Building a Multimodal RAG Pipeline with NVIDIA NeMo Retriever, Hosted NIMs, LanceDB, Reranking, and Grounded Generation

MarkTechPost · 3 weeks ago · 39 ● 2 sources

NVIDIA published a tutorial showing how to build a multimodal retrieval-augmented generation pipeline using NeMo Retriever, which extracts text, tables, charts, and infographics from PDFs, generates embeddings via hosted NIM endpoints, stores vectors in LanceDB, and generates grounded answers with citations using a Nemotron language model. The pipeline processes documents through six stages: offline text extraction, multimodal ingestion with hosted models, dense retrieval, vision-language reranking, filtered search, and RAG answer generation, with a recall-at-k evaluation. The complete workflow enables document intelligence applications to transform PDFs into searchable, citation-ready knowledge bases while delegating model inference to cloud-hosted services.