AWS Machine Learning - AI

FEBRUARY 29, 2024

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.

Knowledge Base 106

Knowledge Base Artificial Inteligence Generative AI AWS 106

AWS Machine Learning - AI

FEBRUARY 19, 2024

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.

Knowledge Base 92

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AWS Machine Learning - AI

FEBRUARY 8, 2024

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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AWS Machine Learning - AI

APRIL 23, 2024

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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Knowledge Base Scalability Applications Generative AI 98

AWS Machine Learning - AI

APRIL 8, 2024

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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AWS Machine Learning - AI

NOVEMBER 22, 2023

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.

Artificial Inteligence 87

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AWS Machine Learning - AI

FEBRUARY 28, 2024

Solution overview There are three critical components in our architecture: Retrieval Augmented Generation (RAG) with database metadata, a multi-step self-correction loop, and Athena as our SQL engine. However, you can also use knowledge bases in Amazon Bedrock to build RAG solutions quickly.

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