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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. Although the principles discussed are applicable across various industries, we use an automotive parts retailer as our primary example throughout this post.
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.
That can become a compliance challenge for industries like healthcare, financial services, insurance, and more. Unclear ROI ChatGPT is currently not accessible via API and the cost of a (hypythetical) API call are unclear. It’s a preview of progress; we have lots of work to do on robustness and truthfulness.”
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.
In the following sections, we explain how AI Workforce enables asset owners, maintenance teams, and operations managers in industries such as energy and telecommunications to enhance safety, reduce costs, and improve efficiency in infrastructure inspections. Security is paramount, and we adhere to AWS best practices across the layers.
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.
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.
Professionals in a wide variety of industries have adopted digital video conferencing tools as part of their regular meetings with suppliers, colleagues, and customers. Amazon Simple Storage Service (Amazon S3) is an object storage service offering industry-leading scalability, data availability, security, and performance.
In addition, they use the developer-provided instruction to create an orchestration plan and then carry out the plan by invoking company APIs and accessing knowledge bases using Retrieval Augmented Generation (RAG) to provide an answer to the users request. This differs from confirmation flows where the agent directly executes API calls.
These intelligent, autonomous systems are poised to become the cornerstone of AI adoption across industries, heralding a new era of human-AI collaboration and problem-solving. As these systems evolve, they will transform industries, expand possibilities, and open new doors for artificial intelligence.
Organizations across industries struggle with automating repetitive tasks that span multiple applications and systems of record. Traditional automation approaches require custom API integrations for each application, creating significant development overhead. Prerequisites AWS Command Line Interface (CLI), follow instructions here.
Intricate workflows that require dynamic and complex API orchestration can often be complex to manage. In industries like insurance, where unpredictable scenarios are the norm, traditional automation falls short, leading to inefficiencies and missed opportunities. With the power of intelligent agents, you can simplify these challenges.
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. Import the API schema from the openapi_schema.json file that you downloaded earlier. Download all three sample data files.
Its inspiring to see how, together, were enabling customers across industries to confidently move AI into production. To give their users even more flexibility, Perplexity complements their own models with services such as Amazon Bedrock , and provides access to additional state-of-the-art models in their API.
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.
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.
Amazon Nova is a new generation of state-of-the-art foundation models (FMs) that deliver frontier intelligence and industry-leading price-performance. How well do these models handle RAG use cases across different industry domains? Amazon Bedrock APIs make it straightforward to use Amazon Titan Text Embeddings V2 for embedding data.
Industrial facilities grapple with vast volumes of unstructured data, sourced from sensors, telemetry systems, and equipment dispersed across production lines. Nevertheless, standalone FMs face limitations in handling complex industrial data with context size constraints (typically less than 200,000 tokens ), which poses challenges.
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.
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.
This enables our sales teams to quickly create initial drafts for sections such as customer overviews, industry analysis, and business priorities, which previously required hours of research across the internet and relied on disparate internal AWS tools. Expanded use of recommendations to provide what next?
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.
Many enterprise customers across various industries are looking to adopt Generative AI to drive innovation, user productivity, and enhance customer experience. This involves creating an OAuth API endpoint in ServiceNow and using the web experience URL from Amazon Q Business as the callback URL.
By choosing View API , you can also access the model using code examples in the AWS Command Line Interface (AWS CLI) and AWS SDKs. For more information on generating JSON using the Converse API, refer to Generating JSON with the Amazon Bedrock Converse API. Additionally, Pixtral Large supports the Converse API and tool usage.
In this post, we discuss two new features of Knowledge Bases for Amazon Bedrock specific to the RetrieveAndGenerate API: configuring the maximum number of results and creating custom prompts with a knowledge base prompt template. You can now choose these as query options alongside the search type. First, we set numberOfResults = 5.
You can retrieve the number of copies of an inference component at any time by making the DescribeInferenceComponent API call and checking the CurrentCopyCount. ApplicationAutoScaling may be in-progress (if configured) or try to increase the capacity by invoking UpdateInferenceComponentRuntimeConfig API. import json scheduler = boto3.client('scheduler')
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.
