A playground for leveraging GPT to answer user prompts using various databases.

vi .env

Paste your API_KEY into the .env file, then close and save with :wq. Your .env file should look like this when finished:

API_KEY=<your API key here>

pip install -r requirements.text

python3 setup.py

code examples.ipynb

• Class takes a table_schema and a list of questions, and generates a list of SQL queries for each question (if GPT isn't confident that the data exists in the database, it returns 'out of scope')
• The query is fed into SQLite3, and the result is returned to the user

TODOs:

• Return a formatted sentence back to the user, not a pd.DataFrame
• Turn on pyre
• Concurrently handle requests

• Class detects obvious mistakes in the query and fixes them automatically
• If the query still throws an error, class is able to go back-and-forth with ChatGPT to create a working solution

• Class can effectively navigate questions involving multiple tables and complex schemas, joining where necessary

• In the event that the class isn't sure exactly what to return, it returns a list of potential answers to each question, sorted by likelihood

• Class can handle various SQL and NoSQL database connections at the same time

• Ability to handle user uploaded files and databases
• Answer open-ended questions like "What products do my customers enjoy most?"
• Connections to GCP and AWS