Hi everyone, i am using local hosted quadratstore to store the index, but i get the timeout for the quadrant_client, anyone has faced a similar issue ?

I am currently having Postgres Vector store, I can see that llama_index creates these vectors as tables, but langchain manages the metadata and stores details regarding the collections.
1.Is there any way we can have cross compatibility irrespective of how the vectorstores are created?

1. Is there anyway to method to keep a track of which embedding model i sused to create a certain collection and the embedding size? Having this info will make the re-use of data easier for large audience

@Logan M

Hey @Logan M , is there anyway we can use the sagemaker model endpoints for building the llm and embedding in llama_index?