Project Highlights

• AtliQ Tees is a t shirt store that sells Adidas, Nike, Van Heusen and Levi's t shirts
• Their inventory, sales and discounts data is stored in a MySQL database
• We will build an LLM based question and answer system that will use following,
  • Google Palm LLM
  • Hugging face embeddings
  • Streamlit for UI
  • Langchain framework
  • Chromadb as a vector store
  • Few shot learning
• In the UI, store manager will ask questions in a natural language and it will produce the answers

Installation

1.Clone this repository to your local machine using:

  git clone https://github.com/patilabhi20/LLM_project

2.Navigate to the project directory:

  cd 4_sqldb_tshirts

3.Install the required dependencies using pip:

  pip install -r requirements.txt

4.Acquire an api key through makersuite.google.com and put it in .env file

  GOOGLE_API_KEY="your_api_key_here"

5.For database setup, run database/db_creation_atliq_t_shirts.sql in your MySQL workbench

Usage

1,Run the Streamlit app by executing:

streamlit run main.py

2.The web app will open in your browser where you can ask questions

Sample Questions

• How many total t shirts are left in total in stock?
• How many t-shirts do we have left for Nike in XS size and white color?
• How much is the total price of the inventory for all S-size t-shirts?
• How much sales amount will be generated if we sell all small size adidas shirts today after discounts?

Project Structure

• main.py: The main Streamlit application script.
• langchain_helper.py: This has all the langchain code
• requirements.txt: A list of required Python packages for the project.
• few_shots.py: Contains few shot prompts
• .env: Configuration file for storing your Google API key.