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Updated 2 months ago

I was thinking it would be example

I was thinking it would be example tweets, like “here’s a listicle of 7 habits of startup founders...” — and other tweets that have performed well. Those would be the dataset. We’d be asking for new ones like those, perhaps based on a certain person’s tweet history or style if that’s possible.
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So, the examples would be stored in llama index actually.

Here's how I might approach this. I'll use a vector index as an example.

Index a bunch of data (in this, it would be a bunch of tweets). For this, I would make each tweet it's own "Document" object (i.e. something like "Here's a tweet: <tweet text>"). Each document would then get an embedding generated for it and saved.

Then, you query your index. i.e. response = index.query("Given related tweets on startup founders, write a new tweet about XX", similarity_top_k=5, response_mode="compact")

Llama index will create an embedding of the query text and fetch the 5 closest matching tweets. Then, using all 5 top matching tweets, it will get the LLM to answer the query given the matched tweets.

Since I specified response_mode="compact", it will stuff as many tweets as possible into each call to the LLM. Without this option, it would make 5 calls to the LLM, one for each matching tweet. If all the text doesn't fit in a single call, it will refine the answer across several calls.

Check out this page for some more inspiration 💪 https://gpt-index.readthedocs.io/en/latest/use_cases/queries.html
Great - I will dig deep into all that. Thank you so much!
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