Build RAG applications using Jina Embeddings v2 on Amazon SageMaker JumpStart

This article discusses how to build retrieval augmented generation (RAG) applications using Jina Embeddings v2 on Amazon SageMaker JumpStart. RAG is an approach to optimize the output of large language models by referencing an authoritative knowledge base before generating a response.

Specifically, the article covers:

• What is RAG and its benefits
• Advantages of using Jina Embeddings v2 for RAG applications
• Overview of Amazon SageMaker JumpStart
• Steps to deploy Jina Embeddings v2 model on SageMaker JumpStart
• Preparing a dataset and indexing text embeddings
• Prompting a generative LLM endpoint and querying it using the indexed context
• Cleaning up resources after use
• Conclusion highlighting the benefits of this approach