LLM Research

This repository contains a collection of Jupyter notebooks exploring various applications of Large Language Models (LLMs). The research focuses on implementing and experimenting with different LLM techniques and frameworks.

Repository Structure

llm-research/
├── RAG/
│   └── docling-langchain-rag.ipynb

Notebooks

RAG (Retrieval-Augmented Generation)

• docling-langchain-rag.ipynb: Implementation of a RAG system with the help of Docling using LangChain framework for document-based question answering.

Getting Started

Prerequisites

• Python 3.8+
• Jupyter Notebook/Lab

Installation

1. Clone the repository:

   git clone https://github.com/shivanshkaushikk/llm-research.git
   cd llm-research

Usage

1. Start Jupyter Lab/Notebook:

   jupyter lab

2. Navigate to the desired notebook and run the cells.