Visualization

If you have an image Document (with possible image data in .uri/.tensor), you can directly visualize it via display().

To better see the Document’s nested structure, you can use summary().

import numpy as np
from docarray import Document

d0 = Document(id='🐲', embedding=np.array([0, 0]))
d1 = Document(id='🐦', embedding=np.array([1, 0]))
d2 = Document(id='🐢', embedding=np.array([0, 1]))
d3 = Document(id='🐯', embedding=np.array([1, 1]))

d0.chunks.append(d1)
d0.chunks[0].chunks.append(d2)
d0.matches.append(d3)

d0.summary()

 <Document ('id', 'embedding', 'chunks', 'matches') at 🐲>
    └─ matches
          └─ <Document ('id', 'adjacency', 'embedding') at 🐯>
    └─ chunks
          └─ <Document ('id', 'parent_id', 'granularity', 'embedding', 'chunks') at 🐦>
              └─ chunks
                    └─ <Document ('id', 'parent_id', 'granularity', 'embedding') at 🐢>

When using Notebook/Colab, this is auto-rendered.