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This solution showcases how to bridge the gap between Google Workspace and AWS services, offering a practical approach to enhancing employee efficiency through conversational AI. The custom Google Chat app, configured for HTTP integration, sends an HTTP request to an API Gateway endpoint.
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. The summary is stored inside an S3 bucket, which can be emptied using the extension’s Clean Up feature.
In this post, we show you an example of a generative AI assistant application and demonstrate how to assess its security posture using the OWASP Top 10 for Large Language Model Applications , as well as how to apply mitigations for common threats. These steps might involve both the use of an LLM and external data sources and APIs.
In this post, we introduce the core dimensions of responsible AI and explore considerations and strategies on how to address these dimensions for Amazon Bedrock applications. For early detection, implement custom testing scripts that run toxicity evaluations on new data and model outputs continuously.
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. Whenever local database information is unavailable, it triggers an online search using the Tavily API. Its used by the weather_agent() function.
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 with a single API, along with a broad set of capabilities to build generative AI applications with security, privacy, and responsible AI.
Amazon Rekognition has two sets of APIs that help you moderate images or videos to keep digital communities safe and engaged. Some customers have asked if they could use this approach to moderate videos by sampling image frames and sending them to the Amazon Rekognition image moderation API.
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.
The best practice for migration is to refactor these legacy codes using the Amazon SageMaker API or the SageMaker Python SDK. We demonstrate how two different personas, a data scientist and an MLOps engineer, can collaborate to lift and shift hundreds of legacy models. SageMaker runs the legacy script inside a processing container.
In the post Secure Amazon SageMaker Studio presigned URLs Part 2: Private API with JWT authentication , we demonstrated how to build a private API to generate Amazon SageMaker Studio presigned URLs that are only accessible by an authenticated end-user within the corporate network from a single account.
In this post, we provide an operational overview of the solution, and then describe how to set it up with the following services: Amazon Bedrock and a knowledge base to generate responses from user questions based on enterprise data sources. The request is sent by the web application to the API. An API created with Amazon 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. In this post, we explore how to use Amazon Bedrock to generate synthetic training data to fine-tune an LLM.
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.
This post shows how to configure an Amazon Q Business custom connector and derive insights by creating a generative AI-powered conversation experience on AWS using Amazon Q Business while using access control lists (ACLs) to restrict access to documents based on user permissions. secrets_manager_client = boto3.client('secretsmanager')
The SageMaker Python SDK provides open-source APIs and containers to train and deploy models on SageMaker, using several different ML and deep learning frameworks. In this post, we walk you through an example of how to build and deploy a custom Hugging Face text summarizer on SageMaker. return tokenized_dataset. If we use an ml.g4dn.16xlarge
If you’re a Zendesk user in a Contact Center environment, you’ll want to be using our Zendesk Agent Scripting app. Pause and Resume: If a ticket is transferred, the supervisor or new agent is taken to the last place in the script, and can see the history of the previous steps taken. New Features in Version 11.
Traditionally, earnings call scripts have followed similar templates, making it a repeatable task to generate them from scratch each time. On the other hand, generative artificial intelligence (AI) models can learn these templates and produce coherent scripts when fed with quarterly financial data.
And thus I thought it’d be fun to design and build something with Nexmo’s Voice and SMS APIs to do just that. This tutorial will cover how to create QR Codes to hide throughout your neighborhood and how to create an app with Express and Node.js Replace the API Key, API Secret, App ID, and your Nexmo Number.
This solution shows how Amazon Bedrock agents can be configured to accept cloud architecture diagrams, automatically analyze them, and generate Terraform or AWS CloudFormation templates. This solution uses Retrieval Augmented Generation (RAG) to ensure the generated scripts adhere to organizational needs and industry standards.
The first allows you to run a Python script from any server or instance including a Jupyter notebook; this is the quickest way to get started. In the following sections, we first describe the script solution, followed by the AWS CDK construct solution. The following diagram illustrates the sequence of events within the script.
This post shows how to use AWS generative artificial intelligence (AI) services , like Amazon Q Business , with AWS Support cases, AWS Trusted Advisor , and AWS Health data to derive actionable insights based on common patterns, issues, and resolutions while using the AWS recommendations and best practices enabled by support data.
In particular, we cover the SMP library’s new simplified user experience that builds on open source PyTorch Fully Sharded Data Parallel (FSDP) APIs, expanded tensor parallel functionality that enables training models with hundreds of billions of parameters, and performance optimizations that reduce model training time and cost by up to 20%.
Amazon Bedrock is a fully managed service that offers a choice of high-performing foundation models (FMs) from leading artificial intelligence (AI) companies like AI21 Labs, Anthropic, Cohere, Meta, Mistral AI, Stability AI, and Amazon through a single API. The scripts for fine-tuning and evaluation are available on the GitHub repository.
In this post, we explore how to remove barriers to adoption, significantly amplifying the effectiveness of your CX strategies. Furthermore, TechSee’s technology can be integrated anywhere through APIs or SDKs. However, the true potential of investing in CX innovation often remains untapped due to barriers that hinder adoption.
