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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.
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.
Observability refers to the ability to understand the internal state and behavior of a system by analyzing its outputs, logs, and metrics. Cost optimization – This solution uses serverless technologies, making it cost-effective for the observability infrastructure. However, some components may incur additional usage-based costs.
With the Amazon Bedrock serverless experience, you can get started quickly, privately customize FMs with your own data, and integrate and deploy them into your applications using the Amazon Web Services (AWS) tools without having to manage infrastructure. The following diagram depicts a high-level RAG architecture.
They have structured data such as sales transactions and revenue metrics stored in databases, alongside unstructured data such as customer reviews and marketing reports collected from various channels. Your tasks include analyzing metrics, providing sales insights, and answering data questions.
Amazon Bedrock KnowledgeBases 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 KnowledgeBase provide a resolution to this issue.
Amazon Bedrock Custom Model Import enables the import and use of your customized models alongside existing FMs through a single serverless, unified API. Accelerate your generative AI application development by integrating your supported custom models with native Bedrock tools and features like KnowledgeBases, Guardrails, and Agents.
By monitoring utilization metrics, organizations can quantify the actual productivity gains achieved with Amazon Q Business. Tracking metrics such as time saved and number of queries resolved can provide tangible evidence of the services impact on overall workplace productivity.
Furthermore, by integrating a knowledgebase containing organizational data, policies, and domain-specific information, the generative AI models can deliver more contextual, accurate, and relevant insights from the call transcripts. and Anthropics Claude Haiku 3.
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.
With deterministic evaluation processes such as the Factual Knowledge and QA Accuracy metrics of FMEval , ground truth generation and evaluation metric implementation are tightly coupled. To scale ground truth generation and curation, you can apply a risk-based approach in conjunction with a prompt-based strategy using LLMs.
It’s serverless, so you don’t have to manage any infrastructure. We benchmark the results with a metric used for evaluating summarization tasks in the field of natural language processing (NLP) called Recall-Oriented Understudy for Gisting Evaluation (ROUGE). Evaluating LLMs is an undervalued part of the machine learning (ML) pipeline.
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.
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.
During the solution design process, Verisk also considered using Amazon Bedrock KnowledgeBases because its purpose built for creating and storing embeddings within Amazon OpenSearch Serverless. This process has been implemented as a periodic job to keep the vector database updated with new documents.
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. The VitechIQ user experience can be split into two process flows: document repository, and knowledge retrieval.
Amazon Bedrock Custom Model Import enables the import and use of your customized models alongside existing FMs through a single serverless, unified API. Accelerate your generative AI application development by integrating your supported custom models with native Bedrock tools and features like KnowledgeBases, Guardrails, and Agents.
Verisk FAST’s AI companion aims to alleviate this burden by not only providing 24/7 support for business processing and configuration questions related to FAST, but also tapping into the immense knowledgebase to provide an in-depth, tailored response. However, they understood that this was not a one-and-done effort.
The AI Service Layer allows Domo to switch between different models provided by Amazon Bedrock for individual tasks and track their performance across key metrics like accuracy, latency, and cost. The serverless access to high-quality LLMs eliminates the need for substantial upfront infrastructure investments typically associated with LLMs.
An LLM is prompted to formulate a helpful answer based on the user’s questions and the retrieved chunks. Amazon Bedrock KnowledgeBases offers a streamlined approach to implement RAG on AWS, providing a fully managed solution for connecting FMs to custom data sources.
Mediasearch Q Business supercharges the way you consume media files by using them as part of the knowledgebase 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.
With the rise of AI, you also need a knowledgebase. These knowledgebases can be hosted in OpenSearch. To prevent this from happening I also included the ApproximateNumberOfMessagesNotVisible next to the ApproximateNumberOfMessages metric. Let me go one step back. Why do we even use OpenSearch?
It combines data from various sourcessuch as logs, metrics, and eventsto analyze system behavior, identify anomalies, and recommend or execute automated remediation actions. This solution also uses Amazon Bedrock KnowledgeBases and Amazon Bedrock Agents.
This includes detailed logging of agent interactions, performance metrics, and system health indicators. Amazon Bedrock Agents and Amazon Bedrock KnowledgeBases as native CrewAI Tools Amazon Bedrock Agents offers you the ability to build and configure autonomous agents in a fully managed and serverless manner on Amazon Bedrock.
Amazon Bedrock offers a serverless experience, so you can get started quickly, privately customize FMs with your own data, and integrate and deploy them into your applications using AWS tools without having to manage infrastructure. Serverless : Customers can access their imported custom models in an on-demand and serverless manner.
By seamlessly integrating foundation models (FMs), prompts, agents, and knowledgebases, organizations can rapidly develop flexible, efficient AI-driven processes tailored to their specific business needs. Experimentation framework The ability to test and compare different prompt variations while maintaining version control.
Each model has different features, price points, and performance metrics, making it difficult to make a confident choice that fits their needs and budget. Additionally, to generate a personalized brochure, relevant images (described as text-based embeddings) are fetched based on the query context.
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