My chunk rewriting method is to use a LLM to generate a title, summary, keyword list, topic, parent topic, and gp topic. Then I embed the concatenation of all of them instead of just the original chunk. This helps a lot.
One fundamental problem of cosine similarity is that it works on surface level. For example, "5+5" won't embed close to "10". Or "The 5th word of this phrase" won't be similar to "this".
If there is any implicit knowledge it won't be captured by simple cosine similarity, that is why we need to draw out those inplicit deductions before embedding. Hence my approach of pre-embedding expansion of chunk semantic information.
I basically treat text like code, and have to "run the code" to get its meaning unpacked.
One fundamental problem of cosine similarity is that it works on surface level. For example, "5+5" won't embed close to "10". Or "The 5th word of this phrase" won't be similar to "this".
If there is any implicit knowledge it won't be captured by simple cosine similarity, that is why we need to draw out those inplicit deductions before embedding. Hence my approach of pre-embedding expansion of chunk semantic information.
I basically treat text like code, and have to "run the code" to get its meaning unpacked.