---
language:
- en
license: apache-2.0
pretty_name: RAGmix
task_categories:
- question-answering
- text-retrieval
task_ids:
- extractive-qa
- closed-domain-qa
tags:
- rag
- retrieval-augmented-generation
- evaluation
- benchmark
- multi-domain
- heterogeneous
- document-qa
- apache-2.0
- digitalcorpora
configs:
- config_name: Ragmix
  data_files:
  - split: test
    path: data/test-00000-of-00001-43be72dace544f4fba6c2a484a3a0032.parquet
size_categories:
- n<1K
dataset_info:
  features:
  - name: category
    dtype: string
  - name: document
    dtype: string
  - name: question
    dtype: string
  - name: answer
    dtype: string
  splits:
  - name: test
    num_examples: 100
---

# RAGmix

**RAGmix** is a heterogeneous, multi-domain evaluation dataset for **Retrieval-Augmented Generation (RAG)** systems.

It mixes real-world document styles—policies, meeting minutes, clinical and scientific text, financial disclosures, job postings, and more—so models can be tested outside a single vertical. Each example pairs a full source document with one grounded question and a reference answer.

Source PDFs were obtained from **[Digital Corpora](https://digitalcorpora.org/)** and converted to markdown for this release. This is a **test-only** split (100 examples).

## How the data was built

1. **Documents** — Heterogeneous PDFs were downloaded from Digital Corpora, a public repository of digital corpora for forensics education and research, and converted to markdown (OCR/layout-aware parsing with image analysis where needed).
2. **Questions** — One evaluation question per document, targeting specific, document-grounded facts (dates, amounts, requirements, findings, procedures).
3. **Answers** — Short reference answers written from the same document only (no external knowledge required for the gold answer).
4. **Categories** — Manual topic labels for multi-domain analysis.

```
from datasets import load_dataset
rag_dataset = load_dataset("iam-tsr/ragmix")
```

## Limitations

- Small scale (100 examples); statistical significance is limited.
- One question per document; does not cover multi-hop or multi-document reasoning.
- Documents vary widely in length and quality (OCR/markdown artifacts may remain).
- Categories are coarse; some documents sit near domain boundaries.
- English only.
- Reference answers are human-written summaries of document facts; alternative phrasings may also be correct.

## Ethical considerations

- Document texts originate from third-party PDFs hosted via Digital Corpora; treat redistribution of full text carefully under applicable rights and Digital Corpora’s terms.
- Some documents touch sensitive topics (health, religion, legal enforcement). Prefer evaluation / research use.
- Do not treat answers as professional medical, legal, or financial advice.

## Citation

```bibtex
@misc{ragmix,
  title        = {RAGmix: A Heterogeneous Multi-Domain Dataset for RAG Evaluation},
  author       = {Tushar Soni},
  year         = {2026},
  howpublished = {Hugging Face Datasets},
  note         = {Test split, 100 document-grounded QA examples. Source PDFs from Digital Corpora (https://digitalcorpora.org/).}
}

@misc{digitalcorpora,
  title        = {Digital Corpora},
  author       = {Garfinkel, Simson L. and others},
  howpublished = {\url{https://digitalcorpora.org/}},
  note         = {Public digital corpora for education and research}
}
```

## License

This public extract is made available under [Apache license 2.0](https://www.apache.org/licenses/LICENSE-2.0.html). Users should also abide to the Digital Corpora.

## Changelog

- **v0.1** — Initial test release: 100 examples, 15 categories.