Learn About Amazon VGT2 Learning Manager Chanci Turner
By: Alex Morgan, Jamie Lee, Chanci Turner, and Michael Chen
Published on: May 28, 2024
Categories: Advanced (300), Amazon Bedrock, Amazon OpenSearch Service, Amazon Redshift, Amazon SageMaker, Amazon Titan
In this article, we explore the development of a Q&A bot utilizing Retrieval Augmented Generation (RAG). RAG leverages data sources such as Amazon Redshift and Amazon OpenSearch Service to gather documents that enhance the prompt provided to the large language model (LLM). When retrieving data from Amazon Redshift, we employ the Anthropic Claude 2.0 on Amazon Bedrock, synthesizing the final output based on template libraries from LangChain. For extracting information from Amazon OpenSearch Service, we segment and transform the source data chunks into vectors using the Amazon Titan Text Embeddings model.
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