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v3.6.1
v3.6.1
  • Deep Lake Docs
  • Vector Store Quickstart
  • Deep Learning Quickstart
  • Storage & Credentials
    • Storage Options
    • User Authentication
    • Storing Deep Lake Data in Your Own Cloud
      • Microsoft Azure
        • Provisioning Federated Credentials
      • Amazon Web Services
        • Provisioning Role-Based Access
        • Enabling CORS
  • List of ML Datasets
  • API Reference
  • 🏢Enterprise Features
    • Compute Engine
      • Tensor Query Language (TQL)
        • TQL Syntax
        • Sampling Datasets
      • Performant Dataloader
    • Tensor Database
      • REST API
      • Migrating Datasets to the Tensor Database
  • 📚EXAMPLE CODE
  • Getting Started
    • Vector Store
      • Step 1: Hello World
      • Step 2: Creating Deep Lake Vector Stores
      • Step 3: Performing Search in the Vector Store
      • Step 4: Customizing Vector Stores
    • Deep Learning
      • 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
  • Tutorials (w Colab)
    • Vector Store Tutorials
      • Vector Search Options
        • Deep Lake Vector Store API
        • REST API
        • LangChain API
      • Deep Lake Vector Store in LangChain
    • Creating Datasets
      • Creating Complex Datasets
      • Creating Object Detection Datasets
      • Creating Time-Series Datasets
      • Creating Datasets with Sequences
      • Creating Video Datasets
    • Training Models
      • 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
  • Playbooks
    • Querying, Training and Editing Datasets with Data Lineage
    • Evaluating Model Performance
    • Training Reproducibility Using Deep Lake and Weights & Biases
    • Working with Videos
  • API Summary
  • 🔬Technical Details
    • Best Practices
      • Creating Datasets at Scale
      • Training Models at Scale
      • Storage Synchronization and "with" Context
      • Restoring Corrupted Datasets
    • Data Layout
    • Version Control and Querying
    • Dataset Visualization
    • Tensor Relationships
    • Visualizer Integration
    • Shuffling in dataloaders
    • How to Contribute
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On this page
  • Overview of Vector Search Options in Deep Lake
  • Overview of Search Computation Execution
  • APIs for Search

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  1. Tutorials (w Colab)
  2. Vector Store Tutorials

Vector Search Options

Overview of Vector Search Options in Deep Lake

Overview of Vector Search Options in Deep Lake

Deep Lake offers a variety of vector search options depending on the storage location of the Vector Store and infrastructure + code that should run the computations.

Search Method
Compute Location
Execution Algorithm
Query Syntax
Required Storage

Python

Client-side

Deep Lake OSS Python Code

LangChain API

In memory, local, user cloud, Tensor Database

Client-side

Deep Lake C++ Compute Engine

LangChain API or TQL

Managed Database

Deep Lake C++ Compute Engine

LangChain API or TQL

Tensor Database

Overview of Search Computation Execution

Python (Client-Side)

Deep Lake OSS offers query execution logic that run on the client (your machine) using OSS code in Python. This compute logic is accessible in all Deep Lake Python APIs and is available for Vector Stores stored in any location. See individual APIs below for details.

Compute Engine (Client-Side)

Deep Lake Compute Engine offers query execution logic that run on the client (your machine) using C++ Code that is called via Python API. This compute logic is accessible in all Deep Lake Python APIs and is only available for Vector Stores stored Deep Lake storage or in user clouds connected to Deep Lake. See individual APIs below for details.

To run queries using Compute Engine, make sure to !pip install "deeplake[enterprise]".

Managed Tensor Database (Server-Side Running Compute Engine)

Deep Lake offers a Managed Tensor Database that executes queries on Deep Lake infrastructure while running Compute Engine under-the-hood. This compute logic is accessible in all Deep Lake Python APIs and is only available for Vector Stores stored in the Deep Lake Managed Tensor Database. See individual APIs below for details.

APIs for Search

Vector search can occur via a variety of APIs in Deep Lake. They are explained in the links below:

PreviousVector Store TutorialsNextDeep Lake Vector Store API

Last updated 2 years ago

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User cloud (), Tensor Database

Deep Lake Vector Store API
REST API
LangChain API
Compute Engine
must be connected to Deep Lake
Tensor Database