Small approach to use RAG for better result in similarity for related content. The best thing is the process of getting vectors are don't only once, then you can reuse them indefinitely.
This isn't RAG, it's using the embedding-based similarity search part of RAG for doing similarity search things.
The use-case presented, finding similar recipes based on ingredients, is interesting. I'm curious how well it works in practice, especially with a generic embedding model like the one used.
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[ 2.5 ms ] story [ 14.8 ms ] threadThe use-case presented, finding similar recipes based on ingredients, is interesting. I'm curious how well it works in practice, especially with a generic embedding model like the one used.