DeepLake

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GitHub C++ Apache-2.0

Description

AI Data Runtime for Agents. Provides serverless Postgres with a multimodal datalake, enabling scalable retrieval and training. Unifies vector storage, dataset management, and streaming data loading for AI agent workflows.

Key Features

  • AI data runtime for agents providing serverless Postgres with multimodal datalake architecture
  • Unified vector storage, dataset management, and streaming data loading for LLM apps and deep learning training
  • Native multi-cloud support with a single API to operate S3, GCP, Azure, and local storage
  • Deep integrations with LangChain, LlamaIndex, Weights & Biases, and other popular AI tools
  • Built-in PyTorch and TensorFlow dataloaders with data versioning and lineage tracking
  • 100+ pre-built public datasets (MNIST, COCO, ImageNet, etc.) with instant visualization in Deep Lake App

Use Cases

💡 Vector database for storing and retrieving document embeddings when building RAG applications
💡 Dataset management and streaming data loading during large-scale deep learning model training
💡 Unified management of images, videos, audio, and text data in multimodal AI systems
💡 Knowledge base storage and efficient retrieval in AI agent workflows with cross-cloud deployment support
💡 Data versioning and annotation management in medical imaging, autonomous driving, and similar domains

Tags

vector-database data-processing python rag database embedding

Categories

📚 RAG Tools

Quick Start

1. Install: pip install deeplake
2. Register at Deep Lake App to get an API token
3. Create a dataset with deeplake.create()
4. Add data and perform vector search
5. Integrate with LLM apps via LangChain or LlamaIndex
Visit GitHub

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