Overview

This is the Helm chart used to deploy end to end pipeline for the NVIDIA AI Blueprint for RAG.

The NVIDIA RAG Blueprint is a reference solution and foundational starting point\nfor building Retrieval-Augmented Generation (RAG) pipelines with NVIDIA NIM microservices.\nIt enables enterprises to deliver natural language question answering grounded in their own data,\nwhile meeting governance, latency, and scalability requirements.\nDesigned to be decomposable and configurable, the blueprint integrates GPU-accelerated components with NeMo Retriever models, Multimodal and Vision Language Models, and guardrailing services,\nto provide an enterprise-ready framework.\nWith a pre-built reference UI, open-source code, and multiple deployment options — including local docker (with and without NVIDIA Hosted endpoints) and Kubernetes —\nit serves as a flexible starting point that developers can adapt and extend to their specific needs.

Source Code

Documentation and source code regarding how to get started can be found here

Additional Resources

Learn more about how to use NVIDIA NIM microservices for RAG through our Deep Learning Institute. Access the course here.

Ethical Considerations

NVIDIA believes Trustworthy AI is a shared responsibility, and we have established policies and practices to enable development for a wide array of AI applications. When downloaded or used in accordance with our terms of service, developers should work with their supporting model team to ensure the models meet requirements for the relevant industry and use case and address unforeseen product misuse. For more detailed information on ethical considerations for the models, please see the Model Card++ Explainability, Bias, Safety & Security, and Privacy Subcards. Please report security vulnerabilities or NVIDIA AI concerns here.

License

This NVIDIA AI BLUEPRINT is licensed under the Apache License, Version 2.0. This project will download and install additional third-party open source software projects and containers. Review the license terms of these open source projects before use.

Use of the models in this blueprint is governed by the NVIDIA AI Foundation Models Community License.

Terms of Use

This blueprint is governed by the NVIDIA Agreements | Enterprise Software | NVIDIA Software License Agreement and the NVIDIA Agreements | Enterprise Software | Product Specific Terms for AI Product. The models are governed by the NVIDIA Agreements | Enterprise Software | NVIDIA Community Model License and the NVIDIA RAG dataset which is governed by the NVIDIA Asset License Agreement.\nThe following models that are built with Llama are governed by the Llama 3.2 Community License Agreement: nvidia/llama-3.2-nv-embedqa-1b-v2 and nvidia/llama-3.2-nv-rerankqa-1b-v2 and llama-3.2-nemoretriever-1b-vlm-embed-v1.

Additional Information

The Llama 3.1 Community License Agreement for the llama-3.1-nemotron-nano-vl-8b-v1, llama-3.1-nemoguard-8b-content-safety and llama-3.1-nemoguard-8b-topic-control models. The Llama 3.2 Community License Agreement for the nvidia/llama-3.2-nv-embedqa-1b-v2, nvidia/llama-3.2-nv-rerankqa-1b-v2 and llama-3.2-nemoretriever-1b-vlm-embed-v1 models. The Llama 3.3 Community License Agreement for the llama-3.3-nemotron-super-49b-v1.5 models. Built with Llama. Apache 2.0 for NVIDIA Ingest and for the nemoretriever-page-elements-v2, nemoretriever-table-structure-v1, nemoretriever-graphic-elements-v1, paddleocr and nemoretriever-ocr-v1 models.

