Citation Attribution

Every scraper gives you an answer’s citation list. CITATION_ATTRIBUTION tells you which of those citations actually made the answer: which answer sentence came from which source passage, with verbatim evidence for every claim. Submit it like any other task — same endpoint, queue, polling and webhooks:

curl -X POST https://api.querying.ai/v1/async/task \
  -H "Authorization: Bearer $QUERYING_API_KEY" -H "Content-Type: application/json" \
  -d '{
    "taskType": "CITATION_ATTRIBUTION",
    "payload": {
      "answer": "…the AI answer, as raw markdown…",
      "prompt": "best korean instant ramen",
      "citations": [
        { "url": "https://example.com/best-ramen", "body": "…the page text…" },
        { "url": "https://another.example.com/review", "body": "…the page text…" }
      ],
      "language": "en"
    }
  }'

The natural pairing is a two-step flow: run an Answers engine (GEMINI, PERPLEXITY, …), then feed its text and sources[].url straight into this task.

Payload

Field	Required	Notes
`answer`	yes	The AI answer as raw markdown, 1–200,000 chars. Keep it unmodified — link fragments in the text are part of the analysis.
`citations[]`	yes	1–25 sources. Supply each source’s text via `body` — citations without a readable body come back as `unfetched`. At most 10 discussion-thread URLs per task.
`citations[].kind`	no	How the answer cites this source: `inline` (linked from the answer text) or `panel` (listed as a source only). Auto-detected when omitted.
`prompt`	no	The question that produced the answer. Improves precision: answer text that merely restates the question is attributed to nothing.
`language`	no	Language for the `insight` narrative. Defaults to the answer’s language.

Result

{
  "influence": {
    "answerChars": 322,
    "attributedChars": 143,
    "coverage": 0.44,                    // share of the answer traceable to the citations
    "ranking": ["https://example.com/…", "https://another.example.com/…"],
    "insight": "The #1 recommendation comes directly from a first-person review…"  // 2–4 sentences, or null
  },
  "attributions": [                       // one per input citation, same order
    {
      "citationUrl": "https://example.com/best-ramen",
      "kind": "panel",
      "outcome": "passage",               // comment | post | passage | none | unfetched
      "influence": 0.28,                  // this citation's verified share of the answer
      "rank": 1,
      "sourceRef": { "kind": "passage", "index": 8 },
      "spans": [
        {
          "answerEvidence": "Shin Ramyun is widely regarded as the best-known",
          "sourceEvidence": "Shin Ramyun is going to be your classic pick.",
          "evidenceVerified": true,        // both sides re-checked against the actual documents
          "strength": "paraphrase",        // verbatim | paraphrase | fact-match
          "method": "judge",
          "answerRange": { "start": 0, "end": 48 },   // character offsets into your answer
          "sourceRef": { "kind": "passage", "index": 8 }
        }
      ],
      "method": "judge"
    }
  ],
  "debug": { "ms": 9800, "judgeCalls": 2, "cacheHits": 0, "unfetched": 0,
             "llmTokens": { "input": 9100, "output": 1200 }, "sourceFetches": 1 }
}

How to read it:

spans[] is the influence map. Each span pairs an exact fragment of your answer (answerEvidence, located at answerRange) with the exact fragment of the source that carries the same information (sourceEvidence). The two sides don’t need to look alike — a paraphrased or translated claim still maps to its source.
evidenceVerified is the trust gate. Both fragments are re-checked to exist verbatim in their own documents. Unverified spans are excluded from all scores; filter them out in your pipeline too.
outcome names what part of the source carried the information: passage for documents; for discussion-thread sources, comment (a specific comment, with its location in sourceRef) or post (the thread’s opening post).
influence and coverage are computed, not estimated — the character share of the answer covered by verified spans. Two citations backing the same sentence both get credit; the totals count shared text once.
kind: "inline" with outcome: "none" means the answer links a citation whose source doesn’t actually support the claim it’s attached to.
A result full of none is a finding, not a failure — the answer didn’t use its citations. AI answers routinely draw most of their text from model knowledge rather than the visible citations; coverage measures influence relative to the sources you provided.
debug.llmTokens and debug.sourceFetches are the usage behind the task’s credit charge.

Limits

25 citations per task; at most 10 discussion-thread citations.
Repeated analyses of the same source reuse a short-lived fetch cache.
Citations without a supplied body are returned as unfetched today; automatic fetching is planned.

Get started

Core concepts

Engines

GEO Research

Migrating

Payload

Result

Limits

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Payload

Result

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