This project applies the core knowledge from the LLMOps module, including the design and implementation of the API Layer, Inference Layer, Observability Layer, Cache Layer, Guardrails Layer, Routing Layer, and the Data Ingestion Pipeline.

LLM tutorial materials include but not limited to NVIDIA NeMo, TensorRT-LLM, Triton Inference Server, and NeMo Guardrails.

💂🏼 Build your Documentation AI with Nemo Guardrails

Responsible AI Masterclass (June 2024 Run)

RAG with Redis. Parallel implementations with/without guardrails.

A simple Retrieval-Augmented Generation (RAG) pipeline for answering questions based on website content. This project combines retrieval of relevant website information with generative models to deliver contextually accurate answers.

An end-to-end AI pipeline that scrapes LinkedIn profile and post data to predict 16-personality (MBTI) types using a RAG-enhanced LLM fine-tuned with LoRA. It automates data collection, preprocessing, storage in PostgreSQL, and personality inference from real-world behavioral and linguistic patterns.

This bootcamp is designed to give NLP researchers an end-to-end overview on the fundamentals of NVIDIA NeMo framework, complete solution for building large language models. It will also have hands-on exercises complimented by tutorials, code snippets, and presentations to help researchers kick-start with NeMo LLM Service and Guardrails.

GitDoc is your ultimate GitHub Documentation Explorer! It's your trusty sidekick for navigating through the vast world of open-source projects, making code exploration and documentation retrieval a breeze. 🚀

Real-time intent preservation layer for LLM agents. Stops hallucination drift before it executes.

Dive into the world of LLM Guardrails using tools like NVIDIA’s NeMo Guardrails. Discover the mechanisms that ensure applications produce reliable, robust, safe, and ethical outputs, and understand their crucial role in LLMs

Generative AI RAG Chatbot for Electricity and Gas Company

AI Application Monitoring and Evaluation Service

Short tutorial on using NVIDIA NeMo Guardrails

Hands-on notebooks for building LLM guardrails using LangChain Middlewares and NVIDIA NeMo Guardrails — covering PII detection, jailbreak protection, topic control, hallucination detection, and human-in-the-loop.

First public benchmark: NVIDIA NIM vs Ollama on RTX 5090 — 7.3x throughput, 13x TTFT, NeMo Guardrails at +2.1% overhead

Hands-on examples for NVIDIA NeMo Guardrails. Learn to add safety guardrails to LLM applications through executable Jupyter notebooks paired with a technical article.

Agentic RAG chat app built on the NVIDIA AI stack — NIM, NeMo Guardrails (Colang 2.x), NeMo Agent Toolkit, Chroma, Next.js. A hands-on reference implementation with a phase-by-phase guide.

Workshop guide for building an agentic RAG chat app on the NVIDIA AI stack — phase-by-phase instructions, solution files, and checkpoints. Companion to nvidia-stack-tutor.

NVIDIA NIM based RAG application deployed locally(LLM ,Embedding model and reranking model)which is optimized to use GPU cluster