Metadata-Version: 2.1
Name: docarray
Version: 0.20.1
Summary: The data structure for unstructured data
Home-page: https://github.com/jina-ai/docarray
Author: DocArray team
Author-email: hello@jina.ai
License: Apache 2.0
Download-URL: https://github.com/jina-ai/docarray/tags
Project-URL: Documentation, https://docarray.jina.ai
Project-URL: Source, https://github.com/jina-ai/docarray/
Project-URL: Tracker, https://github.com/jina-ai/docarray/issues
Description: <p align="center">
        <img src="https://github.com/docarray/docarray/blob/main/docs/_static/logo-light.svg?raw=true" alt="DocArray logo: The data structure for unstructured data" width="150px">
        <br>
        <b>The data structure for unstructured multimodal data</b>
        </p>
        
        <p align=center>
        <a href="https://pypi.org/project/docarray/"><img src="https://img.shields.io/pypi/v/docarray?style=flat-square&amp;label=Release" alt="PyPI"></a>
        <a href="https://codecov.io/gh/docarray/docarray"><img alt="Codecov branch" src="https://img.shields.io/codecov/c/github/docarray/docarray/main?logo=Codecov&logoColor=white&style=flat-square"></a>
        <a href="https://bestpractices.coreinfrastructure.org/projects/6554"><img src="https://bestpractices.coreinfrastructure.org/projects/6554/badge"></a>
        <a href="https://pypistats.org/packages/docarray"><img alt="PyPI - Downloads from official pypistats" src="https://img.shields.io/pypi/dm/docarray?style=flat-square"></a>
        <a href="https://slack.jina.ai"><img src="https://img.shields.io/badge/Slack-3.6k-blueviolet?logo=slack&amp;logoColor=white&style=flat-square"></a>
        </p>
        
        <!-- start elevator-pitch -->
        
        DocArray is a library for nested, unstructured, multimodal data in transit, including text, image, audio, video, 3D mesh, etc. It allows deep-learning engineers to efficiently process, embed, search, recommend, store, and transfer multimodal data with a Pythonic API.
        
        🚪 **Door to multimodal world**: super-expressive data structure for representing complicated/mixed/nested text, image, video, audio, 3D mesh data. The foundation data structure of [Jina](https://github.com/jina-ai/jina), [CLIP-as-service](https://github.com/jina-ai/clip-as-service), [DALL·E Flow](https://github.com/jina-ai/dalle-flow), [DiscoArt](https://github.com/jina-ai/discoart) etc.
        
        🧑‍🔬 **Data science powerhouse**: greatly accelerate data scientists' work on embedding, k-NN matching, querying, visualizing, evaluating via Torch/TensorFlow/ONNX/PaddlePaddle on CPU/GPU.
        
        🚡 **Data in transit**: optimized for network communication, ready-to-wire at anytime with fast and compressed serialization in Protobuf, bytes, base64, JSON, CSV, DataFrame. Perfect for streaming and out-of-memory data.
        
        🔎 **One-stop k-NN**: Unified and consistent API for mainstream vector databases that allows nearest neighbor search including Elasticsearch, Redis, AnnLite, Qdrant, Weaviate.
        
        👒 **For modern apps**: GraphQL support makes your server versatile on request and response; built-in data validation and JSON Schema (OpenAPI) help you build reliable web services.
        
        🐍 **Pythonic experience**: as easy as a Python list. If you can Python, you can DocArray. Intuitive idioms and type annotation simplify the code you write.
        
        🛸 **IDE integration**: pretty-print and visualization on Jupyter notebook and Google Colab; comprehensive autocomplete and type hints in PyCharm and VS Code.
        
        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).
        
        <!-- end elevator-pitch -->
        
        DocArray was released under the open-source [Apache License 2.0](https://github.com/docarray/docarray/blob/main/LICENSE) in January 2022. It is currently a sandbox project under [LF AI & Data Foundation](https://lfaidata.foundation/).
        
        ## [Documentation](https://docarray.jina.ai)
        
        ## Install 
        
        Requires Python 3.7+
        ```shell
        pip install docarray
        ```
        or via Conda:
        ```shell
        conda install -c conda-forge docarray
        ```
        [Commonly used features](https://docarray.jina.ai/#install) can be enabled via `pip install "docarray[common]"`.
        
        
        ## Get Started
        
        DocArray consists of three simple concepts:
        
        - **Document**: a data structure for easily representing nested, unstructured data.
        - **DocumentArray**: a container for efficiently accessing, manipulating, and understanding multiple Documents.
        - **Dataclass**: a high-level API for intuitively representing multimodal data.
        
