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Zakinator123 / ask-airy

An Airtable extension that enables semantic search and Q&A using gpt-3.5
https://askairy.com
MIT License
13 stars 1 forks source link

Deeper explanation #2

Open JpEncausse opened 3 months ago

JpEncausse commented 3 months ago

Hello, I like the way you did it ! This project is 1 year old, is it still relevant or did you change your usage ? Because AI is moving so fast.

I was wondering if I correctly understand your process :

  1. Create a column for embeddings
  2. Convert some fields of a row into an embedding. Store it into the new column

The user ask a question :

  1. Ask an LLM to create a "fake row"
  2. Create an embeddings for this row
  3. Compare distance between this embeddign and the table
  4. Build an answer based on top X matching rows.

I didn't understand the 3. wan you explain where/how this cosine similarity works in the code ? If I have 3000 rows do you iterate on all to run this algorithm ? I don't find how it works. Is it efficient ?

Thanks !