# RAG Eval

> Evaluate your RAG pipeline quality using Ragas metrics (faithfulness, answer relevancy, context precision). PREREQUISITE: You must have a RAG system integrated with OpenClaw (e.g. vector DB + retrieval). Use when: (1) testing RAG answer quality after config changes, (2) checking for hallucinations in retrieved-context answers, (3) running batch regression tests on a golden dataset, (4) comparing RAG performance before/after embedding or chunking changes. NOT for: general LLM chat evaluation without retrieval context, code review, or non-RAG agent outputs.

- Skill: `johnalbertini14-glitch/rag-eval` (Agent Skill, multi-file: 7 files)
- Install (CLI): `npx skillmds@latest add johnalbertini14-glitch/rag-eval`
- Raw SKILL.md: https://api.skillmd.com/api/skills/johnalbertini14-glitch/rag-eval/raw
- Safety review: pending (external: skill-scanner PASS, skillspector CAUTION)
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: DevOps & Infra
- Author: johnalbertini14-glitch (https://skillmd.com/u/johnalbertini14-glitch)
- Updated: 2026-09-08
- Page: https://skillmd.com/skills/johnalbertini14-glitch/rag-eval

---


# RAG Eval — Quality Testing for Your RAG Pipeline

Test and monitor your RAG pipeline's output quality. Measures faithfulness (are answers grounded in retrieved context?), relevancy (does the answer address the question?), and context precision (did retrieval return the right chunks?).

**⚠️ 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
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:

```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: 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`:
```bash
python3 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:
```
🧪 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":[...]}`):

```bash
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:
```bash
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