DocArray
What is DocArray?
Discover how DocArray can enhance your workflow
Key Capabilities
What makes DocArray powerful
Typed Documents
Define explicit schemas for content, embeddings and metadata so every stage of the pipeline consumes and emits compatible objects.
Efficient IO
Use binary serialization and batching to reduce overhead when moving large DocumentArrays between workers and services.
Frameworks and Vectors
Leverage adapters for deep learning frameworks and popular vector databases in cloud or on-prem environments.
Data Quality
Apply type checks and constraints to catch schema drift early and keep experiments reproducible and debuggable.
Key Features
What makes DocArray stand out
- Typed Document and DocumentArray classes for multimodal data
- Fast binary serialization for inter process and network transport
- Field validation and schema versions for reproducibility
- Helpers for chunking splitting and hierarchical docs
- Vector friendly ops for indexing similarity and ranking
- Integrations with PyTorch TensorFlow and ONNX runtimes
- Adapters for common vector databases and cloud stores
- Active community docs examples and release cadence
Use Cases
How DocArray can help you
- RAG pipelines passing chunks and embeddings between steps
- Multimodal search services combining text and images
- ETL jobs moving vectors between stores during migrations
- Evaluation harnesses that track inputs outputs and scores
- Realtime inference systems that batch requests across workers
- Dataset curation with typed metadata for training
- Prototyping in notebooks that later scales to services
- Education demos that teach embeddings and retrieval patterns
Perfect For
Python developers, ML engineers and researchers who need structured multimodal containers and fast, predictable transport across models, vector stores and services
Quick Information
Compare DocArray with Alternatives
See how DocArray stacks up against similar tools
Frequently Asked Questions
What license and cost apply?
Does it require Jina the framework?
Which workloads benefit most?
Is there GPU dependency?
How does it help with reproducibility?
Can it connect to vector databases?
Is there a GUI included?
Where can teams learn quickly?
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