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m98789 t1_j4f135j wrote

Reply to comment by GoodluckH in [D]: Are there models like CODEX but work in a reversed way? by GoodluckH

Got it, this is how I believe it was implemented:

  • Stage 0: All code was split into chunks and had their embeddings taken, and saved into one table for lookups, e.g., code in one field and embedding in the adjacent field.
  • Stage 1: semantic search to find code. Take your query and encode it into an embedding. Then apply dot product over all the code embeddings in the table to find semantically similar code chunks.
  • Stage 2: combine all the top-K similar chunks into one string or list we can call the “context”.
  • Stage 3: stuff the context into a prompt as a preamble, then append the actual question you want to ask.
  • Stage 4: execute the prompt to a LLM like gpt-3 and collect the answer and show it to the user.
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GoodluckH OP t1_j4hiypg wrote

Ahh this makes a lot of sense. Regarding stage 0, how do you split codes? Like just by lines or have some methods to extract functions and classes?

I wrote some script that allows you to extract Python functions using regex, but this is def not scalable to other languages…

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