LLM Application with Google Gemini Pro and Langchain 🚀

Welcome to the LLM (Language Model) application developed using Google Gemini Pro and Langchain.This is the first time I use Gimini, this application allows you to process and analyze multiple PDF documents using FAISS vector embeddings, all presented in a user-friendly interface built with Streamlit.

Prerequisites 🛠️

Make sure you have the following prerequisites installed:

• Google GenerativeAI: The powerful language model from Google.
• Langchain: A library for working with language models.
• Streamlit: For creating interactive web applications with Python.
• python-dotenv: For loading environment variables from a .env file.
• pyPDF2: A library for reading PDF files.
• chromadb: Your description of this library.
• faiss-cpu: A library for efficient similarity search and clustering of dense vectors.
• langchain_google_genai: The Google GenerativeAI extension for Langchain.

Installation and Setup 🚧

1. Clone the repository:

   git clone https://github.com/sayakdeepghosh01/gimini-app-multipdfs.git
   cd your-llm-application

2. Install dependencies:

   pip install -r requirements.txt

Usage 🚀

1. Run the application:

   streamlit run app.py

2. Access the application in your browser: http://localhost:8501

Contributing 👩‍💻👨‍💻

Feel free to contribute to the project. Fork the repository, make changes, and submit a pull request.

Acknowledgments 🙌

-This project is inspired by the teachings and content provided by Krishnaik Sir. A big thanks to him for the valuable insights and guidance in the field of data science and machine learning.

Happy coding! 🎉