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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.
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.
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.
It also uses a number of other AWS services such as Amazon API Gateway , AWS Lambda , and Amazon SageMaker. Some components are categorized in groups based on the type of functionality they exhibit. The component groups are as follows. API Gateway is serverless and hence automatically scales with traffic.
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.
Enabling Global Resiliency for an Amazon Lex bot is straightforward using the AWS Management Console , AWS Command Line Interface (AWS CLI), or APIs. In this section, we associate a Lambda function and CloudWatch group for the BookHotel bot in the source Region ( us-east-1 ) and validate its association in the replica Region ( us-west-2 ).
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.
This involves creating an OAuth API endpoint in ServiceNow and using the web experience URL from Amazon Q Business as the callback URL. The final step of the solution involves enhancing the application environment with a custom plugin for ServiceNow using APIs defined in an OpenAPI schema.
This is guest post by Andy Whittle, Principal Platform Engineer – Application & Reliability Frameworks at The Very Group. At The Very Group , which operates digital retailer Very, security is a top priority in handling data for millions of customers. The adoption of Logstash was initially done seamlessly. text(logData).build();
adds new APIs to customize GraphStorm pipelines: you now only need 12 lines of code to implement a custom node classification training loop. Based on customer feedback for the experimental APIs we released in GraphStorm 0.2, introduces refactored graph ML pipeline APIs. Specifically, GraphStorm 0.3 In addition, GraphStorm 0.3
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. The ReAct approach enables agents to generate reasoning traces and actions while seamlessly integrating with company systems through action groups.
The embedding model, which is hosted on the same EC2 instance as the local LLM API inference server, converts the text chunks into vector representations. The prompt is forwarded to the local LLM API inference server instance, where the prompt is tokenized and is converted into a vector representation using the local embedding model.
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.
We use various AWS services to deploy a complete solution that you can use to interact with an API providing real-time weather information. Amazon Bedrock Agents forwards the details from the user query to the action groups, which further invokes custom Lambda functions. In this solution, we use Amazon Bedrock Agents.
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.
With SSO mode, you set up an SSO user and group in IAM Identity Center and then grant access to either the SSO group or user from the Studio console. For instance, administrators may want to set up IAM permissions for a Studio SSO user based on their Active Directory (AD) group membership.
This VPC endpoint security group only allows traffic originating from the security group attached to your VPC private subnets, adding a layer of protection. Complete the following steps to create the security group: On the Amazon VPC console, choose Security groups in the navigation pane. Choose Create security group.
Check it out below: These changes should affect the following processes: Inviting users Creating a Collection Creating a Dashboard Creating a Card (from a Dashboard) Uploading Excel data (from the Jobs page) Creating a Group Creating a Dashboard Group These changes will soon be reflected in our knowledge base, around when the change goes live.
Both the data processing job and model training job use @remote decorator so that the jobs are running in the SageMaker-associated private subnets and security group from your private VPC. We define the SageMaker-associated private subnets and security group in the configuration file. config_yaml = f""" SchemaVersion: '1.0'
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.
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.
Amazon Q Business uses AWS IAM Identity Center to record the workforce users you assign access to and their attributes, such as group associations. Because Identity Center serves as their common reference of your users and groups, these AWS applications can give your users a consistent experience as they navigate AWS.
Enterprise customers have multiple lines of businesses (LOBs) and groups and teams within them. The workflow steps are as follows: The user authenticates with the Amazon Cognito user pool and receives a token to consume the Studio access API. The user calls the API to access Studio and includes the token in the request.
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.
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",
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. System integration – Agents make API calls to integrated company systems to run specific actions.
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.
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 API Account. To complete this tutorial, you will need a Vonage API account. Prerequisites. The latest version of.NET /.NET
You can authenticate Amazon Q Business to Jira using basic authentication with a Jira ID and Jira API token. To authenticate using basic authentication, you create a secret using AWS Secrets Manager with your Jira ID and Jira API token. See Manage API tokens for your Atlassian account for instructions to create an API token.
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.
They enable applications requiring very low latency or local data processing using familiar APIs and tool sets. Create a security group or select an existing one. Configure the security groups inbound rules to allow traffic only from your clients IP address on port 8080. Delete the security groups and subnets.
You can integrate Smartsheet to Amazon Q Business through the AWS Management Console , AWS Command Line Interface (AWS CLI), or the CreateDataSource API. In Smartsheet Have access to the Smartsheet Event Reporting API. This streamlined process improves client retention, increases accuracy, and elevates overall service quality.
Diagram analysis and query generation : The Amazon Bedrock agent forwards the architecture diagram location to an action group that invokes an AWS Lambda. On receiving confirmation from the user, the agent passes this information to the second action group to generate IaC. Take approval from user.
Launching a machine learning (ML) training cluster with Amazon SageMaker training jobs is a seamless process that begins with a straightforward API call, AWS Command Line Interface (AWS CLI) command, or AWS SDK interaction. For this post, we demonstrate SMP implementation on SageMaker trainings jobs. shuffle(seed=42).select(range(1000)).train_test_split(
Challenge 2: Integration with Wearables and Third-Party APIs Many people use smartwatches and heart rate monitors to measure sleep, stress, and physical activity, which may affect mental health. Third-party APIs may link apps to healthcare and meditation services. However, integrating these diverse sources is not straightforward.
Although it’s recommended to have an IAM Identity Center instance configured (with users federated and groups added) before you start, you can also choose to create and configure an IAM Identity Center instance for your Amazon Q Business application using the Amazon Q console. Similarly for pages and blogs, you use the restrictions page.
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 using a single API, along with a broad set of capabilities you need to build generative AI applications with security, privacy, and responsible AI.
The action is an API that the model can invoke from an allowed set of APIs. Action groups are tasks that the agent can perform autonomously. Action groups are mapped to an AWS Lambda function and related API schema to perform API calls. A set of actions comprise an action group.
The main AWS services used are SageMaker, Amazon EMR , AWS CodeBuild , Amazon Simple Storage Service (Amazon S3), Amazon EventBridge , AWS Lambda , and Amazon API Gateway. Real-time recommendation inference The inference phase consists of the following steps: The client application makes an inference request to the API gateway.
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.
Amazon Bedrock enables access to powerful generative AI models like Stable Diffusion through a user-friendly API. The user chooses Call API to invoke API Gateway to begin processing on the backend. The API invokes a Lambda function, which uses the Amazon Bedrock API to invoke the Stability AI SDXL 1.0
You can use federated groups to define access control, and a user is charged only one time for their highest tier of Amazon Q Business subscription. The client application makes an AssumeRoleWithWebIdentity (OIDC mode) or AssumeRoleWithSAML (SAML mode) API call to AWS Security Token Service (AWS STS) to acquire AWS Sig V4 credentials.
AWS Prototyping successfully delivered a scalable prototype, which solved CBRE’s business problem with a high accuracy rate (over 95%) and supported reuse of embeddings for similar NLQs, and an API gateway for integration into CBRE’s dashboards. The following diagram illustrates the web interface and API management layer.
Step Functions orchestrates AWS services like AWS Lambda and organization APIs like DataStore to ingest, process, and store data securely. For example, the Datastore API might require certain input like date periods to query data. This step can be used to define the date periods to be used by the Map state as an input.
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