feat(ragas): RAGAS evaluation — LLM-as-judge for RAG pipelines

[lianghsun]

Summary

• RAGASExtractor: parses judge LLM's JSON response for 4 metric scores
• RAGASScorer: threshold-based scoring (avg of faithfulness, answer_relevancy, context_precision, context_recall >= 0.5)
• Consolidated judge prompt: 1 LLM call per sample (vs official RAGAS's 6-8 multi-step calls), ~1/6 API cost
• Example dataset: 10 rows from explodinggradients/WikiEval (5 good + 3 ungrounded + 2 poor answers)
• Config template: config.ragas.template.yaml
• Tests: 36 tests covering extractor, scorer, presets, example dataset
• Docs: docs/evals/ragas.md

Key Design Decision

RAGAS is fundamentally different from other benchmarks — it's a metric framework that requires LLM-as-judge. The LLM in config.yaml serves as the judge (not the model being evaluated). The dataset contains pre-assembled judge prompts with pre-existing RAG outputs.

Judge Results (Devstral-Small-2-24B as judge)

Answer Type	Faithfulness	Relevancy	Precision	Recall	Average
good (×5)	1.00	1.00	1.00	1.00	1.00
ungrounded (×3)	0.00–0.80	0.00–0.90	1.00	1.00	0.50–0.93
poor (×2)	0.50	0.70	1.00	1.00	0.80

Judge correctly distinguishes good/ungrounded/poor answers.

Test plan

• python3 -m pytest tests/test_ragas.py -v — 36 passed
• python3 -m pytest tests/ -v — 324 passed, 2 pre-existing failures, 1 skipped
• Actual judge evaluation with Devstral 24B on WikiEval dataset

Closes #63, #64, #65, #66, #67, #68

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