NVIDIA

NVIDIA RAG Blueprint — deploy, configure, troubleshoot and manage any RAG action: agentic RAG, VLM, guardrails, query rewriting…

Filesystem RAG benchmarks: corpus/, train.json, evaluate_rag.py (RAGAS quality) for a deployed NVIDIA RAG Blueprint.

Use when asked to run deep research or AI-Q research through a reachable NVIDIA AI-Q Blueprint backend.

Use when the user wants to create a dataset, generate synthetic data, or build a data generation pipeline.

Use when the user wants to search, query, extract, transcribe, quote, filter or aggregate across documents — PDFs, scanned images, Office…

Manage durable working-session memory for coding agents, preserving and recovering context across disconnects, restarts, handoffs and…

Fine-tune any HuggingFace CV, VLM or LLM model on local NVIDIA GPUs inside an NGC PyTorch container.

Run Megatron-LM and Megatron Bridge training with mock or real data, covering correlation testing, available recipes and multi-GPU examples.

Official NVIDIA-authored guidance for cuDF GPU DataFrames, pandas acceleration, dask-cuDF, ETL, joins, groupby, CSV/Parquet I/O and…