blog_posts: 388548e968
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id | createdDate | title | link | postExcerpt | featuredImageUrl | hash | contributors | modifiedDate | displayDate |
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blog-posts#33-61483 | 2024-05-28 16:56:35 | Build a decentralized semantic search engine on heterogeneous data stores using autonomous agents | https://aws.amazon.com/blogs/big-data/build-a-decentralized-semantic-search-engine-on-heterogeneous-data-stores-using-autonomous-agents/ | In this post, we show how to build a Q&A bot with RAG (Retrieval Augmented Generation). RAG uses data sources like Amazon Redshift and Amazon OpenSearch Service to retrieve documents that augment the LLM prompt. For getting data from Amazon Redshift, we use the Anthropic Claude 2.0 on Amazon Bedrock, summarizing the final response based on pre-defined prompt template libraries from LangChain. To get data from Amazon OpenSearch Service, we chunk, and convert the source data chunks to vectors using Amazon Titan Text Embeddings model. | https://d2908q01vomqb2.cloudfront.net/b6692ea5df920cad691c20319a6fffd7a4a766b8/2024/03/25/BDB-3835_001new-300x160.png | 388548e968 | Dhaval Shah, Jon Handler, Hrishikesh Karambelkar, Jianwei Li, Soujanya Konka | 2024-05-29 14:44:48 | 28 May 2024 |
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