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🌊 Deep Lake: Multi-Modal AI Database

Why Deep Lake:

  • Powerful Search: Build AI Search Applications on multi-modal data from multiple data sources
  • Production Ready: Scale to billions of data points with high-performance vector search and intelligent query agents
  • Cloud-Native Storage: Direct integration with cloud storage (S3, GCS, Azure) for cost-efficient data management

Key Features:

  • Optimized data streaming with efficient indexing
  • Fast search on billions of data points with minimal caching
  • Direct integration with cloud storage (S3, GCS, Azure)
  • Native compatibility with PyTorch and TensorFlow

Getting Started

  1. Install Deep Lake: 

    pip install deeplake

  2. Use it in Python: 

    import deeplake

# Create a dataset
ds = deeplake.create("s3://my-bucket/dataset")  # or local path

# Add data columns
ds.add_column("images", deeplake.types.Image())
ds.add_column("embeddings", deeplake.types.Embedding(768))
ds.add_column("labels", deeplake.types.Text())

  3. Check out our Quickstart to learn more.

For practical examples: - RAG Applications Guide - Deep Learning Tutorial

Join our Slack Community for support and discussions!

What's New in Deep Lake V4

  • Advanced Indexing: Multiple index types (embedding, lexical, inverted) for fast search with minimal caching
  • Concurrent Operations: Support for concurrent writes with eventual consistency
  • Performance Boost: Significantly faster reads/writes using C++ core