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Knowledge Bases for Amazon Bedrock now supports hybrid search

AWS Machine Learning - AI

At AWS re:Invent 2023, we announced the general availability of Knowledge Bases for Amazon Bedrock. With a knowledge base, you can securely connect foundation models (FMs) in Amazon Bedrock to your company data for fully managed Retrieval Augmented Generation (RAG).

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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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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. Knowledge Bases for Amazon Bedrock provides fully managed RAG to supply the agent with access to your data.

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Implementing Knowledge Bases for Amazon Bedrock in support of GDPR (right to be forgotten) requests

AWS Machine Learning - AI

However, if you want to use an FM to answer questions about your private data that you have stored in your Amazon Simple Storage Service (Amazon S3) bucket, you need to use a technique known as Retrieval Augmented Generation (RAG) to provide relevant answers for your customers. The following diagram depicts a high-level RAG architecture.

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

Cloudera

This AMP is built on the foundation of one of our previous AMP s, with the additional enhancement of enabling customers to create a knowledge base from data on their own website using Cloudera DataFlow (CDF) and then augment questions to the chatbot from that same knowledge base in Pinecone.

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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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Vitech uses Amazon Bedrock to revolutionize information access with AI-powered chatbot

AWS Machine Learning - AI

Retrieval Augmented Generation vs. fine tuning Traditional LLMs don’t have an understanding of Vitech’s processes and flow, making it imperative to augment the power of LLMs with Vitech’s knowledge base. These documents are uploaded and stored in Amazon Simple Storage Service (Amazon S3), making it the centralized data store.