Hi everyone
I'm trying to use PrivateGPT which is mostly based on llama_index. For now I have an issue when running embeding inference with on aws sagemaker. I'm using the
BAAI/bge-large-en-v1.5 embedding which, I think, is quite common.
In PGPT, sagemaker embedding is made through Custom embedding in llama_index, the
current implementation is available hereFor now :
- PGPT is able to request the model
- The model send back a vector
- (I think) that this vector do not fit my vector database possibly for a wrong dimensionality
Currently I have the following issue (in attachment).
Do you guys have any idea where to look ?
For now my guess is to look for some parameters like max_length (that would reduce the dimension of my vector) but don't know if it's a good idea and where to look ... Any help appreciated π