RAG Evaluation Toolkit

Evaluate a specifically provided RAG question, answer, and retrieved contexts with Ragas metrics. Use when the user explicitly requests RAG evaluation or hallucination analysis and has chosen a local or cloud judge; do not persist raw content unless requested.

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 @jonathanjing/rag-eval."

2. Manual Installation (CLI)

If you prefer the terminal, run:

bash

openclaw skills install @jonathanjing/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. Configure one judge:
   • OPENAI_API_KEY (default, uses GPT-4o)
   • ANTHROPIC_API_KEY (uses Claude Haiku)
   • RAGAS_LLM=ollama/llama3 (for local/offline evaluation)

For a cloud judge, also set RAGAS_ALLOW_CLOUD=1 after confirming the input may leave the machine.

Setup (first run only)

bash

bash "{baseDir}/scripts/setup.sh"

This script intentionally refuses global installation. Activate a virtual environment first, then run it.

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:

bash

# 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: Run the evaluator. Raw content is not saved by default.
python3 "{baseDir}/scripts/run_eval.py" --input-file /tmp/rag-eval-input.json

# Step 3: Delete the temporary input with the available file tool.

Alternatively, use --input-file:

bash

python3 "{baseDir}/scripts/run_eval.py" --input-file /tmp/rag-eval-input.json

Output JSON:

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:

text

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

Persist only when the user explicitly requests it by adding --save. The saved record contains the raw question, answer, and contexts.

Batch Evaluation

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

bash

python3 "{baseDir}/scripts/batch_eval.py" --input ./dataset.jsonl --output ./batch-eval.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:

bash

python3 "{baseDir}/scripts/run_eval.py" --explain --metric faithfulness

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

Notes

• OpenAI and Anthropic judges transmit the question, answer, and contexts to that provider. Obtain explicit approval for confidential data or use a local RAGAS_LLM.
• The evaluator does not persist raw inputs by default.
• Evaluation costs ~$0.01-0.05 per response depending on length
• For offline use, set RAGAS_LLM=ollama/llama3 in environment