AWS Machine Learning - AI

JULY 1, 2024

Generative artificial intelligence (AI)-powered chatbots play a crucial role in delivering human-like interactions by providing responses from a knowledge base without the involvement of live agents. Create new generative AI-powered intent in Amazon Lex using the built-in QnAIntent and point the knowledge base.

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AWS Machine Learning - AI

APRIL 23, 2024

Generative artificial intelligence (AI) has gained significant momentum with organizations actively exploring its potential applications. 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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AWS Machine Learning - AI

AUGUST 5, 2024

Finding relevant content usually requires searching through text-based metadata such as timestamps, which need to be manually added to these files. In this solution, audio files stored in mp3 format are first uploaded to Amazon Simple Storage Service (Amazon S3) storage. We begin by specifying a data source.

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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.

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

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AWS Machine Learning - AI

JULY 10, 2024

Knowledge Bases for Amazon Bedrock is a fully managed service that helps you implement the entire Retrieval Augmented Generation (RAG) workflow from ingestion to retrieval and prompt augmentation without having to build custom integrations to data sources and manage data flows, pushing the boundaries for what you can do in your RAG workflows.

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AWS Machine Learning - AI

OCTOBER 29, 2024

We have built a custom observability solution that Amazon Bedrock users can quickly implement using just a few key building blocks and existing logs using FMs, Amazon Bedrock Knowledge Bases , Amazon Bedrock Guardrails , and Amazon Bedrock Agents. Additionally, you can choose what gets logged.

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AWS Machine Learning - AI

APRIL 10, 2025

They offer fast inference, support agentic workflows with Amazon Bedrock Knowledge Bases and RAG, and allow fine-tuning for text and multi-modal data. To do so, we create a knowledge base. Complete the following steps: On the Amazon Bedrock console, choose Knowledge Bases in the navigation pane. Choose Next.

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AWS Machine Learning - AI

MARCH 4, 2025

Knowledge base integration Incorporates up-to-date WAFR documentation and cloud best practices using Amazon Bedrock Knowledge Bases , providing accurate and context-aware evaluations. The following diagram illustrates the solutions technical architecture. These documents form the foundation of the RAG architecture.

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AWS Machine Learning - AI

DECEMBER 11, 2024

When Amazon Q Business became generally available in April 2024, we quickly saw an opportunity to simplify our architecture, because the service was designed to meet the needs of our use caseto provide a conversational assistant that could tap into our vast (sales) domain-specific knowledge bases.

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AWS Machine Learning - AI

NOVEMBER 22, 2024

Instead of handling all items within a single execution, Step Functions launches a separate execution for each item in the array, letting you concurrently process large-scale data sources stored in Amazon Simple Storage Service (Amazon S3), such as a single JSON or CSV file containing large amounts of data, or even a large set of Amazon S3 objects.

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AWS Machine Learning - AI

SEPTEMBER 24, 2024

We will walk you through deploying and testing these major components of the solution: An AWS CloudFormation stack to set up an Amazon Bedrock knowledge base, where you store the content used by the solution to answer questions. This solution uses Amazon Bedrock LLMs to find answers to questions from your knowledge base.

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AWS Machine Learning - AI

NOVEMBER 15, 2024

As Principal grew, its internal support knowledge base considerably expanded. Principal wanted to use existing internal FAQs, documentation, and unstructured data and build an intelligent chatbot that could provide quick access to the right information for different roles. 2024, Principal Financial Services, Inc.

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AWS Machine Learning - AI

JANUARY 29, 2025

Accelerate your generative AI application development by integrating your supported custom models with native Bedrock tools and features like Knowledge Bases, Guardrails, and Agents. Sufficient local storage space, at least 17 GB for the 8B model or 135 GB for the 70B model. The following diagram illustrates the end-to-end flow.

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AWS Machine Learning - AI

NOVEMBER 14, 2024

Solution overview This solution uses the Amazon Bedrock Knowledge Bases chat with document feature to analyze and extract key details from your invoices, without needing a knowledge base. The storage layer uses Amazon Simple Storage Service (Amazon S3) to hold the invoices that business users upload.

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AWS Machine Learning - AI

NOVEMBER 15, 2024

With RAG, you can provide the context to the model and tell the model to only reply based on the provided context, which leads to fewer hallucinations. With Amazon Bedrock Knowledge Bases , you can implement the RAG workflow from ingestion to retrieval and prompt augmentation.

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AWS Machine Learning - AI

JUNE 26, 2024

Artificial intelligence (AI)-powered assistants can boost the productivity of a financial analysts, research analysts, and quantitative trading in capital markets by automating many of the tasks, freeing them to focus on high-value creative work. The following diagram illustrates the technical architecture.

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AWS Machine Learning - AI

MARCH 7, 2025

Solution overview The policy documents reside in Amazon Simple Storage Service (Amazon S3) storage. During the solution design process, Verisk also considered using Amazon Bedrock Knowledge Bases because its purpose built for creating and storing embeddings within Amazon OpenSearch Serverless.

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AWS Machine Learning - AI

APRIL 18, 2024

Generative artificial intelligence (AI) with Amazon Bedrock directly addresses these challenges. This standardization is made possible by using advanced prompts in conjunction with Knowledge Bases for Amazon Bedrock , which stores information on organization-specific Terraform modules. Access to Amazon Bedrock models.

