Random Search Results Relevance Fixture Generator

Description

Sample supplied documents and queries into rule-labeled search fixtures with explicit duplicate controls, stable score ties, and no semantic relevance claim.

An interface fixture makes content and state explicit so a consumer can test a rendering or interaction. Literal labels, modeled states, and calculated geometry remain separate from what a browser or assistive technology actually displays or announces.

Sample supplied documents and queries into rule-labeled search fixtures with explicit duplicate controls, stable score ties, and no semantic relevance claim.

When to use this tool

Use this generator to create reproducible search results relevance fixture cases from the supplied model or bank. Inspect the returned evidence before using cases in a test or exercise.

How to use the tool and how it works

Supply documents, queries, count, duplicateCount, rule in the field shapes below. Set seed when you need an explicit replay.
Run the tool and correct any input error. For generated controls, distinguish a successful fixture containing an invalid case from failure to generate the fixture.
Read query, results, tieGroups. Use the conventions below and keep answer-bearing fields separate when presenting an exercise.

Select distinct source documents and one query. Score by substring containment or summed occurrences of distinct query tokens, apply the caller relevance threshold, append exact requested duplicates, and sort by descending score with tie groups.^[1]

Delegated operations used for this result: contains checker; word frequency. Their calculation or parsing conventions apply to the corresponding result fields.^{[2, 3]}

Input field	Meaning and units	Accepted input
documents	Documents	Required. Array of Object; no unknown keys.; minimum items 1; maximum items 128.
documents[].id	Literal identifier used to match records, independently of their display labels.	Required. Text; minimum UTF-16 length 1; maximum UTF-16 length 128.
documents[].text	Text	Required. Text; maximum UTF-16 length 4096.
queries	Queries	Required. Array of Text; minimum UTF-16 length 1; maximum UTF-16 length 4096.; minimum items 1; maximum items 32.
count	Requested count; the mechanics explain whether one draw expands into several returned records.	Required. Safe integer; minimum 1; maximum 128.
duplicateCount	Duplicate Count	Required. Safe integer; minimum 0; maximum 127.
rule	Rule	Required. Object; no unknown keys.
rule.mode	Mode	Required. Text; one of "substring", "token-occurrences".
rule.caseSensitive	Case Sensitive	Required. Boolean.
rule.minimumScore	Minimum Score	Required. Safe integer; minimum 0.
seed	Optional numeric seed; takes precedence over context randomness. Without either, use the fixed tool.io default seed.	Optional. Safe integer.

Output field	Interpretation	Returned shape
query	Query	Required. Text; maximum UTF-16 length 4096.
results	Results	Required. Array of Object; no unknown keys.; minimum items 1; maximum items 128.
results[].id	Literal identifier used to match records, independently of their display labels.	Required. Text; minimum UTF-16 length 1; maximum UTF-16 length 128.
results[].documentId	Document Id	Required. Text; minimum UTF-16 length 1; maximum UTF-16 length 128.
results[].text	Text	Required. Text; maximum UTF-16 length 4096.
results[].containsQuery	Contains Query	Required. Boolean.
results[].score	Score	Required. Safe integer; minimum 0.
results[].relevant	Relevant	Required. Boolean.
results[].duplicateOf	Duplicate Of	Required. Text; minimum UTF-16 length 1; maximum UTF-16 length 128. or Null.
tieGroups	Tie Groups	Required. Array of Object; no unknown keys.; minimum items 0; maximum items 64.
tieGroups[].score	Score	Required. Safe integer.
tieGroups[].resultIds	Result Ids	Required. Array of Text; minimum UTF-16 length 1; maximum UTF-16 length 128.; minimum items 2; maximum items 128.
randomness	Replay metadata: source is seed or context; seed is the normalized 32-bit seed or null for a context source.	Required. Object; no unknown keys.
randomness.seed	Optional numeric replay seed; normalization and precedence are explained below.	Required. Safe integer. or Null.
randomness.source	Source	Required. Text; one of "context", "seed".

An explicit safe-integer seed takes precedence over injected context randomness and is normalized to an unsigned 32-bit integer. With no seed and no context source, replay uses the fixed tool.io default seed. A context source must return finite values from zero inclusive to one exclusive; its advancing state must be retained separately. randomness records the source and normalized seed, or null for context. These generators are not cryptographic random sources.

json

{
  "input": {
    "documents": [
      {
        "id": "a",
        "text": "red blue"
      },
      {
        "id": "b",
        "text": "blue blue"
      },
      {
        "id": "c",
        "text": "green"
      }
    ],
    "queries": [
      "red",
      "blue"
    ],
    "count": 2,
    "duplicateCount": 1,
    "rule": {
      "mode": "token-occurrences",
      "caseSensitive": false,
      "minimumScore": 1
    },
    "seed": 42
  },
  "output": {
    "query": "blue",
    "results": [
      {
        "id": "result-1",
        "documentId": "b",
        "text": "blue blue",
        "containsQuery": true,
        "score": 2,
        "relevant": true,
        "duplicateOf": null
      },
      {
        "id": "result-2",
        "documentId": "c",
        "text": "green",
        "containsQuery": false,
        "score": 0,
        "relevant": false,
        "duplicateOf": null
      },
      {
        "id": "result-3",
        "documentId": "c",
        "text": "green",
        "containsQuery": false,
        "score": 0,
        "relevant": false,
        "duplicateOf": "result-2"
      }
    ],
    "tieGroups": [
      {
        "score": 0,
        "resultIds": [
          "result-2",
          "result-3"
        ]
      }
    ],
    "randomness": {
      "seed": 42,
      "source": "seed"
    }
  }
}

Limitations and assumptions

Relevance is the supplied lexical rule, not semantic search quality. Duplicate results retain their original document and score. count excludes added duplicates and total output is bounded.
A replay requires the same complete input and implementation. Randomized construction does not guarantee uniform coverage of every feasible output; finite samples can repeat unless a distinct-selection rule is explicitly stated.
The field tables state structural bounds; cross-field constraints and bounded search or output budgets also apply. Unknown keys are rejected where the object contract disallows them. Input errors are distinct from null fields and deliberate invalid controls in a successful output.

Alternative or Complementary methods

For a supplied example, inspect the returned construction or delegated check directly. To evaluate a consumer, also test cases chosen for its own boundaries; a generated finite corpus does not establish exhaustive coverage.

References

Tool behavior and example: Random Search Results Relevance Fixture Generator — tool.io
Calculation or validation: text/contains-checker — tool.io
Calculation or validation: text/word-frequency — tool.io

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