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Introducing guardrails in Knowledge Bases for Amazon Bedrock

AWS Machine Learning

Knowledge Bases for Amazon Bedrock is a fully managed capability that helps you securely connect foundation models (FMs) in Amazon Bedrock to your company data using Retrieval Augmented Generation (RAG). In the following sections, we demonstrate how to create a knowledge base with guardrails.

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Knowledge Bases for Amazon Bedrock now supports hybrid search

AWS Machine Learning

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

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

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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The SaaS Guide to Customer Engagement, Retention, and Advocacy

After onboarding, most SaaS customers have to find their own way to success—with little more than a few CSM calls (if they’re a large enough account to have one), and a knowledge base to get them there.

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Implement web crawling in Knowledge Bases for Amazon Bedrock

AWS Machine Learning

Knowledge Bases for Amazon Bedrock enables you to aggregate data sources into a repository of information. With knowledge bases, you can effortlessly build an application that takes advantage of RAG. By integrating web crawlers into the knowledge base, you can gather and utilize this web data efficiently.

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Metadata filtering for tabular data with Knowledge Bases for Amazon Bedrock

AWS Machine Learning

Knowledge Bases for Amazon Bedrock is a fully managed capability that helps you implement the entire RAG workflow, from ingestion to retrieval and prompt augmentation. However, the metadata fields need to be configured during the knowledge base ingestion process. The following screenshot shows an example of the dataset.

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Leadership Panel: Lessons Learnt from a Global Support Community

Speaker: Panel hosted by Adrian Speyer, Head of Community, Vanilla Forums

Join us to learn: How to integrate your knowledge base (and KCS) with your community. Vanilla’s Head of Community, Adrian Speyer leads the panel to uncover and discuss their common initiatives and their individual journeys to success. How to establish a successful ambassador program.

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6 Killer Applications for Artificial Intelligence in the Customer Engagement Contact Center

If Artificial Intelligence for businesses is a red-hot topic in C-suites, AI for customer engagement and contact center customer service is white hot. This white paper covers specific areas in this domain that offer potential for transformational ROI, and a fast, zero-risk way to innovate with AI.