faiss-rs VS pgvecto.rs

Rewriting pgvector in Rust can enable the code to be organized in a more modern and extensible way. Rust's ecosystem is also very rich, with existing Rust bindings such as faiss-rs.

I know rust has beings to FAISS (see https://github.com/Enet4/faiss-rs), I don't know if there's anything that would be considered comparable. Alot of work has gone into FAISS

To evaluate the performance metrics in comparison to the original vector approach, we conducted benchmarking using the dbpedia-entities-openai3-text-embedding-3-large-3072-1M dataset. The benchmark was performed on a Google Cloud virtual machine (VM) with specifications of n2-standard-8, which includes 8 virtual CPUs and 32GB of memory. We used pgvecto.rs v0.2.1 as the vector database.

Please check out our documentation for more details. We encourage you to try out pgvecto.rs, benchmark it against your workloads, and contribute your indexing innovations. Join our Discord community to connect with the developers and other users working to improve pgvecto.rs!

It's kind of a tradeoff. Performance is just one factor when choosing the vector database. In pgvecto.rs https://github.com/tensorchord/pgvecto.rs, we store the index separately from PostgreSQL's internal storage, unlike pgvector's approach. This enable us to get multi-threaded indexing, async indexing without blocking the insertion, and faster search speed comparing to pgvector.

I don't see any fundamental reason why the index in Postgres would be slower than a specialized vector database. The query pattern of the vector database is simply a point query using an index, similar to other queries in an OLTP system.

The only limitation I see is scalability. It's not easy to make PostgreSQL distributed, but solutions like Citus exist, making it still possible.

(I'm the author of pgvecto.rs)

Hi, we've solved the problem you mentioned! Please take a look on our open source postgres vector extension https://github.com/tensorchord/pgvecto.rs.

Our index building process is significantly faster than pgvector on hnsw because we can utilize all the cores, whereas pgvector can only use one core. And for the filter support, we do support pre-filtering, which will guarantee enough results no matter the condition is.

Hi,

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.

Yeah, I think in many cases you just need a vector search lib, instead of a DB.

And in some other cases, you may want postgres vector extension e.g. https://github.com/tensorchord/pgvecto.rs instead of a specialized vector db.

Seems that pgvector has a viable competitor extension: https://github.com/tensorchord/pgvecto.rs

We are thrilled to announce the release of https://github.com/tensorchord/pgvecto.rs, a powerful Postgres extension for vector similarity search written in Rust. Its HNSW algorithm is 20x faster than pgvector at 90% recall. But speed is just the start - pgvecto.rs is architected to add new algorithms easily. We've made it an extensible architecture for contributors to implement the new indexes quickly, and we look forward to the open-source community driving pgvecto.rs to new heights!