The data structure for unstructured data
DocArray is a library for nested, unstructured data such as text, image, audio, video, 3D mesh. It allows deep learning engineers to efficiently process, embed, search, recommend, store, transfer the data with Pythonic API.
🌌 **All data types**: super-expressive data structure for representing complicated/mixed/nested text, image, video, audio, 3D mesh data.
🐍 **Pythonic experience**: designed to be as easy as Python list. If you know how to Python, you know how to DocArray. Intuitive idioms and type annotation simplify the code you write.
🧑🔬 **Data science powerhouse**: greatly accelerate data scientists work on embedding, matching, visualizing, evaluating via Torch/Tensorflow/ONNX/PaddlePaddle on CPU/GPU.
🚡 **Portable**: ready-to-wire at anytime with efficient and compact serialization from/to Protobuf, bytes, JSON, CSV, dataframe.
Read more on [why should you use DocArray](https://docarray.jina.ai/get-started/what-is/) and [comparison to alternatives](https://docarray.jina.ai/get-started/what-is/#comparing-to-alternatives).
## Install
Requires Python 3.7+ and `numpy` only:
```shell
pip install docarray
```
or via Conda:
```shell
conda install -c conda-forge docarray
```
[Additional features](https://docarray.jina.ai/#install) can be enabled by installing the full dependencies: `pip install "docarray[full]"`.
## [Documentation](https://docarray.jina.ai)
## Get Started
DocArray consists of two simple concepts:
- **Document**: a data structure for easily representing nested, unstructured data.
- **DocumentArray**: a container for efficiently accessing, manipulating, and understanding multiple Documents.
### A 10-liners text matching
We search for top-5 similar sentences of she smiled too much in "Pride and Prejudice".
```python
from docarray import Document, DocumentArray
d = Document(uri='https://www.gutenberg.org/files/1342/1342-0.txt').load_uri_to_text()
da = DocumentArray(Document(text=s.strip()) for s in d.text.split('\n') if s.strip())
da.apply(lambda d: d.embed_feature_hashing())
q = (Document(text='she smiled too much')
.embed_feature_hashing()
.match(da, metric='jaccard', use_scipy=True))
print(q.matches[:5, ('text', 'scores__jaccard__value')])
```
```text
[['but she smiled too much.',
'_little_, she might have fancied too _much_.',
'She perfectly remembered everything that had passed in',
'tolerably detached tone. While she spoke, an involuntary glance',
'much as she chooses.”'],
[0.3333333333333333, 0.6666666666666666, 0.7, 0.7272727272727273, 0.75]]
```
Here the feature embedding is done by simple [feature hashing](https://en.wikipedia.org/wiki/Feature_hashing) and distance metric is [Jaccard distance](https://en.wikipedia.org/wiki/Jaccard_index). You got better embedding? Of course you do! Looking forward to seeing your results.
### A complete workflow of visual search
Let's use DocArray and [Totally Looks Like](https://sites.google.com/view/totally-looks-like-dataset) dataset to build simple meme image search. The dataset contains 6016 image-pairs stored in `/left` and `/right`. Images that shares the same filename are perceptually similar. For example,
| left/00018.jpg |
right/00018.jpg |
left/00131.jpg |
right/00131.jpg |
 |
 |
 |
 |
Our problem is given an image from `/left` and find its most-similar image in `/right` (without looking at the filename of course).
### Load images
First load images and preprocess them with standard computer vision techniques:
```python
from docarray import DocumentArray
left_da = DocumentArray.from_files('left/*.jpg')
```
To get a feeling of the data you will handle, plot them in one sprite image:
```python
left_da.plot_image_sprites()
```
### Apply preprocessing
Let's do some standard computer vision preprocessing:
```python
from docarray import Document
def preproc(d: Document):
return (d.load_uri_to_image_blob() # load
.set_image_blob_normalization() # normalize color
.set_image_blob_channel_axis(-1, 0)) # switch color axis for the pytorch model later
left_da.apply(preproc)
```
Did I mention `apply` work in parallel?
### Embed images
Now convert images into embeddings using a pretrained ResNet50:
```python
import torchvision
model = torchvision.models.resnet50(pretrained=True) # load ResNet50
left_da.embed(model, device='cuda') # embed via GPU to speedup
```
This step takes ~30 seconds on GPU. Beside PyTorch, you can also use Tensorflow, PaddlePaddle, ONNX models in `.embed(...)`.
