myenv/lib/python3.12/site-packages/chromadb/utils/data_loaders.py

import importlib
import multiprocessing
from typing import Optional, Sequence, List, Tuple
import numpy as np
from chromadb.api.types import URI, DataLoader, Image, URIs
from concurrent.futures import ThreadPoolExecutor


class ImageLoader(DataLoader[List[Optional[Image]]]):
    def __init__(self, max_workers: int = multiprocessing.cpu_count()) -> None:
        try:
            self._PILImage = importlib.import_module("PIL.Image")
            self._max_workers = max_workers
        except ImportError:
            raise ValueError(
                "The PIL python package is not installed. Please install it with `pip install pillow`"
            )

    def _load_image(self, uri: Optional[URI]) -> Optional[Image]:
        return np.array(self._PILImage.open(uri)) if uri is not None else None

    def __call__(self, uris: Sequence[Optional[URI]]) -> List[Optional[Image]]:
        with ThreadPoolExecutor(max_workers=self._max_workers) as executor:
            return list(executor.map(self._load_image, uris))


class ChromaLangchainPassthroughDataLoader(DataLoader[List[Optional[Image]]]):
    # This is a simple pass through data loader that just returns the input data with "images"
    # flag which lets the langchain embedding function know that the data is image uris
    def __call__(self, uris: URIs) -> Tuple[str, URIs]:  # type: ignore
        return ("images", uris)