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See supported formats in the code and docs at https://github.com/NVIDIA-AI-Blueprints/rag/tree/main.","## Overview\nThis is the Helm chart used to deploy end to end pipeline for the NVIDIA AI Blueprint for RAG.\n\nThe NVIDIA RAG Blueprint is a reference solution and foundational starting point\nfor building Retrieval-Augmented Generation (RAG) pipelines with NVIDIA NIM microservices.\nIt enables enterprises to deliver natural language question answering grounded in their own data,\nwhile meeting governance, latency, and scalability requirements.\nDesigned to be decomposable and configurable, the blueprint integrates GPU-accelerated components with NeMo Retriever models, Multimodal and Vision Language Models, and guardrailing services,\nto provide an enterprise-ready framework.\nWith a pre-built reference UI, open-source code, and multiple deployment options — including local docker (with and without NVIDIA Hosted endpoints) and Kubernetes —\nit serves as a flexible starting point that developers can adapt and extend to their specific needs.\n\n\n## Source Code\nDocumentation and source code regarding how to get started can be found [here](https://github.com/NVIDIA-AI-Blueprints/rag)\n\n## Additional Resources\nLearn more about how to use NVIDIA NIM microservices for RAG through our Deep Learning Institute. Access the course [here](https://learn.nvidia.com/courses/course-detail?course_id=course-v1:DLI+S-FX-16+V1). \n\n## Ethical Considerations\nNVIDIA believes Trustworthy AI is a shared responsibility, and we have established policies and practices to enable development for a wide array of AI applications. When downloaded or used in accordance with our terms of service, developers should work with their supporting model team to ensure the models meet requirements for the relevant industry and use case and address unforeseen product misuse. For more detailed information on ethical considerations for the models, please see the Model Card++ Explainability, Bias, Safety & Security, and Privacy Subcards. Please report security vulnerabilities or NVIDIA AI concerns [here](https://www.nvidia.com/en-us/support/submit-security-vulnerability/).\n\n## License\n\nThis NVIDIA AI BLUEPRINT is licensed under the [Apache License, Version 2.0](https://github.com/NVIDIA-AI-Blueprints/rag/blob/main/LICENSE). This project will download and install additional third-party open source software projects and containers. Review [the license terms of these open source projects](https://github.com/NVIDIA-AI-Blueprints/rag/blob/main/LICENSE-3rd-party.txt) before use.\n\nUse of the models in this blueprint is governed by the [NVIDIA AI Foundation Models Community License](https://docs.nvidia.com/ai-foundation-models-community-license.pdf).\n\n\n## Terms of Use\nThis blueprint is governed by the [NVIDIA Agreements | Enterprise Software | NVIDIA Software License Agreement](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-software-license-agreement/) and the [NVIDIA Agreements | Enterprise Software | Product Specific Terms for AI Product](https://www.nvidia.com/en-us/agreements/enterprise-software/product-specific-terms-for-ai-products/). The models are governed by the [NVIDIA Agreements | Enterprise Software | NVIDIA Community Model License](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-community-models-license/) and the [NVIDIA RAG dataset](https://github.com/NVIDIA-AI-Blueprints/rag/blob/main/data) which is governed by the [NVIDIA Asset License Agreement](https://github.com/NVIDIA-AI-Blueprints/rag/blob/main/data/LICENSE.DATA).\nThe following models that are built with Llama are governed by the Llama 3.2 Community License Agreement: nvidia/llama-3.2-nv-embedqa-1b-v2 and nvidia/llama-3.2-nv-rerankqa-1b-v2 and llama-3.2-nemoretriever-1b-vlm-embed-v1.\n\n## Additional Information\n\nThe [Llama 3.1 Community License Agreement](https://www.llama.com/llama3_1/license/) for the llama-3.1-nemotron-nano-vl-8b-v1, llama-3.1-nemoguard-8b-content-safety and llama-3.1-nemoguard-8b-topic-control models. The [Llama 3.2 Community License Agreement](https://www.llama.com/llama3_2/license/) for the nvidia/llama-3.2-nv-embedqa-1b-v2, nvidia/llama-3.2-nv-rerankqa-1b-v2 and llama-3.2-nemoretriever-1b-vlm-embed-v1 models. The [Llama 3.3 Community License Agreement](https://github.com/meta-llama/llama-models/blob/main/models/llama3_3/LICENSE) for the llama-3.3-nemotron-super-49b-v1.5 models. Built with Llama. Apache 2.0 for NVIDIA Ingest and for the nemoretriever-page-elements-v2, nemoretriever-table-structure-v1, nemoretriever-graphic-elements-v1, paddleocr and nemoretriever-ocr-v1 models.","NVIDIA RAG Blueprint",["D",1780341960613],"/api/image/nvidia/blueprint/helm-charts/nvidia-blueprint-rag","",1406325,"bias","explainability","privacy","safetyAndSecurity",[],[297,298,299],[],[],[],{"_91":142,"_128":142,"_129":303,"_131":132},{"_91":140,"_128":140,"_129":302,"_131":132},{"_91":300,"_128":300,"_129":301,"_131":132},"NSPECT-O8B9-SHZ8",{"_133":132,"_128":300},{"_133":132,"_128":140},{"_133":132,"_128":142},{"_169":1474,"_171":1475},"ngcApps",[1427],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1408":1409,"_1410":1411,"_1412":1413,"_1414":1415,"_1416":315,"_201":339,"_1417":1418,"_1419":51,"_1420":149,"_1421":149,"_1422":1423,"_1424":149,"_1425":1426},"amplitude",{"_1405":1406,"_1011":-7,"_1407":26},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmNoaW5hTmltRGlzdHJpYnV0b3JzUmVkZXNpZ24lMjIlM0F0cnVlJTJDJTIyZW5hYmxlQ3NhV2lkZ2V0JTIyJTNBZmFsc2UlMkMlMjJlbmFibGVQcm9kdWN0R3JvdXBpbmdCeVN1YnNjcmlwdGlvbiUyMiUzQWZhbHNlJTJDJTIyY29udGFpbmVyU2Nhbm5pbmdSZWRlc2lnbk0xJTIyJTNBdHJ1ZSU3RA==","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1399":1400,"_1401":1402,"_1403":1404},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"locale","en","ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"1.7.3","defaultSearchSuggestions",{"_1286":286,"_1287":1288,"_1289":1290,"_1291":1292},"announcements",[],"consentState",{"_377":1276,"_1277":149,"_1278":26,"_1279":26,"_1280":1281},"consentConfig",{"_359":360,"_361":362,"_363":364,"_365":366,"_367":368,"_369":370,"_371":372,"_373":374,"_375":376,"_377":378},"showContactAdmin",1784162923400,[],[],[353,354,355,356,357,358],{"_40":55,"_42":65,"_44":63,"_46":-5,"_48":-5,"_50":51},{"_40":55,"_42":64,"_44":63,"_46":-5,"_48":-5,"_50":51},{"_40":55,"_42":62,"_44":63,"_46":-5,"_48":-5,"_50":51},{"_40":55,"_42":60,"_44":63,"_46":-5,"_48":-5,"_50":51},{"_40":52,"_42":53,"_44":63,"_46":-5,"_48":-5,"_50":51},{"_40":41,"_42":43,"_44":45,"_46":47,"_48":49,"_50":51},"mainInfoText","We and our third-party partners (including social media, advertising, and analytics partners) use cookies and other tracking technologies to collect, store, monitor, and process certain information about you when you visit our website. The information collected might relate to you, your preferences, or your device. We use that information to make the site work, analyze performance and traffic on our website, provide a more personalized web experience, and assist in our marketing efforts.

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