AI’nt That Easy #5: Advanced PDF RAG

Navigating the Complexities of Text, Table, and Image Extraction

At first glance, Retrieval-Augmented Generation (RAG) for PDFs might sound straightforward: extract text, retrieve relevant information, and generate responses. However, when you dive into the implementation, especially when dealing with PDFs containing not just text but also tables and images, the intricacy quickly becomes apparent. Let’s explore a real-world implementation of an advanced PDF RAG system and unpack its architecture.

The Complexity Behind the Scenes

Our sample application is a Streamlit-based chat interface that allows users to upload multiple PDF files, including those with tables and images, and then ask questions based on their content. While this sounds simple, the underlying architecture involves multiple components working in harmony to handle the variety of content within PDFs:

1. Advanced PDF Processing: The system extracts text, tables, and performs OCR on images within PDFs.
2. Text Splitting: Extracted content is split into manageable chunks for efficient processing.
3. Embedding Generation: These content chunks are converted into vector embeddings.
4. Vector Storage: Embeddings are stored in a vector database for quick retrieval.
5. Language Model Integration: A large language model is used for generating responses.
6. Conversation Management: The…