Most developers building "chat with your data" apps hit the exact same wall. You chunk the text, embed it, dump it in a vector database, and the retrieval is still terrible. The model hallucinates or completely scrambles tables.
People think data ingestion is just text extraction. It isn't. In 2026, text extraction is a solved, boring problem. The actual hard part is layout. If your ingestion layer doesn't know that a bold header implies hierarchy, or that a two-column page isn't just one long string of text read left-to-right, your LLM is reading garbage.
Markdown won the ingestion war
We've mostly stopped treating PDFs as plain text. Markdown is now the default format for document ingestion, simply because it preserves structure.
Modern ingestion tools don't just dump strings. They output Markdown where headers, lists, and tables actually mean something. This gives the LLM the context it needs to figure out where a piece of information lived in the original document, which makes citations and retrieval significantly more accurate.
Local engines vs. Vision models
Right now, there are basically two ways to handle this layout problem.
First, you have local deterministic engines like IBM's Docling or OpenDataLoader PDF. Docling has quietly become a standard for enterprise RAG because it natively handles the whole Office suite and spits out clean Markdown. It runs locally without a GPU. OpenDataLoader does something similar. If you have a massive volume of private documents, this is the realistic path.
Then you have the Vision-Language Model (VLM) approach. Instead of trying to parse messy PDF code, tools like Mistral OCR and LlamaParse just look at the document as an image. They see it the way we do. This completely bypasses the nightmare of multi-column layouts and nested tables that broke older parsers.
The tradeoff
VLM parsing feels like magic, but it's expensive. If you process millions of pages, running everything through a cloud vision API will destroy your budget.
If I'm building a RAG pipeline today, my default is a robust local engine like Docling for the bulk of the documents. I only reach for the expensive VLM calls when a PDF is too visually complex for the local parser to figure out.
Whatever you do, don't use legacy libraries like PyPDF or pdfminer for RAG anymore. If your ingestion layer isn't outputting structured Markdown or using vision to understand layout, your app is broken before the prompt even starts.
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