testland/elasticsearch-relevance-tests
Author search-engine relevance regression tests for Elasticsearch, OpenSearch, and Apache Solr. Core workflow on the Elasticsearch Ranking Evaluation API (`POST {index}/_rank_eval`) - judgment lists (query + expected docs at ranks), per-query metrics (Precision@K, Recall@K, MRR, DCG, ERR), reproducible test corpora; pair with Quepid + Splainer for interactive judgment authoring. Per-engine references cover the OpenSearch delta (Search Relevance Workbench, neural query DSL, hybrid BM25 + neural pipelines, ES-to-OS migration parity) and the Apache Solr delta (no _rank_eval: debugQuery score explain, LTR feature/model store REST, eDisMax qf/pf/mm tuning, external nDCG harness). Use before changing analyzers, synonyms, boosts, or query templates on an Elasticsearch, OpenSearch, or Solr index that serves user-facing search, so the NDCG / MRR baseline is captured first.
74
93%
Does it follow best practices?
Run evals on this skill
Adds up to 20 points to the overall score
View guide
Low
Low-risk findings worth noting
SKILL.md
- name:
- elasticsearch-relevance-tests
- description:
- Author search-engine relevance regression tests for Elasticsearch, OpenSearch, and Apache Solr. Core workflow on the Elasticsearch Ranking Evaluation API (`POST {index}/_rank_eval`) - judgment lists (query + expected docs at ranks), per-query metrics (Precision@K, Recall@K, MRR, DCG, ERR), reproducible test corpora; pair with Quepid + Splainer for interactive judgment authoring. Per-engine references cover the OpenSearch delta (Search Relevance Workbench, neural query DSL, hybrid BM25 + neural pipelines, ES-to-OS migration parity) and the Apache Solr delta (no _rank_eval: debugQuery score explain, LTR feature/model store REST, eDisMax qf/pf/mm tuning, external nDCG harness). Use before changing analyzers, synonyms, boosts, or query templates on an Elasticsearch, OpenSearch, or Solr index that serves user-facing search, so the NDCG / MRR baseline is captured first.
- metadata:
- {"keywords":"elasticsearch, rank-eval, relevance-testing, judgment-list, search-quality"}
elasticsearch-relevance-tests
Per the Elasticsearch Rank Eval API, the _rank_eval endpoint
"evaluates search result quality across typical queries using
relevance metrics."
Engine routing
This skill is the single home for term-based engine relevance testing.
The core _rank_eval workflow below is authored against Elasticsearch;
the per-engine deltas live in references:
| Engine | Where | Delta |
|---|---|---|
| Elasticsearch | this SKILL.md | Built-in _rank_eval; the canonical workflow |
| OpenSearch | references/opensearch.md | _rank_eval-compatible fork; Search Relevance Workbench, neural query DSL, hybrid BM25 + neural pipelines, ES-to-OS migration parity |
| Apache Solr | references/solr.md | No _rank_eval; debugQuery explain, LTR feature/model store REST, eDisMax tuning, external nDCG harness |
When to use
- Search-driven product (e-commerce, docs site, internal portal) where relevance regression directly affects business outcomes.
- Pre-deploy gate before changing analyzers, synonyms, boosts, query templates.
- A/B baseline: capture today's NDCG/MRR before tuning so you can prove improvement (or detect regression).
Step 1 - Build the judgment list
A judgment is (query, doc_id, rating). Ratings: 0 = irrelevant,
1 = somewhat, 2 = relevant, 3 = highly relevant (4-point scale).
Build judgments via:
| Source | Method |
|---|---|
| Query logs + click data | Click model (clicked = ≥1, multi-click = ≥2) |
| Quepid (open source) | Interactive UI for judges to rate per-query results |
| Splainer | Diagnose why a doc ranked where it did |
| Domain SMEs | High-stakes queries; manual rating |
Judgment list format (CSV is common):
query,doc_id,rating
"running shoes",sku-1234,3
"running shoes",sku-5678,2
"running shoes",sku-9999,0
"red dress",sku-2222,3Step 2 - Define metrics for your domain
Per the Elasticsearch Rank Eval API:
| Metric | When to use |
|---|---|
| Precision@K | flat top-K accuracy; no graded weighting |
| Recall@K | completeness of the relevant set within top K |
| MRR | one good answer suffices (navigational, Q&A) |
| DCG / NDCG | graded relevance, rank-discounted; default for graded judgments |
| ERR | user-stops-at-first-relevant; rank-decay sensitive |
For e-commerce with graded judgments → NDCG@10 + MRR. For Q&A → MRR
- Precision@1.
