Add RAGAS evaluation system with golden dataset#26
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robayedl
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May 5, 2026
robayedl
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- eval/golden.jsonl: 30-question golden dataset from "Attention Is All You Need" (15 factual, 8 reasoning, 5 multi_hop, 2 out_of_scope)
- eval/run.py: evaluation runner — calls DocuMind pipeline directly, computes RAGAS metrics with Gemini 2.5 Flash as judge, prints per-question table, saves timestamped JSON + overwrites latest.json, auto-updates README scores after each run
- eval/results/latest.json: latest evaluation results (30 questions)
- eval/EVALUATION_GUIDE.md: dataset format, usage, cost estimates
- Makefile: run / ui / test / lint / eval / update-readme targets
- requirements.txt: bump ragas 0.0.22 → >=0.2.0
- README.md: evaluation section with live scores, split code blocks,
- Remove legacy eval/ragas_eval.py, eval/run_eval.py, eval/test_queries.json
- eval/golden.jsonl: 30-question golden dataset from "Attention Is All You Need" (15 factual, 8 reasoning, 5 multi_hop, 2 out_of_scope) - eval/run.py: evaluation runner — calls DocuMind pipeline directly, computes RAGAS metrics with Gemini 2.5 Flash as judge, prints per-question table, saves timestamped JSON + overwrites latest.json, auto-updates README scores after each run - eval/results/latest.json: latest evaluation results (30 questions) - eval/EVALUATION_GUIDE.md: dataset format, usage, cost estimates - Makefile: run / ui / test / lint / eval / update-readme targets - requirements.txt: bump ragas 0.0.22 → >=0.2.0 - README.md: evaluation section with live scores, split code blocks, - Remove legacy eval/ragas_eval.py, eval/run_eval.py, eval/test_queries.json
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