#semantic-search

#semantic-search

Amazon Web Services has launched S3 Vectors, a new storage solution that dramatically cuts the cost of handling vectorized data in AI by as much as 90%.

Basic RAG performed well for general topics but struggled with questions involving time, like identifying workshops next month, due to lack of metadata filtering.

Extracting metadata from research papers facilitates better academic search and retrieval dynamics. By building semantic embeddings for metadata, users gain enhanced search capabilities that match user queries with relevant content.

The goal of the new data structure is, in short, to create a new "Set alike" data type, similar to Sorted Sets, where instead of having a scalar as a score, you have a vector... asking for elements similar to a given query vector.