request.