Atomic Fact Pipeline: Python Scripts Decompose Unstructured PDFs to Mass-Locate Insertion Points for Original Data on Indexed Pages

To scale unique information gain across legacy content, the Atomic Fact Pipeline uses Python and the Gemini API to extract unstructured data from internal PDFs and locate insertion points for it on existing URLs, details Lidia Infante.

The multi-layered Google Colab stack converts raw research into standardized units and maps them directly to content gaps.

First, the script feeds raw PDFs and spreadsheets into the LLM.

The API decomposes these massive documents into standardized atomic facts formatted as a [Metric, Statement, Source] database.

The script then passes this extracted array back through the LLM to tag each row against the site's internal content taxonomy, yielding a clean dataset mapped to specific topic clusters.

For the injection phase, a secondary process analyzes existing indexed pages.

The script scrapes the HTML but explicitly strips out global navigation and footers to isolate the core article body.

Finally, the LLM cross-references the taxonomized facts against these isolated page summaries and outputs the exact contextual HTML insertion point for the new data.