---

name: search-and-ranking description: "Marqo tensor, lexical, hybrid, recommend, filters, ranking controls, score modifiers, facets, collapse, recency, sort, and relevance-cutoff workflows." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

Search and Ranking

Use this sub-skill for Marqo query construction, ranking knobs, filters, facets, collapse, recency, sort, relevance cutoff, weak-and / second-phase behavior, recommend payloads, and safe request builders.

Route elsewhere

• Public HTTP route wiring, auth, and response mapping → ../documents-and-api/
• Index schema, field types, and collapse-field configuration → ../index-and-vespa/
• Vectorisation, model loading, and /vectorise internals → ../inference-and-models/

Open first

1. references/search-recipes.md
2. references/ranking-parameters.md
3. references/filter-and-modifier-reference.md
4. references/troubleshooting.md
5. scripts/search_payload_examples.py

Operating rules

• Start from the smallest valid payload, then add one ranking feature at a time.
• Prefer camelCase payload examples because they match the public request shape.
• Keep examples offline; the bundled script prints JSON only and does not call Marqo or Vespa.
• When a request mixes search, recommend, or filter logic, use the bundled references before trying to improvise a payload.

What this sub-skill owns

• Tensor, lexical, hybrid, multimodal, and custom-vector search payloads
• Recommendation payloads built from document ids and interpolation
• Search filters, score modifiers, boost handling, and custom score rerank keys
• Search ranking controls: alpha, rrfK, rerank depth, weak-and, second phase, recency, sort, collapse, facets, and relevance cutoff
• Failure recovery when a payload is valid JSON but invalid for the chosen search method