Nvidia Developer 02月16日
New NVIDIA AI Blueprint: Build a Customizable RAG Pipeline
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NVIDIA AI Blueprint for RAG为开发者提供了一个基础的起点,用于构建可扩展和可定制的检索管道,从而提供高精度和高吞吐量。通过使用该蓝图,开发者可以创建一个RAG应用程序,该程序能够提供上下文感知的响应,将LLM连接到大型企业数据语料库,从而实现基于相关数据的可行性见解。该蓝图可以按原样使用,也可以与其他NVIDIA蓝图结合使用,以解决更高级的用例,包括数字人和用于客户服务的AI助手。该架构旨在增强决策能力和生产力。

🔑 NVIDIA AI Blueprint for RAG旨在为开发者提供一个构建可扩展和可定制的检索管道的基础,从而提供高精度和高吞吐量,其默认使用API端点,无需GPU即可轻松体验。

🗣️ 该蓝图支持多轮对话、多集合、多会话,以及多语言和跨语言检索,能够提供上下文感知的响应,将LLM连接到大型企业数据语料库,从而实现基于相关数据的可行性见解。

⚙️ 该蓝图提供了NIM微服务的可配置选项和NIM端点,并优化了数据存储,可以与其他的NVIDIA蓝图结合使用,以解决更高级的用例,包括数字人和用于客户服务的AI助手。

💻 自托管蓝图的推荐系统要求是8XH100-80GB或8XA100-80GB,配备Llama 3.1 70b NIM、NeMo Retriever嵌入和重新排序NIM,以及由NVIDIA cuVS加速的Milvus数据库。

The NVIDIA AI Blueprint for RAG provides developers with a foundational starting point for building scalable and customizable retrieval pipelines that deliver high-accuracy and throughput. Use the blueprint to create a RAG application that delivers context-aware responses, connecting LLMs to large corpora of enterprise data, to enable actionable insights grounded in relevant data. The blueprint can be used as is, or combined with other NVIDIA Blueprints to address more advanced use cases including digital humans and AI assistants for customer service. Get started with this reference architecture to enhance decision-making and productivity.Architecture DiagramKey FeaturesOpenAI-compatible APIsMulti-turn conversationsMulti-collectionMulti-session supportMultilingual and cross-lingual retrievalOptimized data storageConfigurability options for NIM selection and NIM endpointsReranking usageMinimum System RequirementsHardware RequirementsThe blueprint by default uses API endpoints, making it very easy to experience without needing GPUs.It is expected that the NIM microservices will need to be self-hosted as you progress in your RAG development. For self-hosting the blueprint with these microservices locally deployed, the recommended system requirement is 8XH100-80GB or 8XA100-80GB with the Llama 3.1 70b NIM, the NeMo Retriever embedding and reranking NIM, and the Milvus database accelerated with NVIDIA cuVS.OS RequirementsDeployment OptionsSoftware used in this blueprintNIM microservicesNVIDIA Technology3rd Party SoftwareEthical ConsiderationsNVIDIA 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.LicenseUse of the models in this blueprint is governed by the NVIDIA AI Foundation Models Community License.Terms of UseGOVERNING TERMS: The software and materials are governed by the NVIDIA Software License Agreement and the Product-Specific Terms for NVIDIA AI Products, except that models are governed by the AI Foundation Models Community License Agreement and the NVIDIA RAG dataset is governed by the NVIDIA Asset License Agreement. ADDITIONAL INFORMATION: for Meta/llama-3.1-70b-instruct model the Llama 3.1 Community License Agreement, for nvidia/llama-3.2-nv-embedqa-1b-v2model the Llama 3.2 Community License Agreement, and for the nvidia/llama-3.2-nv-rerankqa-1b-v2 model the Llama 3.2 Community License Agreement. Built with Llama.

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NVIDIA AI蓝图 RAG LLM NIM微服务
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