@_hep , you can also try with more advanced retrieval techniques. For capturing the value of nuances, embedding addaptors may be the most appropriate (akin to retraining an embedding model).
For this, you will want to start with a batch that are either human-validated or run through an LLM for synthetic validation. Checkout the deeplearning.ai course from Chroma on advanced retrieval to get started if you’ve never heard of this (free).
Still, for a use case like this I agree with @_j that extracting entities and mathing those is likely to render a better final result than embedding space distance.