flash

PageIndex Flash

Builds the PageIndex tree structure from a PDF using layout statistics without an LLM. Augmenting the tree with summaries and refining it for retrieval needs an LLM.

Usage

Python

from pageindex.flash import page_index_flash

tree = page_index_flash("paper.pdf")                                 # optimized tree + summaries
tree = page_index_flash("paper.pdf", summary=False, optimize=False)  # raw tree only, no LLM
tree = page_index_flash("paper.pdf", optimize="merge")               # deterministic merge, no LLM expand

Takes a file path or an io.BytesIO stream and returns the tree as a dict. Summaries are on by default and need an LLM API key.

Command line

python3 run_pageindex.py --mode flash --pdf_path document.pdf                             # optimized tree + summaries
python3 run_pageindex.py --mode flash --pdf_path document.pdf --no-summary --optimize off # raw tree only, no LLM

Writes the tree to results/<name>_structure.json.

Output

{
    "doc_name": str,
    "doc_title": str,
    "structure": [
        {
            "title": str,
            "node_id": str,       # 4-digit, zero-padded
            "start_index": int,   # 1-based, inclusive
            "end_index": int,
            "summary": str,
            "key_items": [str],   # optimize only: titles of subsections merged away
            "nodes": [...],
        }
    ],
}

Benchmark

Nine PDFs, each run end to end with tree optimization: PDF parse, layout outline, merge, LLM expand, then a summary for every node.

Document	Pages	Input tokens	Output tokens
Bitcoin whitepaper	9	8,715	4,673
Attention Is All You Need	15	26,805	10,183
KIMI K3	47	85,704	35,217
DeepSeek-R1	86	68,398	26,351
Situational Awareness	165	115,130	54,347
Federal Reserve 2023 report	222	280,975	136,982
9/11 Commission Report	585	720,624	200,202
Pattern Recognition and Machine Learning	758	857,983	277,675
Machine Learning: A Probabilistic Perspective	1,098	1,587,265	646,958