pdf-ingestion

To extract text from a PDF while preserving structure, you can ingest the document natively or use the `pdftotext` tool. Native input maintains layout for short documents, while extraction preserves content for prose-heavy files.

The best way to summarize a long PDF is to extract the text, chunk it into smaller sections, and summarize each part sequentially. This method maintains context relevance and prevents the model's context window from overflowing.

Yes, you need external tools like `pdftotext` or `pypdf` dependencies for text extraction from long-form PDFs. These tools convert PDF content into text, enabling the model to chunk and summarize large documents efficiently.

Native PDF input is ideal for short documents where preserving layout is crucial. For prose-heavy documents, using text extraction tools like `pdftotext` is recommended to capture content without being constrained by complex layout structures.

PDF text extraction loses layout but preserves content because tools like `pdftotext` flatten the document into raw text strings. This approach strips visual formatting to focus purely on the textual prose, optimizing it for model ingestion.

A key limitation of extracting PDF text is the loss of visual structure and layout formatting. While content is preserved, complex formatting like tables or columns may be flattened, requiring chunking and summarization for long documents.