Gemini Quizify Mission

Overview

This project provides a comprehensive solution for processing PDF documents, extracting text, generating embeddings, and storing the data in a vector store for efficient querying. Additionally, the project leverages a Large Language Model (LLM) to generate quiz questions from the processed text, making it an excellent tool for educational purposes.

Features

• PDF Processing: Upload and process multiple PDF documents.

• Text Splitting: Split documents into smaller text chunks for efficient embedding.

• Embedding Generation: Generate embeddings for text chunks using a pre-configured embedding model.

• Vector Store Creation: Store text chunks and their embeddings in a Chroma in-memory vector store.

• Querying: Query the vector store to retrieve documents similar to a given query.

• Quiz Question Generation: Automatically generate quiz questions from the processed text using an LLM.

Required Python libraries

• streamlit
• langchain
• langchain_community
• chromadb
• re

Aknowledgement

• Streamlit
• Langchain
• Chroma
• Large Language Model (LLM)