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README.md

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----
-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
----
+---
+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**: 11,000+ 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.