This article introduces Vision RAG, an evolution of traditional RAG systems designed to enable search and retrieval on complex, multimodal documents beyond plain text. It leverages next-generation multimodal embedding models, like Voyage AI's voyage-multimodal-3, to index visual and textual content simultaneously, overcoming limitations of OCR-based methods for enterprise data. The system design focuses on unified embeddings for efficient vector search and feeding relevant visual assets to vision-capable LLMs for grounded answers.
Read original on MongoDB BlogTraditional Retrieval-Augmented Generation (RAG) systems primarily work with plain text, leaving a vast amount of enterprise knowledge in complex documents (PDFs, slides, diagrams, dashboards) untapped. Relying on Optical Character Recognition (OCR) or other parsing techniques to extract text from these multimodal sources presents significant engineering challenges, including high costs, brittleness across various formats and layouts, and accuracy issues. This problem highlights a critical gap in enabling LLMs to access comprehensive organizational knowledge.
Vision RAG extends the core principles of text-based RAG—retrieval and generation—to encompass multimodal content. Instead of costly and error-prone text extraction, Vision RAG uses advanced multimodal embedding models. These models can ingest both text and images (or screenshots of documents) and generate a single, dense vector representation that captures the semantic meaning and structural context of the content. This unified representation allows direct indexing and vector search on entire documents, slides, and images, even when they contain interleaved text and visuals.
Key Architectural Shift: Unified Multimodal Embeddings
The innovation in Vision RAG lies in the multimodal embedding model. Unlike older models that used separate encoders for text and images (leading to a 'modality gap' and unreliable cross-modal retrieval), modern models like Voyage AI's voyage-multimodal-3 employ a single encoder. This ensures textual and visual features are processed consistently within the same vector space, enabling true multimodal retrieval and more accurate semantic search.
Vision RAG significantly reduces engineering complexity and cost associated with traditional preprocessing pipelines for multimodal data. By enabling native access to rich, multimodal enterprise information for LLM-based systems, it enhances the accuracy and relevance of AI-generated responses. This approach provides a more robust and scalable solution for knowledge retrieval in data-rich environments where information is not confined to plain text.