Expose `fetch_and_sort_papers()` parameters in `literature_review()`

[sanjaynagi]

I think it would be great to be able to configure the following in the literature_review() function:

• keyword combinations
• year_range
• top_n papers

[sanjaynagi]

feel free to assign to me @eimenhmdt , though I'm sure you can handle that... :P

[eimenhmdt]

Thanks for the suggestions! What do you mean by keyword combinations?

Regarding the top_n papers, we need to be cautious to not create a context that's too large due to token limitations of GPT 3.5. What are your thoughts on this?

[sanjaynagi]

I was thinking that if users wanted to, they could pass their own keyword_combinations which would be passed to semanticScholar, avoiding the first call to the openAI API :)

Thats true! have you any idea what that context would look like atm in terms of number of papers? If I get time today Ill try run the algorithm with quite a few and see. We would probably want a limit anyway so a user doesn't accidentally choose 1000 top papers or something and burn through their tokens.

[eimenhmdt]

re keywords: I think that's a good idea. Many researchers probably already know very well which keyword combinations they would want to use. If you want, it would be really cool if you could build this. :)

re context: Depends on the model you use. Currently, AutoResearcher uses GPT-3.5 turbo by default. But, it's also possible to use GPT-4. I think if we want to extract useful & sufficient information from each paper, 20–25 papers (GPT-4 2x this number) should be max in the context. But, I could also think of a refined algorithm that circumvents these limitations, i.e. adding additional steps, using a vector store etc.

[sanjaynagi]

Interesting!

Sure - got my PhD viva in a few days though but will tackle after that.

[eimenhmdt]

Oh, cool. Best of luck for your viva!!