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https://github.com/NVIDIA/GenerativeAIExamples

Generative AI reference workflows optimized for accelerated infrastructure and microservice architecture.
https://github.com/NVIDIA/GenerativeAIExamples

gpu-acceleration large-language-models llm llm-inference microservice nemo rag retrieval-augmented-generation tensorrt triton-inference-server

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Generative AI reference workflows optimized for accelerated infrastructure and microservice architecture.

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README 

        



![](docs/images/[email protected])



# NVIDIA Generative AI Examples



This repository is a starting point for developers looking to integrate with the NVIDIA software ecosystem to speed up their generative AI systems. Whether you are building RAG pipelines, agentic workflows, or fine-tuning models, this repository will help you integrate NVIDIA, seamlessly and natively, with your development stack.



## Table of Contents



* [What's New?](#whats-new)

  * [Knowledge Graph RAG](#knowledge-graph-rag)

  * [Agentic Workflows with Llama 3.1](#agentic-workflows-with-llama-31)

  * [RAG with Local NIM Deployment and LangChain](#rag-with-local-nim-deployment-and-langchain)

* [Try it Now!](#try-it-now)

* [RAG](#rag)

  * [RAG Notebooks](#rag-notebooks)

  * [RAG Examples](#rag-examples)

  * [RAG Tools](#rag-tools)

  * [RAG Projects](#rag-projects)

* [Documentation](#documentation)

  * [Getting Started](#getting-started)

  * [How To's](#how-tos)

  * [Reference](#reference)

* [Community](#community)



## What's New?



### Knowledge Graph RAG



This example implements a GPU-accelerated pipeline for creating and querying knowledge graphs using RAG by leveraging NIM microservices and the RAPIDS ecosystem to process large-scale datasets efficiently.



- [Knowledge Graphs for RAG with NVIDIA AI Foundation Models and Endpoints](community/knowledge_graph_rag)



### Agentic Workflows with Llama 3.1



- Build an Agentic RAG Pipeline with Llama 3.1 and NVIDIA NeMo Retriever NIM microservices [[Blog](https://developer.nvidia.com/blog/build-an-agentic-rag-pipeline-with-llama-3-1-and-nvidia-nemo-retriever-nims/), [Notebook](RAG/notebooks/langchain/agentic_rag_with_nemo_retriever_nim.ipynb)]

- [NVIDIA Morpheus, NIM microservices, and RAG pipelines integrated to create LLM-based agent pipelines](https://github.com/NVIDIA/GenerativeAIExamples/blob/v0.7.0/experimental/event-driven-rag-cve-analysis)



### RAG with Local NIM Deployment and LangChain



- Tips for Building a RAG Pipeline with NVIDIA AI LangChain AI Endpoints by Amit Bleiweiss. [[Blog](https://developer.nvidia.com/blog/tips-for-building-a-rag-pipeline-with-nvidia-ai-langchain-ai-endpoints/), [Notebook](https://github.com/NVIDIA/GenerativeAIExamples/blob/v0.7.0/notebooks/08_RAG_Langchain_with_Local_NIM.ipynb)]



For more information, refer to the [Generative AI Example releases](https://github.com/NVIDIA/GenerativeAIExamples/releases/).



## Try it Now!



Experience NVIDIA RAG Pipelines with just a few steps!



1. Get your NVIDIA API key.

   1. Go to the [NVIDIA API Catalog](https://build.ngc.nvidia.com/explore/).

   1. Select any model.

   1. Click **Get API Key**.

   1. Run:

      ```console

      export NVIDIA_API_KEY=nvapi-...

      ``` 



1. Clone the repository.



   ```console

   git clone https://github.com/nvidia/GenerativeAIExamples.git

   ```



1. Build and run the basic RAG pipeline.



   ```console

   cd GenerativeAIExamples/RAG/examples/basic_rag/langchain/

   docker compose up -d --build

   ```



1. Go to  and submit queries to the sample RAG Playground.



1. Stop containers when done. 

  

   ```console

   docker compose down

   ``` 

      



## RAG



### RAG Notebooks



NVIDIA has first-class support for popular generative AI developer frameworks like [LangChain](https://python.langchain.com/v0.2/docs/integrations/chat/nvidia_ai_endpoints/), [LlamaIndex](https://docs.llamaindex.ai/en/stable/examples/llm/nvidia/), and [Haystack](https://haystack.deepset.ai/integrations/nvidia). These end-to-end notebooks show how to integrate NIM microservices using your preferred generative AI development framework.



