Phase 2: Enterprise Data Engineering · 60 min · Python · PyMuPDF · unstructured.io

Document Processing for RAG Ingestion

The document is messy. The ingestion must be clean.

What you will learn

Extract text from PDFs using PyMuPDF, pdfplumber, and unstructured.io
Run OCR on scanned documents using Tesseract and cloud OCR services
Extract and preserve table structures: detecting boundaries, maintaining row/column relationships
Implement chunking strategies: fixed-size, sentence-based, semantic chunking at 512-1024 tokens
Extract metadata: titles, sections, page numbers, access control labels

Objective

What you'll learn

This lesson takes approximately 60 min. By the end, you will be able to:

Extract text from PDFs using PyMuPDF, pdfplumber, and unstructured.io
Run OCR on scanned documents using Tesseract and cloud OCR services
Extract and preserve table structures: detecting boundaries, maintaining row/column relationships
Implement chunking strategies: fixed-size, sentence-based, semantic chunking at 512-1024 tokens
Extract metadata: titles, sections, page numbers, access control labels

The Problem

Enterprise documents are messy. PDFs with embedded images, scanned contracts run through OCR, Word docs with tracked changes, Excel files with merged cells, HTML with boilerplate navigation. Your RAG ingestion pipeline needs to handle all of these and produce clean, chunkable text.

Unlock the full lesson

You've read the first 2 sections. The rest of this lesson covers The Concept, Build, Key Terms & Next — plus a hands-on lab, quiz, and project artifact.

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