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Amazon Bedrock announces the preview launch of Session Management APIs, a new capability that enables developers to simplify state and context management for generative AI applications built with popular open source frameworks such as LangGraph and LlamaIndex. Building generative AI applications requires more than model API calls.
In this post, we guide you through integrating Amazon Bedrock Agents with enterprise data APIs to create more personalized and effective customer support experiences. An automotive retailer might use inventory management APIs to track stock levels and catalog APIs for vehicle compatibility and specifications.
Many businesses want to integrate these cutting-edge AI capabilities with their existing collaboration tools, such as Google Chat, to enhance productivity and decision-making processes. The custom Google Chat app, configured for HTTP integration, sends an HTTP request to an API Gateway endpoint.
We demonstrate how generative AI along with external tool use offers a more flexible and adaptable solution to this challenge. The solution uses the FMs tool use capabilities, accessed through the Amazon Bedrock Converse API. For more details on how tool use works, refer to The complete tool use workflow.
It also uses a number of other AWS services such as Amazon API Gateway , AWS Lambda , and Amazon SageMaker. API Gateway is serverless and hence automatically scales with traffic. API Gateway also provides a WebSocket API. Incoming requests to the gateway go through this point.
By harnessing the latest advancements in generative AI, we empower employees to unlock new levels of efficiency and creativity within the tools they already use every day. Note that these APIs use objects as namespaces, alleviating the need for explicit imports. Sonnet).
The solution also uses Amazon Cognito user pools and identity pools for managing authentication and authorization of users, Amazon API Gateway REST APIs, AWS Lambda functions, and an Amazon Simple Storage Service (Amazon S3) bucket. To launch the solution in a different Region, change the aws_region parameter accordingly.
This integration brings Anthropics visual perception capabilities as a managed tool within Amazon Bedrock Agents, providing you with a secure, traceable, and managed way to implement computer use automation in your workflows. Add the Amazon Bedrock Agents supported computer use action groups to your agent using CreateAgentActionGroup API.
At AWS, we help our customers transform responsible AI from theory into practice—by giving them the tools, guidance, and resources to get started with purpose-built services and features, such as Amazon Bedrock Guardrails. These dimensions make up the foundation for developing and deploying AI applications in a responsible and safe manner.
Intricate workflows that require dynamic and complex API orchestration can often be complex to manage. In this post, we explore how chaining domain-specific agents using Amazon Bedrock Agents can transform a system of complex API interactions into streamlined, adaptive workflows, empowering your business to operate with agility and precision.
Amazon Bedrock Flows offers an intuitive visual builder and a set of APIs to seamlessly link foundation models (FMs), Amazon Bedrock features, and AWS services to build and automate user-defined generative AI workflows at scale. Test the flow Youre now ready to test the flow through the Amazon Bedrock console or API.
Prerequisites Before proceeding, make sure that you have the necessary AWS account permissions and services enabled, along with access to a ServiceNow environment with the required privileges for configuration. AWS Have an AWS account with administrative access. For more information, see Setting up for Amazon Q Business. Choose Next.
This approach, which we call intelligent metadata filtering, uses tool use (also known as function calling ) to dynamically extract metadata filters from natural language queries. Function calling allows LLMs to interact with external tools or functions, enhancing their ability to process and respond to complex queries.
One important aspect of this foundation is to organize their AWS environment following a multi-account strategy. In this post, we show how you can extend that architecture to multiple accounts to support multiple LOBs. In this post, we show how you can extend that architecture to multiple accounts to support multiple LOBs.
Weve seen our sales teams use this capability to do things like consolidate meeting notes from multiple team members, analyze business reports, and develop account strategies. Amazon Q Business provides a number of out-of-the-box connectors to popular data sources like relational databases, content management systems, and collaboration tools.
Enabling Global Resiliency for an Amazon Lex bot is straightforward using the AWS Management Console , AWS Command Line Interface (AWS CLI), or APIs. If this option isn’t visible, the Global Resiliency feature may not be enabled for your account. To better understand the solution, refer to the following architecture diagram.
For more information about the SageMaker AI API, refer to the SageMaker AI API Reference. 8B-Instruct to DeepSeek-R1-Distill-Llama-8B, but the new model version has different API expectations. In this use case, you have configured a CloudWatch alarm to monitor for 4xx errors, which would indicate API compatibility issues.
MLflow , a popular open-source tool, helps data scientists organize, track, and analyze ML and generative AI experiments, making it easier to reproduce and compare results. SageMaker is a comprehensive, fully managed ML service designed to provide data scientists and ML engineers with the tools they need to handle the entire ML workflow.
Amazon Bedrock is a fully managed service that offers a choice of high-performing foundation models (FMs) from leading AI companies such as 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.
Evaluations are also a fundamental tool during application development to validate the quality of prompt templates. It functions as a standalone HTTP server that provides various REST API endpoints for monitoring, recording, and visualizing experiment runs. This allows you to keep track of your ML experiments.
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. In this post, we demonstrate how to use Amazon Bedrock Agents with a web search API to integrate dynamic web content in your generative AI application.
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.
SageMaker Feature Store now makes it effortless to share, discover, and access feature groups across AWS accounts. With this launch, account owners can grant access to select feature groups by other accounts using AWS Resource Access Manager (AWS RAM).
