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Documentation Index

Fetch the complete documentation index at: https://docs.factagora.com/llms.txt

Use this file to discover all available pages before exploring further.

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1. Embed a watermark

Send your content to /fingerprint/embed to extract the TKG and embed the watermark. 
curl -X POST "https://api.factagora.com/api/v1/fingerprint/embed" \
  -H "Authorization: Bearer fct_your_api_key" \
  -H "Content-Type: application/json" \
  -d '{
    "content": "The European Central Bank raised interest rates by 25 basis points on Thursday, citing persistent core inflation in the eurozone. Analysts at Goldman Sachs expect one more hike before a pause in Q4.",
    "content_type": "news",
    "metadata": {
      "author_id": "editor_jane",
      "published_at": "2024-06-15T08:00:00Z",
      "source_id": "article_12345"
    }
  }'

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Response

{
  "watermarked_content": "The European Central Bank raised interest rates by 25 basis points...",
  "fingerprint_id": "fp_l1p8OPCwGhvu",
  "tkg_snapshot": {
    "entities": ["European Central Bank", "Interest rate", "Goldman Sachs"],
    "timeseries": ["2024-06-15"],
    "relations": [
      { "from": "European Central Bank", "rel": "raises", "to": "Interest rate" }
    ],
    "argument_map": [
      {
        "premise": "Persistent core inflation in the eurozone",
        "evidence": "ECB policy meeting decision",
        "conclusion": "Interest rates raised by 25 basis points"
      }
    ]
  },
  "embed_timestamp": "2024-06-15T08:01:23.456Z",
  "meta": { "executionTimeMs": 1234 }
}
Distribute watermarked_content, not the original. The watermarked version looks identical to the original but contains invisible zero-width characters that enable watermark-based detection.
Store fingerprint_id alongside your internal article ID. You’ll need it for re-scoring and reporting.

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2. Detect content reuse

When you encounter content that looks like it might originate from your article — even if it’s been rewritten — send it to /fingerprint/detect. 
curl -X POST "https://api.factagora.com/api/v1/fingerprint/detect" \
  -H "Authorization: Bearer fct_your_api_key" \
  -H "Content-Type: application/json" \
  -d '{
    "content": "ECB hiked rates 25bp Thursday due to stubborn inflation. Goldman analysts predict one final increase before year-end pause."
  }'

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Response

{
  "match_found": true,
  "confidence_score": 0.87,
  "query_fingerprint_id": "fp_tmpQueryId01",
  "matches": [
    {
      "fingerprint_id": "fp_l1p8OPCwGhvu",
      "content_type": "news",
      "metadata": { "author_id": "editor_jane", "source_id": "article_12345" },
      "embed_timestamp": "2024-06-15T08:01:23.456Z",
      "score": 0.87,
      "similarity_breakdown": {
        "entity_match": 0.92,
        "timeseries_match": 1.0,
        "causal_pattern_match": 0.78
      },
      "overlap": {
        "entities": ["european central bank", "interest rate", "goldman sachs"],
        "timeseries": ["2024-06-15"],
        "relations": ["european central bank|raises|interest rate"]
      },
      "watermark_match": false,
      "watermark_correlation": null
    }
  ],
  "meta": {
    "scanned": 156,
    "executionTimeMs": 342,
    "weights": { "entity": 0.5, "time": 0.2, "causal": 0.3 },
    "watermark_detected": false
  }
}
Notice that the query text is completely rewritten — “ECB hiked rates 25bp” vs “European Central Bank raised interest rates by 25 basis points” — yet the API matched it with 87% confidence by comparing the underlying causal structure.

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3. Detect with watermark (highest confidence)

If the watermarked version of your content was copied with the invisible characters intact, the watermark layer fires for near-certain provenance: 
{
  "matches": [
    {
      "fingerprint_id": "fp_l1p8OPCwGhvu",
      "score": 0.87,
      "watermark_match": true,
      "watermark_correlation": 0.98,
      ...
    }
  ],
  "meta": {
    "watermark_detected": true
  }
}
When watermark_match: true, the match is cryptographically verified — the exact watermarked content (or a portion of it) was used.

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4. Tune detection with custom weights

Different content types benefit from different scoring emphasis. Override the defaults when needed: 
{
  "content": "...",
  "weights": { "entity": 0.3, "time": 0.1, "causal": 0.6 }
}
The weights used are always echoed back in meta.weights so you can verify what was applied.

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5. Re-score an existing fingerprint

To periodically check if new content matches a fingerprint you already embedded, pass the fingerprint_id directly: 
curl -X POST "https://api.factagora.com/api/v1/fingerprint/detect" \
  -H "Authorization: Bearer fct_your_api_key" \
  -H "Content-Type: application/json" \
  -d '{"fingerprint_id": "fp_l1p8OPCwGhvu"}'
This reuses the stored TKG snapshot without re-extracting, making it faster and ideal for scheduled monitoring.

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Summary

StepEndpointWhat it does
EmbedPOST /fingerprint/embedExtracts TKG, embeds watermark, stores fingerprint
DetectPOST /fingerprint/detectRuns watermark + TKG matching against stored fingerprints
Re-scorePOST /fingerprint/detect (with fingerprint_id)Re-scores existing fingerprint against new candidates