Conversational Search for Cloud Support
Help developer search and troubleshoot AWS Cloud Services
Overview
Conversational Search for Cloud Support is powered by Amazon Kendra and Generative AI that enables employees and builders to quickly find important AWS cloud services related information and troubleshooting information. This demo uses AWS product documentation and AWS re:Post knowledge center ariticales. This demo is applicable to customers looking for Generative Search and Discovery applications such as 1/ Q&A Chatbots with Generative capabilities 2/ Conversational Virtual Assistants for cloud services builders 3/ Contact center solutions such as Agent Assist.
How it works
Amazon Kendra is used to index official ariticles, troubleshooting guides, and documents from sources like the AWS documentation site and AWS re:Post. This makes the cloud services realted ariticles easily searchable via natural language.
Large pretrained language models is then used to understand user queries, summarize and extract key details from the tax documents, and provide concise answers.
By combining the search capabilities of Kendra with the natural language prowess of these models, we can build an interface that helps developers and engineers quickly find relevant Cloud services related information through interactive conversations.
The language models can summarize key product infromation and troubleshooting procedures, while Kendra provides access to the original documentation for reference. Together, they become more powerful than either individual approach.
Demo Prompts
- How durable is amazon s3?
- Can I see my s3 storage usage?
- Getting 403 access denied when accessing s3 bucket with public read
- My admin asks me turn on MFA for S3 delete. how do I enable it?
- Why I cannot connect to my EC2 instance?
Architecture
Conversational Search for Cloud Support is powered by Amazon Kendra and Generative AI. It utilizes Amazon Lex for managing conversation sessions and intent detection. AWS Lambda and LangChain framework is used to orchestrate the process of retrieving relevant excerpts from enterprise data using Amazon Kendra and pass the context to Large Language Model for generative content creation.
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