test: add rag retrieval baseline

eval/rag-retrieval/README.md

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+# RAG Retrieval Baseline
+
+This directory contains the offline retrieval baseline for the RAG refactor.
+
+The baseline is intentionally narrower than full diagnosis evaluation. It checks
+whether fixed retrieval queries can recover expected documents, breadcrumbs, and
+evidence keywords before changing L0 behavior, query augmentation, evidence
+post-processing, or Spring AI VectorStore integration.
+
+## Layout
+
+```text
+eval/rag-retrieval/
+  cases/golden-cases.json      Fixed retrieval golden cases
+  fixtures/*.json              Saved retrieval candidates for each case
+  reports/baseline.json        Machine-readable baseline report
+  reports/baseline.md          Human-readable baseline report
+```
+
+## Run
+
+From the repository root:
+
+```bash
+python scripts/eval_rag_retrieval.py
+```
+
+Custom paths are also supported:
+
+```bash
+python scripts/eval_rag_retrieval.py \
+  --cases eval/rag-retrieval/cases/golden-cases.json \
+  --fixtures eval/rag-retrieval/fixtures \
+  --json-report eval/rag-retrieval/reports/baseline.json \
+  --markdown-report eval/rag-retrieval/reports/baseline.md
+```
+
+## Hit Levels
+
+- `strong`: expected document is found and breadcrumb or evidence keyword coverage is satisfied.
+- `medium`: expected document is found, but breadcrumb or keyword coverage is incomplete.
+- `weak`: expected evidence keyword is found, but expected document is missing.
+- `miss`: expected document and expected evidence are not found.
+
+`Recall@K` counts `strong` and `medium` as retrieved.
+
+## Scope
+
+This baseline runs fully offline and does not call MySQL, Redis, Milvus, an LLM,
+or the Spring Boot application. It is a regression harness for retrieval behavior,
+not a claim that live production retrieval accuracy is complete.