Datasets:

tarekmasryo
/

RAG-QA-Logs-Corpus

license: cc-by-4.0
task_categories:
  - tabular-classification
  - tabular-regression
  - question-answering
  - text-generation
language:
  - en
tags:
  - rag
  - retrieval-augmented-generation
  - evaluation
  - hallucination
  - meta-modeling
  - risk-scoring
  - logs
  - telemetry
  - tabular-data
  - multi-table
  - machine-learning
  - open-dataset
  - synthetic
  - simulated
  - agent
  - code
pretty_name: RAG QA Logs & Corpus
size_categories:
  - 100K<n<1M

🧠 RAG QA Evaluation Logs & Corpus

Multi-Table RAG Telemetry for Quality, Hallucinations, Latency, and Cost

A multi-table dataset modeling a production-style RAG (Retrieval-Augmented Generation) system end-to-end:

a document corpus
a chunk-level index
retrieval events per query
QA evaluation runs with correctness, hallucination, latency, and cost signals
scenario templates for QA use cases
a data dictionary documenting every column

All records are fully synthetic but realistic system logs and corpus data — designed to look and behave like real RAG telemetry while remaining safe to share and experiment with.

The dataset is intended for RAG quality analysis, meta-modeling, hallucination risk scoring, and dashboard-style telemetry.

🔐 Privacy & Synthetic Data

This dataset is fully synthetic.

No real users, customers, patients, or organisations are represented.
No personally identifiable information (PII) is included.
All IDs, queries, documents, and logs were programmatically generated to mimic realistic RAG system behaviour while preserving privacy.

The design aims to balance realism (for meaningful analysis and modeling) with strong privacy guarantees, making it suitable for open research, teaching, demos, and public dashboards.

📘 Dataset Overview

Field	Description
Files	`rag_corpus_documents.csv`, `rag_corpus_chunks.csv`, `rag_qa_eval_runs.csv`, `rag_retrieval_events.csv`, `rag_qa_scenarios.csv`, `rag_qa_data_dictionary.csv`
Tables	6 (documents, chunks, QA runs, retrieval events, data dictionary)
Total rows (approx.)	~103K across all tables (103,273 rows in total)
Main targets	`is_correct`, `hallucination_flag`, `faithfulness_label`
Type	Multi-table tabular logs + short text fields

Tables are linked by stable identifiers such as doc_id, chunk_id, run_id, example_id, and scenario_id, making joins explicit and reliable.

📂 Files

rag_corpus_documents.csv – document-level corpus metadata
rag_corpus_chunks.csv – chunk-level index and text content
rag_qa_eval_runs.csv – QA runs with labels, metrics, and configurations
rag_retrieval_events.csv – per-chunk retrieval telemetry
rag_qa_scenarios.csv – scenario-level QA templates and use cases
rag_qa_data_dictionary.csv – column-level documentation for all tables

All files are in CSV format, use snake_case column names, and are designed to be ML- and analytics-ready.

📊 Table Summary

Approximate size per table: | Table | Rows | Columns | Granularity | |--------------------------|--------|---------|-------------------------------------------------------| | `rag_corpus_documents` | 658 | 19 | One row per document in the RAG corpus | | `rag_corpus_chunks` | 5,237 | 6 | One row per text chunk derived from a document | | `rag_qa_eval_runs` | 3,824 | 46 | One row per QA evaluation example | | `rag_retrieval_events` | 93,375 | 9 | One row per retrieved chunk for a given QA example | | `rag_qa_scenarios` | 88 | 11 | One row per scenario-level QA template / use case | | `rag_qa_data_dictionary` | 91 | 5 | One row per column definition across all tables |

🧱 Table Structure

1️⃣ Document Corpus — `rag_corpus_documents.csv`

High-level view of the RAG knowledge base. Granularity: 1 row = 1 document Key fields:

doc_id — unique document identifier
domain — e.g. support_faq, hr_policies, product_docs, developer_docs, policies, financial_reports, medical_guides, research_papers, customer_success, data_platform_docs, mlops_docs, marketing_analytics
title — document title
source_type — e.g. pdf_manual, spreadsheet, wiki_page
language — currently en
n_sections, n_tokens, n_chunks, avg_chunk_tokens — structural and size indicators
created_at_utc, last_updated_at_utc — lifecycle timestamps
is_active, contains_tables — operational flags
pii_risk_level, security_tier — risk and access level
owner_team, embedding_model, search_index, top_keywords — ownership and indexing metadata
Use this table to understand what kind of corpus the RAG system is built on and how corpus properties relate to downstream performance.

