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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 105
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Automate chatbot for document and data retrieval using Agents and Knowledge Bases for Amazon Bedrock

AWS Machine Learning

Numerous customers face challenges in managing diverse data sources and seek a chatbot solution capable of orchestrating these sources to offer comprehensive answers. This post presents a solution for developing a chatbot capable of answering queries from both documentation and databases, with straightforward deployment.

Chatbots 127
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What Timeframe for an AI Chatbot Project?

Inbenta

Depending on the context in which the chatbot project takes place, and therefore its scope of action, its implementation may take more or less time. Indeed, the development of a chatbot implies creating new jobs such as the one of Botmaster for example. How long does it take to deploy an AI chatbot? Let’s see what these can be.

Chatbots 140
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Delight your customers with great conversational experiences via QnABot, a generative AI chatbot

AWS Machine Learning

In this post, we discuss how to use QnABot on AWS to deploy a fully functional chatbot integrated with other AWS services, and delight your customers with human agent like conversational experiences. After authentication, Amazon API Gateway and Amazon S3 deliver the contents of the Content Designer UI.

Chatbots 128
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GenAI for Aerospace: Empowering the workforce with expert knowledge on Amazon Q and Amazon Bedrock

AWS Machine Learning

This post shows how aerospace customers can use AWS generative AI and ML-based services to address this document-based knowledge use case, using a Q&A chatbot to provide expert-level guidance to technical staff based on large libraries of technical documents. Sign in to the Amazon Q console.

APIs 108
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Building AI chatbots using Amazon Lex and Amazon Kendra for filtering query results based on user context

AWS Machine Learning

When the user signs in to an Amazon Lex chatbot, user context information can be derived from Amazon Cognito. The Amazon Lex chatbot can be integrated into Amazon Kendra using a direct integration or via an AWS Lambda function. The use of the AWS Lambda function will provide you with fine-grained control of the Amazon Kendra API calls.

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How Q4 Inc. used Amazon Bedrock, RAG, and SQLDatabaseChain to address numerical and structured dataset challenges building their Q&A chatbot

AWS Machine Learning

Enterprises turn to Retrieval Augmented Generation (RAG) as a mainstream approach to building Q&A chatbots. The end goal was to create a chatbot that would seamlessly integrate publicly available data, along with proprietary customer-specific Q4 data, while maintaining the highest level of security and data privacy.

Chatbots 124