auth VS pgvector

I've been happy with Ente Auth

https://github.com/ente-io/auth

I can highly recommend Ente Photos[0]. It has automatic backup, and a very good replication architecture for maximum reliability[1]. It's also super easy to use and everything is encrypted by default.

[0] https://ente.io/

Running machine learning on device.

Context: I'm working on an e2ee alternative to Google Photos[1] where we have to cluster embeddings (for face recognition) and run similarity searches (for semantic search[2]) on device.

[1]: https://ente.io

[2]: https://openai.com/research/clip

> there is no better alternative

If your primary device is Android, please check out Ente[1].

We are an E2EE alternative to Google Photos. We had launched on HN[2] a while ago, and have been working towards feature parity. We aren't "there" yet, but hope to soon be.

If you've any feedback, please share it with [email protected], I'd be grateful!

[1]: https://ente.io

[2]: https://news.ycombinator.com/item?id=28347439

I use: Signal, ente.io and the Proton suite of producs. And tresorit as well. I kinda trust those things.

Proton is starting to become more like https://ente.io

# 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: