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Amazon Bedrock announces the preview launch of Session ManagementAPIs, 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.
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 managementAPIs to track stock levels and catalog APIs for vehicle compatibility and specifications.
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.
Ultimately, this systematic approach to managing models, prompts, and datasets contributes to the development of more reliable and transparent generative AI applications. MLflow is an open source platform for managing the end-to-end ML lifecycle, including experimentation, reproducibility, and deployment.
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. It contains services used to onboard, manage, and operate the environment, for example, to onboard and off-board tenants, users, and models, assign quotas to different tenants, and authentication and authorization microservices.
Bruce McMahon, VP of Product Management, shares updates from the 2023.09 release including enhancements to bulk export API capabilities, giving customers even more control over their data.
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.
One can quickly host such application on the AWS Cloud without managing the underlying infrastructure, for example, with Amazon Simple Storage Service (S3) and Amazon CloudFront. Note that these APIs use objects as namespaces, alleviating the need for explicit imports. For testing, one can sideload an Office Add-in.
The Amazon Bedrock single API access, regardless of the models you choose, gives you the flexibility to use different FMs and upgrade to the latest model versions with minimal code changes. Amazon Titan FMs provide customers with a breadth of high-performing image, multimodal, and text model choices, through a fully managedAPI.
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.
To enable the video insights solution, the architecture uses a combination of AWS services, including the following: Amazon API Gateway is a fully managed service that makes it straightforward for developers to create, publish, maintain, monitor, and secure APIs at scale.
Amazon Q Business is a fully managed, generative AI-powered assistant designed to enhance enterprise operations. This integration empowers you to use advanced search capabilities and intelligent email management using natural language. Access to AWS Secrets Manager. On the API Library page, search for and choose Admin SDK API.
Intricate workflows that require dynamic and complex API orchestration can often be complex to manage. Using natural language processing (NLP) and OpenAPI specs, Amazon Bedrock Agents dynamically managesAPI sequences, minimizing dependency management complexities.
Building generative AI applications presents significant challenges for organizations: they require specialized ML expertise, complex infrastructure management, and careful orchestration of multiple services. You can obtain the SageMaker Unified Studio URL for your domains by accessing the AWS Management Console for Amazon DataZone.
By using the power of LLMs and combining them with specialized tools and APIs, agents can tackle complex, multistep tasks that were previously beyond the reach of traditional AI systems. LangGraph is essential to our solution by providing a well-organized method to define and manage the flow of information between agents.
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. Today, were announcing computer use support within Amazon Bedrock Agents using Anthropics Claude 3.5
Enterprises that have adopted ServiceNow can improve their operations and boost user productivity by using Amazon Q Business for various use cases, including incident and knowledge management. For a complete list of AWS Identity and Access Management (IAM) roles for Amazon Q Business, see IAM roles for Amazon Q Business.
Recognizing this need, we have developed a Chrome extension that harnesses the power of AWS AI and generative AI services, including Amazon Bedrock , an AWS managed service to build and scale generative AI applications with foundation models (FMs). For additional details, refer to Creating a new user in the AWS Management Console.
Make sure that you have the correct AWS Identity and Access Management (IAM) permissions to use Amazon Bedrock. Clone the repo To get started, clone the repository by running the following command, and then switch to the working directory: git clone [link] Build your guardrail To build the guardrail, you can use the CreateGuardrail API.
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.
Generative AI scoping framework Start by understanding where your generative AI application fits within the spectrum of managed vs. custom. These steps might involve both the use of an LLM and 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.
Amazon Bedrock Knowledge Bases is a fully managed capability that simplifies the management of the entire RAG workflow, empowering organizations to give foundation models (FMs) and agents contextual information from your private data sources to deliver more relevant and accurate responses tailored to your specific needs.
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. Amazon Bedrock Agents offers a fully managed solution for creating, deploying, and scaling AI agents on AWS.
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.
In 2024, this activity took an account manager (AM) up to 40 hours per customer. Data synthesis: The assistant can pull relevant information from multiple sources including from our customer relationship management (CRM) system, financial reports, news articles, and previous APs to provide a holistic view of our customers.
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
The new feature expands the possibilities for managing SageMaker inference endpoints. It allows you to configure the endpoints so they can scale to zero instances during periods of inactivity, providing an additional tool for resource management.
With Global Resiliency, you no longer need to manually manage separate bots across Regions, because the feature automatically replicates and keeps Regional configurations in sync. Enabling Global Resiliency for an Amazon Lex bot is straightforward using the AWS Management Console , AWS Command Line Interface (AWS CLI), or APIs.
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. This can be information like the title, description, or movie genre.
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")
In this article, well explore what a call center knowledge management system (KMS) is and how it can bridge the gaps between your agents, information storage, and customer service. As self-service systems get smarter, your agents are left to manage more complex customer issues. What is a knowledge management system?
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.
AWS is the first major cloud provider to deliver Pixtral Large as a fully managed, serverless model. By choosing View API , you can also access the model using code examples in the AWS Command Line Interface (AWS CLI) and AWS SDKs. Additionally, Pixtral Large supports the Converse API and tool usage.
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. Create a new flow if required.
Within SageMaker JumpStart, the private model hub feature allows organizations to create their own internal repository of ML models, enabling teams to share and manage models securely within their organization. No model artifacts need to be managed by the customer. The SageMaker team will manage version or security updates.
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. Pre-built templates tailored to various use cases are included, significantly enhancing both employee and customer experiences.
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
Cross-Region inference enables you to seamlessly manage unplanned traffic bursts by utilizing compute across different Regions. Importantly, cross-Region inference prioritizes the connected Amazon Bedrock API source Region when possible, helping minimize latency and improve overall responsiveness.
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.
An AWS Identity and Access Management (IAM) role to access Amazon Bedrock Marketplace and Amazon SageMaker endpoints. You can find detailed usage instructions, including sample API calls and code snippets for integration. Our prompt and input payload are as follows: system_prompt='''You are a catalog manager for an ecommerce portal.
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.
Today, we’re excited to introduce two powerful new features for Amazon Bedrock: Prompt Management and Prompt Flows, in public preview. You can use the Prompt Management and Flows features graphically on the Amazon Bedrock console or Amazon Bedrock Studio, or programmatically through the Amazon Bedrock SDK APIs.
Amazon Bedrock Knowledge Bases offers a fully managed Retrieval Augmented Generation (RAG) feature that connects large language models (LLMs) to internal data sources. It also provides developers with greater control over the LLMs outputs, including the ability to include citations and manage sensitive information.
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