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Enhance customer support with Amazon Bedrock Agents by integrating enterprise data APIs

AWS Machine Learning

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.

APIs 128
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Automate building guardrails for Amazon Bedrock using test-driven development

AWS Machine Learning

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.

APIs 115
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Track LLM model evaluation using Amazon SageMaker managed MLflow and FMEval

AWS Machine Learning

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.

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Record a Call in Ruby with Vonage Voice API WebSockets

Nexmo

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.

APIs 125
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Enterprise-grade natural language to SQL generation using LLMs: Balancing accuracy, latency, and scale

AWS Machine Learning

Augmenting SQL DDL definitions with metadata to enhance LLM inference This involves enhancing the LLM prompt context by augmenting the SQL DDL for the data domain with descriptions of tables, columns, and rules to be used by the LLM as guidance on its generation. The set of few-shot examples of user queries and corresponding SQL statements.

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Getting started with computer use in Amazon Bedrock Agents

AWS Machine Learning

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.

APIs 133
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Using natural language in Amazon Q Business: From searching and creating ServiceNow incidents and knowledge articles to generating insights

AWS Machine Learning

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.

APIs 90