### Visualize embeddings
You can visualize the embeddings via tSNE in an interactive embedding projector:
```python
left_da.plot_embeddings()
```
Fun is fun, but recall our goal is to match left images against right images and so far we have only handled the left. Let's repeat the same procedure for the right:
```python
right_da = (DocumentArray.from_files('right/*.jpg')
.apply(preproc)
.embed(model, device='cuda'))
```
### Match nearest neighbours
We can now match the left to the right and take the top-9 results.
```python
left_da.match(right_da, limit=9)
```
Let's inspect what's inside `left_da` matches now:
```python
for d in left_da:
for m in d.matches:
print(d.uri, m.uri, m.scores['cosine'].value)
```
```text
left/02262.jpg right/03459.jpg 0.21102
left/02262.jpg right/02964.jpg 0.13871843
left/02262.jpg right/02103.jpg 0.18265384
left/02262.jpg right/04520.jpg 0.16477376
...
```
Or shorten the loop as one-liner using the element & attribute selector:
```python
print(left_da['@m', ('uri', 'scores__cosine__value')])
```
Better see it.
```python
(DocumentArray(left_da[8].matches, copy=True)
.apply(lambda d: d.set_image_blob_channel_axis(0, -1)
.set_image_blob_inv_normalization())
.plot_image_sprites('result.png'))
```
What we did here is reverting the preprocessing steps (i.e. switching axis and normalizing) on the copied matches, so that one can visualize them using image sprites.
### Quantitative evaluation
Serious as you are, visual inspection is surely not enough. Let's calculate the recall@K. First we construct the groundtruth matches:
```python
groundtruth = DocumentArray(
Document(uri=d.uri, matches=[Document(uri=d.uri.replace('left', 'right'))]) for d in left_da)
```
Here we create a new DocumentArray with real matches by simply replacing the filename, e.g. `left/00001.jpg` to `right/00001.jpg`. That's all we need: if the predicted match has the identical `uri` as the groundtruth match, then it is correct.
Now let's check recall rate from 1 to 5 over the full dataset:
```python
for k in range(1, 6):
print(f'recall@{k}',
left_da.evaluate(
groundtruth,
hash_fn=lambda d: d.uri,
metric='recall_at_k',
k=k,
max_rel=1))
```
```text
recall@1 0.02726063829787234
recall@2 0.03873005319148936
recall@3 0.04670877659574468
recall@4 0.052194148936170214
recall@5 0.0573470744680851
```
More metrics can be used such as `precision_at_k`, `ndcg_at_k`, `hit_at_k`.
If you think a pretrained ResNet50 is good enough, let me tell you with [Finetuner](https://github.com/jina-ai/finetuner) one could do much better in just 10 extra lines of code. [Here is how](https://finetuner.jina.ai/get-started/totally-looks-like/).
### Save results
You can save a DocumentArray to binary, JSON, dict, dataframe, CSV or Protobuf message with/without compression. In its simplest form,
```python
left_da.save('left_da.bin')
```
To reuse it, do `left_da = DocumentArray.load('left_da.bin')`.
If you want to transfer a DocumentArray from one machine to another or share it with your colleagues, you can do:
```python
left_da.push(token='my_shared_da')
```
Now anyone who knows the token `my_shared_da` can pull and work on it.
```python
left_da = DocumentArray.pull(token='my_shared_da')
```
Intrigued? That's only scratching the surface of what DocArray is capable of. [Read our docs to learn more](https://docarray.jina.ai).
## Support
- Use [Discussions](https://github.com/jina-ai/docarray/discussions) to talk about your use cases, questions, and
support queries.
- Join our [Slack community](https://slack.jina.ai) and chat with other community members about ideas.
- Join our [Engineering All Hands](https://youtube.com/playlist?list=PL3UBBWOUVhFYRUa_gpYYKBqEAkO4sxmne) meet-up to discuss your use case and learn Jina's new features.
- **When?** The second Tuesday of every month
- **Where?**
Zoom ([see our public events calendar](https://calendar.google.com/calendar/embed?src=c_1t5ogfp2d45v8fit981j08mcm4%40group.calendar.google.com&ctz=Europe%2FBerlin)/[.ical](https://calendar.google.com/calendar/ical/c_1t5ogfp2d45v8fit981j08mcm4%40group.calendar.google.com/public/basic.ics))
and [live stream on YouTube](https://youtube.com/c/jina-ai)
- Subscribe to the latest video tutorials on our [YouTube channel](https://youtube.com/c/jina-ai)
## Join Us
DocArray is backed by [Jina AI](https://jina.ai) and licensed under [Apache-2.0](./LICENSE). [We are actively hiring](https://jobs.jina.ai) AI engineers, solution engineers to build the next neural search ecosystem in opensource.