The tutorial describes building a multimodal retrieval-augmented generation (RAG) pipeline using NVIDIA NeMo Retriever. It begins by configuring a Python 3.12 environment, installing required packages, and performing offline PDF text extraction without a GPU or external API key. The workflow then extends to hosted NVIDIA NIM endpoints to detect page elements, extract tables, charts, and infographics, generate dense vector embeddings, and store processed content in LanceDB. Finally, it implements 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. The code asserts that the Python version is 3.12.x, as nemo-retriever requires it. It installs nemo-retriever and openai packages if needed, imports components like create_ingestor and Retriever, and downloads a sample PDF named "multimodal_test.pdf" from a GitHub repository. Stage 1 performs offline text extraction using the pdfium method with extract_text set to True and other extraction options set to False, then inspects the extracted rows, columns, and page content.