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NOTE: this PR also includes the sample dataset from #11 (same commit) in order to have data to work on.
Converted this PR to draft while I'm working to expand it with a Generative QA approach
Updated with the current version that adds 3 generative QA types: RAG, LFQA and OpenAI-based.
Context now includes the full ROSA docs (plus the ROSA workshop and the MOBB material in the data/external samples)
Results are not great, I'm still trying to see if they can be improved a bit - also need to elaborate/document.
Ok, I believe this is ready for another review.
The RAG version is not working well for some reason that so far has escaped me. @Shreyanand @suppathak if you have suggestions especially on that part they would be most welcome.
I have added the retrieval of the whole ROSA docs from S3 storage, and these docs together with the in-repo samples (ROSA workshop and MOBB) are used for context.
There are now more comments/docs and the structure has also been updated, hopefully making it more easy to follow.
Another update:
I now removed the RAG generator test: it does not work, and deepset plan to remove the RAG generator tutorial.
I found a problem with the Markdown pre-processor. I mention it as "fixmes" in the notebook. I am inclined NOT to try to fix these in this notebook though: the purpose of this PR is to review Haystack as a framework, and it there are issues with the markdown pre-processor they should be mentioned, right?
In #9 we are exploring various QA systems.
This PR provides a simple experiment of Extractive and Generative QA using Haystack