Each vector is 1536 numbers. I don't know how many bits per number, but I'll assume 64 bits (8 bytes). So total size is 1536 * 115K * 8 / 1024^2 gives 1.3GB.
So yes, not a lot.
I still haven't set it up so I don't know how much space it really will take, but my 40K doc one took 2-3 GB of RAM. It's not pandas DF, but in an in-memory DB so perhaps there's a lot of overhead per row? I haven't debugged.
To be clear, I'm totally fine with your approach if it works. I have very limited time so I was using txtai instead of rolling my own - it's nice to get a RAG up and running in just a few lines of code. But for sure, if the overhead of txtai is really that significant, I'll need to switch to pure pandas.
Each vector is 1536 numbers. I don't know how many bits per number, but I'll assume 64 bits (8 bytes). So total size is 1536 * 115K * 8 / 1024^2 gives 1.3GB.
So yes, not a lot.
I still haven't set it up so I don't know how much space it really will take, but my 40K doc one took 2-3 GB of RAM. It's not pandas DF, but in an in-memory DB so perhaps there's a lot of overhead per row? I haven't debugged.
To be clear, I'm totally fine with your approach if it works. I have very limited time so I was using txtai instead of rolling my own - it's nice to get a RAG up and running in just a few lines of code. But for sure, if the overhead of txtai is really that significant, I'll need to switch to pure pandas.