{"categories":[{"label":"Software Development","url":"https://skillfed.io/packages/category/software-development/16"},{"label":"Libraries","url":"https://skillfed.io/packages/category/software-development-libraries/9"},{"label":"Scientific/Engineering","url":"https://skillfed.io/packages/category/scientific-engineering/7"},{"label":"Python Modules","url":"https://skillfed.io/packages/category/software-development-libraries-python-modules/19"},{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence/10"},{"label":"Mathematics","url":"https://skillfed.io/packages/category/scientific-engineering-mathematics/3"},{"label":"Video","url":"https://skillfed.io/packages/category/multimedia-video"},{"label":"Image Recognition","url":"https://skillfed.io/packages/category/scientific-engineering-image-recognition"},{"label":"Database Engines/Servers","url":"https://skillfed.io/packages/category/database-database-engines-servers"},{"label":"Indexing/Search","url":"https://skillfed.io/packages/category/internet-www-http-indexing-search"}],"enrichment":{"capability":"DocArray provides a Python data structure for representing, transmitting, storing, and retrieving multimodal data, with built-in support for tensors from NumPy, PyTorch, TensorFlow, and JAX.","skillfed_tags":["multimodal-data","tensor-handling","ml-data-structures"],"use_cases":["Define typed schemas for multimodal training data with tensor shape validation, then batch them for model training.","Build API endpoints that accept and return structured multimodal documents with automatic validation.","Organize and transmit image, text, and embedding data together in a single document structure for neural search applications.","Compose nested document hierarchies (e.g., a document containing both image and text sub-documents) for complex data pipelines.","Serialize multimodal collections to JSON or Protobuf for inter-service communication in microservice architectures."],"what_it_does":"DocArray is a Python library for defining, organizing, and working with multimodal data in machine learning workflows. It provides Pydantic-based schema definitions that let you declare document types with typed fields\u2014including tensors with explicit shapes\u2014and then collect them into vectorized or list-based containers for batch processing. The library integrates with NumPy and other tensor frameworks, and is designed to work seamlessly with web frameworks and microservice platforms.\n\nYou use DocArray when you need to represent complex, heterogeneous data (images, text, embeddings, metadata) in a structured way that mirrors how machine learning models consume it. It handles both single documents and bulk collections: DocVec stacks tensors for efficient batch operations, while DocList preserves individual tensor structures for streaming or re-ranking. The library also supports nested document composition and can serialize data as JSON over HTTP or Protobuf over gRPC.","worth_installing":"Yes. DocArray is actively maintained, has low install friction, carries a permissive Apache 2.0 license, and solves a real problem for machine learning workflows involving multimodal data. It integrates well with the Python ecosystem and is particularly valuable if you're building systems that need structured tensor and metadata handling. No known vulnerabilities."},"id":"docarray","links":{"html":"https://skillfed.io/packages/docarray","md":"https://skillfed.io/packages/docarray.md","pypi":"https://pypi.org/project/docarray/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2025-03-21","license_spdx":null,"license_treatment":"permissive","name":"docarray","python_support":"supports_current","summary":"The data structure for multimodal data"},"popularity":{"monthly_downloads":109045,"position":12531,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"0.41.0"}