Use Adaptive Retrieval to improve query performance with OpenAI's new embedding models| Supabase
pgvector 0.6.0 brings a significant improvement: parallel index builds for HNSW. Building an HNSW index is now up to 30x faster for unlogged tables.| Supabase
Increase performance in pgvector using HNSW indexes| Supabase
Increase performance in pgvector by using embedding vectors with fewer dimensions| Supabase
Supabase recently contributed to the OpenAI Retrieval Plugin repo with a Postgres and a Supabase implementation to help developers build ChatGPT plugins using pgvector.| Supabase
Creating a ChatGPT interface for the Supabase documentation.| Supabase
An example of how to build an AI-powered search engine using OpenAI's embeddings and PostgreSQL.| Supabase