How to extract meaning from charts and tables in PDFs

Late-interaction multi-vector retrieval offers an alternative to conventional PDF RAG pipelines that rely on OCR, text chunking, and single-vector text embeddings, which can miss information contained in charts, tables, layouts, and diagrams. Using Weaviate’s hosted multi2multivec-weaviate model, each PDF page is rendered as an image and represented by multiple vectors, allowing MaxSim-based matching between query concepts and relevant visual regions without text extraction or chunking. A demonstration using NVIDIA’s FY2026 quarterly investor presentations shows that the system can retrieve specific pages containing revenue and gross-margin trends even when the query wording does not appear on those pages. Weaviate Cloud supports drag-and-drop ingestion and can pair retrieval with its Query Agent to generate synthesized, page-cited answers, while an equivalent production workflow can be implemented in Python using PyMuPDF to rasterize pages and Weaviate to store and vectorize them. The approach is most suitable for visually rich, chart-heavy PDF collections, whereas conventional text embeddings may remain less expensive and adequate for text-dominant corpora; hybrid systems can combine both methods.

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