Currently we use LLMs to generate a summary, used as an additional chunk. As you might guess, this can take time, so we postpone the summarization at the end (the current default pipeline is: extract, partition, gen embedding, save embeddings, summarize, gen embeddings (of the summary), save emb)
Initial tests though are showing that summaries are affecting the quality of answers, so we'll probably remove it from the default flow and use it only for specific data types (e.g. chat logs).
There's a bunch of synthetic data scenarios we want to leverage LLMs for. Without going too much into details, sometimes "reading between the lines", and for some memory consolidation patterns (e.g. a "dream phase"), etc.
Initial tests though are showing that summaries are affecting the quality of answers, so we'll probably remove it from the default flow and use it only for specific data types (e.g. chat logs).
There's a bunch of synthetic data scenarios we want to leverage LLMs for. Without going too much into details, sometimes "reading between the lines", and for some memory consolidation patterns (e.g. a "dream phase"), etc.