In this post, we explore how to remove barriers to adoption, significantly amplifying the effectiveness of your CX strategies. Furthermore, TechSee’s technology can be integrated anywhere through APIs or SDKs. However, the true potential of investing in CX innovation often remains untapped due to barriers that hinder adoption.
In this first post, we focus on the basics of RAG architecture and how to optimize text-only RAG. The second post outlines how to work with multiple data formats such as structured data (tables, databases) and images. In part 2 of this post, we will discuss how to extend this capability to images and structured data.
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.
If you’re a Zendesk user in a Contact Center environment, you’ll want to be using our Zendesk Agent Scripting app. Benefits of the Zendesk Agent Scripting App. Installing the Agent Scripting App into Zendesk. Installing the Agent Scripting App into Zendesk. Enabling Automatic Script Selection.
In this blog post, we show how we optimized torch.compile performance on AWS Graviton3-based EC2 instances, how to use the optimizations to improve inference performance, and the resulting speedups. We benchmarked 45 models using the scripts from the TorchBench repo. Starting with PyTorch 2.3.1, Starting with PyTorch 2.3.1,
In this post, we demonstrate how to use Amazon Rekognition , Amazon SageMaker JumpStart , and Amazon OpenSearch Service to solve this business problem. These models have been packaged to be securely and easily deployable via Amazon SageMaker APIs. MatchConfidence – A match confidence score that can be used to control API behavior.
The function then searches the OpenSearch Service image index for images matching the celebrity name and the k-nearest neighbors for the vector using cosine similarity using Exact k-NN with scoring script. Go to the CloudFormation console, choose the stack that you deployed through the deploy script mentioned previously, and delete the stack.
In this post, we demonstrate how to build a RAG workflow using Knowledge Bases for Amazon Bedrock for a drug discovery use case. You can also use the StartIngestionJob API to trigger the sync via the AWS SDK. We use the Knowledge Bases for Amazon Bedrock retrieve_and_generate and retrieve APIs with Amazon Bedrock LangChain integration.
Lets delve into a basic Colang script to see how it works: define user express greeting "hello" "hi" "what's up?" define bot ask how are you "How are you doing?" model API exposed by SageMaker JumpStart properly. define bot express greeting "Hey there!" The Llama 3.1
Lastly the model is tested against a set of known genome sequences using some inference API calls. In the sample Jupyter notebook we show how to download FASTA files from GenBank, convert them into FASTQ files, and then load them into a HealthOmics sequence store. Then we deploy that model as a SageMaker real-time inference endpoint.
An asynchronous API and Amazon OpenSearch Service connector make it easy to integrate the model into your neural search applications. In this post, we walk through how to use the Titan Image Generator and Titan Multimodal Embeddings models via the AWS Python SDK. For Python scripts, you can use the AWS SDK for Python (Boto3).
Similar to the process of PyTorch integration with C++ code, Neuron CustomOps requires a C++ implementation of an operator via a NeuronCore-ported subset of the Torch C++ API. Finally, the custom library is built by calling the load API. For more information, refer to Custom Operators API Reference Guide [Experimental].
In a previous post , we showed you how to build and publish a Web Component. Now it’s time to see how to use a top feature of Web Components: Custom components and widgets build on the Web Component standards will work across modern browsers, and they can be used with any JavaScript library or framework that works with HTML.
Writing a call script is a must for contact centers that want to excel in their prospecting effort. If you write it according to the rules of the game, the script is an observable, cost-effective, and efficient method of attracting and maintaining prospects and clients. What exactly is call scripting? Why do scripts exist?
In this post, we show you how to unlock new levels of efficiency and creativity by bringing the power of generative AI directly into your Slack workspace using Amazon Bedrock. We show how to create a Slack application, configure the necessary permissions, and deploy the required resources using AWS CloudFormation.
You can fine-tune and deploy JumpStart models using the UI in Amazon SageMaker Studio or using the SageMaker Python SDK extension for JumpStart APIs. This post focuses on how we can implement MLOps with JumpStart models using JumpStart APIs, Amazon SageMaker Pipelines , and Amazon SageMaker Projects. sm_client = boto3.client("sagemaker")
You must also associate a security group for your VPC with these endpoints to allow all inbound traffic from port 443: SageMaker API: com.amazonaws.region.sagemaker.api. This is required to communicate with the SageMaker API. Solution walkthrough In this blog post, we demonstrate how to deploy the Terraform solution.
Here are some features which we will cover: AWS CloudFormation support Private network policies for Amazon OpenSearch Serverless Multiple S3 buckets as data sources Service Quotas support Hybrid search, metadata filters, custom prompts for the RetreiveAndGenerate API, and maximum number of retrievals.
In this post, we show how to activate privacy-preserving ML predictions for the most highly regulated environments. Although this example shows how to perform this for inference operations, you can extend the solution to training and other ML steps. It’s also important to phrase all computations as linear equations. resource("s3").Bucket
This post demonstrates how to use Amazon SageMaker to fine tune a Sentence Transformer embedding model and deploy it with an Amazon SageMaker Endpoint. The following code snippet demonstrates how to load a training dataset from a JSON file, prepares the data for training, and then fine tunes the pre-trained model.
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