Practical overview
When it fits
Use DocArray when a Python machine-learning workflow needs structured multimodal records, collections suited to batch or list processing, or documented data exchange between components.
Who it is for
- Python developers building machine-learning applications with multimodal records.
- Teams organizing tensor and other document fields for batch processing or streaming.
- Developers preparing data for model-serving, microservice or distributed workflows.
Documented uses
- Define typed records combining fields such as text, image URLs and tensors.
- Group records in DocVec for stacked tensor batches or DocList for list-like processing.
- Serialize and deserialize documents using formats including JSON and Protobuf.
- Transmit data using HTTP or gRPC and connect document data to storage workflows.
Requirements & limits
DocArray is installed as a Python library from the command line. The documented installation action does not indicate an account requirement.
- DocVec stacks document tensors for batch processing; DocList retains tensors as-is and supports list operations.
- The documentation describes native support for NumPy, PyTorch, TensorFlow and JAX, as well as compatibility with FastAPI and Jina.
- Data transmission options described include JSON over HTTP and Protobuf over gRPC.
- The documentation notes significant changes after version 0.21; the current README is for versions above 0.30.
Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.
Install DocArray and define a multimodal document collection
Install DocArray, model records with a schema, and collect them in a typed document list.
Before you start
- Python environment with the required dependencies for the example, including PyTorch and NumPy.
- A DocArray version compatible with the documented API; the documentation distinguishes versions above 0.30 from versions at or below 0.21.
Steps
Install DocArray from the CLI with the command below.
pip install -U docarrayDefine a document schema by subclassing
BaseDocand declaring fields such as text, image URL, or a shaped tensor.Create records that follow the schema and collect them in
DocList[YourSchema](...).Access collection fields in bulk, or use the documented
append,insert, and delete operations to modify the list.
Expected outcome
A schema-defined collection of multimodal documents that can be accessed and modified as a typed list.
Guide limitations
- The documented example uses specific tensor and image types; corresponding libraries and valid data are needed for those fields.
- The documentation states that DocArray versions at or below 0.21 require
pip install docarray==0.21and refer users to the older documentation and codebase. - This workflow prepares document data; it does not demonstrate a complete model-training pipeline.
Prepare DocList data for a PyTorch DataLoader
Expose schema-defined DocArray records through the documented PyTorch dataset interface.
Before you start
- DocArray installed in a Python environment.
- A document schema and records collected in a
DocList. - PyTorch available for the DataLoader workflow.
Steps
Define a document type by subclassing
BaseDocand declaring the data fields needed by the dataset.Create records of that type and collect them in
DocList[YourSchema](...).Use the DocList dataset interface with PyTorch's
DataLoaderas described in the DocArray documentation.
Expected outcome
DocArray document data is prepared for loading through a PyTorch DataLoader in a model workflow.
Guide limitations
- The documentation describes dataset use inside a PyTorch DataLoader but does not demonstrate a complete model-training pipeline.
- The detailed DataLoader setup is not included in the available page content.
Sources & review
Source record details
- Source availability
- Active
- Sources checked
- 12 October 2026