Shape recipe: TRANSLATION_TASK
What this shape is
The user has a working query, request body, or DSL fragment in another search engine (Solr, Elasticsearch ≥ 7.11 syntax that needs OS-side adjustments, raw Lucene syntax, or vendor-specific dialect) and wants the OpenSearch equivalent. The deliverable is drop-in JSON or code that the user can paste into _search, _msearch, a client SDK call, or an OpenSearch Dashboards Dev Tools tab.
This shape is purely a syntactic + semantic mapping exercise. It is NOT a migration assessment, NOT a sizing exercise, and NOT a relevance-tuning engagement.
When to dispatch here
Detect TRANSLATION_TASK when the user prompt contains any of:
- "translate this Solr query"
- "convert this DSL"
- "what's the OpenSearch equivalent of …"
- "rewrite this for OpenSearch"
- "this is my Solr
q=…&fq=…&qf=…, give me OpenSearch JSON" - A pasted Solr URL query string (
/select?q=…&qf=…&pf=…&mm=…&tie=…) - A pasted ES query that uses post-fork-only features (e.g.
runtime_mappingsin 7.12+, ES 8.xknntop-level field) and the user is targeting AOS managed - A pasted Lucene
q=string with field-prefix syntax (title:headphones AND brand:sony)
Do NOT dispatch here when:
- The user pasted
schema.xmlor an ES mapping → that's SCHEMA_CONVERSION. - The user wants migration tooling guidance ("how do I move my Solr docs to OS") → that's FULL_ASSESSMENT or FOCUSED_OPERATIONAL.
- The user asks "how do I write a query that does X" with no source DSL → that's a search-recipe lookup; serve from
references/search-recipes.mddirectly.
Required output template
Produce these sections in order, nothing more:
1. Source restatement (1 sentence)
"Translating Solr 8.11 eDisMax
q=wireless headphones&qf=title^3 description&pf=title^5&mm=2<-25%&q.op=AND&tie=0.3to OpenSearch 2.x_search."
State source engine + version (if known) + the specific query type (eDisMax, dismax, standard, function query, JSON Facet API, …) + target endpoint.
2. Drop-in JSON / code (the deliverable)
A single fenced code block, valid JSON, ready to paste into Dev Tools. Preserve field names exactly. Include query, from/size, sort, aggs, highlight blocks as the source request had them.
3. Translation fidelity table
For every non-trivial Solr/ES parameter in the source, one row showing source param → OpenSearch param → fidelity (verbatim / mapped / approximation). This is the heart of the shape — every syntactic element must be either preserved or explicitly mapped, with no silent drops.
4. Approximation caveats (inline, only if any rows in the table are "approximation")
A short bullet list of behavior drift the user must be aware of. Examples:
pf(phrase boost): modeled as ashouldclause withmulti_match type: phrase. Scoring shape differs — Solr'spfboosts the whole phrase score additively; OpenSearch'sshouldadds a separately-scored phrase match. Re-tune boost values against your judgment list.tie_breakerdefault: OpenSearchmulti_match best_fieldsdefaultstie_breaker: 0.0(winner-takes-all); Solr eDisMax defaultstie=0.0as well, but if the source omittedtie, set it explicitly to avoid surprises if you later upgrade.q.op=AND: OpenSearchquery_stringdefaults to OR. Setdefault_operator: ANDexplicitly or results will diverge.
5. Verification snippet (optional, 1–3 lines)
If the translation is non-trivial, give the user a 1-line _validate/query?explain=true or a 2-doc sanity check they can run to confirm the rewrite parses and scores reasonably.
NOT REQUIRED — explicitly OMIT
Do NOT produce these sections in TRANSLATION_TASK:
- Timeline & Resourcing — removed from the suite. Do NOT estimate engineer-weeks, sprint count, or calendar duration anywhere.
- Executive Summary — translation is tactical, not a deliverable that needs an exec frame.
- Source / Target / Migration Path / Risks — the four big assessment sections; not applicable here.
- Sizing / Readiness scorecard — translation has no infra footprint.