        Let's see DocArray in action with some examples.
        
        ### Example 1: represent multimodal data in a dataclass
        
        You can easily represent the following news article card with `docarray.dataclass` and type annotation:
        
        
        <table>
        <tr>
        <td> 
        
        <img src="https://github.com/docarray/docarray/blob/main/docs/fundamentals/dataclass/img/image-mmdoc-example.png?raw=true" alt="A example multimodal document" width="300px">
             
        </td>
        <td>
        
        ```python
        from docarray import dataclass, Document
        from docarray.typing import Image, Text, JSON
        
        
        @dataclass
        class WPArticle:
            banner: Image
            headline: Text
            meta: JSON
        
        
        a = WPArticle(
            banner='https://.../cat-dog-flight.png',
            headline='Everything to know about flying with pets, ...',
            meta={
                'author': 'Nathan Diller',
                'Column': 'By the Way - A Post Travel Destination',
            },
        )
        
        d = Document(a)
        ```
        
        </td>
        </tr>
        </table>
        
        
        ### Example 2: text matching in 10 lines
        
        Let's search for top-5 similar sentences of <kbd>she smiled too much</kbd> 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(Document.embed_feature_hashing, backend='process')
        
        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 have better embeddings? Of course you do! We look forward to seeing your results!
        
        ### Example 3: external storage for out-of-memory data
        
        When your data is too big, storing in memory is not the best idea. DocArray supports [multiple storage backends](https://docarray.jina.ai/advanced/document-store/) such as SQLite, Weaviate, Qdrant and AnnLite. They're all unified under **the exact same user experience and API**. Take the above snippet: you only need to change one line to use SQLite:
        
        ```python
        da = DocumentArray(
            (Document(text=s.strip()) for s in d.text.split('\n') if s.strip()),
            storage='sqlite',
        )
        ```
        
        The code snippet can still run **as-is**. All APIs remain the same, the subsequent code then runs in an "in-database" manner. 
        
        Besides saving memory, you can leverage storage backends for persistence and faster retrieval (e.g. on nearest-neighbor queries).
        
        ### Example 4: complete workflow of visual search 
        
        Let's use DocArray and the [Totally Looks Like](https://sites.google.com/view/totally-looks-like-dataset) dataset to build a simple meme image search. The dataset contains 6,016 image-pairs stored in `/left` and `/right`. Images that share the same filename appear similar to the human eye. For example:
        
        <table>
        <thead>
          <tr>
            <th>left/00018.jpg</th>
            <th>right/00018.jpg</th>
            <th>left/00131.jpg</th>
            <th>right/00131.jpg</th>
          </tr>
        </thead>
        <tbody>
          <tr align="center">
            <td><img src="https://github.com/docarray/docarray/blob/main/.github/README-img/left-00018.jpg?raw=true" alt="Visualizing top-9 matches using DocArray API" width="50%"></td>
            <td><img src="https://github.com/docarray/docarray/blob/main/.github/README-img/right-00018.jpg?raw=true" alt="Visualizing top-9 matches using DocArray API" width="50%"></td>
            <td><img src="https://github.com/docarray/docarray/blob/main/.github/README-img/left-00131.jpg?raw=true" alt="Visualizing top-9 matches using DocArray API" width="50%"></td>
            <td><img src="https://github.com/docarray/docarray/blob/main/.github/README-img/right-00131.jpg?raw=true" alt="Visualizing top-9 matches using DocArray API" width="50%"></td>
          </tr>
        </tbody>
        </table>
        
        Given an image from `/left`, can we find the most-similar image to it in `/right`? (without looking at the filename).
        
        ### Load images
        
        First we load images. You *can* go to [Totally Looks Like](https://sites.google.com/view/totally-looks-like-dataset)'s website, unzip and load images as below:
        
        ```python
        from docarray import DocumentArray
        
        left_da = DocumentArray.from_files('left/*.jpg')
        ```
        
        Or you can simply pull it from Jina AI Cloud:
        
        ```python
        left_da = DocumentArray.pull('jina-ai/demo-leftda', show_progress=True)
        ```
        
        **Note**
        If you have more than 15GB of RAM and want to try using the whole dataset instead of just the first 1,000 images, remove `[:1000]` when loading the files into the DocumentArrays `left_da` and `right_da`.
        
        
        You'll see a progress bar to indicate how much has downloaded.
        
