Amazon Bedrock’s KnowledgeBases is truly a serverless way to build a RAG pipeline rapidly, which allows the developer to connect almost all types of enterprise data sources including Jira or Confluence pages. This capability simplifies the process for developers looking to integrate document storage, chunking, retrieval, and analysis into their generative AI applications without spending much time writing code for document ingestion or deciding the chunking strategies, etc. 

For instance, if a developer has a large set of customer support documents stored in Amazon S3, they can designate this storage location as the source for Bedrock. From there, Bedrock automatically manages the entire ingestion and retrieval workflow: it fetches documents from S3, splits them into manageable chunks, creates vector embeddings, and stores these in a chosen vector database. This architecture orchestrates the efficient retrieval of relevant information when a user query is submitted and the whole process is serverless.

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