- Citations section — only include if you make ≥3 version-volatile claims (e.g. "this only works in OS 2.13+"). For a normal Solr→OS query rewrite, citations are noise.
- Migration tooling discussion — do NOT pivot to "and to move your data, use Migration Assistant for Amazon OpenSearch Service…". Stay in the lane.
- Dollar costs — universal hard constraint; never produce a dollar figure.
- Persona-aware framing — the asker self-selected by pasting DSL; treat them as a search engineer.
Solr → OpenSearch query translation reference table
This is the canonical lookup. Use it; do NOT re-derive per request.
| Solr (or ES 7.x dialect) | OpenSearch | Fidelity | Notes |
|---|---|---|---|
q=headphones |
{"multi_match": {"query": "headphones", "fields": ["title", "description"]}} |
mapped | Solr searches df (default field); OS has no _all — name fields explicitly. |
q=title:headphones |
{"match": {"title": "headphones"}} |
verbatim | Field-scoped match. |
q.op=AND |
"default_operator": "AND" (on query_string) or "operator": "AND" (on match / multi_match) |
verbatim | OpenSearch defaults to OR. #1 cause of result divergence. Always set explicitly. |
defType=edismax |
multi_match type: best_fields |
mapped | Closest semantic equivalent; not byte-identical scoring. |
qf=title^3 description^1 tags^2 |
"fields": ["title^3", "description^1", "tags^2"] |
verbatim | Boosts pass through unchanged. |
pf=title^5 (phrase boost) |
should: [{"multi_match": {"query": "<q>", "type": "phrase", "fields": ["title^5"]}}] |
approximation | Scoring shape differs — see caveats. |
pf2=title^3 / pf3=title^2 |
Two should clauses with match_phrase and slop adjustment |
approximation | Solr's bigram/trigram phrase boost has no exact OS equivalent. |
tie=0.3 |
"tie_breaker": 0.3 (on multi_match best_fields) |
verbatim | Same semantics. |
mm=2<-25% |
"minimum_should_match": "2<-25%" |
verbatim | Syntax passes UNCHANGED — same parser. |
mm=100% |
"minimum_should_match": "100%" |
verbatim | All clauses must match. |
fq=in_stock:true |
bool.filter: [{"term": {"in_stock": true}}] |
verbatim | Filter context — no scoring, cacheable. |
bq=category:electronics^2 (boost query) |
should: [{"term": {"category": {"value": "electronics", "boost": 2}}}] |
verbatim | Additive scoring boost. |
bf=recip(ms(NOW,timestamp),3.16e-11,1,1) (boost function) |
function_score with gauss or exp decay on timestamp |
mapped | Solr recip is a hyperbolic decay; OS gauss/exp give equivalent shape — re-tune scale. |
sort=score desc, price asc |
"sort": [{"_score": "desc"}, {"price": "asc"}] |
verbatim | score → _score. |
start=20&rows=20 |
"from": 20, "size": 20 |
verbatim | |
fl=title,price,_score |
"_source": ["title", "price"] (_score always returned) |
verbatim | |
hl=true&hl.fl=title |
"highlight": {"fields": {"title": {}}} |
verbatim | |
facet=true&facet.field=category |
"aggs": {"by_category": {"terms": {"field": "category.keyword"}}} |
mapped | Use .keyword sub-field for terms agg. |
facet.range=price&facet.range.start=0&facet.range.end=100&facet.range.gap=20 |
"aggs": {"price_ranges": {"range": {"field": "price", "ranges": [{"to":20},{"from":20,"to":40},…]}}} |
mapped | OS uses explicit range buckets. |
mlt=true |
more_like_this query |
mapped | Same semantics, different request shape. |
wt=json |
(default) Accept: application/json |
verbatim | OS speaks JSON natively. |
group=true&group.field=brand |
"collapse": {"field": "brand.keyword"} |
mapped | OS collapse ≈ Solr field collapsing for top-1 per group. |
cursorMark=* |
search_after + tiebreaker sort |
mapped | OS deep pagination via PIT + search_after. |
Worked exemplar
User: Translate this Solr query:
q=wireless headphones&defType=edismax&qf=title^3 description^1 tags^2&pf=title^5&mm=2<-25%&q.op=AND&tie=0.3&fq=in_stock:true&start=0&rows=20&sort=score desc, price asc&hl=true&hl.fl=titleAssistant response (this is the shape):
Translating Solr 8.x eDisMax q=wireless headphones (qf/pf/mm/tie/q.op/fq/sort/highlight) to OpenSearch 2.x _search.
