How to Make Documentation Images and Videos Searchable in One API Call

Most AI-powered search systems have a blind spot that nobody talks about: they cannot see images or videos. They parse text, index text, and retrieve text. Everything else - screenshots, diagrams, video walkthroughs, annotated UI captures - is invisible. For documentation-heavy products, this means a significant portion of the knowledge base is simply unreachable. The gap is architectural, not incidental.

This post walks through why text-only retrieval misses so much, and how a single API call - POST https://api.ragionex.com/v1/knowledge/search - can return answers from documentation that includes screenshots and videos. Ragionex ships this in its Developer Preview today.

The Scale of the Problem

According to Gartner (2023), 80 to 90 percent of all enterprise data is unstructured, and that percentage is growing at 55 to 65 percent annually - three times faster than structured data. A large share of that unstructured data is visual: images, videos, diagrams, presentations, and screen recordings.

Technical documentation is no exception. Open any modern product documentation - VS Code, AWS, OpenAI, Figma - and count the screenshots per page. Product docs are not walls of text anymore. They are rich, visual guides where screenshots show exactly which button to click, which menu to open, which dialog to configure. Video walkthroughs demonstrate multi-step workflows that would take paragraphs to describe in text.

Visual content improves comprehension and retention for procedural tasks - which is why modern product documentation has shifted toward screenshots, annotated diagrams, and video walkthroughs rather than dense text descriptions. The trend is not slowing down.

Yet most RAG (Retrieval-Augmented Generation) systems treat all of this visual content as if it does not exist.

What Gets Lost When You Ignore Visual Content

Consider what a typical product screenshot contains:

Button labels and their exact positions - "Click the gear icon in the bottom-left corner"
Menu hierarchies - The full path from top-level menu to nested submenu item
UI states - Whether a toggle is on or off, which tab is active, what a selected item looks like
Configuration dialogs - Every field, dropdown option, checkbox, and default value
Error messages - The exact text of warnings, alerts, and error dialogs as they appear on screen
Color coding and visual indicators - Status badges, severity levels, progress bars
Layout and spatial relationships - Where panels are relative to each other, what the sidebar contains

None of this information exists in the surrounding text. Technical writers do not transcribe every pixel of a screenshot into prose - that would be redundant. The image IS the documentation. The text around it provides context: "To configure auto-save, open the Settings panel shown below." The screenshot shows the actual Settings panel with all its options.

Videos contain even more. A 30-second screen recording of a refactoring operation in an IDE shows the right-click context menu, the specific refactoring option, the preview dialog, the confirmation step, and the final result. Describing that flow in text requires multiple paragraphs. In video, it is self-evident.

When a user asks "Where is the settings button?" or "What options are in the refactoring menu?" - the answer lives in a screenshot or video. A text-only RAG system will never find it.

Why Traditional RAG Cannot Solve This

Traditional RAG systems follow a straightforward pipeline: split documents into text passages, generate vector representations for each passage, store them in a search index, and retrieve the most semantically similar passages when a query arrives.

The problem is in the first step. When the system encounters an image reference like ![Settings panel](images/settings.png), it sees a markdown image tag - a few words of alt text at best. The actual content of the image, everything visible in that screenshot, is discarded. The same applies to video elements. The rich visual information never enters the search index, so it can never be retrieved.

This creates a systematic blind spot. The system is not occasionally missing results - it is architecturally incapable of returning answers that exist only in visual content.

Research benchmarking multimodal RAG systems (Yang et al., arXiv:2502.14864, 2025) confirms the technical challenges: unified vector retrieval methods fail entirely on chart-dense documents, and even under ideal ground-truth retrieval conditions, coverage scores plateau below 75 percent. Traditional approaches to incorporating visual content fail to capture the detailed information needed for accurate retrieval.

The common workarounds each have serious limitations:

Using basic alt text. Most image alt text is a short descriptive phrase: "Settings panel" or "Code editor showing refactoring." This captures almost none of the detailed information visible in the image. You cannot answer "What font sizes are available in the settings?" from an alt text that says "Settings dialog."

Ignoring the problem. Many teams accept that their RAG system only covers text content and treat it as a known limitation. This works until users start asking questions about things they saw in screenshots - which happens constantly with visual documentation.

