Building a Multimodal RAG Pipeline with NVIDIA NeMo Retriever, Hosted NIMs, LanceDB, Reranking, and Grounded Generation
This technical tutorial outlines the construction of a multimodal retrieval-augmented generation (RAG) pipeline using NVIDIA's NeMo Retriever and NIM endpoints. It covers the end-to-end process from document ingestion and vector embedding to reranking and grounded response generation.
Why it matters
As multimodal AI becomes more prevalent, developers need standardized workflows to integrate complex document analysis into enterprise applications.
In this tutorial, we build an advanced multimodal retrieval-augmented generation pipeline with NVIDIA NeMo Retriever . We begin by configuring a Python 3.12 environment, installing the required packages, and performing offline PDF text extraction without relying on a GPU or external API key. We then extend the workflow with hosted NVIDIA NIM endpoints to detect page elements, extract tables, charts, and infographics, generate dense vector embeddings, and store the processed content in LanceDB. Finally, we implement dense retrieval, vision-language reranking, metadata-filtered search, grounded response generation with inline citations, and a lightweight recall-at-k evaluation to validate retrieval quality across multimodal document content.
Get smarter about the news
Sign up free for a feed built around what you actually care about, Dive Deeper research on any story, and the full text of every article.
Create free accountAlready have an account? Sign in