AI BriefingImage Indexing Methods for RAG
Key point
For cost efficiency in RAG systems, this proposes storing images by describing them as text at the indexing stage.
Details
This covers an engineering approach to efficiently leverage screenshots, diagrams, and tables in technical documents within a RAG (Retrieval-Augmented Generation) pipeline.
The most common approach, Query-time multimodal, collects all relevant images and passes them to a vision model, but it is highly uneconomical. Actual testing showed that passing images directly increases the cost per query by up to 27% for GPT and 51% for Claude.
To address this, the approach describes images as text at indexing time.
- Indexing stage: A cheap vision model is used to describe the content of each image as text just once.
- Storage and retrieval: The generated descriptions are stored alongside regular text chunks, and at query time, this text is retrieved and passed to the model.
Applying this approach minimizes per-query overhead to 1%~6% compared to the text-only approach, while still statistically significantly improving answer quality. In particular, it greatly improves the actionability of answers by providing visual context alongside the process of finding specific settings.
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