Introduction
Deep Lake is a database for AI optimized for storing, querying and versioning vectors and multimodal data (images, video, audio, text). It enables building LLM applications, training deep learning models at scale, and streaming data into PyTorch/TensorFlow for efficient training.
Key features
- Multi-cloud support (S3, GCP, Azure) and local usage scenarios.
- Native vector and multimodal data support with visualization via the Deep Lake App.
- Integrations with popular tools: LangChain, LlamaIndex, PyTorch/TensorFlow data loaders, and vector stores.
- Data versioning and streaming support for large-scale training pipelines.
Use cases
- Vector store for RAG applications and LLM apps.
- Managing large image/video/audio datasets for model training and research.
- Data visualization, version control and collaborative dataset management in enterprise or academic settings.
Technical details
- Implemented in Python with comprehensive APIs and tutorials ( https://docs.deeplake.ai/) .
- Provides a Deep Lake App for dataset visualization and supports real-time streaming to training frameworks.
- Licensed under Apache-2.0; active community and frequent releases.