Lecture Notes: Creating a Retrieval-Augmented Generation (RAG) LLM App with Llama Index and OpenAI

Introduction

• Presenter: Krishak
• Platform: YouTube
• Topic: Building a RAG system to query multiple PDFs using Llama Index and OpenAI.

Overview

• RAG (Retrieval-Augmented Generation): Combines the power of retrieval and generation models.
• Purpose: Query information from multiple PDFs.
• Tools Used: Llama Index, OpenAI, vector databases, external databases.

Setup and Basics

• Environment Setup: (Using Conda)
  • Create a virtual environment: conda create -n VENV python=3.10
  • Activate the environment: conda activate VENV
  • Install libraries:
    • LlamaIndex
    • OpenAPI
    • pypdf
    • IPyKernel
    • python-dotenv

Project Structure

• Data Folder: Contains PDFs for processing (e.g., attention.pdf and yolo.pdf).
• Environment Variables: Store API keys securely.
• Requirements File: Lists all necessary libraries for the project.
• Jupyter Notebook: For initial testing and implementation: test.ipynb.

Implementation Steps

1. Import Libraries: OS, dotenv, LlamaIndex
2. Set Up Environment Variables: Loading API keys from .env file.
3. Reading PDFs: Using SimpleDirectoryReader from Llama Index to read PDFs into documents.
4. Convert Documents to Vectors: Creating vector embeddings from PDF content: