Building a Multimodal RAG Pipeline with NVIDIA NeMo Retriever, Hosted NIMs, LanceDB, Reranking, and Grounded Generation
NVIDIA's NeMo Retriever + hosted NIMs now support multimodal RAG at scale — here's how to build it.
Why it matters
A practical engineering guide to production RAG with NVIDIA's stack: shows practitioners how to wire NeMo Retriever, LanceDB, and reranking into a working multimodal pipeline without GPU overhead for indexing.
The key facts
12 to knowNVIDIA NeMo Retriever integration with hosted NIM endpoints
Multimodal RAG pipeline architecture (text + images)
Offline PDF extraction without GPU or API key requirement
LanceDB vector database integration
Reranking and grounded generation included
Python 3.12 environment setup
NVIDIA NeMo Retriever integration
Hosted NVIDIA NIM endpoints
LanceDB vector store
Python 3.12 environment
Offline PDF text extraction
Multimodal retrieval pipeline
Go to the source
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Publisher excerpt: 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…