Building a Multimodal RAG Pipeline with NVIDIA NeMo Retriever, Hosted NIMs, LanceDB, Reranking, and Grounded Generation
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. F
Get smart on it
A multimodal retrieval-augmented generation pipeline combines multiple types of data processing to extract and organize information from documents. This tutorial demonstrates building such a pipeline using NVIDIA NeMo Retriever and related tools, which extract text, tables, charts, and infographics from PDFs, convert them to vector embeddings, and store them in a database for intelligent retrieval. The pipeline includes steps for offline text extraction, multimodal content detection through hosted APIs, deduplication, embedding generation, and storage in LanceDB, followed by retrieval and response generation with citations. This approach enables systems to search and generate responses based on diverse document content types rather than just text alone.
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