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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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Works with any time-based content

Timeseries organizes content chronologically, it works on both event-based content (news articles, reports, announcements) and structured data pages. Each data point captures what happened at a specific point in time as a text description, not a numeric value. 
{ "timestamp": "2023-03", "label": "Italy temporarily bans ChatGPT over privacy concerns" }
{ "timestamp": "2024-08", "label": "EU AI Act enters into force" }

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Timestamps are pre-normalized

Timestamps are always returned in one of three formats: YYYY, YYYY-MM, or YYYY-MM-DD. You can sort and compare them safely as strings. 
// Timestamps are already normalized, no conversion needed
dataPoints.sort((a, b) => a.timestamp.localeCompare(b.timestamp));

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Use URLs pointing to content-rich pages

URLs pointing to articles, reports, or data pages with clear time-based information yield the best results. Sparse or navigation-only pages may return fewer data points. 
{ "url": "https://en.wikipedia.org/wiki/Timeline_of_artificial_intelligence" }

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Use the file endpoint for PDFs and reports

For annual reports, whitepapers, or research documents, upload the file directly with the /api/v1/timeseries/file endpoint rather than passing a URL. 
curl -X POST "https://api.factagora.com/api/v1/timeseries/file" \
  -H "Authorization: Bearer fag_your_api_key" \
  -F "file=@annual-report-2024.pdf"

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Combine with Causality Graph

After extracting a timeseries, use Causality Graph to understand the causal relationships behind the sequence of events. 
Timeseries (event sequence) → Causality Graph (why did each event happen?)