qdrant-0.10-multiple-vectors-pipeline-definition.py

from sklearn.pipeline import make_pipeline
from embetter.grab import ColumnGrabber
from embetter.vision import ImageLoader, TimmEncoder
from embetter.text import SentenceEncoder

output_directory = Path("./images")

image_pipeline = make_pipeline(
   ColumnGrabber("URL"),
   DownloadFile(output_directory),
   ImageLoader(),
   TimmEncoder("vit_base_patch16_224"),
)

text_pipeline = make_pipeline(
   ColumnGrabber("TEXT"),
   SentenceEncoder("all-MiniLM-L6-v2"),
)