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 108

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AWS Machine Learning - AI

APRIL 1, 2025

Amazon Bedrock Agents enables this functionality by orchestrating foundation models (FMs) with data sources, applications, and user inputs to complete goal-oriented tasks through API integration and knowledge base augmentation. In the first flow, a Lambda-based action is taken, and in the second, the agent uses an MCP server.

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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. versions, catering to different programming preferences.

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AWS Machine Learning - AI

SEPTEMBER 25, 2024

Amazon Bedrock Knowledge Bases is a fully managed capability that helps you implement the entire RAG workflow—from ingestion to retrieval and prompt augmentation—without having to build custom integrations to data sources and manage data flows. Latest innovations in Amazon Bedrock Knowledge Base provide a resolution to this issue.

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AWS Machine Learning - AI

DECEMBER 4, 2024

The following diagram illustrates the conceptual architecture of an AI assistant with Amazon Bedrock IDE. Solution architecture The architecture in the preceding figure shows how Amazon Bedrock IDE orchestrates the data flow.

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AWS Machine Learning - AI

MARCH 20, 2025

It integrates with existing applications and includes key Amazon Bedrock features like foundation models (FMs), prompts, knowledge bases, agents, flows, evaluation, and guardrails. Before we dive deep into the deployment of the AI agent, lets walk through the key steps of the architecture, as shown in the following diagram.

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AWS Machine Learning - AI

MARCH 18, 2025

This transcription then serves as the input for a powerful LLM, which draws upon its vast knowledge base to provide personalized, context-aware responses tailored to your specific situation. These data sources provide contextual information and serve as a knowledge base for the LLM.

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AWS Machine Learning - AI

NOVEMBER 7, 2024

It also uses a number of other AWS services such as Amazon API Gateway , AWS Lambda , and Amazon SageMaker. 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.

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AWS Machine Learning - AI

NOVEMBER 1, 2024

Although tagging is supported on a variety of Amazon Bedrock resources —including provisioned models, custom models, agents and agent aliases, model evaluations, prompts, prompt flows, knowledge bases, batch inference jobs, custom model jobs, and model duplication jobs—there was previously no capability for tagging on-demand foundation models.

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AWS Machine Learning - AI

JULY 11, 2024

This solution shows how Amazon Bedrock agents can be configured to accept cloud architecture diagrams, automatically analyze them, and generate Terraform or AWS CloudFormation templates. Solution overview Before we explore the deployment process, let’s walk through the key steps of the architecture as illustrated in Figure 1.

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AWS Machine Learning - AI

SEPTEMBER 24, 2024

It’s a fully serverless architecture that uses Amazon OpenSearch Serverless , which can run petabyte-scale workloads, without you having to manage the underlying infrastructure. The following diagram illustrates the solution architecture. This solution uses Amazon Bedrock LLMs to find answers to questions from your knowledge base.

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AWS Machine Learning - AI

APRIL 18, 2024

With Amazon Bedrock, teams can input high-level architectural descriptions and use generative AI to generate a baseline configuration of Terraform scripts. AWS Landing Zone architecture in the context of cloud migration AWS Landing Zone can help you set up a secure, multi-account AWS environment based on AWS best practices.

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AWS Machine Learning - AI

FEBRUARY 25, 2025

Solution overview Before we dive into the deployment process, lets walk through the key steps of the architecture as illustrated in the following figure. This function invokes another Lambda function (see the following Lambda function code ) which retrieves the latest error message from the specified Terraform Cloud workspace.

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AWS Machine Learning - AI

OCTOBER 11, 2024

The assistant can filter out irrelevant events (based on your organization’s policies), recommend actions, create and manage issue tickets in integrated IT service management (ITSM) tools to track actions, and query knowledge bases for insights related to operational events. It has several key components.

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Cloud AWS Serverless Policies 89

AWS Machine Learning - AI

APRIL 24, 2024

It uses the provided conversation history, action groups, and knowledge bases to understand the context and determine the necessary tasks. This is based on the instructions that are interpreted by the assistant as per the system prompt and user’s input. Additionally, you can access device historical data or device metrics.

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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. The Content Designer AWS Lambda function saves the input in Amazon OpenSearch Service in a questions bank index.

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CIO

APRIL 25, 2023

Five years later, transformer architecture has evolved to create powerful models such as ChatGPT. ChatGPT was trained with 175 billion parameters; for comparison, GPT-2 was 1.5B (2019), Google’s LaMBDA was 137B (2021), and Google’s BERT was 0.3B (2018). GPT stands for generative pre-trained transformer.

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AWS Machine Learning - AI

JUNE 26, 2024

You can now use Agents for Amazon Bedrock and Knowledge Bases for Amazon Bedrock to build specialized agents and AI-powered assistants that run actions based on natural language input prompts and your organization’s data. Both the action groups and knowledge base are optional and not required for the agent itself.

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AWS Machine Learning - AI

OCTOBER 15, 2024

By using Amazon Bedrock Agents , action groups , and Amazon Bedrock Knowledge Bases , we demonstrate how to build a migration assistant application that rapidly generates migration plans, R-dispositions, and cost estimates for applications migrating to AWS.

