Does PageIndex work well for synthesizing a literature review or survey report from dozens of separate documents

[wangjiawen2013]

Hi,

PageIndex can transform lengthy PDF documents into a semantic tree structure, similar to a “table of contents” but optimized for use with Large Language Models (LLMs). It's ideal for: financial reports, regulatory filings, academic textbooks, legal or technical manuals, and any document that exceeds LLM context limits.

I understand PageIndex excels at handling a single long document (e.g., a 200‑page annual report or a textbook). However, my use case is different: I have dozens of individual PDF documents (research papers, technical reports, or project summaries) – not one single book. My goal is to leverage these documents to write a literature review or a survey report.

Would PageIndex be suitable for this multi‑document scenario? Specifically:

1. Can PageIndex build a unified “tree index” across multiple independent files, or would I need to treat each document as a separate tree?

2. How does the reasoning‑based retrieval work when the answer likely spans several documents (e.g., comparing findings from paper A and paper B)?

3. Are there any known limitations in terms of the number of documents (e.g., 20–50 PDFs) or total page count?

4. If multi‑document synthesis is possible, what is the recommended workflow – should I pre‑merge all PDFs into one large file, or can PageIndex natively handle a collection of files?

Any guidance, best practices, or pointers to examples would be greatly appreciated.

Thank you!