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Gemini for extracting structured content from complex PDFs

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Gemini integrates with Drive and Vertex AI to extract usable content from uploaded PDFs.

Gemini offers two core pathways for working with PDFs: Google Drive summaries for everyday users, and Vertex AI endpoints for developers who require structured data. When a PDF is opened in Google Drive, Gemini automatically generates a sidebar summary with key bullet points, section headings, and—when applicable—embedded tables. This summary includes a “Copy to Sheets” button, enabling one-click transfer of table data into a spreadsheet.


This feature was rolled out broadly in June 2025 and has been designed to function seamlessly within the Google Workspace interface. File uploads up to 100 MB are supported, although performance is optimal under that threshold. Larger files are accepted but may result in partial or incomplete extractions depending on layout complexity.



Drive summary cards highlight tables, propose follow-up actions, and simplify export.

When Gemini detects tables in a PDF, the Drive summary interface proposes structured follow-up actions. Examples include:

  • “Copy table to Sheets”, which preserves tabular formatting.

  • “Summarize this PDF” or “Draft a response based on content”.

  • “List key takeaways”, especially in business, legal, or HR documents.


This functionality is contextual: if Gemini detects formal structure (tables, headings, numbered sections), it surfaces export tools. If the document is unstructured or scanned as an image, Gemini attempts OCR but with limited formatting accuracy.

Workspace admins can disable or restrict these features in the Admin Console under “Drive → Features → AI summaries,” applying domain-level DLP if needed.


Vertex AI allows developers to extract structured JSON from PDF files.

For technical workflows, Gemini can extract structured information—tables, section headings, key-value lists—via the generativeContent endpoint in Vertex AI. This is accessible from:

  • Google Cloud Console

  • REST API

  • Python, Node.js, and C# SDKs


To use this method, a developer uploads a PDF to a Google Cloud Storage (GCS) bucket and calls the Gemini model with:

  • mimeType: application/pdf

  • structuredOutputConfig enabled

  • A custom prompt instructing the model to extract specific elements (e.g., tables, headings, metadata)


Example prompt schema:

{
  "task": "Extract tables as JSON with columns and row numbers",
  "output_format": "array of objects",
  "range": "pages 3–12 only"
}

The output is a fully structured JSON array or dictionary, which can then be passed to downstream apps or analytics pipelines.


Prompt engineering and layout awareness are key to accurate table extraction.

Gemini performs well on moderately complex documents, but results may vary when handling:

  • Merged cells or multilevel headers: table headers may drop or misalign. Use prompts like “Flatten merged cells; repeat headers in each row” to improve output.

  • Scanned image-based PDFs: OCR is available but struggles with skewed scans. Preprocessing with Google Cloud Vision's detectDocumentText improves results.

  • Dense diagrams or vector drawings: non-textual data may inflate token size or trigger truncation. For better performance, instruct Gemini to analyze only specific page ranges.

In real-world testing, Gemini consistently handled documents up to 300 pages, although Google does not officially guarantee performance at that size. The best results are consistently achieved on files under 100 MB with standardized table layouts.


Summary cards in Drive and JSON extraction in Vertex AI meet different user needs.

Feature

Google Drive + Gemini

Vertex AI + Gemini API

Target user

General Workspace user

Developer / analyst

Input method

Drag-and-drop in Drive

Upload to GCS bucket

Output

Bullet summary, action cards

Structured JSON (tables, headings)

File size

Up to 100 MB recommended

Tested up to ~300 pages

OCR support

Yes (built-in)

Yes, with Vision API optional

Admin control

Can disable in Admin Console

Follows GCP IAM and retention policies

End users gain quick insights and export tools through Drive, while enterprise developers can perform complex PDF parsing at scale using Gemini’s generative capabilities. This dual-layer model makes Gemini suitable for tasks ranging from daily reading to automated document intelligence across an organization.


Data stays within Google’s infrastructure and respects workspace governance.

Gemini’s PDF features comply with Google Workspace and Google Cloud’s security policies:

  • In Drive: Data never leaves the user’s domain; summaries follow Drive’s file permissions.

  • In Vertex AI: Uploaded files reside in the customer’s encrypted GCS bucket; output retention and audit logs follow GCP IAM and DLP settings.

  • Training exclusion: Gemini does not use Drive or Vertex AI user data for training unless explicitly permitted under enterprise settings.


This ensures that extracted data remains confidential and under organizational control, even when Gemini is used for advanced parsing, analysis, or transformation of complex documents.


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