Create Knowledge for Your Models - Document Processing

Converting complex documents like PDFs into knowledge that AI models can effectively use is often underestimated. While it might seem straightforward, flawed document processing is frequently the hidden culprit behind poor AI performance, yet the model often takes the blame. Even sophisticated tools like Optical Character Recognition (OCR) and Vision Language Models (VLMs) aren’t perfect¹; they can misinterpret layouts, miss text, struggle with tables, and generate inaccurate descriptions for images, leading to “garbage in” for your AI system.

This tutorial guides you through building a document processing pipeline designed to fix some of these issues. And it does it with completely local tools. We’ll leverage Docling² to convert PDFs into structured Markdown, incorporating advanced features like OCR via RapidOCR³ and automated image descriptions using SmolVLM⁴. Crucially, we will emphasize the absolute necessity of visually inspecting the conversion output to catch errors early. You’ll learn how to go beyond simple text extraction by integrating visual context and refining the structure.

Furthermore, we’ll explore strategies for transforming the processed text into genuinely useful knowledge components. This involves employing Large Language Models (LLMs) first for intelligent, semantic chunking - breaking the document into meaningful sections - and then for contextual enrichment, adding summaries that help situate each chunk within the document’s broader narrative. This careful, step-by-step process transforms raw documents into high-quality, context-aware inputs ready for effective use in downstream AI applications like Retrieval-Augmented Generation (RAG).

Setup