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AWS Machine Learning - AI

JULY 11, 2024

Figure 1 : High level overview of creating Infrastructure as Code from architecture diagram Initial Input through the Amazon Bedrock chat console : The user begins by entering the name of their Amazon Simple Storage Service (Amazon S3) bucket and the object (key) name where the architecture diagram is stored into the Amazon Bedrock chat console.

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AWS Machine Learning - AI

MARCH 19, 2024

With Knowledge Bases for Amazon Bedrock , you can simplify the RAG development process to provide more accurate anomaly root cause analysis for plant workers. A knowledge base of these files is generated in Amazon Bedrock with a Titan text embeddings model and a default OpenSearch Service vector store.

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AWS Machine Learning - AI

APRIL 24, 2024

Conversational artificial intelligence (AI) assistants are engineered to provide precise, real-time responses through intelligent routing of queries to the most suitable AI functions. It uses the provided conversation history, action groups, and knowledge bases to understand the context and determine the necessary tasks.

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AWS Machine Learning - AI

AUGUST 2, 2024

Generative artificial intelligence (AI) is rapidly emerging as a transformative force, poised to disrupt and reshape businesses of all sizes and across industries. However, their knowledge is static and tied to the data used during the pre-training phase. The following diagram illustrates this architecture.

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AWS Machine Learning - AI

AUGUST 15, 2024

QnABot on AWS (an AWS Solution) now provides access to Amazon Bedrock foundational models (FMs) and Knowledge Bases for Amazon Bedrock , a fully managed end-to-end Retrieval Augmented Generation (RAG) workflow. If a knowledge base ID is configured , the Bot Fulfillment Lambda function forwards the request to the knowledge base.

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AWS Machine Learning - AI

DECEMBER 18, 2024

Enterprises that have adopted ServiceNow can improve their operations and boost user productivity by using Amazon Q Business for various use cases, including incident and knowledge management. Navigate to the deployed web experience URL and sign with your AWS IAM Identity Center credentials.

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AWS Machine Learning - AI

APRIL 9, 2025

The Unsuccessful query responses and Customer feedback metrics help pinpoint gaps in the knowledge base or areas where the system struggles to provide satisfactory answers. These logs can be delivered to multiple destinations, such as CloudWatch, Amazon Simple Storage Service (Amazon S3), or Amazon Data Firehose.

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AWS Machine Learning - AI

MARCH 18, 2025

If a user has a role configured with a specific guardrail requirement (using the bedrock:GuardrailIdentifier condition), they shouldnt use that same role to access services like Amazon Bedrock Knowledge Bases RetrieveAndGenerate or Amazon Bedrock Agents InvokeAgent.

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AWS Machine Learning - AI

JULY 10, 2024

Imagine this—all employees relying on generative artificial intelligence (AI) to get their work done faster, every task becoming less mundane and more innovative, and every application providing a more useful, personal, and engaging experience. More knowledge base updates can be found in the News Blog.

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AWS Machine Learning - AI

SEPTEMBER 20, 2024

There are many challenges that can impact employee productivity, such as cumbersome search experiences or finding specific information across an organization’s vast knowledge bases. Knowledge management: Amazon Q Business helps organizations use their institutional knowledge more effectively.

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AWS Machine Learning - AI

MARCH 28, 2024

It provides a modular and flexible framework for combining LLMs with other components, such as knowledge bases, retrieval systems, and other AI tools, to create powerful and customizable applications. There was no monitoring, load balancing, auto-scaling, or persistent storage at the time.

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AWS Machine Learning - AI

MAY 30, 2024

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.

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AWS Machine Learning - AI

MAY 14, 2024

Recent advances in artificial intelligence have led to the emergence of generative AI that can produce human-like novel content such as images, text, and audio. We built the RAG solution as detailed in the following GitHub repo and used SageMaker documentation as the knowledge base. Question : What is Amazon SageMaker?

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AWS Machine Learning - AI

OCTOBER 24, 2024

They use the developer-provided instruction to create an orchestration plan and then carry out the plan by invoking company APIs and accessing knowledge bases using Retrieval Augmented Generation (RAG) to provide a final response to the end user. We use Amazon Bedrock Agents with two knowledge bases for this assistant.

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AWS Machine Learning - AI

NOVEMBER 7, 2024

Depending on the use case and data isolation requirements, tenants can have a pooled knowledge base or a siloed one and implement item-level isolation or resource level isolation for the data respectively. You also need to consider the operational characteristics and noisy neighbor risks.

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AWS Machine Learning - AI

NOVEMBER 27, 2023

Generative artificial intelligence ( generative AI ) models have demonstrated impressive capabilities in generating high-quality text, images, and other content. Read and write access to an Amazon Simple Storage Service (Amazon S3) bucket. Access to Amazon Textract. Access to Amazon OpenSearch as a vector database.

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AWS Machine Learning - AI

SEPTEMBER 12, 2024

RAG overview Retrieval-Augmented Generation (RAG) is a technique that enables the integration of external knowledge sources with FM. First, relevant content is retrieved from an external knowledge base based on the user’s query. Based in Dallas, Texas, he and his family love to travel and go on long road trips.

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AWS Machine Learning - AI

SEPTEMBER 18, 2024

After the profile is converted into text that explains the profile, a RAG framework is launched using Amazon Bedrock Knowledge Bases to retrieve related industry insights (articles, pain points, and so on). Building your knowledge base for the industry insights document is the final prerequisite.

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