| --- |
| license: cc-by-4.0 |
| configs: |
| - config_name: default |
| data_files: |
| - split: train |
| path: data/train-* |
| dataset_info: |
| features: |
| - name: id |
| dtype: string |
| - name: domain |
| dtype: string |
| - name: question_type |
| dtype: string |
| - name: dynamism |
| dtype: string |
| - name: question |
| dtype: string |
| - name: reference_answer |
| dtype: string |
| - name: sources |
| list: |
| - name: filename |
| dtype: string |
| - name: id |
| dtype: string |
| - name: pages |
| sequence: int64 |
| splits: |
| - name: train |
| num_bytes: 35785 |
| num_examples: 100 |
| download_size: 21165 |
| dataset_size: 35785 |
| --- |
| |
|
|
| # EntRAG Benchmark: Question Answering Dataset |
|
|
| ## Description |
|
|
| EntRAG is a specialized benchmark dataset designed for evaluating Retrieval-Augmented Generation (RAG) systems in enterprise contexts. |
| The dataset addresses the unique challenges of business environments where information comes from heterogeneous sources including structured databases, documents, and dynamic mock APIs. |
|
|
| The dataset comprises 100 manually constructed question-answer pairs across six enterprise domains: Finance, Technical Documentation, Environment, Legal and Compliance, Human Resources, and Marketing and Sales. |
| Questions are designed to evaluate both static document retrieval and dynamic API integration scenarios, reflecting realistic enterprise information needs. |
|
|
| ## Dataset Structure |
|
|
| ### Columns |
|
|
| * `id`: Unique identifier for each question-answer pair |
| * `domain`: The subject area or field of knowledge the question pertains to (e.g., "Technical Documentation", "Finance", "Healthcare") |
| * `question_type`: The category of reasoning required (e.g., "comparison", "factual", "analytical", "procedural") |
| * `dynamism`: Indicates whether the answer content changes over time ("static" for timeless information, "dynamic" for evolving content) |
| * `question`: A natural language question that requires information retrieval and reasoning to answer accurately |
| * `reference_answer`: The correct, comprehensive answer that serves as the ground truth for evaluation |
| * `sources`: Array of source documents that contain the information needed to answer the question, including: |
| * `id`: Unique identifier for the source |
| * `filename`: Name of the source document or API endpoint |
| * `pages`: Array of specific page numbers where relevant information is found (empty for API sources) |
|
|
| ## Use Cases |
|
|
| This dataset is particularly valuable for: |
|
|
| * **RAG System Evaluation**: Testing RAG systems with realistic business scenarios and multi-source information integration |
| * **Hybrid System Assessment**: Evaluating systems that combine document retrieval with API-based data access |
| * **Domain-Specific Analysis**: Understanding RAG performance across different business domains |
| * **Dynamic Information Handling**: Assessing systems that work with both static documents and real-time data sources |
|
|
| ## Accessing the Dataset |
|
|
| You can load this dataset via the Hugging Face Datasets library using the following Python code: |
|
|
| ```python |
| from datasets import load_dataset |
| |
| # Load the dataset |
| dataset = load_dataset("fkapsahili/EntRAG") |
| |
| # Access the data |
| for example in dataset['train']: |
| print(f"Domain: {example['domain']}") |
| print(f"Question Type: {example['question_type']}") |
| print(f"Dynamism: {example['dynamism']}") |
| print(f"Question: {example['question']}") |
| print(f"Answer: {example['reference_answer']}") |
| print(f"Sources: {len(example['sources'])} documents") |
| print("---") |
| ``` |
|
|
| ### Alternative Loading Methods |
|
|
| For direct integration with evaluation frameworks: |
|
|
| ```python |
| import json |
| from datasets import load_dataset |
| |
| # Load and convert to list format |
| dataset = load_dataset("fkapsahili/EntRAG", split="train") |
| qa_pairs = [dict(item) for item in dataset] |
| ``` |
|
|
| ## Integration with RAG Frameworks |
|
|
| This dataset supports evaluation of various RAG architectures and can be integrated with existing evaluation pipelines. |
| The format is compatible with standard RAG evaluation frameworks and supports both document-based and API-integrated systems. |
|
|
| ## Dataset Statistics |
|
|
| * **Total QA Pairs**: 100 manually constructed questions |
| * **Domains**: 6 domains (Finance, Technical Documentation, Environment, Legal and Compliance, Human Resources, Marketing and Sales) |
| * **Question Types**: 7 reasoning patterns (simple queries, comparison, aggregation, multi-hop reasoning, simple with conditions, factual contradiction, post-processing) |
| * **Dynamism Distribution**: |
| * Static questions: 28% (document-based retrieval) |
| * Dynamic questions: 72% (requiring real-time API integration) |
| * **Source Documents**: 9,500+ pages from authentic enterprise documents across 10 major companies |
| * **Company Sectors**: Technology, healthcare, e-commerce, retail, automotive, and energy |
| * **Mock APIs**: 4 domain-specific APIs (finance, SEC filings, HR statistics, web search) |
|
|
| ## Citation |
|
|
| If you use this dataset in your research, please cite: |
|
|
| ```bibtex |
| @dataset{entrag_2025, |
| title={EntRAG: Enterprise RAG Benchmark}, |
| author={Fabio Kapsahili}, |
| year={2025}, |
| publisher={Hugging Face}, |
| url={https://huggingface.co/datasets/fkapsahili/EntRAG} |
| } |
| ``` |
|
|
| ## License |
|
|
| This dataset is released under Creative Commons Attribution 4.0. Please see the LICENSE file for full details. |
|
|
| ## Additional Resources |
|
|
| * **Evaluation Code**: https://github.com/fkapsahili/EntRAG |
|
|
| For questions, issues, please open an issue in the associated GitHub repository. |
