In our previous post titled “Do we really need a specialized vector database?” on HN (https://news.ycombinator.com/item?id=37097004) we discussed the importance of using a Postgres-based solution for vector search. However, we acknowledged that existing Postgres vector extensions lack support for metadata filtering.
We are excited to announce that we have now addressed this limitation. We are proud to be the first (https://github.com/tensorchord/pgvecto.rs) to enable conditional filtering directly on HNSW indexes within Postgres. This breakthrough allows for efficient and effective metadata filtering in combination with vector search, eliminating the tradeoff previously associated with using Postgres for this purpose.
We invite you to explore our updated offering and experience the benefits of seamless metadata filtering within a Postgres-based vector search system.
In our previous post titled “Do we really need a specialized vector database?” on HN (https://news.ycombinator.com/item?id=37097004) we discussed the importance of using a Postgres-based solution for vector search. However, we acknowledged that existing Postgres vector extensions lack support for metadata filtering.
We are excited to announce that we have now addressed this limitation. We are proud to be the first (https://github.com/tensorchord/pgvecto.rs) to enable conditional filtering directly on HNSW indexes within Postgres. This breakthrough allows for efficient and effective metadata filtering in combination with vector search, eliminating the tradeoff previously associated with using Postgres for this purpose.
We invite you to explore our updated offering and experience the benefits of seamless metadata filtering within a Postgres-based vector search system.