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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. If you want more control, Knowledge Bases lets you control the chunking strategy through a set of preconfigured options.

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Harness the Power of Pinecone with Cloudera’s New Applied Machine Learning Prototype

Cloudera

The AMP demonstrates how organizations can create a dynamic knowledge base from website data, enhancing the chatbot’s ability to deliver context-rich, accurate responses. An overview of the RAG architecture with a vector database used to minimize hallucinations in the chatbot application.

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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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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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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. This post explores the new enterprise-grade features for Knowledge Bases on Amazon Bedrock and how they align with the AWS Well-Architected Framework.

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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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Simplify data prep for generative AI with Amazon SageMaker Data Wrangler

AWS Machine Learning - AI

Read and write access to an Amazon Simple Storage Service (Amazon S3) bucket. The choice of vector database is an important architectural decision. Preparing a prompt After we create a knowledge base out of our PDF, we can test it by searching the knowledge base for a few sample queries.