I recommend you look deeper at LangChain if you are not already familiar with it. You can also look at the aws-samples Github page; they have some great examples to get you started. For example, you could add Amazon Kendra to the mix. Connect it with one of its many sources, like Atlassian Confluence, and set up Langchain to utilize the Kendra retriever. And now you have a chatbot that can answer questions based... - Source: dev.to / 7 months ago

If you're doing this on AWS they already have a really contained solution for this. I'm sure Azure has a similar solution. I'll assume AWS - if so, AWS Kendra is a good place to start. This will give you performant natural language understanding and enterprise search support. Then you just need to map the rest of your desired functions to core AWS solutions. Source: about 1 year ago

> > One of the most important capabilities of Bedrock is how easy it is to customize a model. Customers simply point Bedrock at a few labeled examples in Amazon S3, and the service can fine-tune the model for a particular task without having to annotate large volumes of data (as few as 20 examples is enough) I can't even. Does anyone remember Amazon Kendra [1]? They promised the same there. "Here's an ML powered... - Source: Hacker News / about 1 year ago

Amazon Kendra is now FedRAMP High Compliant. Amazon Kendra is now authorized as FedRAMP High in AWS GovCloud (US-West) Region. Amazon Kendra is a highly accurate intelligent search service powered by machine learning. Kendra reimagines enterprise search for your websites and applications so your employees and customers can easily find the content they are looking for, even when it's scattered across multiple... - Source: dev.to / over 1 year ago

It supports using Lambdas for fulfilling specific duties, such as placing orders, as well as integrating into Kendra which is a ML powered search service. You can effectively have a conversation asking for information and it can intelligently look through your business domain and respond with answers in a naturally conversational way. - Source: dev.to / over 1 year ago

We’re going to setup a Kafka cluster using confluent.io, create a producer and consumer as well as enhance our behavior driven tests to include the new interface. We’re going to update our helm chart so that the updates are seamless to Kubernetes and we’re going to leverage our observability stack to propagate the traces in the published messages. Source: over 2 years ago