From the course: Introduction to AI-Native Vector Databases
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Solution: Vector DBs
From the course: Introduction to AI-Native Vector Databases
Solution: Vector DBs
You've taken the time to work on this challenge. Now, I'll walk you through my solution. So, challenge five is interesting because we're going to revisit our tiny jeopardy-like data set, and we're going to get a chatbot powered by a large language model to answer some of our jeopardy questions. So, here what we're really doing is implementing the RAG workflow. We're going to retrieve the questions from the vector database that we've stored them in. And we're going to augment and pass them to a large language model so that it can read the question and then generate an answer. And then, we're going to complete the circle by looking at how many answers it got correct and how many answers it got wrong. So, to start off, we'll load in our data set here. Here, we've got the ten question data set. We're going to start up Weaviate, and then we're going to insert all of our data into Weaviate. And to make sure that everything is in there, we just do a simple aggregate query here to make sure…
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