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  • Displaying 7 results
    NVIDIA
    NVIDIA
    AI-Q Research Assistant Backend
    The NVIDIA AI-Q Research Assistant Blueprint gives developers a foundational starting point for building a deep research assistant that can run on-premise. The backend container provides the RESTful API service.
    Container
    NVIDIA
    NVIDIA
    RAG Playground - NVIDIA AI Blueprint for RAG
    This blueprint container is a reference solution for the Retrieval Augmented Generation (RAG) pipeline. Use this sample user interface container for a quick start with the blueprint.
      Container
      NVIDIA
      NVIDIA
      NVIDIA RAG Blueprint
      An end to end Helm chart for the NVIDIA RAG Blueprint. See supported formats in the code and docs at https://github.com/NVIDIA-AI-Blueprints/rag/tree/main.
        Helm Chart
        NVIDIA
        NVIDIA
        AI-Q Research Assistant Frontend
        The NVIDIA AI-Q Research Assistant Blueprint gives developers a foundational starting point for building a deep research assistant that can run on-premise. The frontend container provides a demo web UI.
        Container
        NVIDIA
        NVIDIA
        AI-Q Research Assistant File Loader
        The NVIDIA AI-Q Research Assistant Blueprint gives developers a foundational starting point for building a deep research assistant that can run on-premise. This container includes a utility to create two default datasets used by the demo web UI.
        Container
        Collection
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        AI-Q Research Assistant
        The NVIDIA AI-Q Research Assistant Blueprint gives developers a foundational starting point for building a deep research assistant that can run on-premise, allowing anyone to create detailed research reports using on-premise data.
        31
        Collection
        NVIDIA
        NVIDIA
        Blueprint - Build an enterprise rag pipeline
        The NVIDIA AI Blueprint for RAG gives developers a foundational starting point for building scalable, customizable retrieval pipelines that deliver both high accuracy and throughput with multi-modal data.
          31

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