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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.
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. There are multiple components to a guardrail for Amazon Bedrock.
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.
The Vonage Voice API WebSockets feature recently left Beta status and became generally available. Vonage API Account. To complete this tutorial, you will need a Vonage API account. Once you have an account, you can find your API Key and API Secret at the top of the Vonage API Dashboard. Building the Server.
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.
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.
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.
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.
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.
Designing the prompt Before starting any scaled use of generative AI, you should have the following in place: A clear definition of the problem you are trying to solve along with the end goal. Refer to Getting started with the API to set up your environment to make Amazon Bedrock requests through the AWS API. client = boto3.client("bedrock-runtime",
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.
It allows developers to build voice and text messaging features through Application programming interfaces (API). Software engineers familiar with using APIs can operate CPaaS. The post What is CCaaS (Contact Center as a Service): Definition, and Benefits appeared first on Justcall Blog. Tips for Choosing CCaaS Providers.
In this post, we will show you how you can use the Vonage Voice API to protect one of their most valuable data assets—their phone number. In a few simple steps we will build a parental control application that will mask your child’s phone number using the Voice API and Android 10. Android 10 Call Redirection Feature. Prerequisites.
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. The class definition is similar to the LangChain ConversationalChatAgent class.
However, there are benefits to building an FM-based classifier using an API service such as Amazon Bedrock, such as the speed to develop the system, the ability to switch between models, rapid experimentation for prompt engineering iterations, and the extensibility into other related classification tasks.
It’s also possible to use the Nicereply API to build an entirely customized survey flow. There are definitely Customer Effort Score detractors out there. They need definitely more research to confirm the connection between difficult experiences and disloyalty. This aids in the future analysis of effort score trends and churn.
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
Amazon Bedrock is a fully managed service that provides access to high-performing foundation models (FMs) from leading AI startups and Amazon through a unified API. Measures Assistant is a microservice deployed in a Kubernetes on AWS environment and accessed through a REST API.
You can deploy or fine-tune models through an intuitive UI or APIs, providing flexibility for all skill levels. Task definition (count_task) This is a task that we want this agent to execute. Amazon SageMaker JumpStart offers a diverse selection of open and proprietary FMs from providers like Hugging Face, Meta, and Stability AI.
Moreover, this capability prioritizes the connected Amazon Bedrock API source/primary region when possible, helping to minimize latency and improve responsiveness. Compatibility with existing Amazon Bedrock API No additional routing or data transfer cost and you pay the same price per token for models as in your source/primary region.
The best practice for migration is to refactor these legacy codes using the Amazon SageMaker API or the SageMaker Python SDK. Step Functions is a serverless workflow service that can control SageMaker APIs directly through the use of the Amazon States Language. We do so using AWS SDK for Python (Boto3) CreateProcessingJob API calls.
Luckily there is an easy way to make this happen: API integration. What is an API? An API (Application Programming Interface) is a kind of universal translator for software. So the API sits between them and translates. Ok – so what’s “API integration”? API integration helps them to understand one another. .
Forecasting Core Features The Ability to Consume Historical Data Whether it’s from a copy/paste of a spreadsheet or an API connection, your WFM platform must have the ability to consume historical data. Scheduling Core Features Matching Schedules to Forecasted Volume The common definition of WFM is “right people, right place, right time”.
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.
The Cloud-Based Software Behind the Scenes Beyond a simple definition of the term, its impossible to talk about the omnichannel contact center without talking about omnichannel contact center software solutions that make them possible. API and Integrations: WFO solutions arent the only thing your contact center software should connect with.
The frontend UI interacts with the extract microservice through a RESTful interface provided by Amazon API Gateway. For each image, it will apply the following ML features to extract information: Recognize celebrity faces using the Amazon Rekognition celebrity API. Detect text using the Amazon Rekognition text detection API.
Rather than using probabilistic approaches such as traditional machine learning (ML), Automated Reasoning tools rely on mathematical logic to definitively verify compliance with policies and provide certainty (under given assumptions) about what a system will or wont do. However, its important to understand its limitations.
