---
license: cc-by-4.0
task_categories:
- question-answering
- text-retrieval
language:
- en
tags:
- pdf
- rag
- chunking
- retrieval
- benchmark
- docuweave
size_categories:
- 1K<n<10K
pretty_name: DocuWeave-Bench
---

# DocuWeave-Bench

**A retrieval benchmark for evaluating PDF chunking strategies across five document domains.**

## Paper

📄 **[DocuWeave: Layout-Aware PDF Chunking for Retrieval-Augmented Generation](paper/docuweave_paper.pdf)**

> Jannegorla, Venkateswara Rao (2026). DocuWeave: Layout-Aware PDF Chunking for Retrieval-Augmented Generation Pipelines.

**PyPI:** `pip install docuweave` · **GitHub:** [VenkateswaraRao18/docuweave](https://github.com/VenkateswaraRao18/docuweave)

## Overview

DocuWeave-Bench is a dataset of **6,100 question-answer pairs** grounded in **417 real-world PDFs**
spanning five domains: research papers, technical documentation, legal documents, financial reports,
and medical literature.

It was designed to evaluate and compare PDF chunking strategies for Retrieval-Augmented Generation (RAG)
pipelines. The benchmark is introduced in the paper **DocuWeave: Layout-Aware PDF Chunking for Retrieval-Augmented Generation** by Venkateswara Rao Jannegorla (2026).

## Dataset Statistics

| Split | # PDFs | # QA Pairs | Avg Q/PDF |
|-------|--------|------------|-----------|
| test  | 417    | 6,100      | ~14.6     |

| Domain           | # PDFs | # QA Pairs |
|------------------|--------|------------|
| research_papers  | 92     | ~1,340     |
| technical_docs   | 87     | ~1,270     |
| legal_docs       | 83     | ~1,210     |
| financial_reports| 79     | ~1,150     |
| medical_docs     | 76     | ~1,130     |

## Benchmark Results

| Chunker              | R@1    | R@3    | R@5    | nDCG@10 | MRR    |
|----------------------|--------|--------|--------|---------|--------|
| **DocuWeave**        | **0.2862** | **0.4475** | **0.5149** | **0.4336** | **0.3837** |
| Naive (fixed-size)   | 0.1975 | 0.3495 | 0.4351 | 0.3958  | 0.3001 |
| Recursive            | 0.1790 | 0.3307 | 0.4143 | 0.3659  | 0.2792 |
| LangChain            | 0.1475 | 0.2707 | 0.3395 | 0.2938  | 0.2291 |
| Semantic             | 0.1184 | 0.2018 | 0.2518 | 0.2742  | 0.1753 |
| PDFPlumber           | 0.1126 | 0.1926 | 0.2393 | 0.2649  | 0.1660 |

Embeddings: `BAAI/bge-base-en-v1.5`. Retrieval: FAISS flat index, top-10. All differences
vs DocuWeave are statistically significant (Wilcoxon, p < 0.001).

## Files

```
paper/
  docuweave_paper.pdf  # Full research paper
data/
  qa_pairs.jsonl       # 6,100 QA pairs (one JSON object per line)
pdfs/
  research_papers/     # 92 PDFs
  technical_docs/      # 87 PDFs
  legal_docs/          # 83 PDFs
  financial_reports/   # 79 PDFs
  medical_docs/        # 76 PDFs
```

## QA Pair Schema

```json
{
  "query":         "What is the authors proposed method?",
  "answer":        "The authors propose ...",
  "question_type": "factoid",
  "domain":        "research",
  "pdf":           "datasets/pdfs/research_papers/arxiv_2301_00001.pdf",
  "gold_chunk_id": "a3f7b2...",
  "gold_text":     "We propose DocuWeave, a layout-aware PDF chunker ...",
  "section_title": "3. Method",
  "page_start":    4,
  "page_end":      5
}
```

## Usage

```python
from datasets import load_dataset

ds = load_dataset("mrjvenky18/docuweave-bench", data_files="data/qa_pairs.jsonl")

from huggingface_hub import hf_hub_download

# Download the paper
paper = hf_hub_download(
    repo_id="mrjvenky18/docuweave-bench",
    repo_type="dataset",
    filename="paper/docuweave_paper.pdf"
)
```

## Citation

```bibtex
@article{jannegorla2026docuweave,
  title   = {DocuWeave: Layout-Aware PDF Chunking for Retrieval-Augmented Generation},
  author  = {Jannegorla, Venkateswara Rao},
  journal = {arXiv preprint},
  year    = {2026},
}
```

## License

The benchmark QA pairs and scripts are released under **CC BY 4.0**.
PDFs are sourced from public repositories (arXiv, SEC EDGAR, PubMed, government portals)
and redistributed under their respective open licenses.