If you already have a Postgresql database, this makes sense, but if you are starting from scratch, you could also use vector databases that allow hybrid search, which most do. and this also allows for more options in respect to embedding models which could be very critical for a given use case.
Interesting take on embedding management! Love the sync mechanics. What would you say are the main commonalities and differences between pgvector, pgvectorscale and pgai?
This is really great! I've been running into so many limitations using chromadb and redis (with redisearch). ChromaDB in particular uses a lot of memory as the number of embeddings increases, which gets very expensive to host. Is that also the case with pgai? Is it affordable? Also, random question, but what vscode extension are you using to run the script as a jupyter notebook at th-cam.com/video/8oTnUtFYAes/w-d-xo.htmlsi=J21f6uJF09ipZ7sj&t=903?
If you already have a Postgresql database, this makes sense, but if you are starting from scratch, you could also use vector databases that allow hybrid search, which most do. and this also allows for more options in respect to embedding models which could be very critical for a given use case.
Interesting take on embedding management! Love the sync mechanics. What would you say are the main commonalities and differences between pgvector, pgvectorscale and pgai?
Great video! Can you make a video about security for RAG-Applications, options to avoid exploits etc?
Excellent! 🙏for sharing!
My pleasure!
where is lexical search ? even a separate lexical search index ?
Check out this video: th-cam.com/video/TbtBhbLh0cc/w-d-xo.html
This is really great! I've been running into so many limitations using chromadb and redis (with redisearch). ChromaDB in particular uses a lot of memory as the number of embeddings increases, which gets very expensive to host. Is that also the case with pgai? Is it affordable? Also, random question, but what vscode extension are you using to run the script as a jupyter notebook at th-cam.com/video/8oTnUtFYAes/w-d-xo.htmlsi=J21f6uJF09ipZ7sj&t=903?
Keep it up bro
How efficient and feasible pgvector store i wanna scale it to production?!
It can handle production workloads with millions of vectors using pgvectorscale
@daveebbelaar also if i use timescale db image, wouldn't it cause any trouble down the line?
@ nope, its just Postgres in the backend with added extensions.
Awesome
First #notificationGang
You fast haha!