This configuration takes the form of a Directed Acyclic Graph (DAG) represented as a JSON pipeline definition. The DevOps engineer can then use the Kubernetes APIs provided by ACK to submit the pipeline definition and initiate one or more pipeline runs in SageMaker. amazonaws.com/sagemaker-xgboost:1.7-1",
Amazon Transcribe The transcription for the entire video is generated using the StartTranscriptionJob API. The solution runs Amazon Rekognition APIs for label detection , text detection, celebrity detection , and face detection on videos. The metadata generated for each video by the APIs is processed and stored with timestamps.
Large language model (LLM) agents are programs that extend the capabilities of standalone LLMs with 1) access to external tools (APIs, functions, webhooks, plugins, and so on), and 2) the ability to plan and execute tasks in a self-directed fashion. Note that the next action may or may not involve using a tool or API.
Problem definition Traditionally, the recommendation service was mainly provided by identifying the relationship between products and providing products that were highly relevant to the product selected by the customer. Lambda receives the list of recommendations and provides them to the API gateway.
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. These approaches allow for the definition of more sophisticated logic and dynamic workflows, often called prompt flows.
In this post, we explain the common practice of live stream visual moderation with a solution that uses the Amazon Rekognition Image API to moderate live streams. There are charges for Amazon S3 storage, Amazon S3 API calls that Amazon IVS makes on behalf of the customer, and serving the stored video to viewers.
For interacting with AWS services, the AWS Amplify JS library for React simplifies the authentication, security, and API requests. The backend uses several serverless and event-driven AWS services, including AWS Step Functions for low-code workflows, AWS AppSync for a GraphQL API, and Amazon Translate. 1 – Translating a document.
Amazon Bedrock is a fully managed service that offers an easy-to-use API for accessing foundation models for text, image, and embedding. Amazon Location offers an API for maps, places, and routing with data provided by trusted third parties such as Esri, HERE, Grab, and OpenStreetMap. Point function requires importing shapely library.
Configuring IP-restricted presigned URLs for SageMaker Ground Truth The new IP restriction feature for presigned URLs in SageMaker Ground Truth can be enabled through the SageMaker API or the AWS Command Line Interface (AWS CLI). You can also perform these operations through the SageMaker API using the AWS SDK.
The standard definition of PDD can be defined as: “the time or delay that occurs from the time a number has been dialed, until the caller or called party hears ringing.” This moment that a customer is impatiently waiting in and getting annoyed by is known as post dial delay (PDD). What is post dial delay (PDD) ?
The next stage is the extraction phase, where you pass the collected invoices and receipts to the Amazon Textract AnalyzeExpense API to extract financially related relationships between text such as vendor name, invoice receipt date, order date, amount due, amount paid, and so on. It is available both as a synchronous or asynchronous API.
We’ve made several pushes of our scalable API solution, but for anyone who isn’t a developer, those three letters can look pretty scary. For the developers out there reading this, our API will be enterprise-ready following Oauth2 protocol. One thing is for sure: name pronunciation is definitely not going to be the same.
Customer success is a rapidly changing field, and the past ten years or so are definitely evidence of this fact. What kinds of API integrations do you provide to seamlessly connect with our other vendors? Asking the right questions now means you can make a lasting investment no matter how your customer’s needs change. Learn more.
Building proofs of concept is relatively straightforward because cutting-edge foundation models are available from specialized providers through a simple API call. Cohere language models in Amazon Bedrock The Cohere Platform brings language models with state-of-the-art performance to enterprises and developers through a simple API call.
This model is then deployed on Amazon Elastic Container Service (Amazon ECS), which enables quick auto scaling and easy deployment definition and management. You can easily do this with PyTorch Neuron using the torch.neuron.DataParallel API. As you can see, compiler arguments can be passed to the torch.neuron API directly.
You’ll need a Vonage API Account. Please take note of your accounts API Key, API Secret, and the number that comes with it. The Class definition should look like this: public class TranslationEngine : IDisposable. For now, we will add an empty API controller called VoiceController to our Controllers folder.
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