This project creates embeddings for every set of a few paragraphs from the source dataset, in order to do real-time semantic search and question answering on them.

The very barebones of the project is currently in testingsrc/.ipynb. file which contains:

To try out, add a src/config.py file which contains your OPENAI_API_KEY.

TODO:

Getting data:
- Figure out the right format for the dataset
- Get entirety of data
- Searches for new posts/papers/etc and scrape them, runs once a day
Semantic search:
- Test out other techniques than just vector similarity (e.g. LSH-index, see Dense Retrieval methods (here)[https://medium.com/@aikho/deep-learning-in-information-retrieval-part-ii-dense-retrieval-1f9fecb47de9])
- Test other embeddings models ((SimCSE)[https://github.com/princeton-nlp/SimCSE] possibly SOTA?)
Question answering:
- Test out other models prompts to see which is best
Summarization:
- Test out other models and prompts to see which is best (Forefront?)
Info extraction from PDF:
- Specifically mentioned by Anson. Look into methods by Mely.ai to extract tables from PDFs maybe?
- Test various techniques to make it more performant
Finetuning:
- Finetune embeddings model
- Finetune q&a model
- Finetune summarization model
- Finetune info extraction model
Create website/other. Not sure what would be most helpful here
- Find someone that can figure this out

Source dataset: Kirchner, J. H., Smith, L., Thibodeau, J., McDonnell, K., and Reynolds, L. "Understanding AI alignment research: A Systematic Analysis." arXiv preprint arXiv:2022.4338861 (2022).