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  • 🏠Deep Lake Docs
  • List of ML Datasets
  • 🏗️SETUP
    • Installation
    • User Authentication
      • Workload Identities (Azure Only)
    • Storage and Credentials
      • Storage Options
      • Setting up Deep Lake in Your Cloud
        • Microsoft Azure
          • Configure Azure SSO on Activeloop
          • Provisioning Federated Credentials
          • Enabling CORS
        • Google Cloud
          • Provisioning Federated Credentials
          • Enabling CORS
        • Amazon Web Services
          • Provisioning Role-Based Access
          • Enabling CORS
  • 📚Examples
    • Deep Learning
      • Deep Learning Quickstart
      • Deep Learning Guide
        • Step 1: Hello World
        • Step 2: Creating Deep Lake Datasets
        • Step 3: Understanding Compression
        • Step 4: Accessing and Updating Data
        • Step 5: Visualizing Datasets
        • Step 6: Using Activeloop Storage
        • Step 7: Connecting Deep Lake Datasets to ML Frameworks
        • Step 8: Parallel Computing
        • Step 9: Dataset Version Control
        • Step 10: Dataset Filtering
      • Deep Learning Tutorials
        • Creating Datasets
          • Creating Complex Datasets
          • Creating Object Detection Datasets
          • Creating Time-Series Datasets
          • Creating Datasets with Sequences
          • Creating Video Datasets
        • Training Models
          • Splitting Datasets for Training
          • Training an Image Classification Model in PyTorch
          • Training Models Using MMDetection
          • Training Models Using PyTorch Lightning
          • Training on AWS SageMaker
          • Training an Object Detection and Segmentation Model in PyTorch
        • Updating Datasets
        • Data Processing Using Parallel Computing
      • Deep Learning Playbooks
        • Querying, Training and Editing Datasets with Data Lineage
        • Evaluating Model Performance
        • Training Reproducibility Using Deep Lake and Weights & Biases
        • Working with Videos
      • Deep Lake Dataloaders
      • API Summary
    • RAG
      • RAG Quickstart
      • RAG Tutorials
        • Vector Store Basics
        • Vector Search Options
          • LangChain API
          • Deep Lake Vector Store API
          • Managed Database REST API
        • Customizing Your Vector Store
        • Image Similarity Search
        • Improving Search Accuracy using Deep Memory
      • LangChain Integration
      • LlamaIndex Integration
      • Managed Tensor Database
        • REST API
        • Migrating Datasets to the Tensor Database
      • Deep Memory
        • How it Works
    • Tensor Query Language (TQL)
      • TQL Syntax
      • Index for ANN Search
        • Caching and Optimization
      • Sampling Datasets
  • 🔬Technical Details
    • Best Practices
      • Creating Datasets at Scale
      • Training Models at Scale
      • Storage Synchronization and "with" Context
      • Restoring Corrupted Datasets
      • Concurrent Writes
        • Concurrency Using Zookeeper Locks
    • Deep Lake Data Format
      • Tensor Relationships
      • Version Control and Querying
    • Dataset Visualization
      • Visualizer Integration
    • Shuffling in Dataloaders
    • How to Contribute
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  • Overview of Deep Lake's Managed Tensor Database
  • User Interfaces
  • Further Information:

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  1. Examples
  2. RAG

Managed Tensor Database

Deep Lake Managed Database

PreviousLlamaIndex IntegrationNextREST API

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Overview of Deep Lake's Managed Tensor Database

Deep Lake offers a serverless Managed Tensor Database that eliminates the complexity of self-hosting and substantially lowers costs. Currently, it only supports dataset queries, including vector search, but additional features for creating and modifying data being added in December 2023.

User Interfaces

LangChain and LlamaIndex

To use the Managed Vector Database in LangChain or Llama Index, specify dataset_path = hub://org_id/dataset_name and runtime = {"tensor_db": True} during Vector Store creation.

REST API

A standalone REST API is available for interacting with the Managed Database:

Further Information:

📚
REST API
Migrating Datasets to the Tensor Database
Comparison of Deep Lake as a Managed Database vs Embedded Database