How to Build a RAG Pipeline from PDFs Using Python

Short summary

A tutorial on building a production-grade RAG pipeline that fixes ingestion failures by converting PDFs to structured Markdown before chunking. Uses Nutrient's Python SDK to preserve headings, lists, and tables, then applies a heading-aware chunker that splits on section boundaries instead of arbitrary character counts. Includes Python code for PDF-to-Markdown conversion and a regex-based chunking strategy.

• •Most RAG pipelines fail at ingestion, not retrieval — raw PDF text breaks chunking
• •Convert PDFs to structured Markdown with Nutrient SDK to preserve headings, lists, and tables
• •Chunk on headings and paragraph breaks instead of fixed character counts for coherent retrieval

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