Manual workarounds. Some teams try to manually supplement their image references with additional text. This does not scale. A documentation set with thousands of images cannot be manually maintained as the product evolves.

The Real Cost of Visual Blindness

This is not an academic problem. It affects real user interactions every day.

When a support chatbot powered by RAG cannot answer "How do I enable dark mode?" because the answer is in a screenshot showing the appearance settings panel, the user gets a generic or incorrect response. When an internal knowledge base search misses the deployment architecture diagram that shows exactly how services connect, an engineer wastes time asking colleagues. When a developer asks "What does the error dialog look like when the build fails?" and gets nothing back because that information is in a video walkthrough, the documentation might as well not exist.

The cost compounds. Organizations invest heavily in creating visual documentation - recording walkthroughs, capturing annotated screenshots, building diagrams. Then they deploy a search system that cannot access any of it. The investment in visual content creation is effectively wasted for AI-powered retrieval.

Making Visual Content Searchable

The solution requires treating visual content as a first-class citizen in the retrieval system - not an afterthought, not a nice-to-have, but a core part of the searchable knowledge base.

When a user asks "where is the settings button?" the system should be able to find the answer regardless of whether it lives in a paragraph of text or in a screenshot. The user does not care about the format - they care about getting the right answer.

We process the visual content at build time, alongside text. Natural language queries find answers in either - written content, screenshots, or videos.

The key architectural insight is that this processing happens at build time, not at query time. There is no per-query cost for handling visual content. The heavy lifting is done once, and every subsequent search benefits from it.

Ragionex: Visual Content Search in Practice

Most search engines pretend images don't exist. Half your docs do. Ragionex is a Context Engine that makes screenshots, videos, and diagrams searchable - same API call as text. Users find answers wherever they live.

The developer preview is built on VS Code documentation - a realistic dataset where visuals matter. VS Code docs rely heavily on screenshots to show editor features, settings panels, extension interfaces, and debugging workflows. Video walkthroughs demonstrate refactoring operations, debugging sessions, and multi-step configurations.

With Ragionex, you can ask questions about things that are only visible in screenshots or videos:

"Where is the Azure sign-in option in VS Code?" - The answer comes from a VS Code sidebar screenshot.
"What does the refactoring context menu look like?" - The answer comes from a video showing a right-click menu with refactoring options.
"What options are available in the font settings?" - The answer is in a screenshot of the settings panel.

A text-only RAG system returns nothing for these queries. Ragionex returns the specific answer because the visual content has been processed and made searchable.

The API is straightforward. Send a question, get back relevant answers with source references:

POST /v1/knowledge/search
{
    "question": "How to sign in to Azure in VS Code?",
    "results": 5,
    "collection": "vscode-docs"
}

The response shape is the same regardless of source format - text, image, or video.

You can try it for free at ragionex.com.

The Future Is Multimodal

The trend is clear: documentation is becoming more visual, not less. Video tutorials are replacing written guides. Interactive screenshots with annotations are replacing plain text instructions. Product tours with on-page screen recordings are becoming standard.

According to Research and Markets, the multimodal search market is projected to reach $13.97 billion by 2029 - driven by the recognition that text-only search is fundamentally incomplete. The broader visual search technology market is on a similar growth trajectory as multimodal AI capabilities become standard enterprise infrastructure.

Organizations that build AI applications on top of text-only retrieval systems are building on an incomplete foundation. As their documentation becomes richer and more visual, the gap between what users ask and what the system can find will widen.

Making images and videos searchable is not a nice-to-have feature. It is a requirement for any retrieval system that claims to actually understand documentation. The information in a screenshot is just as valid and just as searchable as the text around it - if the system is built to handle it.

The tools to solve this problem exist today. Whether visual content ends up in your search index is now an architectural choice, not a technical constraint.

Related reading: 7 RAG API Patterns Most Developers Skip covers exactly how to keep the visual content prose available to your downstream LLM and stripped from end-user output. For the agent-side analogue - where retrieval also needs to surface non-textual context - see Your Agent Doesn't Have a Memory Problem. It Has a Retrieval Problem.

Ragionex is a Context Engine that makes documentation - including images and videos - searchable for AI applications. Try the free developer preview at ragionex.com.