AI’nt That Easy #12: Advanced PDF RAG with Ollama and llama3

A Step-by-Step Guide

The strengths of retrieval-based and generation-based approaches, RAG allows us to create more accurate, context-aware, and knowledge-grounded AI applications.

In this blog post, we’ll explore how to build a RAG application using Ollama and the llama3 model, focusing on processing PDF documents. We’ll dive into the complexities involved, the benefits of using Ollama, and provide a comprehensive architectural overview with code snippets. By the end of this guide, you’ll have a solid understanding of how to implement an open-source RAG solution for your own projects.

The Complexity of RAG Applications

Implementing a RAG application involves several intricate components working together seamlessly:

1. Document Processing: Extracting and parsing text from various file formats (in our case, PDFs).
2. Text Chunking: Breaking down large texts into manageable pieces for efficient processing.
3. Embedding Generation: Converting text chunks into numerical representations for semantic similarity comparisons.
4. Vector Storage: Efficiently storing and retrieving embedded text chunks.
5. Query Processing: Transforming user queries into a format suitable for retrieval and generation.
6. Retrieval: Finding the most relevant text chunks based on the user’s…