When constructing a Retrieval-Augmented Generation (RAG) system for documents containing mixed content like text, tables, and charts, the effectiveness of multimodal embeddings was compared to text embeddings. Tests were conducted using 150 queries on datasets such as DocVQA, ChartQA, and AI2D. Results showed that multimodal embeddings significantly outperformed text embeddings for tables (88% vs. 76%) and had a slight advantage with charts (92% vs. 90%), while text embeddings excelled in pure text scenarios (96% vs. 92%). These findings suggest that multimodal embeddings are preferable for visual documents, whereas text embeddings suffice for pure text content. This matters because choosing the right embedding approach can significantly enhance the performance of systems dealing with diverse document types.