        To get a feeling of the data, we can plot them in one sprite image. You need matplotlib and torch installed to run this snippet:
        
        ```python
        left_da.plot_image_sprites()
        ```
        
        <p align="center">
        <a href="https://docarray.jina.ai"><img src="https://github.com/docarray/docarray/blob/main/.github/README-img/sprite.png?raw=true" alt="Load totally looks like dataset with docarray API" width="60%"></a>
        </p>
        
        ### Apply preprocessing
        
        Let's do some standard computer vision pre-processing:
        
        ```python
        from docarray import Document
        
        
        def preproc(d: Document):
            return (
                d.load_uri_to_image_tensor()  # load
                .set_image_tensor_normalization()  # normalize color
                .set_image_tensor_channel_axis(-1, 0)
            )  # switch color axis for the PyTorch model later
        
        
        left_da.apply(preproc)
        ```
        
        Did I mention `apply` works in parallel?
        
        ### Embed images
        
        Now let's 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 speed up
        ```
        
        This step takes ~30 seconds on GPU. Beside PyTorch, you can also use TensorFlow, PaddlePaddle, or ONNX models in `.embed(...)`.
        
        ### Visualize embeddings
        
        You can visualize the embeddings via tSNE in an interactive embedding projector. You will need to have pydantic, uvicorn and FastAPI installed to run this snippet:
        
        ```python
        left_da.plot_embeddings(image_sprites=True)
        ```
        
        <p align="center">
        <a href="https://docarray.jina.ai"><img src="https://github.com/docarray/docarray/blob/main/.github/README-img/tsne.gif?raw=true" alt="Visualizing embedding via tSNE and embedding projector" width="90%"></a>
        </p>
        
        Fun is fun, but 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:
        
        <table>
        <tr>
        <th> Pull from Cloud </th> 
        <th> Download, unzip, load from local </th>
        </tr>
        <tr>
        <td> 
        
        ```python
        right_da = (
            DocumentArray.pull('jina-ai/demo-rightda', show_progress=True)
            .apply(preproc)
            .embed(model, device='cuda')[:1000]
        )
        ```
             
        </td>
        <td>
        
        ```python
        right_da = (
            DocumentArray.from_files('right/*.jpg')[:1000]
            .apply(preproc)
            .embed(model, device='cuda')
        )
        ```
        
        </td>
        </tr>
        </table>
        
        ### Match nearest neighbors
        
        Now we can 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 m in left_da[0].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 to a one-liner using the element and 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_tensor_channel_axis(
                    0, -1
                ).set_image_tensor_inv_normalization()
            )
            .plot_image_sprites()
        )
        ```
        
        <p align="center">
        <a href="https://docarray.jina.ai"><img src="https://github.com/jina-ai/docarray/blob/main/.github/README-img/9nn-left.jpeg?raw=true" alt="Visualizing top-9 matches using DocArray API" height="250px"></a>
        <a href="https://docarray.jina.ai"><img src="https://github.com/jina-ai/docarray/blob/main/.github/README-img/9nn.png?raw=true" alt="Visualizing top-9 matches using DocArray API" height="250px"></a>
        </p>
        
        Here we reversed the preprocessing steps (i.e. switching axis and normalizing) on the copied matches, so you 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 created 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
        ```
        
        You can also use other metrics like `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) you can do much better with [just another ten lines of code](https://finetuner.jina.ai/notebooks/image_to_image/).
        
        
        ### 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 that DocumentArray's data, use `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('my_shared_da')
        ```
        
        Now anyone who knows the token `my_shared_da` can pull and work on it.
        
        ```python
        left_da = DocumentArray.pull('<username>/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).
        
        
        <!-- start support-pitch -->
        ## Support
        - Join our [Slack community](https://jina.ai/slack) and chat with other community members about ideas.
        
        
        > DocArray is a trademark of LF AI Projects, LLC
        
Keywords: docarray deep-learning data-structures cross-modal multi-modal unstructured-data nested-data neural-search
Platform: UNKNOWN
Classifier: Development Status :: 5 - Production/Stable
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Education
Classifier: Intended Audience :: Science/Research
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Unix Shell
Classifier: Environment :: Console
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Operating System :: OS Independent
Classifier: Topic :: Database :: Database Engines/Servers
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Internet :: WWW/HTTP :: Indexing/Search
Classifier: Topic :: Scientific/Engineering :: Image Recognition
Classifier: Topic :: Multimedia :: Video
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Scientific/Engineering :: Mathematics
Classifier: Topic :: Software Development
Classifier: Topic :: Software Development :: Libraries
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Description-Content-Type: text/markdown
Provides-Extra: common
Provides-Extra: full
Provides-Extra: qdrant
Provides-Extra: annlite
Provides-Extra: weaviate
Provides-Extra: elasticsearch
Provides-Extra: redis
Provides-Extra: milvus
Provides-Extra: benchmark
Provides-Extra: test
