rag-harness 0.1.1

⚡ CLI tool to evaluate and compare RAG systems.

⚡ RAG Harness

The fastest way to evaluate and compare RAG systems from your terminal.

📦 Install

pip install rag-harness

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🚀 Why this exists

Evaluating RAG systems is messy.

• Different metrics everywhere
• No standard CLI tools
• Hard to compare models

👉 RAG Harness fixes that.

🎥 Demo

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🔥 Features

• ⚡ One-command RAG evaluation
• 📊 Exact Match + F1 + Context metrics
• 🧠 RAGAS-style scoring (no API required)
• ⚔️ Compare multiple RAG systems
• 📁 Works with JSONL / JSON / CSV

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⚡ Quick Start

git clone https://github.com/yourname/rag-harness.git
cd rag-harness
python -m venv .venv
.\.venv\Scripts\activate
pip install -e .  

▶️ Run Evaluation

rag-harness evaluate examples/dataset.jsonl examples/predictions_a.jsonl

Output

📊 RAG Evaluation Summary

Exact Match        0.5000
F1 Score           0.8333
Context Precision  1.0000
Context Recall     1.0000

🧠 RAGAS Score     0.9000

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⚔️ Compare Systems

rag-harness compare examples/dataset.jsonl examples/predictions_a.jsonl examples/predictions_b.jsonl

Output

⚔️ RAG Systems Comparison

Metric              A        B
--------------------------------
Exact Match         0.50     0.00
F1 Score            0.83     0.00
RAGAS Score         0.90     0.00

🏆 System A wins

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📁 Dataset Format

Dataset

{"id":"1","question":"Who wrote Hamlet?","answer":"William Shakespeare","contexts":["William Shakespeare wrote Hamlet."]}

Predictions

{"id":"1","answer":"Shakespeare","contexts":["William Shakespeare wrote Hamlet."]}

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🧠 RAGAS-style Scoring

We provide a lightweight RAGAS-inspired score:

RAGAS = 0.6 * F1 + 0.4 * Context Recall

• No API keys required
• Fast and deterministic
• Extendable to real RAGAS later

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🤝 Contributing

PRs, Ideas, Improvements welcome.