[vadman97]

> Have you looked into finetuning the embedding model to your use case?

Not yet, though this is definitely one of the next steps for us. The `gte-large` model we use is trained on a variety of text, but a hypothesis is one trained or fine-tuned on technical / code-related content may work better.

> Have you considered faster foundation models?

Any in particular that you would suggest? We're still pretty new to this so would love to learn about other recommendations. Would a foundation model perform as well at this task?

> How far could you go with this idea? Could this be the basis of a new monitoring platform?

Certainly; there are traditional ML approaches that could be applied to monitoring as well and we're heavily exploring this (ie. for metric anomaly detection). Another area we're exploring embeddings grouping is for filtering ingest to help folks only ingest / retain data that they actually want, but without the overhead of strict filter rules. Tons more to explore in this space, and you will certainly be hearing more from us here.