Multimodal RAG: Retrieve the Page, Not Just the Paragraph
How to build retrieval systems that preserve tables, diagrams, screenshots, layout, and citations instead of flattening every document into text.
A PDF is not a bag of sentences. Meaning can live in a table header, the position of a label on a diagram, a screenshot, or the relationship between a caption and an image. Text-only extraction often discards exactly the evidence a user needs.
Preserve structure during ingestion
Store page boundaries, headings, coordinates, table relationships, image references, and document metadata alongside text. Generate representations that support both semantic retrieval and exact keyword matches, then retain a path back to the original page.
- Use layout-aware parsing for columns, tables, forms, and footnotes
- Create image or page-level representations when visual meaning matters
- Keep source, version, page, and section metadata on every retrievable unit
- Rerank candidates with the user’s full question and document context
- Return a citation that opens the precise page or region used in the answer
Retrieve complementary evidence
A question about a chart may need the visual, its legend, and the paragraph that explains methodology. The retrieval layer should assemble those pieces as a coherent evidence bundle rather than choosing one isolated chunk.
Evaluate by document type
Invoices, manuals, research reports, slide decks, and dashboards fail differently. Build retrieval tests for each source class and score citation accuracy, not only final-answer similarity.
Primary sources
First-party documentation and announcements used to ground this field note.
