Enhance PDF Parsing Pipeline with GPT-4o Image Analysis for Rich Content Extraction

[aliamerj]

We currently extracting raw text from PDFs, which works fine for straightforward documents but struggles with slide decks, diagrams, tables, and other visually rich content. To deliver a more comprehensive retrieval-augmented generation (RAG) experience, let’s augment our pipeline by:

1. Dual Extraction Strategy

• Text Layer: Continue using pdfminer (via plumpdf) to grab the “machine” text.
• Image Layer: Convert each page into an image (pdf2image) and feed it into GPT-4o’s vision mode to describe diagrams, tables, and layout nuances.

2. Integration Outline

• Add a preprocessing step that checks page complexity (e.g., presence of images or tables).
• Route simple pages through text-only extraction; route complex pages through GPT-4o image analysis.
• Merge the two outputs into a unified chunked corpus (e.g., interleave or concatenate based on page order).

3. Benefits & Use Cases

• Slide Decks: Extract slide titles, bullet points, and visuals in context.
• Exported Web Pages: Preserve both textual and graphical insights (e.g., charts).
• Enhanced QA: Users querying on “How does the process flow?” will get both narrative and diagrammatic explanations.