# docarray/docarray

Represent, send, store and search multimodal data

Repository: https://github.com/docarray/docarray
Canonical: https://ross.abutalabs.com/products/docarray
Homepage: https://docs.docarray.org/
Language: Python
License: Apache-2.0
License Family: permissive
Topics: docarray, data-structures, multimodal, cross-modal, neural-search, deep-learning, nested-data, qdrant, weaviate, nearest-neighbor-search, protobuf, elasticsearch, dataclass, multi-modal, semantic-search, machine-learning, pytorch, fastapi, pydantic
Last push: 2026-03-27T16:19:09+00:00

## Health v2 (maintenance only)
Score: 56/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 74, release rhythm 8, longevity 100
- inputs: {"age_days": 1723, "days_push": 159, "days_rel": 530, "gap_med": null, "n_releases_24m": 1}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 3124, forks 243 (observed 2026-08-28T04:07:44.402038+00:00)

## What it is
DocArray is a Python library for representing, transmitting, storing, and retrieving multimodal data such as text, images, audio, and video. Built on Pydantic, it integrates with NumPy, PyTorch, TensorFlow, and JAX, and supports vector databases like Weaviate, Qdrant, Elasticsearch, and Redis.

## Use cases
- represent multimodal data for machine learning models
- store and search embeddings in a vector database
- build semantic search over images and text
- send documents as JSON over HTTP or Protobuf over gRPC
- structure training data for PyTorch or TensorFlow
- build multimodal AI applications with FastAPI

## When to choose
- you need typed data structures for mixed media (text, image, audio) in ML pipelines
- you want a unified interface to multiple vector databases
- you need Pydantic/FastAPI-compatible document models for ML services

## When to avoid
- you only need plain tabular data (use pandas)
- you need a full vector database rather than a client-side data structure
- you rely on DocArray <=0.21 APIs, which differ significantly from 0.30+

## Facets
- artifact type: library
- maturity: active
- function: serialization, vector-database, search-engine, machine-learning, rag
- domain: machine-learning, deep-learning, data-science, large-language-models
- platform: python, cross-platform
- tags: multimodal-data, data-structures, pydantic, nearest-neighbor-search, embeddings, protobuf, doclist, search, retrieval-augmented-generation

## Member repositories
- docarray/docarray (main) score 56

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:07:44.402038+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T07:26:31.509782+00:00, confidence not recorded.
  - readme: https://github.com/docarray/docarray (fetched 2026-08-28T04:07:44.402038+00:00, sha d5a805e1fd15)
  - homepage: https://docs.docarray.org/ (fetched 2026-08-29T09:41:29.640080+00:00, sha 248672121972)
  - site_page: https://docs.docarray.org/API_reference/doc_store/doc_store (fetched 2026-08-29T09:41:29.650348+00:00, sha c64487f723a5)
  - site_page: https://docs.docarray.org/API_reference/doc_store/file_doc_store (fetched 2026-08-29T09:41:29.652543+00:00, sha 54d34c48e21e)
  - site_page: https://docs.docarray.org/API_reference/doc_store/s3_doc_store (fetched 2026-08-29T09:41:29.654563+00:00, sha 40cf3519e70e)
  - site_page: https://docs.docarray.org/API_reference/utils/maps_docs (fetched 2026-08-29T09:41:29.656797+00:00, sha 79d1c567aba1)
- Data as of 2026-08-30T08:39:29.467469+00:00.