pgvecto.rs VS pgvector

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!

# Install Postgres and pgvector !apt-get update && apt install postgresql postgresql-server-dev-14 !git clone --branch v0.6.2 https://github.com/pgvector/pgvector.git !cd pgvector && make && make install # Start database !service postgresql start !sudo -u postgres psql -U postgres -c "ALTER USER postgres PASSWORD 'pass';"

When talking about Vector Databases, in the market we can find the specialized ones and multi-model, most of the major database providers like Oracle, PostgreSQL or MongoDB, for mention some of them, have integrated a specific solution to retrieve vector data.

For this example, I wanted to focus on how pgvector  – an open-source vector similarity search for Postgres – can be used to identify data similarities that exist in enterprise data.

pgvector supports dense vector search well, but it does not have plan to support sparse vector.

There's an issue in the pgvector repo about someone having several ~10-20million row tables and getting acceptable performance with the right hardware and some performance tuning: https://github.com/pgvector/pgvector/issues/455

I'm in the early stages of evaluating pgvector myself. but having used pinecone I currently am liking pgvector better because of it being open source. The indexing algorithm is clear, one can understand and modify the parameters. Furthermore the database is postgresql, not a proprietary document store. When the other data in the problem is stored relationally, it is very convenient to have the vectors stored like this as well. And postgresql has good observability and metrics. I think when it comes to flexibility for specialized applications, pgvector seems like the clear winner. But I can definitely see pinecone's appeal if vector search is not a core component of the problem/business, as it is very easy to use and scales very easily

The Spring AI project aims to streamline the development of applications that incorporate artificial intelligence functionality without unnecessary complexity. On this example we use features like: Embedding, Prompts, ETL and save all embedding on PGvector(Postgres Vector database)

Official GitHub repository of the pgvector extension

pgvector 0.6.0 was just released and will be available on Supabase projects soon. Again, a special shout out to Andrew Kane and everyone else who worked on parallel index builds.

The pgvector extension adds vector similarity search capabilities to your PostgreSQL database. To use the extension, you have to first create it in your database. You can install the extension, by connecting to your database and running the CREATE EXTENSION command from the psql command prompt: