* DEV: Better strategies for summarization
The strategy responsibility needs to be "Given a collection of texts, I know how to summarize them most efficiently, using the minimum amount of requests and maximizing token usage".
There are different token limits for each model, so it all boils down to two different strategies:
Fold all these texts into a single one, doing the summarization in chunks, and then build a summary from those.
Build it by combining texts in a single prompt, and truncate it according to your token limits.
While the latter is less than ideal, we need it for "bart-large-cnn-samsum" and "flan-t5-base-samsum", both with low limits. The rest will rely on folding.
* Expose summarized chunks to users
* Minor... use username suggester in case username already exists
* FIX: ensure we truncate long prompts
Previously we
1. Used raw length instead of token counts for counting length
2. We totally dropped a prompt if it was too long
New implementation will truncate "raw" if it gets too long maintaining
meaning.
Roman Rizzi
Rafael dos Santos Silva
Sam