RAG

Deepika, that helps.

When I search for a python library to read PDF files containing scanned documents, I get the suggestions OCRmyPDF, IronPDF and pdfplumber. The last one, pdfplumber, does n ot have OCR but OCR could be added. Depending on the percent of the amount of data in the scanned images OCR is likely critical. OCRmyPDF uses Tesseract that is complicated. The complexity is likely an advantage but is likely to take time to configure.

What I Recommend for Your Use Cases (Building RAG in Python)

Given your use cases (simple text, multi-column, non-English, images, scanned docs, tables):

1. If you want maximum robustness (mixed PDFs) and minimal manual handling ? Start with olmOCR.

   • It handles native PDFs + scans + complex layouts + tables + non-English text.

   • Useful when you don’t know the layout ahead of time (e.g. batch ingest of many heterogeneous PDFs).

   • Ideal for building a vector store: you get a linearized, well-ordered text that preserves document structure reasonably well.

2. If you mostly deal with “digital” PDFs (not scanned), and want more control / custom pre- or post-processing ? Use a hybrid of PyMuPDF (or PDFPlumber/pdfminer) + Camelot/Tabula-py (for tables).

   • This gives you the flexibility to selectively extract what you need (text, tables, metadata), optionally preserve layout coordinates (e.g. for chunking, context-windowing, embeddings, etc.).

   • You can integrate cleanly with Python data stacks (Pandas, JSON, etc.).

Optionally, if you want layout + semantic-aware extraction (columns, footnotes, images, reading order, caption detection) and are comfortable experimenting — track Éclair (or similar research-tools) for possible future upgrade.

?? Some Practical Considerations / Caveats

• Even the best extractors may struggle with very messy scans, handwritten text, or complexly nested tables/forms. You might need manual cleanup or fallback strategies.

• For table-heavy PDFs: no tool is perfect. Camelot/Tabula-py will fail if tables are irregular or lack borders; ML-based extractors (like olmOCR) may mis-recognize rows/columns — validate the output.

• Multilingual / non-Latin scripts — test carefully. While tools like olmOCR claim support, performance may vary based on source quality and script.

• For building a Vector store / RAG: beyond extraction, you’ll need to decide how to chunk the extracted content (by paragraph, page, section, etc.), whether to preserve layout metadata (for context), and how to embed images/tables if you want multimodal search.

? My Recommendation (as your “go-to”)

If I were you and starting from scratch — for a RAG application needing high flexibility — I’d pick olmOCR as the primary extractor. Then:

• Use it for first-pass ingestion of all PDFs (digital or scanned).

• For PDFs where table fidelity matters (financial reports, tabular data), cross-validate / augment with Camelot or Tabula-py.

• Store results in a structured format (e.g. Markdown + metadata JSON, or JSON + Pandas DataFrames for tables), before embedding/chunking for vector storage.

here is no one “perfect” PDF extractor for RAG — the right tool depends on the mix of your use-cases (native text vs scanned images, tables vs narrative text, embeddings vs structured storage). But I can recommend a few of the strongest tools (and combinations) using Python today — plus one new “state of the art” entrant — that are very good for RAG pipelines

@Sam Hobbs I am building RAG application using Python language and need to work with documents that are in PDF form.

Requirement is to extract information from PDF documents which require flexibility when defining the page layout and Vector store that information .

Here are use cases :

PDF Document contains :

• Simple Text

• Multi-Column

• Non-English PDFs

• Complex Layout with Images

• Scanned Documents

• Table-Heavy (tabular data)

The best tool for me might be lousy for you.

Is there something specific about RAG that is unique about reading PDF files or are there any requirements unique to your application? If so then it is important to provide such details. As far as I know, in general, a RAG application would read PDF files the same as any other application.

The language you are using is also important. If you are using Visual Studio or Visual Studio Code for your IDE then there are many choices available using the NuGet Package Manager. That probably will not help you if you are using Python therefore it helps to know what you are using.