Deep Lake Docs

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Activeloop Deep Lake

Deep Lake as a Vector Store for LLM Applications

• Store and search embeddings and their metadata including text, jsons, images, audio, video, and more. Save the data locally, in your cloud, or on Deep Lake storage.

• Build LLM Apps using or integrations with LangChain and LlamaIndex

• Run computations locally or on our Managed Tensor Database

Deep Lake as a Data Lake For Deep Learning

• Store images, audios, videos, text and their metadata (i.e. annotations) in a data format optimized for Deep Learning. Save the data locally, in your cloud, or on Activeloop storage.

• Rapidly train PyTorch and TensorFlow models while streaming data with no boilerplate code.

• Run version control, dataset queries, and distributed workloads using a simple Python API.

Deep Lake Architecture for Inference and Model Development Applications.

To start using Deep Lake ASAP, check out our Vector Store Quickstart, Deep Learning Quickstart, Getting Started Guides, Tutorials, and Playbooks.

Please check out Deep Lake's GitHub repository and give us a ⭐ if you like the project.

Join our Slack Community if you need help or have suggestions for improving documentation!

Deep Lake Docs Overview

Vector Store Quickstart

Deep Learning Quickstart

Storage & Credentials

Getting Started

Tutorials (w Colab)

Playbooks

Dataset Visualization

Best Practices

Low-Level API Summary