rag-eval

name	rag-eval
description	Evaluate your RAG pipeline quality using Ragas metrics (faithfulness, answer relevancy, context precision).
version	1.2.1
metadata	{"openclaw":{"emoji":"🧪","requires":{"anyBins":"[Truncated]","anyEnv":"[Truncated]"},"envVars":{"OPENAI_API_KEY":"[Truncated]","ANTHROPIC_API_KEY":"[Truncated]","RAGAS_LLM":"[Truncated]","RAGAS_PASS_THRESHOLD":"[Truncated]","RAGAS_REVIEW_THRESHOLD":"[Truncated]","RAGAS_OPENAI_MODEL":"[Truncated]","RAGAS_ANTHROPIC_MODEL":"[Truncated]"}}}

RAG Eval — Quality Testing for Your RAG Pipeline

Test and monitor your RAG pipeline's output quality.

🛠️ Installation

1. Ask OpenClaw (Recommended)

Tell OpenClaw: "Install the rag-eval skill." The agent will handle the installation and configuration automatically.

2. Manual Installation (CLI)

If you prefer the terminal, run:

clawhub install rag-eval

⚠️ Prerequisites

1. Your OpenClaw must have a RAG system (vector DB + retrieval pipeline). This skill evaluates the output quality of that pipeline — it does not provide RAG functionality itself.
2. At least one LLM API key is required — Ragas uses an LLM as judge internally. Set one of:
   • OPENAI_API_KEY (default, uses GPT-4o)
   • ANTHROPIC_API_KEY (uses Claude Haiku)
   • RAGAS_LLM=ollama/llama3 (for local/offline evaluation)

Setup (first run only)

bash scripts/setup.sh

This installs ragas, datasets, and other dependencies.

Single Response Evaluation

When user asks to evaluate an answer, collect:

1. question — the original user question
2. answer — the LLM output to evaluate
3. contexts — list of text chunks used to generate the answer (retrieved docs)

⚠️ SECURITY: Never interpolate user content directly into shell commands. Write the input to a temp JSON file first, then pipe it to the evaluator:

# Step 1: Write input to a temp file (agent should use the write/edit tool, NOT echo)
# Write this JSON to /tmp/rag-eval-input.json using the file write tool:
# {"question": "...", "answer": "...", "contexts": ["chunk1", "chunk2"]}

# Step 2: Pipe the file to the evaluator
python3 scripts/run_eval.py < /tmp/rag-eval-input.json

# Step 3: Clean up
rm -f /tmp/rag-eval-input.json

Alternatively, use --input-file:

python3 scripts/run_eval.py --input-file /tmp/rag-eval-input.json

Output JSON:

{
  "faithfulness": 0.92,
  "answer_relevancy": 0.87,
  "context_precision": 0.79,
  "overall_score": 0.86,
  "verdict": "PASS",
  "flags": []
}

Post results to user with human-readable summary:

🧪 Eval Results
• Faithfulness: 0.92 ✅ (no hallucination detected)
• Answer Relevancy: 0.87 ✅
• Context Precision: 0.79 ⚠️ (some irrelevant context retrieved)
• Overall: 0.86 — PASS

Save to memory/eval-results/YYYY-MM-DD.jsonl.

Batch Evaluation

For a JSONL dataset file (each line: {"question":..., "answer":..., "contexts":[...]}):

python3 scripts/batch_eval.py --input references/sample_dataset.jsonl --output memory/eval-results/batch-YYYY-MM-DD.json

Score Interpretation

Score	Verdict	Meaning
0.85+	✅ PASS	Production-ready quality
0.70-0.84	⚠️ REVIEW	Needs improvement
< 0.70	❌ FAIL	Significant quality issues

Faithfulness Deep-Dive

If faithfulness < 0.80, run:

python3 scripts/run_eval.py --explain --metric faithfulness

This outputs which sentences in the answer are NOT supported by context.

Notes

• Ragas uses an LLM internally as judge (uses your configured OpenAI/Anthropic key)
• Evaluation costs ~$0.01-0.05 per response depending on length
• For offline use, set RAGAS_LLM=ollama/llama3 in environment