Retrieval systems typically split long documents into chunks, but doing so strips away the surrounding context.
Retrieval systems typically split long documents into chunks, but doing so strips away the surrounding context.
Contextual embedding models solve this problem by encoding the entire document at once and then aggregating the chunk vectors afterward.
They are usually trained on one golden chunk per query.