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In this post, we propose an end-to-end solution using Amazon Q Business to simplify integration of enterprise knowledgebases at scale. Boosting performance When working with your specific dataset in Amazon Q Business, you can use relevance tuning to enhance the performance and accuracy of search results.
In this post, we demonstrate how to create an automated email response solution using Amazon Bedrock and its features, including Amazon Bedrock Agents , Amazon Bedrock KnowledgeBases , and Amazon Bedrock Guardrails. Monitoring – Monitors system performance and user activity to maintain operational reliability and efficiency.
KnowledgeBases 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 knowledgebase with guardrails.
Seamless integration of latest foundation models (FMs), Prompts, Agents, KnowledgeBases, Guardrails, and other AWS services. Flexibility to define the workflow based on your business logic. Reduced time and effort in testing and deploying AI workflows with SDK APIs and serverless infrastructure.
One way to enable more contextual conversations is by linking the chatbot to internal knowledgebases and information systems. Integrating proprietary enterprise data from internal knowledgebases enables chatbots to contextualize their responses to each user’s individual needs and interests.
This post explores the new enterprise-grade features for KnowledgeBases on Amazon Bedrock and how they align with the AWS Well-Architected Framework. AWS Well-Architected design principles RAG-based applications built using KnowledgeBases for Amazon Bedrock can greatly benefit from following the AWS Well-Architected Framework.
The solution presented in this post takes approximately 15–30 minutes to deploy and consists of the following key components: Amazon OpenSearch Service Serverless maintains three indexes : the inventory index, the compatible parts index, and the owner manuals index. The following diagram illustrates the workflow of the agent.
At AWS re:Invent 2023, we announced the general availability of KnowledgeBases for Amazon Bedrock. With a knowledgebase, you can securely connect foundation models (FMs) in Amazon Bedrock to your company data for fully managed Retrieval Augmented Generation (RAG).
KnowledgeBases 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. RAG is a popular technique that combines the use of private data with large language models (LLMs).
Generative artificial intelligence (AI)-powered chatbots play a crucial role in delivering human-like interactions by providing responses from a knowledgebase without the involvement of live agents. You can simply connect QnAIntent to company knowledge sources and the bot can immediately handle questions using the allowed content.
The Lambda function interacts with Amazon Bedrock through its runtime APIs, using either the RetrieveAndGenerate API that connects to a knowledgebase, or the Converse API to chat directly with an LLM available on Amazon Bedrock. If you don’t have an existing knowledgebase, refer to Create an Amazon Bedrock knowledgebase.
Knowledgebase integration Incorporates up-to-date WAFR documentation and cloud best practices using Amazon Bedrock KnowledgeBases , providing accurate and context-aware evaluations. Brijesh specializes in AI/ML solutions and has experience with serverless architectures.
As the complexity and scale of these applications grow, providing comprehensive observability and robust evaluation mechanisms are essential for maintaining high performance, quality, and user satisfaction. Cost optimization – This solution uses serverless technologies, making it cost-effective for the observability infrastructure.
An end-to-end RAG solution involves several components, including a knowledgebase, a retrieval system, and a generation system. Solution overview The solution provides an automated end-to-end deployment of a RAG workflow using KnowledgeBases for Amazon Bedrock. txt,md,html,doc/docx,csv,xls/.xlsx,pdf).
At the forefront of this evolution sits Amazon Bedrock , a fully managed service that makes high-performing foundation models (FMs) from Amazon and other leading AI companies available through an API. The following demo recording highlights Agents and KnowledgeBases for Amazon Bedrock functionality and technical implementation details.
Amazon Bedrock is a fully managed service that offers a choice of high-performing foundation models (FMs) from leading AI companies through a single API, along with a broad set of capabilities to build generative AI applications with security, privacy, and responsible AI. Included with Amazon Bedrock is KnowledgeBases for Amazon Bedrock.
Amazon Bedrock is a fully managed service that offers a choice of high-performing FMs from leading AI companies like AI21 Labs, Anthropic, Cohere, Meta, Mistral AI, Stability AI, and Amazon through a single API, along with a broad set of capabilities to build generative AI applications with security, privacy, and responsible AI.
Their DeepSeek-R1 models represent a family of large language models (LLMs) designed to handle a wide range of tasks, from code generation to general reasoning, while maintaining competitive performance and efficiency. The resulting distilled models, such as DeepSeek-R1-Distill-Llama-8B (from base model Llama-3.1-8B
Without specialized structured query language (SQL) knowledge or Retrieval Augmented Generation (RAG) expertise, these analysts struggle to combine insights effectively from both sources. SageMaker Unified Studio setup SageMaker Unified Studio is a browser-based web application where you can use all your data and tools for analytics and AI.
Since Amazon Bedrock is serverless, you don’t have to manage any infrastructure, and you can securely integrate and deploy generative AI capabilities into your applications using the AWS services you are already familiar with. Haiku model to receive answers to an array of questions because it’s a performant, fast, and cost-effective option.
The complexity of developing and deploying an end-to-end RAG solution involves several components, including a knowledgebase, retrieval system, and generative language model. Solution overview The solution provides an automated end-to-end deployment of a RAG workflow using KnowledgeBases for Amazon Bedrock.
General productivity Amazon Q Business specializes in Retrieval Augmented Generation (RAG) over enterprise and domain-specific datasets, and can also perform general knowledge retrieval and content generation tasks. We deliver our chatbot experience through a custom web frontend, as well as through a Slack application.
