Most agentic retrieval demos assume clean, well-structured documents. Enterprise reality is often different, consisting of messy PDFs where …

Most agentic retrieval demos assume clean, well-structured documents. Enterprise reality is often different, consisting of messy PDFs where critical information is buried across tables, figures, and complex layouts. That’s why we teamed up with @lancedb to explore how LiteParse (our lightning-fast parser) combined with LanceDB’s native multimodal storage can improve retrieval quality and agent response accuracy. By separating PDFs into multiple information layers - pages (text + screenshots + embeddings), chunks, and extracted assets - and storing them in LanceDB for fast multimodal retrieval, we built a hybrid pipeline that unlocks information traditional RAG systems often miss. Instead of relying on chunk-level retrieval alone, agents can retrieve and reason across pages, chunks, and visual assets, making complex enterprise PDFs far more accessible. The result is a significantly stronger retrieval foundation for agentic workflows. 📖 Read the full breakdown in the blog post: https://www.lancedb.com/blog/from-messy-pdfs-to-verifiable-answers-with-liteparse-and-lancedb 💻 Explore the full implementation on GitHub: https://github.com/lancedb/liteparse-lancedb-pdf-qa

Source: Jerry Liu (X) | 2026-07-06