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Forget keyword matching—similarity search lets your AI find what you mean, not just what you type. Ricardo Ferreira walks through building a similarity search with Redis as a vector store in LangChain, using a Marvel anti-hero dataset. See how embeddings and distance algorithms power smarter, fuzzier lookups, and how to fine-tune your queries for speed and precision.

Key topics

1. Build and optimize similarity search using Redis as a vector store in LangChain
2. How embeddings and distance algorithms make search results smarter and more intuitive