Generative AI is reshaping businesses and unlocking new opportunities across various industries. We explore how you can use Amazon Bedrock Agents with generative AI and cutting-edge AWS technologies, which offer a transformative approach to supporting sales reps across this industry (and beyond). Haiku and Sonnet, Meta Llama 3.1,
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 Bedrock Guardrails provides configurable safeguards that help organizations build generative AI applications with industry-leading safety protections. In this role, he uses his expertise in cloud-based architectures to develop innovative generative AI solutions for clients across diverse industries.
The integration of generative AI capabilities is driving transformative changes across many industries. We use various AWS services to deploy a complete solution that you can use to interact with an API providing real-time weather information. This Lambda function used a weather API to fetch up-to-date meteorological data.
However, some geographies and regulated industries bound by data protection and privacy regulations have sought to combine generative AI services in the cloud with regulated data on premises. With this mechanism, you can build distributed RAG applications for highly regulated industries subject to data residency requirements.
Reduced time and effort in testing and deploying AI workflows with SDK APIs and serverless infrastructure. Thomson Reuters transforms the way professionals work by delivering innovative tech and GenAI powered by trusted expertise and industry-leading insights. Flexibility to define the workflow based on your business logic.
Amazon Bedrock Marketplace is a new capability in Amazon Bedrock that enables developers to discover, test, and use over 100 popular, emerging, and specialized foundation models (FMs) alongside the current selection of industry-leading models in Amazon Bedrock. To begin using Pixtral 12B, choose Deploy.
Importantly, cross-Region inference prioritizes the connected Amazon Bedrock API source Region when possible, helping minimize latency and improve overall responsiveness. v2 using the Amazon Bedrock console or the API by assuming the custom IAM role mentioned in the previous step ( Bedrock-Access-CRI ).
When enterprises fine-tune curated models, they can specialize general-purpose solutions for their specific industry needs and gain competitive advantages through improved performance on their proprietary data. Update models in the private hub Modify your existing private HubContent by calling the new sagemaker:UpdateHubContent API.
Companies across all industries are harnessing the power of generative AI to address various use cases. Cloud providers have recognized the need to offer model inference through an API call, significantly streamlining the implementation of AI within applications. Here, we map the description through with no change and use $$.Map.Item.Value
During these live events, F1 IT engineers must triage critical issues across its services, such as network degradation to one of its APIs. This impacts downstream services that consume data from the API, including products such as F1 TV, which offer live and on-demand coverage of every race as well as real-time telemetry.
The user’s request is sent to AWS API Gateway , which triggers a Lambda function to interact with Amazon Bedrock using Anthropic’s Claude Instant V1 FM to process the user’s request and generate a natural language response of the place location. It will then return the place name with the highest similarity score.
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.
The data sources may include seismic surveys, well logs, core samples, geochemical analyses, and production histories, with some of it in industry-specific formats. Industrial maintenance – We built a solution that combines maintenance logs, equipment manuals, and visual inspection data to optimize maintenance schedules and troubleshooting.
Amazon Bedrock Marketplace is a new capability in Amazon Bedrock that developers can use to discover, test, and use over 100 popular, emerging, and specialized foundation models (FMs) alongside the current selection of industry-leading models in Amazon Bedrock. It doesnt support Converse APIs or other Amazon Bedrock tooling.
Agent Creator is a versatile extension to the SnapLogic platform that is compatible with modern databases, APIs, and even legacy mainframe systems, fostering seamless integration across various data environments. The integration with Amazon Bedrock is achieved through the Amazon Bedrock InvokeModel APIs.
Introduction to Amazon Nova models Amazon Nova is a new generation of foundation model (FM) offering frontier intelligence and industry-leading price-performance. Create a fine-tuning job Fine-tuning Amazon Nova models through the Amazon Bedrock API is a streamlined process: On the Amazon Bedrock console, choose us-east-1 as your AWS Region.
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