"Query-Document Mismatch: This model assumes that query embedding and the content embedding are similar in the embedding space, which is not always true based on the text you're trying to search over."
There are embeddings models that take this into account, which are pretty fascinating.
I've been exploring https://huggingface.co/intfloat/e5-large-v2 which lets you calculate two different types of embeddings in the same space. Example from their README:
passage: As a general guideline, the CDC's average requirement of protein for women ages 19 to 70 is 46 grams per day
query: how much protein should a female eat
You can then build your embedding database out of "passage: " embeddings, then run "query: " embeddings against it to try and find passages that can answer the question.
I've had pretty great initial results trying that out against paragraphs from my blog: https://til.simonwillison.net/llms/embed-paragraphs#user-con...
This won't help address other challenges mentioned in that post, like "what problems did we fix last week?" - but it's still a useful starting point.
There are embeddings models that take this into account, which are pretty fascinating.
I've been exploring https://huggingface.co/intfloat/e5-large-v2 which lets you calculate two different types of embeddings in the same space. Example from their README:
You can then build your embedding database out of "passage: " embeddings, then run "query: " embeddings against it to try and find passages that can answer the question.I've had pretty great initial results trying that out against paragraphs from my blog: https://til.simonwillison.net/llms/embed-paragraphs#user-con...
This won't help address other challenges mentioned in that post, like "what problems did we fix last week?" - but it's still a useful starting point.