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 knowledgebases for insights related to operational events. It has several key components.
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. This solution uses Amazon Bedrock LLMs to find answers to questions from your knowledgebase. Choose your new knowledgebase to open it.
API Gateway is serverless and hence automatically scales with traffic. The advantage of using Application Load Balancer is that it can seamlessly route the request to virtually any managed, serverless or self-hosted component and can also scale well. It’s serverless so you don’t have to manage the infrastructure.
In addition, customers are looking for choices to select the most performant and cost-effective machine learning (ML) model and the ability to perform necessary customization (fine-tuning) to fit their business use cases. The LLM generated text, and the IR system retrieves relevant information from a knowledgebase.
To address this challenge, we recently enabled customers to perform free text searches on the event management page, allowing new users to run queries with minimal product knowledge. The following screenshot shows an example of the event filters (1) and time filters (2) as seen on the filter bar (source: Cato knowledgebase ).
To add to these challenges, they must think critically under time pressure and perform their tasks quickly to keep up with the pace of the market. Both the action groups and knowledgebase are optional and not required for the agent itself. The following diagram illustrates the technical architecture.
It integrates with existing applications and includes key Amazon Bedrock features like foundation models (FMs), prompts, knowledgebases, agents, flows, evaluation, and guardrails. The Lambda function performs the actions by calling the JIRA API or database with the required parameters provided from the agent.
Amazon Bedrock is a fully managed service that offers a choice of high-performing foundation models (FMs) from leading AI companies like AI21 Labs, Anthropic, Cohere, Meta, Stability AI, and Amazon through a single API, along with a broad set of capabilities to build generative AI applications with security, privacy, and responsible AI.
Voice-based assistants like Alexa demonstrate how we are entering an era of conversational interfaces. We explore how to build a fully serverless, voice-based contextual chatbot tailored for individuals who need it. All the services that we use are serverless and fully managed by AWS. We discuss this later in the post.
Reducing data leakage and malicious use Although generative AI has the potential to be a force for good, models might also be exploited by adversaries looking to extract sensitive information or perform harmful actions. The offline evaluation pipeline uses tools like Giskard to detect performance, bias, and security issues in AI systems.
With Amazon Bedrock KnowledgeBases , you securely connect FMs in Amazon Bedrock to your company data for RAG. Amazon Bedrock KnowledgeBases facilitates data ingestion from various supported data sources; manages data chunking, parsing, and embeddings; and populates the vector store with the embeddings.
This is a fully managed service that offers a choice of high-performing foundation models (FMs) from leading artificial intelligence (AI) companies like AI21 Labs, Anthropic, Cohere, Meta, Stability AI, and Amazon through a single API. It’s serverless, so you don’t have to manage any infrastructure.
Overview of key metrics Amazon Q Business Insights (see the following screenshot) offers a comprehensive set of metrics that provide valuable insights into user engagement and system performance. By using CloudWatch Log Insights, administrators can create custom queries to extract and analyze detailed performance data.
Asure anticipated that generative AI could aid contact center leaders to understand their teams support performance, identify gaps and pain points in their products, and recognize the most effective strategies for training customer support representatives using call transcripts.
An approach to product stewardship with generative AI Large language models (LLMs) are trained with vast amounts of information crawled from the internet, capturing considerable knowledge from multiple domains. However, their knowledge is static and tied to the data used during the pre-training phase.
Amazon Bedrock is a fully managed service that offers a choice of high-performing foundation models (FMs) from leading AI companies like AI21 Labs, Anthropic, Cohere, Meta, Mistral AI, Stability AI, and Amazon through a single API, along with a broad set of capabilities to build generative AI applications with security, privacy, and responsible AI.
They need a full range of capabilities to build and scale generative AI applications that are tailored to their business and use case —including apps with built-in generative AI, tools to rapidly experiment and build their own generative AI apps, a cost-effective and performant infrastructure, and security controls and guardrails.
Built using Amazon Bedrock KnowledgeBases , Amazon Lex , and Amazon Connect , with WhatsApp as the channel, our solution provides users with a familiar and convenient interface. The solution’s scalability quickly accommodates growing data volumes and user queries thanks to AWS serverless offerings.
With Bedrock’s serverless experience, one can get started quickly, privately customize FMs with their own data, and easily integrate and deploy them into applications using the AWS tools without having to manage any infrastructure. However, for this use case, the complexity associated with fine-tuning and the costs were not warranted.
These benchmarks are essential for tracking performance drift over time and for statistically comparing multiple assistants in accomplishing the same task. Additionally, they enable quantifying performance changes as a function of enhancements to the underlying assistant, all within a controlled setting. 201% $12.2B
Their DeepSeek-R1 models represent a family of large language models (LLMs) designed to handle a wide range of tasks, from code generation to general reasoning, while maintaining competitive performance and efficiency. The resulting distilled models, such as DeepSeek-R1-Distill-Llama-8B (from base model Llama-3.1-8B
Amazon Bedrock is a fully managed service that offers a choice of high-performing foundation models (FMs) from leading AI companies like AI21 Labs, Anthropic, Cohere, Meta, Stability AI, and Amazon through a single API, along with a broad set of capabilities you need to build generative AI applications with security, privacy, and responsible AI.
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