NVIDIA-AI-Blueprints/rag

This NVIDIA RAG blueprint serves as a reference solution for a foundational Retrieval Augmented Generation (RAG) pipeline.

What it solves

This project provides a reference architecture for building enterprise‑grade Retrieval‑Augmented Generation (RAG) pipelines. It addresses the need for AI responses that are grounded in trusted, real‑time enterprise data to reduce hallucinations, ensure accuracy, and maintain compliance and freshness of information.

How it works

The system uses a modular orchestration layer (based on LangChain) to coordinate several GPU‑accelerated components:

  • Data Ingestion: Extracts multimodal content (text, tables, charts, infographics, and audio) from documents using specialized NIM microservices.
  • Retrieval: Converts queries into embeddings and searches a GPU‑accelerated vector database (such as Elasticsearch or Milvus) using hybrid search (dense and sparse).
  • Refinement: Employs reranking models to improve the precision of retrieved passages.
  • Generation: Uses LLMs (like Nemotron) to generate grounded responses, with optional reflection steps and programmable guardrails for safety.
  • Agentic RAG: For complex queries, it implements a LangGraph plan‑and‑execute pipeline that handles multi‑hop and cross‑document searches through scope discovery and parallel task execution.

Who it’s for

Developers and enterprises looking to deploy scalable, low‑latency RAG systems that can handle multimodal data and complex, multi‑step reasoning queries.

Highlights

  • Multimodal Support: Capable of extracting and processing data from tables, charts, and audio within documents.
  • Agentic Capabilities: Includes a plan‑and‑execute pipeline for ambiguous or multi‑hop queries.
  • GPU Acceleration: Leverages NVIDIA NIM microservices and cuVS for high‑performance indexing and search.
  • Enterprise Ready: Includes a reference UI, OpenAI‑compatible APIs, and support for deployment via Docker or Kubernetes/OpenShift.
  • Evaluation Tools: Integrated RAGAS framework scripts for quality benchmarking.