Natural language is converted into sql queries using Gemini llm and langchain which is a framework to create applications upon llm.
This llm will have access to our database and converts the question to sql query and returns the answer. We need to add complex questions in few shot learning where llm
can't understand any complex questions given by the user.So, it uses semanticsimilarity examples to learn from it.
Home page:
flask run
streamlit run main.py
Used local database , the sql query file is given at database/sql.sql
And also the csv files in data folder.
To install all the dependencies run
pip install -r requirements.txt
-> Deployed on flask
-> app.py is the main server which handles all the request. Go to \app.py to see
-> Used local sql database
-> Used easygoogletranslate for tranlation of different languages
-> Added contactus page, where the user can share his issue or problem
-> Used MySQLdb python library to automatically store the users response (name,email,message) into the local sql database
There are no models linked
There are no models linked
