Pre-requisites:

SQL Database (if using sample data, use Azure SQL with Adventure Works dataset)
Azure AI Search

Instructions

Create an Azure SQL DB using the Adventure Works Sample Dataset (https://learn.microsoft.com/en-us/sql/samples/adventureworks-install-configure?view=sql-server-ver16&tabs=ssms)
Update .env and constants.py with your environment variables
Gather sample questions and answers and put in a CSV (as shown in src/data/sql_query_examples.csv)
Extract Schema from SQL DB - may need to be updated depending on customer scenario. The purpose of this step is to extract a schema description into a .txt file to be used as context for LLM calls for SQL generation.

python extract_descriptions.py
Generate vector database

python generate_ai_search_vectors.py
Command to start the application streamlit run app.py

Authentication Options

Networking Guidance / Recommendations

TO DO

Update authentication
Add instructions
auth options - use entra ID rather than DB password
Consider MySQL, Postgres (could be long-term)
Networking - currently it is whitelisted