GET products/_search
{
"query": {
"bool": {
"must": [
{
"multi_match": {
"query": "wireless headphones",
"type": "best_fields",
"fields": ["title^3", "description^1", "tags^2"],
"operator": "AND",
"minimum_should_match": "2<-25%",
"tie_breaker": 0.3
}
}
],
"should": [
{
"multi_match": {
"query": "wireless headphones",
"type": "phrase",
"fields": ["title^5"]
}
}
],
"filter": [
{"term": {"in_stock": true}}
]
}
},
"from": 0,
"size": 20,
"sort": [{"_score": "desc"}, {"price": "asc"}],
"highlight": {"fields": {"title": {}}}
}| Solr param | OpenSearch | Fidelity |
|---|---|---|
defType=edismax |
multi_match type: best_fields |
mapped |
qf=title^3 description^1 tags^2 |
fields: ["title^3","description^1","tags^2"] |
verbatim |
pf=title^5 |
should + multi_match type: phrase |
approximation |
mm=2<-25% |
minimum_should_match: "2<-25%" |
verbatim |
q.op=AND |
operator: "AND" |
verbatim |
tie=0.3 |
tie_breaker: 0.3 |
verbatim |
fq=in_stock:true |
filter term clause |
verbatim |
sort=score desc, price asc |
sort: [{"_score":"desc"},{"price":"asc"}] |
verbatim |
hl=true&hl.fl=title |
highlight.fields.title |
verbatim |
Approximation caveats:
pfphrase boost: modeled as a separateshouldclause; Solr eDisMax adds thepfscore to the eDisMax sum, OpenSearch adds theshouldscore to the bool sum. Top-N order should be similar but not identical — re-tunepfboost (using the example value^5) against your judgment list.q.op=AND: set asoperator: "AND"onmulti_match(cleaner thanquery_stringfor this case). OpenSearch defaults to OR — without this, recall would explode.
To sanity-check parsing: POST products/_validate/query?explain=true with the same body — confirms the BoolQuery / DisjunctionMaxQuery structure matches expectation.
Pre-emit checklist (TRANSLATION_TASK-specific)
Before sending the response, tick every box:
- First sentence is the source restatement (engine + version + query type + target endpoint), not tool narration.
- Output contains a single, valid, copy-pasteable JSON block for
_search(or the right endpoint). - Every parameter from the source request is either present in the OS JSON or explicitly mapped in the fidelity table — no silent drops.
-
q.op/default_operatoris handled explicitly: if the source had AND (Solrq.op=ANDOR an explicitAND/&&boolean operator inside a Lucenequery_stringquery), it MUST appear in the JSON itself —operator: "AND"onmatch/multi_match, ordefault_operator: "AND"onquery_string. Discussing it only in prose or in the approximation caveats does NOT satisfy this rule (the customer is told to drop the JSON in directly, so the JSON has to be correct on its own). Apply this to every query in a multi-query translation, not only the first. -
mm/minimum_should_matchpreserved verbatim (same parser; do not "simplify"2<-25%). - eDisMax
qfboosts preserved verbatim inmulti_match.fields. - Any
pf/pf2/pf3is in a separateshouldclause AND flagged in caveats as approximation. -
tie_breakeris set explicitly when the source hadtie(do not rely on defaults). - Field names that need
.keywordsub-field forterm/terms agg/sortare using it. - NO
Timeline & Resourcingsection. NO Executive Summary. NO Sizing. NO migration tooling pivot. - NO dollar figures anywhere.
- No marketing tone ("seamless", "robust", "best-in-class", "elegant", "cleanly").
- Citations section omitted unless ≥3 version-volatile claims were made.
- If approximation rows exist, the caveats bullet list is present — never leave approximation unflagged.