The goal of this is to create a program that acts as an NYC bar/happy hour recommender. It uses OpenAI's embeddings API to base the recommendations off a list of bars found in the csv files in the
data
directory.
Note: This README needs some TLC that's simply not available from me at 2am.
- Create a
.env
file in the repository (see.env.example
) and add your OpenAI credentials. - Run
yarn start "The prompt goes here"
. - Usually the first run will request and create the embeddings, but I'm commiting the embeddings to this repo for
drinks-happy-hour.csv
.
If you want to try out another data file you can create a new embedding context by basically copying the existing code in src/index.ts
.
Expect a 30-60 second first run of the program when doing this. Subsequent executions will be significantly faster at around 5 seconds.
I'm pretty damn impressed with how good it is off the bat. Pretty much no tweaking of anything, no error handling (lol), no logging, no prompt engineering.