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@jannem @mntmn
AFAIK the way these LLM tools work is they have an embedding of words into a vector space, they index text by converting every word in a every document to a vector, and storing it in a database together with ID of the document it came from, and then when you search, they turn each of the query words into vectors, and search for K nearest neighbors in the vector space for each of them.

Then they feed the documents they found to an LLM.

What if you skipped the last step?