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DOCARRAY
Multimodal AI data structures for vector search and neural search
Apache-2.0
ABOUT
AI applications work with diverse data types — text, images, audio, video, 3D meshes — but standard Python data structures (dicts, lists, dataclasses) don't capture the rich relationships between modalities or serialize efficiently to formats compatible with vector databases and search engines. DocArray provides typed, composable document data structures with built-in embedding support, chunking, serialization to JSON/Protobuf, and first-class integration with vector databases like Qdrant, Elasticsearch, and Weaviate.
INSTALL
pip install docarrayINTEGRATION GUIDE
1. Represent multimodal documents with text, images, and embeddings in a single typed data structure
2. Build neural search pipelines that encode, index, and retrieve across text, image, and audio data
3. Chunk and preprocess long documents for RAG while preserving document structure and metadata
4. Serialize AI pipeline intermediates to Protobuf for high-performance inter-service communication
5. Integrate with vector databases directly through DocArray's built-in storage backends
TAGS
multimodaldata-structurespythonembeddingsvector-searchneural-searchjina