In today's era of information explosion, how to efficiently extract required information from massive documents has become a major challenge for enterprises. A recent technical comparison study conducted an in-depth analysis of Vision-RAG and Text-RAG, revealing their advantages and disadvantages in enterprise search.
Text-RAG typically works by converting PDF documents into text, then performing embedding and indexing. However, this process often leads to the loss of document layout information, table structures, and chart semantics due to the imperfections of OCR (Optical Character Recognition) technology. These issues directly affect the accuracy and recall rate of information retrieval.
In contrast, Vision-RAG adopts a more advanced approach. It first converts PDF documents into images and generates high-fidelity embeddings through a Visual Language Model (VLM). This processing not only preserves the document's layout and chart information but also achieves significant improvements in practical applications. The study shows that Vision-RAG can achieve a 25% to 39% overall improvement in retrieval and generation when handling visually rich documents.
Additionally, the study found that using high-resolution visual models significantly improves inference quality, as the resolution's fineness is crucial when dealing with small fonts, symbols, and charts. However, the cost of Vision-RAG is usually higher than that of Text-RAG, mainly because the number of tokens increases significantly during image processing.
When designing a Vision-RAG system for production environments, experts recommend that enterprises ensure embedding alignment between different modalities, use trained encoders for text and image interaction matching, and prioritize high-quality image input in the retrieval process. At the same time, by utilizing efficient retrieval and re-ranking mechanisms, enterprises can effectively manage token costs and improve the accuracy of information retrieval.
Key Points:
🌟 Vision-RAG can improve overall retrieval accuracy by 25% to 39% compared to Text-RAG when handling visually rich documents.
📈 High-resolution visual models can significantly enhance information inference quality, especially when dealing with small fonts and complex charts.
💰 Although Vision-RAG has a higher cost, its advantage in information retrieval accuracy makes it an ideal choice for enterprise search.