Solution overview Our solution implements a verified semantic cache using the Amazon Bedrock Knowledge Bases Retrieve API to reduce hallucinations in LLM responses while simultaneously improving latency and reducing costs. The function checks the semantic cache (Amazon Bedrock Knowledge Bases) using the Retrieve API.
Cloud providers have recognized the need to offer model inference through an API call, significantly streamlining the implementation of AI within applications. Although a single API call can address simple use cases, more complex ones may necessitate the use of multiple calls and integrations with other services.
Integration with the AWS Well-Architected Tool pre-populates workload information and initial assessment responses. Integration with the AWS Well-Architected Tool Creates a Well-Architected workload milestone for the assessment and prepopulates answers for WAFR questions based on generative AI-based assessment.
Besides the common AI functionalities like text and image generation, it allows them to interact with internal data, tools, and workflows through natural language queries. The implementation uses Slacks event subscription API to process incoming messages and Slacks Web API to send responses.
This blog post delves into how these innovative tools synergize to elevate the performance of your AI applications, ensuring they not only meet but exceed the exacting standards of enterprise-level deployments. Lets dive in and discover how these powerful tools can help you build more effective and reliable AI-powered solutions.
Amazon Bedrock is a fully managed service provided by AWS that offers developers access to foundation models (FMs) and the tools to customize them for specific applications. It allows developers to build and scale generative AI applications using FMs through an API, without managing infrastructure.
Amazon Bedrock , a fully managed service offering high-performing foundation models from leading AI companies through a single API, has recently introduced two significant evaluation capabilities: LLM-as-a-judge under Amazon Bedrock Model Evaluation and RAG evaluation for Amazon Bedrock Knowledge Bases.
Each category necessitates specialized generative AI-powered tools to generate insights. An alternative approach to routing is to use the native tool use capability (also known as function calling) available within the Bedrock Converse API. The dataframes may contain nans, so make sure you account for those in your code. -
Enhancing AWS Support Engineering efficiency The AWS Support Engineering team faced the daunting task of manually sifting through numerous tools, internal sources, and AWS public documentation to find solutions for customer inquiries. For example, the Datastore API might require certain input like date periods to query data.
Traditional annotation tools, with basic playback and marking capabilities, often fall short in capturing these nuanced details. Through custom human annotation workflows , organizations can equip annotators with tools for high-precision segmentation. On the SageMaker console, choose Labeling workforces.
Amazon Bedrock is a fully managed service that makes FMs from leading AI startups and Amazon available through an API, so you can choose from a wide range of FMs to find the model that is best suited for your use case. Solution overview The solution comprises two main steps: Generate synthetic data using the Amazon Bedrock InvokeModel API.
After achieving the desired accuracy, you can use this ground truth data in an ML pipeline with automated machine learning (AutoML) tools such as AutoGluon to train a model and inference the support cases. Refer to Getting started with the API to set up your environment to make Amazon Bedrock requests through the AWS API.
Luckily for us, Vonage has a fantastic API for tracking phone calls ! We’ll use the Vonage API and build a.NET Core application that stores and displays this information by using event sourcing. Vonage APIAccount. To complete this tutorial, you will need a Vonage APIaccount. Prerequisites. Using ngrok.
A recent search about how Chat GPT can and will assist in customer service, contact center and customer experience shows many ways ChatGPT can be a potentially valuable tool including: Answering Frequently Asked Questions (FAQs) : Chat GPT can handle repetitive and commonly asked questions by providing instant and accurate responses.
It provides access to the most comprehensive set of tools for each step of ML development, from preparing data to building, training, deploying, and managing ML models. After you stop the Space, you can modify its settings using either the UI or API via the updated SageMaker Studio interface and then restart the Space.
Prerequisites To try Mistral-Small-24B-Instruct-2501 in SageMaker JumpStart, you need the following prerequisites: An AWS account that will contain all your AWS resources. At the time of writing this post, you can use the InvokeModel API to invoke the model. It doesnt support Converse APIs or other Amazon Bedrock tooling.
Amazon Bedrock is a fully managed service that makes foundation models (FMs) from leading AI startups and Amazon available through an API, so you can choose from a wide range of FMs to find the model that’s best suited for your use case. The deployment will output the API Gateway endpoint URL and an API key.
The Amazon Bedrock API returns the output Q&A JSON file to the Lambda function. The container image sends the REST API request to Amazon API Gateway (using the GET method). API Gateway communicates with the TakeExamFn Lambda function as a proxy. The JSON file is returned to API Gateway.
For example, it enables user subscription management across Amazon Q offerings and consolidates Amazon Q billing from across multiple AWS accounts. Additionally, Q Business conversation APIs employ a layer of privacy protection by leveraging trusted identity propagation enabled by IAM Identity Center. Finally, you have an OAuth 2.0
These delays can lead to missed security errors or compliance violations, especially in complex, multi-account environments. Amazon Bedrock Agents is a fully managed service that helps developers create AI agents that can break down complex tasks into steps and execute them using FMs and APIs to accomplish specific business objectives.
They enable applications requiring very low latency or local data processing using familiar APIs and tool sets. Prerequisites To run this demo, complete the following prerequisites: Create an AWS account , if you dont already have one. Enable the Local Zones in Los Angeles and Honolulu in the parent Region US West (Oregon).
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