The retail shopping advisor AI workflow demonstrates how to use NVIDIA NIM inference microservices to develop a generative AI-powered shopping advisor that provides interactive customer experiences using retrieval-augmented generation (RAG).
The NVIDIA retail shopping advisor AI workflow example provides an advanced starting point for generating conversational, accurate and precise responses tailored to consumer shopping.
Leverage the NVIDIA NIMs–including Llama 3 70b and NeMo Retriever embedding models–and our end-to-end sample RAG pipeline to integrate with LangChain and a GPU-accelerated Milvus vector database. Use the provided sample Jupyter notebook to adapt this example for your product catalog data and customer needs. Deploy with ease via Docker compose on a single GPU with optimized LLM inference performance and scaling.
Key benefits of the retail shopping advisor AI workflow include:
Deliver contextually accurate responses to naturally formed questions based on your company’s product catalog.
Simplify the orchestration of scaling RAG pods on Kubernetes in production.
Deploy on your preferred on-premises or cloud platform.
To get started, review the documentation linked below and learn what is included in the retail shopping advisor workflow, and how to run the workflow.
Request a free 90-day evaluation license for NVIDIA AI Enterprise and try the retail shopping advisor AI workflow.
Learn more about how to use NVIDIA NIM microservices for RAG through our Deep Learning Institute. Access the course here.
Contact NVIDIA to learn more about how you can purchase NVIDIA AI Enterprise for your production deployment.
By downloading or using NVIDIA NIM inference microservices included in the retail shopping advisor workflow you agree to the terms of the NVIDIA Software License Agreement and Product-specific Terms for AI products.
By downloading or using the product catalog in the retail shopping advisor workflow you agree to the terms of the NVIDIA Asset License.
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