Remove APIs Remove Scripts Remove Tools
article thumbnail

Integrate dynamic web content in your generative AI application using a web search API and Amazon Bedrock Agents

AWS Machine Learning

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.

APIs 101
article thumbnail

Generate customized, compliant application IaC scripts for AWS Landing Zone using Amazon Bedrock

AWS Machine Learning

Tools like Terraform and AWS CloudFormation are pivotal for such transitions, offering infrastructure as code (IaC) capabilities that define and manage complex cloud environments with precision. Traditionally, cloud engineers learning IaC would manually sift through documentation and best practices to write compliant IaC scripts.

Scripts 122
Insiders

Sign Up for our Newsletter

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Trending Sources

article thumbnail

Create a generative AI–powered custom Google Chat application using Amazon Bedrock

AWS Machine Learning

Many businesses want to integrate these cutting-edge AI capabilities with their existing collaboration tools, such as Google Chat, to enhance productivity and decision-making processes. The custom Google Chat app, configured for HTTP integration, sends an HTTP request to an API Gateway endpoint.

APIs 118
article thumbnail

Secure Amazon SageMaker Studio presigned URLs Part 3: Multi-account private API access to Studio

AWS Machine Learning

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.

APIs 75
article thumbnail

How Druva used Amazon Bedrock to address foundation model complexity when building Dru, Druva’s backup AI copilot

AWS Machine Learning

Dru on the backend decodes log data, deciphers error codes, and invokes API calls to troubleshoot. Overall orchestration Originally, we adopted an AI agent approach and relied on the foundation model (FM) to make plans and invoke tools using the reasoning and acting (ReAct) method to answer user questions.

APIs 104
article thumbnail

Amazon Bedrock Custom Model Import now generally available

AWS Machine Learning

This feature empowers customers to import and use their customized models alongside existing foundation models (FMs) through a single, unified API. This unified experience enables developers to use the same tooling and workflows across both base FMs and imported custom models. Be backed by enterprise grade security and privacy tooling.

APIs 134
article thumbnail

Create a multimodal chatbot tailored to your unique dataset with Amazon Bedrock FMs

AWS Machine Learning

The logic flow for generating an answer to a text-image response pair routes as follows: Steps 1 and 2 – To start, a user query and corresponding image are routed through an Amazon API Gateway connection to an AWS Lambda function, which serves as the processing and orchestrating compute for the overall process. us-east-1 or bash deploy.sh

Chatbots 119