Phase 1 · Building with LLMs · Context & retrieval
Similarity search
Find the most relevant text by comparing embeddings.
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Why it matters
Similarity search is the engine inside "related products", support-article suggestions and, above all, retrieval-augmented generation. Before a model can answer from your help center, your brand guide or last quarter's reports, your code has to find the few passages that matter out of thousands. That's this lesson: embed once, compare fast, return the best few.
What you'll be able to do
- Build a small search index by embedding a set of documents once
- Find the top-k most similar documents for a query
- Score every document at once with a matrix of vectors
- Use a minimum score so weak matches aren't returned
Before this lesson
Next in this phase: Chunking documents