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AWS Machine Learning - AI

MARCH 20, 2025

Moreover, Amazon Bedrock offers integration with other AWS services like Amazon SageMaker , which streamlines the deployment process, and its scalable architecture makes sure the solution can adapt to increasing call volumes effortlessly. This is powered by the web app portion of the architecture diagram (provided in the next section).

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AWS Machine Learning - AI

OCTOBER 23, 2024

Built using Amazon Bedrock Knowledge Bases , Amazon Lex , and Amazon Connect , with WhatsApp as the channel, our solution provides users with a familiar and convenient interface. With the ability to continuously update and add to the knowledge base, AI applications stay current with the latest information.

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AWS Machine Learning - AI

MAY 14, 2024

The entire conversation in this use case, starting with generative AI and then bringing in human agents who take over, is logged so that the interaction can be used as part of the knowledge base. We also have another expert group providing feedback using Amazon SageMaker GroundTruth on completion quality for the RLHF based training.

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AWS Machine Learning - AI

DECEMBER 22, 2023

Solution architecture The following diagram illustrates the solution architecture. Diagram 1: Solution Architecture Overview The agent’s response workflow includes the following steps: Users perform natural language dialog with the agent through their choice of web, SMS, or voice channels.

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AWS Machine Learning - AI

MARCH 7, 2025

In this post, we describe the development journey of the generative AI companion for Mozart, the data, the architecture, and the evaluation of the pipeline. The following diagram illustrates the solution architecture. You can create a decoupled architecture with reusable components. Connect with him on LinkedIn.

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AWS Machine Learning - AI

SEPTEMBER 20, 2024

Amazon Bedrock agents use LLMs to break down tasks, interact dynamically with users, run actions through API calls, and augment knowledge using Amazon Bedrock Knowledge Bases. With just a few configuration steps, you can dramatically expand your chatbot’s knowledge base and capabilities, all while maintaining a streamlined UI.

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AWS Machine Learning - AI

DECEMBER 6, 2023

To create AI assistants that are capable of having discussions grounded in specialized enterprise knowledge, we need to connect these powerful but generic LLMs to internal knowledge bases of documents. Then we introduce you to a more versatile architecture that overcomes these limitations.

Artificial Inteligence 114

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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

MARCH 5, 2025

These assistants can be powered by various backend architectures including Retrieval Augmented Generation (RAG), agentic workflows, fine-tuned large language models (LLMs), or a combination of these techniques. Generative AI question-answering applications are pushing the boundaries of enterprise productivity.

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AWS Machine Learning - AI

OCTOBER 7, 2024

By harnessing the power of generative AI and Amazon Web Services (AWS) services Amazon Bedrock , Amazon Kendra , and Amazon Lex , this solution provides a sample architecture to build an intelligent Slack chat assistant that can streamline information access, enhance user experiences, and drive productivity and efficiency within organizations.

Generative AI 93

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AWS Machine Learning - AI

SEPTEMBER 12, 2024

Architecture The solution uses Amazon API Gateway , AWS Lambda , Amazon RDS, Amazon Bedrock, and Anthropic Claude 3 Sonnet on Amazon Bedrock to implement the backend of the application. The following diagram illustrates the solution architecture. User authentication and authorization is done using Amazon Cognito.

Generative AI 117

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AWS Machine Learning - AI

MAY 7, 2024

For a generative AI powered Live Meeting Assistant that creates post call summaries, but also provides live transcripts, translations, and contextual assistance based on your own company knowledge base, see our new LMA solution. Transcripts are then stored in the project’s S3 bucket under /transcriptions/TranscribeOutput/.

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AWS Machine Learning - AI

AUGUST 26, 2024

Our internal AI sales assistant, powered by Amazon Q Business , will be available across every modality and seamlessly integrate with systems such as internal knowledge bases, customer relationship management (CRM), and more. We carefully choose models based on their specific capabilities and the requirements of each summary section.

Generative AI 112

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AWS Machine Learning - AI

JULY 30, 2024

Mediasearch Q Business supercharges the way you consume media files by using them as part of the knowledge base used by Amazon Q Business to generate reliable answers to user questions. Mediasearch Q Business builds on the Mediasearch solution powered by Amazon Kendra and enhances the search experience using Amazon Q Business.

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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

SEPTEMBER 13, 2024

This could be Amazon Elastic Compute Cloud (Amazon EC2), AWS Lambda , AWS SDK , Amazon SageMaker notebooks, or your workstation if you are doing a quick proof of concept. The following diagram illustrates the solution architecture. For the purpose of this post, this code is running on a t3a.micro EC2 instance with Amazon Linux 2023.

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AWS Machine Learning - AI

MARCH 14, 2024

We use AWS Lambda as our orchestration function responsible for interacting with various data sources, LLMs and error correction based on the user query. The following figure illustrates this architecture. The following figures shows the step-by-step procedure of how a query is processed for the text-to-SQL pipeline.

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AWS Machine Learning - AI

SEPTEMBER 12, 2024

Llama 3 uses a decoder-only transformer architecture and new tokenizer that provides improved model performance with 128K size. RAG overview Retrieval-Augmented Generation (RAG) is a technique that enables the integration of external knowledge sources with FM. RAG involves three main steps: retrieval, augmentation, and generation.

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