2️⃣ Chunk Corpus — `rag_corpus_chunks.csv`

What the retriever actually “sees”.

Granularity: 1 row = 1 chunk

Key fields:

chunk_id — unique chunk identifier
doc_id — foreign key to rag_corpus_documents.doc_id
domain — propagated from the parent document
chunk_index — 0-based position of the chunk within the document
estimated_tokens — approximate token length
chunk_text — the text content used for retrieval and ranking

Use this table to rebuild retrieval candidates, inspect chunking strategies, and study how content structure affects retrieval.

3️⃣ QA Evaluation Runs — `rag_qa_eval_runs.csv`

End-to-end evaluation records for question–answer runs, including labels and metrics. Granularity: 1 row = 1 QA example (one query, one answer, one configuration) Key fields: Context & content

example_id, run_id — unique identifiers
domain, task_type, difficulty — scenario and question type
scenario_id — link to rag_qa_scenarios
query — user-style question text
gold_answer — reference answer
has_answer_in_corpus — whether the corpus actually contains sufficient evidence

Quality & hallucination signals

is_correct — main binary correctness flag
correctness_label — descriptive view of correctness (e.g. correct / partial / incorrect)
faithfulness_label — e.g. faithful / unfaithful / unknown
hallucination_flag — binary hallucination indicator
user_feedback_label — simplified user-style feedback
supervising_judge_label — synthetic “expert” judgement
is_noanswer_probe — marks deliberately unanswerable queries

Retrieval metrics

retrieval_strategy, chunking_strategy
n_retrieved_chunks
top1_score, mean_retrieved_score
recall_at_5, recall_at_10, mrr_at_10
has_relevant_in_top5, has_relevant_in_top10

Latency & resource usage

latency_ms_retrieval, latency_ms_generation, total_latency_ms
used_long_context_window, context_window_tokens

Configuration & cost

embedding_model, reranker_model, generator_model
temperature, top_p, max_new_tokens, stop_reason
prompt_tokens, answer_tokens, total_cost_usd

Supervision & usage

doc_ids_used, chunk_ids_used
eval_mode, created_at_utc

This table is the main entry point for meta-modeling, risk scoring, and latency–cost–quality tradeoff analysis.

4️⃣ Retrieval Events — `rag_retrieval_events.csv`

Per-chunk retrieval telemetry for each QA example.

Granularity: 1 row = 1 retrieved chunk for an example

Key fields:

run_id, example_id — link back to rag_qa_eval_runs
chunk_id — link to rag_corpus_chunks
rank — rank position (1 = top)
retrieval_score — retriever score
is_relevant — relevance label for this chunk
domain, difficulty, retrieval_strategy — redundant context fields for easier analysis

Use this table to reconstruct retrieval lists, compute custom ranking metrics, and explore how ranking quality influences final answers.

5️⃣ Scenarios — `rag_qa_scenarios.csv`

Scenario-level templates and use cases for QA runs.

Granularity: 1 row = 1 scenario

Key fields:

scenario_id — links to rag_qa_eval_runs.scenario_id
domain — scenario domain (support, HR, finance, medical, developer docs, etc.)
primary_doc_id — anchor document for the scenario
query, gold_answer — canonical scenario-level QA pair
difficulty_level — e.g. easy / medium / hard
scenario_type — e.g. factual QA, policy lookup, multi-hop reasoning
use_case — short description of the business or product scenario
has_answer_in_corpus — whether the scenario is designed to be answerable from the corpus
n_eval_examples, is_used_in_eval — how many QA examples were generated per scenario and whether it appears in the evaluation runs

This table adds a narrative layer on top of the logs, making it easier to build dashboards, teaching materials, or explainability reports.

6️⃣ Data Dictionary — `rag_qa_data_dictionary.csv`

Column-level documentation across all tables. Granularity: 1 row = 1 column definition Key fields:

table_name — name of the table the column belongs to
column_name — column name in snake_case
dtype — high-level type (int, float, bool, category, datetime, text)
description — human-readable explanation
allowed_values — expected values or ranges where applicable

Use this file as a single source of truth when exploring or building models on top of the dataset.

