An embedding is a multi-dimensional representation of a Document (often a [1, D] vector). It serves as a very important piece of machine learning. The attribute embedding is designed to contain a Document’s embedding information.

Like .tensor, you can assign it with a Python (nested) List/Tuple, Numpy ndarray, SciPy sparse matrix (spmatrix), TensorFlow dense and sparse tensor, PyTorch dense and sparse tensor, or PaddlePaddle dense tensor.

import numpy as np
import scipy.sparse as sp
import torch
import tensorflow as tf

from docarray import Document

d0 = Document(embedding=[1, 2, 3])
d1 = Document(embedding=np.array([1, 2, 3]))
d2 = Document(embedding=np.array([[1, 2, 3], [4, 5, 6]]))
d3 = Document(embedding=sp.coo_matrix([0, 0, 0, 1, 0]))
d4 = Document(embedding=torch.tensor([1, 2, 3]))
d5 = Document(embedding=tf.sparse.from_dense(np.array([[1, 2, 3], [4, 5, 6]])))

Unlike some other packages, DocArray doesn’t actively cast dtype into float32. If the right-hand assignment dtype is float64 in PyTorch, it stays as a PyTorch float64 tensor.

To assign .tensors and .embeddings to multiple Documents in bulk, use DocumentArray. It’s much faster and smarter than using a for-loop.

Fill embedding via neural network#

On multiple Documents use DocumentArray

To embed multiple Documents, don’t use this feature in a for-loop. Instead, put all Documents in a DocumentArray and call .embed(). You can find out more in Embed via Neural Network.

Usually you don’t want to assign an embedding manually, but instead doing something like:

d.tensor   \
d.text   ---> some DNN model ---> d.embedding
d.blob /

Once a Document has its content field set, you can use a deep neural network to embed() it, which means filling its .embedding. For example, take this Document:

q = (Document(uri='/Users/hanxiao/Downloads/left/00003.jpg')
     .set_image_tensor_channel_axis(-1, 0))

Let’s embed it into a vector with ResNet50:

import torchvision
model = torchvision.models.resnet50(pretrained=True)

Find nearest-neighbors#

On multiple Documents use DocumentArray

To match multiple Documents, don’t use this feature in a for-loop. Instead, find out more in Find Nearest Neighbors.

Documents with an .embedding can be “matched” against each other. In this example, we create ten Documents and put them into a DocumentArray, and then use another Document to search against them.

from docarray import DocumentArray, Document
import numpy as np

da = DocumentArray.empty(10)
da.embeddings = np.random.random([10, 256])

q = Document(embedding=np.random.random([256]))

 <Document ('id', 'embedding', 'matches') at 63a39fa86d6911eca6fa1e008a366d49>
    └─ matches
          ├─ <Document ('id', 'adjacency', 'embedding', 'scores') at 63a39aee6d6911eca6fa1e008a366d49>
          ├─ <Document ('id', 'adjacency', 'embedding', 'scores') at 63a399d66d6911eca6fa1e008a366d49>
          ├─ <Document ('id', 'adjacency', 'embedding', 'scores') at 63a39b346d6911eca6fa1e008a366d49>
          ├─ <Document ('id', 'adjacency', 'embedding', 'scores') at 63a3999a6d6911eca6fa1e008a366d49>
          ├─ <Document ('id', 'adjacency', 'embedding', 'scores') at 63a39a626d6911eca6fa1e008a366d49>
          ├─ <Document ('id', 'adjacency', 'embedding', 'scores') at 63a397ba6d6911eca6fa1e008a366d49>
          ├─ <Document ('id', 'adjacency', 'embedding', 'scores') at 63a39a1c6d6911eca6fa1e008a366d49>
          ├─ <Document ('id', 'adjacency', 'embedding', 'scores') at 63a39ab26d6911eca6fa1e008a366d49>
          ├─ <Document ('id', 'adjacency', 'embedding', 'scores') at 63a399046d6911eca6fa1e008a366d49>
          └─ <Document ('id', 'adjacency', 'embedding', 'scores') at 63a399546d6911eca6fa1e008a366d49>