Is This The Cheapest Way to Do Semantic Search?

Summary

The transcript discusses implementing semantic search using S3 vectors, focusing on the process of extracting data, generating embeddings, and storing them in a vector index. The key technical steps involve creating floating point array vectors, using the S3 SDK to store embeddings, and querying the index by embedding search terms with the same model used for the original data. The practical takeaway is that this approach allows for more sophisticated, context-aware search results ranked by semantic similarity rather than traditional keyword matching.