# 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.