Kapa.ai's approach to indexing images for RAG: describing images at indexing time with cheap vision models
By
mooreds
Pure flour-power. Hearty enough to carry you through lunch.
Summary
Kapa.ai describes their approach to handling images in RAG (Retrieval-Augmented Generation) pipelines for technical documentation. Instead of sending images to the model at query time (which is expensive), they use a cheap vision model to describe each image once at indexing time, store those descriptions as text, and retrieve them alongside regular text chunks. This makes indexing a one-time cost with minimal per-query overhead (1-6%). The article details their technical journey, challenges with different image types (screenshots, diagrams, tables), and their solution for making visual content searchable and useful for AI-powered technical Q&A.
Key quotes
· 3 pulledWe don't send images to the model at query time. We describe each image once, at indexing time, with a cheap vision model, store the descriptions as text, and retrieve them alongside ordinary text chunks.
Indexing is a one-time cost; after that, per-query overhead is 1% to 6% over text-only retrieval.
The knowledge bases we process hold millions of images: screenshots, architecture diagrams, circuit schematics, annotated UI walkthroughs.
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