---
pretty_name: "RAG QA Logs & Corpus (Synthetic)"
license: cc-by-4.0
language:
  - en
size_categories:
  - 100K<n<1M

task_categories:
  - question-answering
  - tabular-classification
  - tabular-regression

tags:
  - rag
  - retrieval-augmented-generation
  - evaluation
  - hallucination
  - faithfulness
  - meta-modeling
  - risk-scoring
  - logs
  - telemetry
  - multi-table
  - synthetic-data
  - tabular
  - dataset

# ✅ HF multi-table configs (stable loading + dataset viewer)
configs:
  - config_name: default
    default: true
    data_files:
      - split: train
        path: data/eval_runs.csv

  - config_name: eval_runs
    data_files:
      - split: train
        path: data/eval_runs.csv

  - config_name: retrieval_events
    data_files:
      - split: train
        path: data/rag_retrieval_events.csv

  - config_name: documents
    data_files:
      - split: train
        path: data/rag_corpus_documents.csv

  - config_name: chunks
    data_files:
      - split: train
        path: data/rag_corpus_chunks.csv

  - config_name: scenarios
    data_files:
      - split: train
        path: data/scenarios.csv

  - config_name: data_dictionary
    data_files:
      - split: train
        path: docs/data_dictionary.csv
---

# 🧠📚 RAG QA Logs & Corpus (Synthetic)
### 🧪 Multi-table synthetic RAG telemetry for quality, hallucinations, latency, and cost

A production-style, privacy-safe synthetic dataset that mimics telemetry exported from a real **RAG** system — from **corpus → chunks → retrieval events → eval runs**.

✅ Fully synthetic (no real users / orgs / PII).

---

## ⚡ Quick facts
- **Total rows:** 103,255 across 6 linked tables
- **Labels (in `eval_runs`):** `is_correct`, `hallucination_flag`, `faithfulness_label`
- **Join keys:** `doc_id`, `chunk_id`, `example_id`, `scenario_id`, `query_id`, `run_id`
- **Splits:** `train`, `val`, `test` (example-grain in `eval_runs`)
- **Retrieval strategies:** `dense`, `bm25`, `hybrid`, `dense_then_rerank`, `bm25_then_rerank`

---

## 📦 What’s inside
- `data/eval_runs.csv` — QA evaluation runs (labels + metrics)
- `data/rag_retrieval_events.csv` — per-chunk retrieval telemetry (many rows per example)
- `data/rag_corpus_documents.csv` — document metadata
- `data/rag_corpus_chunks.csv` — chunk index + chunk text
- `data/scenarios.csv` — scenario templates/use-cases
- `docs/data_dictionary.csv` — schema documentation across tables

---

## 🔗 How to join
- Documents → Chunks: `doc_id`
- Eval runs → Retrieval events: `example_id`
- Retrieval events → Chunks: `chunk_id`
- Eval runs → Scenarios: `scenario_id`

---

## 🚀 How to use

### 🤗 Load from Hugging Face (recommended)
```python
from datasets import load_dataset, get_dataset_config_names

repo_id = "tarekmasryo/rag-qa-logs-corpus-data"
print(get_dataset_config_names(repo_id))

runs   = load_dataset(repo_id, "eval_runs")["train"].to_pandas()
events = load_dataset(repo_id, "retrieval_events")["train"].to_pandas()
docs   = load_dataset(repo_id, "documents")["train"].to_pandas()
chunks = load_dataset(repo_id, "chunks")["train"].to_pandas()
```

### 💻 Local (after cloning)
```bash
git clone https://huggingface.co/datasets/tarekmasryo/rag-qa-logs-corpus-data
cd rag-qa-logs-corpus-data
```

```python
import pandas as pd

runs   = pd.read_csv("data/eval_runs.csv")
events = pd.read_csv("data/rag_retrieval_events.csv")
docs   = pd.read_csv("data/rag_corpus_documents.csv")
chunks = pd.read_csv("data/rag_corpus_chunks.csv")
```

### 🧩 Example join (runs → events → chunks → docs)
> Note: after joining `runs → events`, the result becomes **retrieval-grain** (rows ~= `retrieval_events`).
```python
df = (
    runs
    .merge(events, on="example_id", how="left", suffixes=("_run", "_evt"))
    .merge(chunks[["chunk_id", "doc_id", "chunk_index"]], on="chunk_id", how="left")
    .merge(docs[["doc_id", "domain", "title", "source_type"]], on="doc_id", how="left")
)

print(df.shape)
print(runs["split"].value_counts(dropna=False)) 
```

---

## 🧾 Citation
Tarek Masryo. (2025). *RAG QA Logs & Corpus (Synthetic).*

---

## 📜 License
**CC BY 4.0 (Attribution Required)**