# RAG Retrieval Baseline

Offline regression harness for `lookup_knowledge` **after** hybrid retrieval + qualityScore post-process.

It checks whether fixed queries still recover expected documents, breadcrumbs, keywords, and pipeline behaviors (filter / unfiltered retry). It is **not** a full diagnosis-agent E2E.

Production knowledge path: `MilvusHybridKnowledgeStore` with `retrieval.search.mode=hybrid` (dense+BM25+RRF).  
`mode=dense` remains a same-collection baseline for recall comparison (not a second index).

Related design notes:

- `mvp/engineering/rag/RAG-Hybrid质量分与后处理.md`
- `mvp/engineering/rag/RAG-Agent如何读relevance_level.md`
- `mvp/architecture/RAG知识检索架构.md` §6

## Offline vs live

| Layer | What | Needs live stack? |
|-------|------|-------------------|
| **Offline** | `fixtures/*.json` × `golden-cases.json` → pass/fail + baseline diff | **No** (no Milvus/LLM/Boot) |
| **Snapshot generate** | Real `LookupKnowledgeTool` writes fixtures | **Yes** (embedding + Milvus + DB/L0 as configured) |
| **Live smoke** | optional `eval_rag_live_acceptance.py` | Yes (running app) |

Daily CI / local quick check: **offline only**.  
After changing retrieval, indexing, or search mode: **regenerate fixtures**, then offline eval, then update baseline if the diff is intentional.

## Layout

```text
eval/rag-retrieval/
  cases/golden-cases.json      Fixed queries + expectations
  seed-docs/*.md               Canonical docs for live snapshot (kb_scope: rag-eval)
  fixtures/*.json              Frozen lookupResult snapshots (+ searchMode meta)
  reports/baseline.json|md     Last accepted offline report
  reports/baseline-diff.*      Optional diff vs previous report
```

## Fixture shape (minimum)

```text
caseId
query
retrievedAt
searchMode          # hybrid | dense  (required on newly generated fixtures)
kbScope             # e.g. rag-eval when generation used a scope
lookupResult        # found, evidenceBlocks, contextPack, retrievalTrace, rerankTrace, …
```

Offline eval **ignores unknown top-level meta** and does **not** full-JSON-compare.  
It asserts golden key fields only (doc/source, keywords, attempt, fallback, …). Raw scores are not pass criteria.

Older fixtures may omit `searchMode`; regenerate to attach meta.

## Seed docs + import

Live snapshot generation should use seed docs so results do not depend on ad-hoc local KB junk:

```text
eval/rag-retrieval/seed-docs/*.md
```

Frontmatter example:

```yaml
source: mysql-connection-pool
breadcrumb: Database > MySQL > Connection Pool
kb_scope: rag-eval
```

Import via real upload pipeline:

```powershell
.\scripts\prepare_rag_eval_seed.ps1
```

Isolation:

- App default may leave `retrieval.kb-scope` empty (all docs).
- Eval generation passes `-Dretrieval.kb-scope=rag-eval`.
- Category-filter fallback retries without L0 category filter only; **kb_scope still applies**.

Body is chunked/embedded; frontmatter feeds metadata/L0 (decoy keywords in frontmatter alone should not become dense content).

Seeds must live in the **current hybrid collection schema** (`milvus.collection`, default `biz`). If the collection was recreated for BM25 hybrid, re-import seeds after rebuild.

## Offline run (no live stack)

```bash
python scripts/eval_rag_retrieval.py
```

Custom paths:

```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
```

## Generate fixtures (live stack)

```powershell
.\scripts\prepare_rag_eval_seed.ps1
.\scripts\generate_rag_lookup_snapshots.ps1
# default: SearchMode=hybrid, KbScope=rag-eval, then offline eval
```

Dense baseline snapshot (same seed, comparison only):

```powershell
.\scripts\generate_rag_lookup_snapshots.ps1 -SearchMode dense -Fixtures eval\rag-retrieval\fixtures-dense -SkipEval
```

(Dual-directory comparison reports are optional / future; knife-1 only documents the override.)

Maven equivalent:

```text
mvn -q -Dtest=RagLookupSnapshotGeneratorTest \
  -Drag.snapshot.enabled=true \
  -Dretrieval.kb-scope=rag-eval \
  -Dretrieval.search.mode=hybrid \
  test
```

Generator is **off** in normal tests; only runs when `rag.snapshot.enabled=true` (writes files).

If generated fixtures fail offline golden checks: either fix retrieval/index, or update golden/baseline **with an explicit reason** — do not silently overwrite.

## Golden assertions

Supported expectation fields include:

- `expectedSources` / `expectedDocIds`
- `expectedBreadcrumbs` / `expectedKeywords`
- `expectedSelectedAttempt`
- `expectedFallbackReason` / `expectedFallbackReasons`
- `expectedEvidenceStatus`
- `expectedContextSources`
- `expectedRerankTopSource`

Catch regressions such as missing expected source, broken context pack sources, wrong selected attempt, or broken filtered → unfiltered retry.

**Note:** `relevance_level` is not a hard golden gate here (hybrid quality is rank-ordinal; see agent relevance-level doc).

## Baseline diff

```bash
python scripts/eval_rag_retrieval.py \
  --json-report eval/rag-retrieval/reports/current.json \
  --markdown-report eval/rag-retrieval/reports/current.md \
  --compare-to eval/rag-retrieval/reports/baseline.json \
  --diff-json-report eval/rag-retrieval/reports/baseline-diff.json \
  --diff-markdown-report eval/rag-retrieval/reports/baseline-diff.md
```

Diff covers pass rate, recall@K, hit level, first expected rank, attempt, fallback, evidence status, rerank top source.  
Non-zero exit on case failure or regression in diff mode.

## Hit levels

- `strong`: expected document found **and** breadcrumb or keyword coverage OK  
- `medium`: expected document found, coverage incomplete  
- `weak`: keyword hit without expected document  
- `miss`: neither  

`Recall@K` counts `strong` + `medium`.

## Optional live smoke (post-reindex)

After reindex, with app up:

```bash
python scripts/eval_rag_live_acceptance.py --base-url http://127.0.0.1:9900
```

Calls `GET /api/search/similar`. Environment smoke only — does **not** replace offline baseline.