Using respective credentials for both local development as well as
deployment. When deployed on azure, the app authenticates with the SQL
database via Entra ID (formerly active directory) and accesses other
credentials via key vault as a system managed identity.
The docker file was updated to align with the software
requirements of the app. An additional script `install_odbc.sh` is added
to install the required microsoft ODBC driver.
Before, there was no direct description about the usage of the manual
SQL query input field. A plotly dash component was added with a more
precise description for the user.
Add the first working code logic both in terms of backend and
frontend-related tasks. Add a detailled system message for improved
results. Add several UI improvements for result display and user
information. Add text input field for direct SQL code comparison.
The implementation of the openAI backend had to be changed due to strict
rate limits of azure OpenAI free tier and was replaced with a regular
openai API key.
In order to compare the (not yet implemented) SQL query generated by
the LLM with an actual query, another text field was added that parses
the query to `pyodbc`, which connects to our database, stores the
resulting rows in a `pandas` dataframe and then visualizes it as a table
in plotly dash.
The SQL functionalities are implemented in the `sql_utils.py` module.
Additionally, some minor updates to the overall behavior and layout of
the app were implemented.
Tobias Quadfasel