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Nicholas Tindle c1a1767034 feat(docs): Add block documentation auto-generation system (#11707 )

- Add generate_block_docs.py script that introspects block code to
generate markdown
- Support manual content preservation via <!-- MANUAL: --> markers
- Add migrate_block_docs.py to preserve existing manual content from git
HEAD
- Add CI workflow (docs-block-sync.yml) to fail if docs drift from code
- Add Claude PR review workflow (docs-claude-review.yml) for doc changes
- Add manual LLM enhancement workflow (docs-enhance.yml)
- Add GitBook configuration (.gitbook.yaml, SUMMARY.md)
- Fix non-deterministic category ordering (categories is a set)
- Add comprehensive test suite (32 tests)
- Generate docs for 444 blocks with 66 preserved manual sections

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>

<!-- Clearly explain the need for these changes: -->

### Changes 🏗️

<!-- Concisely describe all of the changes made in this pull request:
-->

### Checklist 📋

#### For code changes:
- [x] I have clearly listed my changes in the PR description
- [x] I have made a test plan
- [x] I have tested my changes according to the test plan:
  <!-- Put your test plan here: -->
  - [x] Extensively test code generation for the docs pages



<!-- CURSOR_SUMMARY -->
---

> [!NOTE]
> Introduces an automated documentation pipeline for blocks and
integrates it into CI.
> 
> - Adds `scripts/generate_block_docs.py` (+ tests) to introspect blocks
and generate `docs/integrations/**`, preserving `<!-- MANUAL: -->`
sections
> - New CI workflows: **docs-block-sync** (fails if docs drift),
**docs-claude-review** (AI review for block/docs PRs), and
**docs-enhance** (optional LLM improvements)
> - Updates existing Claude workflows to use `CLAUDE_CODE_OAUTH_TOKEN`
instead of `ANTHROPIC_API_KEY`
> - Improves numerous block descriptions/typos and links across backend
blocks to standardize docs output
> - Commits initial generated docs including
`docs/integrations/README.md` and many provider/category pages
> 
> <sup>Written by [Cursor
Bugbot](https://cursor.com/dashboard?tab=bugbot) for commit
631e53e0f6. This will update automatically
on new commits. Configure
[here](https://cursor.com/dashboard?tab=bugbot).</sup>
<!-- /CURSOR_SUMMARY -->

---------

Co-authored-by: Claude Opus 4.5 <noreply@anthropic.com>

2026-01-19 07:03:19 +00:00

1.6 KiB

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Nvidia Deepfake

Blocks for detecting deepfakes and synthetic image manipulation using Nvidia AI.

Nvidia Deepfake Detect

What it is

Detects potential deepfakes in images using Nvidia's AI API

How it works

This block analyzes images using Nvidia's AI-powered deepfake detection model. It returns a probability score (0-1) indicating the likelihood that an image has been synthetically manipulated.

Set return_image to true to receive a processed image with detection markings highlighting areas of concern.

Inputs

Input	Description	Type	Required
image_base64	Image to analyze for deepfakes	str (file)	Yes
return_image	Whether to return the processed image with markings	bool	No

Outputs

Output	Description	Type
error	Error message if the operation failed	str
status	Detection status (SUCCESS, ERROR, CONTENT_FILTERED)	str
image	Processed image with detection markings (if return_image=True)	str (file)
is_deepfake	Probability that the image is a deepfake (0-1)	float

Possible use case

Content Verification: Verify authenticity of user-uploaded profile photos or identity documents.

Media Integrity: Screen submitted images for signs of AI manipulation.

Trust & Safety: Detect potentially misleading synthetic content in social or news platforms.

1.6 KiB Raw Blame History