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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.
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.
Importantly, cross-Region inference prioritizes the connected Amazon Bedrock API source Region when possible, helping minimize latency and improve overall responsiveness. The customers AWS accounts that are allowed to use Amazon Bedrock are under an Organizational Unit (OU) called Sandbox. Sonnet v2 model using cross-Region inference.
These documents are internally called account plans (APs). In 2024, this activity took an account manager (AM) up to 40 hours per customer. In this post, we showcase how the AWS Sales product team built the generative AI account plans draft assistant. Its a game-changer for serving my full portfolio of accounts.
The custom Google Chat app, configured for HTTP integration, sends an HTTP request to an API Gateway endpoint. Before processing the request, a Lambda authorizer function associated with the API Gateway authenticates the incoming message. The following figure illustrates the high-level design of the solution.
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.
After you set up the connector, you can create one or multiple data sources within Amazon Q Business and configure them to start indexing emails from your Gmail account. The connector supports authentication using a Google service account. We describe the process of creating an account later in this post. Choose Create.
Note that these APIs use objects as namespaces, alleviating the need for explicit imports. API Gateway supports multiple mechanisms for controlling and managing access to an API. AWS Lambda handles the REST API integration, processing the requests and invoking the appropriate AWS services.
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.
We recently announced the general availability of cross-account sharing of Amazon SageMaker Model Registry using AWS Resource Access Manager (AWS RAM) , making it easier to securely share and discover machine learning (ML) models across your AWS accounts. Mitigation strategies : Implementing measures to minimize or eliminate risks.
Prerequisites Before creating your application in Amazon Bedrock IDE, you’ll need to set up a few resources in your AWS account. This includes setting up Amazon API Gateway , AWS Lambda functions, and Amazon Athena to enable querying the structured sales data. Navigate to the AWS Secrets Manager console and find the secret -api-keys.
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.
Prerequisites Before you start, make sure you have the following prerequisites in place: Create an AWS account , or sign in to your existing account. Based on the API response, you can determine the guardrail’s action. Additionally, each row of the API response is saved so the user can explore the response as needed.
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.
The new ApplyGuardrail API enables you to assess any text using your preconfigured guardrails in Amazon Bedrock, without invoking the FMs. In this post, we demonstrate how to use the ApplyGuardrail API with long-context inputs and streaming outputs. For example, you can now use the API with models hosted on Amazon SageMaker.
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.
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.
They use a highly optimized inference stack built with NVIDIA TensorRT-LLM and NVIDIA Triton Inference Server to serve both their search application and pplx-api, their public API service that gives developers access to their proprietary models. The results speak for themselvestheir inference stack achieves up to 3.1
Traditional automation approaches require custom API integrations for each application, creating significant development overhead. Add the Amazon Bedrock Agents supported computer use action groups to your agent using CreateAgentActionGroup API. Prerequisites AWS Command Line Interface (CLI), follow instructions here.
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.
With GraphStorm, you can build solutions that directly take into account the structure of relationships or interactions between billions of entities, which are inherently embedded in most real-world data, including fraud detection scenarios, recommendations, community detection, and search/retrieval problems. Specifically, GraphStorm 0.3
For instance, as a marketing manager for a video-on-demand company, you might want to send personalized email messages tailored to each individual usertaking into account their demographic information, such as gender and age, and their viewing preferences. We then use an Amazon Bedrock API call to generate the personalized email.
Amazon Bedrock APIs make it straightforward to use Amazon Titan Text Embeddings V2 for embedding data. The implementation used the universal gateway provided by the FloTorch enterprise version to enable consistent API calls using the same function and to track token count and latency metrics uniformly. get("message", {}).get("content")
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.
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.
Update models in the private hub Modify your existing private HubContent by calling the new sagemaker:UpdateHubContent API. Refer to the public API documentation for more details. Refer to the public API documentation for more details. Refer to the public API documentation for more usage details.
Reduced time and effort in testing and deploying AI workflows with SDK APIs and serverless infrastructure. We can also quickly integrate flows with our applications using the SDK APIs for serverless flow execution — without wasting time in deployment and infrastructure management.
These steps might involve both the use of an LLM and external data sources and APIs. Agent plugin controller This component is responsible for the API integration to external data sources and APIs. The LLM agent is an orchestrator of a set of steps that might be necessary to complete the desired request.
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).
Using SageMaker with MLflow to track experiments The fully managed MLflow capability on SageMaker is built around three core components: MLflow tracking server This component can be quickly set up through the Amazon SageMaker Studio interface or using the API for more granular configurations.
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.
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 makes these features available through the service API, allowing for a customized look and feel on the frontend.
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.
We use various AWS services to deploy a complete solution that you can use to interact with an API providing real-time weather information. We also use identity pool to provide temporary AWS credentials for the user while they interact with Amazon Bedrock API. In this solution, we use Amazon Bedrock Agents.
Amazon Bedrock is a fully managed service that offers a choice of high-performing foundation models from leading AI companies like AI21 Labs, Anthropic, Cohere, Meta, Stability AI, and Amazon via a single API, along with a broad set of capabilities to build generative AI applications with security, privacy, and responsible AI.
Amazon Bedrock is a fully managed service that makes a wide range of foundation models (FMs) available though an API without having to manage any infrastructure. Amazon API Gateway and AWS Lambda to create an API with an authentication layer and integrate with Amazon Bedrock. An API created with Amazon API Gateway.
Refer to Getting started with the API to set up your environment to make Amazon Bedrock requests through the AWS API. Test the code using the native inference API for Anthropics Claude The following code uses the native inference API to send a text message to Anthropics Claude. client = boto3.client("bedrock-runtime",
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.
The solution uses the FMs tool use capabilities, accessed through the Amazon Bedrock Converse API. This enables the FMs to not just process text, but to actively engage with various external tools and APIs to perform complex document analysis tasks. For more details on how tool use works, refer to The complete tool use workflow.
The workflow steps are as follows: The user submits an Amazon Bedrock fine-tuning job within their AWS account, using IAM for resource access. The fine-tuning job initiates a training job in the model deployment accounts. Provide your account, bucket name, and VPC settings. Choose Create policy.
It allows developers to build and scale generative AI applications using FMs through an API, without managing infrastructure. Customers are building innovative generative AI applications using Amazon Bedrock APIs using their own proprietary data.
Beyond Amazon Bedrock models, the service offers the flexible ApplyGuardrails API that enables you to assess text using your pre-configured guardrails without invoking FMs, allowing you to implement safety controls across generative AI applicationswhether running on Amazon Bedrock or on other systemsat both input and output levels.
Prerequisites You need an AWS account with an AWS Identity and Access Management (IAM) role with permissions to manage resources created as part of the solution. For details, see Creating an AWS account. Note that MLflow tracking starts from the mlflow.start_run() API. You can now open the private-mlflow.ipynb notebook.
Agent architecture The following diagram illustrates the serverless agent architecture with standard authorization and real-time interaction, and an LLM agent layer using Amazon Bedrock Agents for multi-knowledge base and backend orchestration using API or Python executors. Domain-scoped agents enable code reuse across multiple agents.
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