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Thanks so much for sharing your thoughts! I also felt that they are pretty similar. But, I am guessing that SQLite (similar to most relational database systems)

Thanks so much for sharing your thoughts! I also felt that they are pretty similar. But, I am guessing that SQLite (similar to most relational database systems) does not automatically cache the results of functions, do non-trivial cost-based optimization for functions in queries, or reorder function-based predicates based on the estimated cost of running the functions, etc. Edit: I have shared more details on the function-aware optimization in EVA in this post (in case you are interested) -- https://news.ycombinator.com/item?id=35764355#35773608 https://news.ycombinator.com/item?id=35764355#35773608 Sure, we will try it out and keep you posted :)

simonw 3y ago
You can cache function results yourself in Python if you want to - my implementation also sums up the tokens used by the calls to the functions. Influencing optimization isn't possible using regular Python-based custom SQL functions though. I think you can influence that stuff in SQLite if you create more complex virtual table functions, but those aren't exposed through the regular Python sqlite3 module yet.
- jarulraj 3y ago
  Thanks for the clarifications. Token summation is a cool optimization :) Query optimizers in SQL database systems typically optimize based on the time to execute the function on a local server. The token summation optimization generalizes time-based optimization of local functions to dollar-based optimization for remote functions. Execution Time-based optimization: FunctionFoo(input 1) = 2x FunctionFoo(input 2) Dollar-based optimization: ChatGPT(prompt with 100 tokens) = 2x ChatGPT(prompt with 50 tokens) We are also exploring dollar-based optimization in EVA, and will check out your openai-to-sqlite tool for ideas [1]. [1] https://datasette.io/tools/openai-to-sqlite https://datasette.io/tools/openai-to-sqlite

simonw 3y ago

You can cache function results yourself in Python if you want to - my implementation also sums up the tokens used by the calls to the functions. Influencing optimization isn't possible using regular Python-based custom SQL functions though. I think you can influence that stuff in SQLite if you create more complex virtual table functions, but those aren't exposed through the regular Python sqlite3 module yet.

jarulraj 3y ago
Thanks for the clarifications. Token summation is a cool optimization :) Query optimizers in SQL database systems typically optimize based on the time to execute the function on a local server. The token summation optimization generalizes time-based optimization of local functions to dollar-based optimization for remote functions. Execution Time-based optimization: FunctionFoo(input 1) = 2x FunctionFoo(input 2) Dollar-based optimization: ChatGPT(prompt with 100 tokens) = 2x ChatGPT(prompt with 50 tokens) We are also exploring dollar-based optimization in EVA, and will check out your openai-to-sqlite tool for ideas [1]. [1] https://datasette.io/tools/openai-to-sqlite https://datasette.io/tools/openai-to-sqlite