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Use RAG for drug discovery with Knowledge Bases for Amazon Bedrock

AWS Machine Learning - AI

Knowledge Bases for Amazon Bedrock allows you to build performant and customized Retrieval Augmented Generation (RAG) applications on top of AWS and third-party vector stores using both AWS and third-party models. RAG is a popular technique that combines the use of private data with large language models (LLMs).

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Build a contextual chatbot application using Knowledge Bases for Amazon Bedrock

AWS Machine Learning - AI

One way to enable more contextual conversations is by linking the chatbot to internal knowledge bases and information systems. Integrating proprietary enterprise data from internal knowledge bases enables chatbots to contextualize their responses to each user’s individual needs and interests.

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Automate the insurance claim lifecycle using Agents and Knowledge Bases for Amazon Bedrock

AWS Machine Learning - AI

You can now use Agents for Amazon Bedrock and Knowledge Bases for Amazon Bedrock to configure specialized agents that seamlessly run actions based on natural language input and your organization’s data. The following diagram illustrates the solution architecture. The following are some example prompts: Create a new claim.

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Building scalable, secure, and reliable RAG applications using Knowledge Bases for Amazon Bedrock

AWS Machine Learning - AI

However, to unlock the long-term success and viability of these AI-powered solutions, it is crucial to align them with well-established architectural principles. The AWS Well-Architected Framework provides best practices and guidelines for designing and operating reliable, secure, efficient, and cost-effective systems in the cloud.

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Build knowledge-powered conversational applications using LlamaIndex and Llama 2-Chat

AWS Machine Learning - AI

RAG allows models to tap into vast knowledge bases and deliver human-like dialogue for applications like chatbots and enterprise search assistants. Solution overview In this post, we demonstrate how to create a RAG-based application using LlamaIndex and an LLM. Download press releases to use as our external knowledge base.

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The Five Pillars of AWS Well-Architected Framework?—?Understanding What Makes the Cloud Secure

Cloud Conformity

The Five Pillars of AWS Well-Architected Framework?—?Understanding Understanding What Makes the Cloud Secure The 5 pillars to a well-architected house At Cloud Conformity, we often harp on about the AWS Well-Architected Framework and for very good reason. Why should I use the architected framework at all?

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Build a contextual chatbot for financial services using Amazon SageMaker JumpStart, Llama 2 and Amazon OpenSearch Serverless with Vector Engine

AWS Machine Learning - AI

The LLM generated text, and the IR system retrieves relevant information from a knowledge base. In this post, we explore building a contextual chatbot for financial services organizations using a RAG architecture with the Llama 2 foundation model and the Hugging Face GPTJ-6B-FP16 embeddings model, both available in SageMaker JumpStart.