Use these [notebooks](./RAG/notebooks/README.md) to learn about the LangChain and LlamaIndex connectors.



#### LangChain Notebooks



- RAG

  - [Basic RAG with CHATNVIDIA LangChain Integration](./RAG/notebooks/langchain/langchain_basic_RAG.ipynb)

  - [RAG using local NIM microservices for LLMs and Retrieval](./RAG/notebooks/langchain/RAG_Langchain_with_Local_NIM.ipynb)

  - [RAG for HTML Documents](./RAG/notebooks/langchain/RAG_for_HTML_docs_with_Langchain_NVIDIA_AI_Endpoints.ipynb)

  - [Chat with NVIDIA Financial Reports](./RAG/notebooks/langchain/Chat_with_nvidia_financial_reports.ipynb)

- Agents

  - [NIM Tool Calling 101](https://github.com/langchain-ai/langchain-nvidia/blob/main/cookbook/nvidia_nim_agents_llama3.1.ipynb)

  - [Agentic RAG with NeMo Retriever](./RAG/notebooks/langchain/agentic_rag_with_nemo_retriever_nim.ipynb)

  - [Agents with Human in the Loop](./RAG/notebooks/langchain/LangGraph_HandlingAgent_IntermediateSteps.ipynb)



#### LlamaIndex Notebooks



- [Basic RAG with LlamaIndex Integration](./RAG/notebooks/llamaindex/llamaindex_basic_RAG.ipynb)



### RAG Examples



By default, these end-to-end [examples](RAG/examples/README.md) use preview NIM endpoints on [NVIDIA API Catalog](https://catalog.ngc.nvidia.com). Alternatively, you can run any of the examples [on premises](./RAG/examples/local_deploy/).



#### Basic RAG Examples



  - [LangChain example](./RAG/examples/basic_rag/langchain/README.md)

  - [LlamaIndex example](./RAG/examples/basic_rag/llamaindex/README.md)



#### Advanced RAG Examples



  - [Multi-Turn](./RAG/examples/advanced_rag/multi_turn_rag/README.md)

  - [Multimodal Data](./RAG/examples/advanced_rag/multimodal_rag/README.md)

  - [Structured Data](./RAG/examples/advanced_rag/structured_data_rag/README.md) (CSV)

  - [Query Decomposition](./RAG/examples/advanced_rag/query_decomposition_rag/README.md)



### RAG Tools



Example tools and tutorials to enhance LLM development and productivity when using NVIDIA RAG pipelines.



- [Evaluation](./RAG/tools/evaluation/README.md)

- [Observability](./RAG/tools/observability/README.md)



### RAG Projects



- [NVIDIA Tokkio LLM-RAG](https://docs.nvidia.com/ace/latest/workflows/tokkio/text/Tokkio_LLM_RAG_Bot.html): Use Tokkio to add avatar animation for RAG responses.

- [Hybrid RAG Project on AI Workbench](https://github.com/NVIDIA/workbench-example-hybrid-rag): Run an NVIDIA AI Workbench example project for RAG.



## Documentation



### Getting Started



- [Prerequisites](./docs/common-prerequisites.md)



### How To's



- [Changing the Inference or Embedded Model](./docs/change-model.md)

- [Customizing the Vector Database](./docs/vector-database.md)

- [Customizing the Chain Server](./docs/chain-server.md):

  - [Chunking Strategy](./docs/text-splitter.md)

  - [Prompting Template Engineering](./docs/prompt-customization.md)

- [Configuring LLM Parameters at Runtime](./docs/llm-params.md)

- [Supporting Multi-Turn Conversations](./docs/multiturn.md)

- [Speaking Queries and Listening to Responses with NVIDIA Riva](./docs/riva-asr-tts.md)



### Reference



- [Support Matrix](./docs/support-matrix.md)

- [Architecture](./docs/architecture.md)

- [Using the Sample Chat Web Application](./docs/using-sample-web-application.md)

- [RAG Playground Web Application](./docs/frontend.md)

- [Software Component Configuration](./docs/configuration.md)



## Community

We're posting these examples on GitHub to support the NVIDIA LLM community and facilitate feedback.

We invite contributions! Open a GitHub issue or pull request! See [contributing](docs/contributing.md) Check out the [community](./community/README.md) examples and notebooks.