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Python script converts PDFs to structured Markdown for RAG pipelines

By PulseAugur Editorial · [1 sources] ·

This article presents a Python script using the Apify platform to convert PDFs into structured Markdown chunks suitable for retrieval-augmented generation (RAG) pipelines. The script addresses common issues with PDF text extraction, such as lost headings and interleaved columns, by utilizing an Apify Actor that parses, performs OCR on scanned pages, and splits content based on headings and paragraphs. The output includes metadata like section headings, which can be stored alongside vector embeddings for more contextually relevant AI responses. The solution handles various document formats and offers a cost-effective approach for preparing data for vector databases. AI

IMPACT Simplifies data preparation for RAG, enabling more accurate AI responses by preserving document structure.

RANK_REASON The item describes a specific tool/script for processing documents for AI applications.

Read on dev.to — LLM tag →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Python script converts PDFs to structured Markdown for RAG pipelines

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The item describes a specific tool/script for processing documents for AI applications.
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  1. dev.to — LLM tag TIER_1 English(EN) · Caspian Data ·

    Turn PDFs into Markdown chunks for RAG in 10 lines of Python (with OCR for scanned pages)

    <p>If you have ever fed PDFs into an LLM pipeline, you know the problem: <code>pdftotext</code> gives you one long wall of text. Headings are gone, page numbers and running headers are glued into sentences, two-column papers come out interleaved, and scanned pages come out empty.…

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