From the course: Fundamentals of AI Engineering: Principles and Practical Applications
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Challenge
From the course: Fundamentals of AI Engineering: Principles and Practical Applications
Challenge
(bright music) - [Instructor] Now that you've learned about embeddings, models and efficient processing techniques, it's time to put that knowledge into practice with a real world application. Open up chapter four and navigate to 04_05.py. This is the first challenge of the course. In this challenge, you'll build a semantic search system for a customer support team. The team needs to quickly find relevant documentation based on user queries, a perfect application for the embedding techniques we've covered so far. Let me walk you through what we'll be implementing. You'll be creating semantic search engine that uses sentence transformers to generate embeddings for both documents and queries. You'll organize these embeddings in a way that allows for efficient searching. Second, you'll be implementing embeddings caching. Remember how we discuss that generating embeddings can be computationally expensive? By caching embeddings for common queries, you'll avoid regenerating them, saving…
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