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How we index images for RAG

kapa.ai

June 2, 2026

8 min read

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58/100

Summary

Kapa.ai indexes millions of images, including screenshots and diagrams, to enhance AI assistants that answer technical questions. Images are processed in a way that they are not sent to the model at query time, optimizing their utility in the retrieval-augmented generation (RAG) pipeline.

Key Takeaways

Kapa.ai indexes images for RAG by describing each image with a vision model at indexing time, storing the descriptions as text, and retrieving them alongside text chunks during queries.
The use of images in technical documentation improves the quality of answers, with LLM judges preferring image-context answers by a statistically significant margin.
Query-time multimodal approaches were found to be economically unfeasible and structurally unsuitable for technical questions, leading Kapa.ai to adopt a one-time indexing method.
Images in documentation serve two roles: illustrative, enhancing clarity of text, and load-bearing, containing essential information that cannot be conveyed through text alone.

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Community Sentiment

Mixed

Positives

Describing images at indexing time with a cheap vision model enhances retrieval outcomes, demonstrating an effective method for integrating visual data into text-based systems.
The approach of generating text descriptions for important images allows agents to better understand visual content, significantly improving search and retrieval capabilities.

Concerns

The non-deterministic nature of LLMs raises concerns that new models may interpret data differently, potentially revealing context that was previously overlooked and necessitating reprocessing.