🎯 Targets & Tasks

Typical learning targets:

is_correct — classification: did the system answer correctly?
hallucination_flag — classification: is the answer hallucinated?
faithfulness_label — multi-class view of answer faithfulness
Paired with rich system signals (retrieval metrics, latency, cost, configuration), these enable:
Meta-models that estimate answer quality before showing it to users
Risk scores driving block / escalate / rerun decisions
Policy design for when to switch retrieval strategy or model configuration

🚀 Example Usage

Using plain `pandas` with the raw files: `python import pandas as pd base_path = "path/to/data" # or the local dataset path docs = pd.read_csv(f"{base_path}/rag_corpus_documents.csv") chunks = pd.read_csv(f"{base_path}/rag_corpus_chunks.csv") qa_runs = pd.read_csv(f"{base_path}/rag_qa_eval_runs.csv") retrieval_events = pd.read_csv(f"{base_path}/rag_retrieval_events.csv") scenarios = pd.read_csv(f"{base_path}/rag_qa_scenarios.csv") dictionary = pd.read_csv(f"{base_path}/rag_qa_data_dictionary.csv")` Example join: attach the top-ranked retrieved chunk to each QA example: `python top = ( retrieval_events.query("rank == 1") .merge(chunks[["chunk_id", "chunk_text"]], on="chunk_id", how="left") ) qa_with_top_chunk = qa_runs.merge( top[["run_id", "chunk_text"]], on="run_id", how="left", suffixes=("", "_top_chunk"), )` You can then train a simple meta-model on `qa_with_top_chunk` to predict `is_correct` or `hallucination_flag` from retrieval and configuration features.

🔬 Research & Applications

RAG meta-modeling
- Predict correctness or hallucination risk from retrieval and latency metrics
- Build guardrails that decide when to block, escalate, or rerun answers
Retrieval & ranking analysis
- Compare retrieval strategies across domains and difficulty levels
- Explore how rank, score, and recall relate to final correctness
Latency & cost trade-offs
- Study how total_latency_ms, context_window_tokens, and total_cost_usd interact with answer quality
- Prototype “fast vs careful” modes for RAG systems
Teaching & dashboards
- Demonstrate a realistic RAG pipeline without exposing real logs
- Build dashboards that visualise quality, latency, cost, and configuration over time

🧭 Ethical Considerations

All records are fully synthetic system logs and corpus content, not collected from real users or organisations.
No personally identifiable information (PII) is included.
The dataset is intended for research, teaching, benchmarking, and prototyping,
not for validating real-world systems in high-stakes domains (e.g. clinical, legal, financial decisions).

📚 Citation

When using this dataset in research, demos, or teaching material, please cite the dataset URL on Hugging Face and:

“RAG QA Evaluation Logs & Corpus — Synthetic Multi-Table Benchmark by Tarek Masryo”

📜 License

CC BY 4.0 (Attribution Required)
You are free to use, share, and modify this dataset, provided that appropriate credit is given.

🧠 RAG QA Evaluation Logs & Corpus

Multi-Table RAG Telemetry for Quality, Hallucinations, Latency, and Cost

🔐 Privacy & Synthetic Data

📘 Dataset Overview

📂 Files

All files are in CSV format, use snake_case column names, and are designed to be ML- and analytics-ready.

📊 Table Summary

🧱 Table Structure

1️⃣ Document Corpus — rag_corpus_documents.csv

2️⃣ Chunk Corpus — rag_corpus_chunks.csv

3️⃣ QA Evaluation Runs — rag_qa_eval_runs.csv

4️⃣ Retrieval Events — rag_retrieval_events.csv

5️⃣ Scenarios — rag_qa_scenarios.csv

6️⃣ Data Dictionary — rag_qa_data_dictionary.csv

🎯 Targets & Tasks

🚀 Example Usage

🔬 Research & Applications

🧭 Ethical Considerations

📚 Citation

📜 License

1️⃣ Document Corpus — `rag_corpus_documents.csv`

2️⃣ Chunk Corpus — `rag_corpus_chunks.csv`

3️⃣ QA Evaluation Runs — `rag_qa_eval_runs.csv`

4️⃣ Retrieval Events — `rag_retrieval_events.csv`

5️⃣ Scenarios — `rag_qa_scenarios.csv`

6️⃣ Data Dictionary — `rag_qa_data_dictionary.csv`