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. 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.

We configure the Python 3.12 environment, install NVIDIA NeMo Retriever, and import the required ingestion and retrieval components. We download the sample multimodal PDF and define it as the input document for the pipeline. We then perform CPU-based offline text extraction with PDFium and inspect the extracted rows, columns, and page content.

We securely load the NVIDIA API key and define the hosted NIM endpoints for layout detection, OCR, table extraction, graphic analysis, embedding, reranking, and generation. We create a multimodal ingestion pipeline that extracts text, tables, charts, and infographics while applying token-aware chunking and content deduplication. We generate embeddings for the extracted content and upload the resulting vectors and metadata to a LanceDB table.

We inspect the extracted multimodal elements and convert the processed document into page-level and full-document Markdown. We configure a dense retriever that embeds user queries and searches the LanceDB vector index for the most relevant document chunks. We test both individual and batched queries while displaying page numbers, similarity scores, and retrieved text previews.

We create a vision-language reranking pipeline that retrieves a wider candidate set and reorders the results according to semantic relevance. We also apply a text-based filter to narrow retrieval results to chunks containing specific content from the document. We directly inspect the LanceDB table to verify the number of stored records and examine the indexed text.

We combine the retrieved document chunks with a hosted Nemotron language model to generate answers grounded only in the supplied context. We include numbered source references and page metadata so the generated responses remain traceable to the original document. We conclude by calculating recall at k for a small set of expected answers and report the final vector database and Markdown artifacts.

In conclusion, we created a complete multimodal RAG system that transforms structured and unstructured PDF content into searchable, citation-ready knowledge. We used NeMo Retriever to coordinate extraction, deduplication, chunking, embedding, vector database indexing, retrieval, and reranking while keeping the Colab runtime lightweight by delegating model inference to hosted NVIDIA NIM services. We also generated grounded answers with a Nemotron language model and measured retrieval effectiveness with a simple recall-at-k test. By completing this workflow, we established a reusable foundation for building document intelligence applications that process text, tables, charts, and visual elements through a unified retrieval pipeline.