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iam-tsr/ragmix

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 and converted to markdown for this… See the full description on the dataset page: https://huggingface.co/datasets/iam-tsr/ragmix.

sourceHugging Faceapache-2.0updated 2mo agoView on Hugging Face
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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. 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. 2.Questions — One evaluation question per document, targeting specific, document-grounded facts (dates, amounts, requirements, findings, procedures).
  3. 3.Answers — Short reference answers written from the same document only (no external knowledge required for the gold answer).
  4. 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. Users should also abide to the Digital Corpora.

Changelog

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