Hi,
I’m currently using OpenAI embeddings to index some texts and was tinkering with OpenAI CLIP which would let me use image in addition.
Questions:
- Does it make sense to average OpenAI embeddings with OpenAI CLIP embeddings? Will semantic search performance be degraded / improved?
The bigger context is that I use postgres to index my vectors and there is a possibility that I use multiple models to embed my content but would prefer if I have to deal with a single column for the vector. (example: index content with clip, openai embeddings, sentence bert, etc)
This is my table
create table documents (
id text primary key,
data text,
embedding vector (1536),
hash text,
dataset_id text,
user_id text,
metadata json
);
create index on documents
using ivfflat (embedding vector_cosine_ops)
with (lists = 100);
Unsure I can use json for embeddings (could be {“clip”: […], “openai_embeddings”: […]…}), need to index dynamically or something
Well thats a bit messy thought, just if you could help with this question:
- Does it make sense to average OpenAI embeddings with OpenAI CLIP embeddings? Will semantic search performance be degraded / improved?