#multimodal data

DataChain enables efficient organization of unstructured data into scalable datasets, integrating seamlessly with AI models without abstraction. Features include Pythonic pipelines, multimodal data support, and metadata generation via AI models, alongside optimized operations through parallelization and vector search. Supports integration with PyTorch and TensorFlow.

LanceDB is an open-source database specialized in multimodal AI, offering serverless, scalable vector search, and integration with tools like LangChain and LlamaIndex. Supporting Python and JavaScript, its Rust-based core enhances machine learning efficiency.

DocArray offers a versatile Python library for handling multimodal data, including representation, storage, and retrieval, tailored for AI applications. It integrates smoothly with frameworks like PyTorch, TensorFlow, and FastAPI, supporting vector databases such as Weaviate and Redis. It also facilitates data transmission using JSON or Protobuf, fitting well within distributed systems and microservice frameworks. By utilizing Pydantic for defining machine-learning data models, it aids in efficient data management, making it a valuable tool for AI developers.