Vision-RAG vs Text-RAG: Improving Retrieval Accuracy in Enterprise Search

Vision-RAG vs Text-RAG: Improving Retrieval Accuracy in Enterprise Search

This article delves into the technical comparison between Vision-RAG and Text-RAG techniques in the context of enterprise search systems. It highlights how most Retrieval-Augmented Generation (RAG) failures stem from the retrieval phase rather than from the generation phase itself. Traditional text-first pipelines suffer from loss of layout semantics and structure during PDF to text conversion, which significantly degrades recall and precision.

Vision-RAG addresses these challenges by retrieving entire rendered pages using vision-language embeddings, preserving visual context and improving retrieval accuracy on visually rich documents. This approach offers material end-to-end gains for enterprises relying on accurate search over complex documents, such as reports and manuals. For developers and AI teams, adopting Vision-RAG could reshape how enterprise search solutions handle visually complex data, enhancing both the quality and reliability of information retrieval.

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