Step 3 - Submit a rank_eval request
Per the Elasticsearch Rank Eval API:
POST products/_rank_eval
{
"requests": [
{
"id": "running_shoes_query",
"request": {
"query": { "match": { "name": "running shoes" } }
},
"ratings": [
{ "_index": "products", "_id": "sku-1234", "rating": 3 },
{ "_index": "products", "_id": "sku-5678", "rating": 2 },
{ "_index": "products", "_id": "sku-9999", "rating": 0 }
]
},
{
"id": "red_dress_query",
"request": { "query": { "match": { "name": "red dress" } } },
"ratings": [
{ "_index": "products", "_id": "sku-2222", "rating": 3 }
]
}
],
"metric": {
"dcg": { "k": 10, "normalize": true }
}
}Response shape:
{
"metric_score": 0.84,
"details": {
"running_shoes_query": { "metric_score": 0.91, "unrated_docs": [...] },
"red_dress_query": { "metric_score": 0.77, "unrated_docs": [...] }
}
}Step 4 - Wrap as a test
import requests, csv
def load_judgments(path):
by_query = {}
with open(path) as f:
for row in csv.DictReader(f):
by_query.setdefault(row["query"], []).append({
"_index": "products",
"_id": row["doc_id"],
"rating": int(row["rating"]),
})
return by_query
def test_search_relevance_baseline():
judgments = load_judgments("tests/judgments.csv")
requests_payload = [
{
"id": q.replace(" ", "_"),
"request": { "query": { "match": { "name": q } } },
"ratings": ratings,
}
for q, ratings in judgments.items()
]
body = {
"requests": requests_payload,
"metric": { "dcg": { "k": 10, "normalize": true } },
}
r = requests.post("http://localhost:9200/products/_rank_eval", json=body)
result = r.json()
# Baseline NDCG must not regress vs known-good
assert result["metric_score"] >= 0.80, f"NDCG@10 regressed: {result['metric_score']}"Step 5 - Per-query regression detection
Aggregate metric only catches large shifts. Track per-query:
def test_no_query_drops_more_than_10_percent():
current = run_rank_eval()
baseline = json.loads(Path("tests/baseline.json").read_text())
for query_id, baseline_score in baseline["details"].items():
current_score = current["details"][query_id]["metric_score"]
delta = current_score - baseline_score["metric_score"]
assert delta >= -0.10, \
f"Query {query_id} dropped {delta:.2f} (was {baseline_score['metric_score']:.2f}, now {current_score:.2f})"Advanced topics
Binary-metric relevant_rating_threshold config, snapshotting a
reproducible test corpus for CI, and Quepid + Splainer judgment
authoring live in
references/rank-eval-guide.md.
Anti-patterns
| Anti-pattern | Why it fails | Fix |
|---|---|---|
| Use binary judgments only | Loses graded info; NDCG degrades to Precision | 4-point scale (Step 1) |
| Rebuild judgments per test run | Bias from current ranking | Pinned judgment list (Step 1) |
| Track only aggregate NDCG | Hides per-query regressions | Per-query tracking (Step 5) |
| Test against changing index | Baselines move under your feet | Snapshot corpus (advanced guide) |
| 100% click-derived judgments | Click bias to top results, position bias | Mix click + SME judgments |
Limitations
- Judgments are expensive; budget hundreds-to-thousands of query-doc pairs for a meaningful test set.
- Click-derived judgments have position bias; correct using click models (cascade, dynamic Bayesian).
- Rank Eval API doesn't natively support relevance graded > 4 or pairwise comparisons.
- Synonyms, language analyzers, custom scoring matter - pin in CI.
References
- Elasticsearch Rank Eval API - request/response schema, metrics
- Quepid (judgment authoring UI) - github.com/o19s/quepid
- Splainer (debug per-doc ranking) - github.com/o19s/splainer-search
- references/opensearch.md - OpenSearch delta (compatible API; Workbench, neural, hybrid)
- references/solr.md - Apache Solr delta (LTR, debugQuery, external nDCG)
vector-search-recall-tests- vector search analogue