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v3.9.16
v3.9.16
  • 🏠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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  • How Deep Lake Implements an Index for ANN Search
  • Using the Index
  • Limitations

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  1. Examples
  2. Tensor Query Language (TQL)

Index for ANN Search

Overview of Deep Lake's Index implementation for ANN search.

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How Deep Lake Implements an Index for ANN Search

Deep Lake implements the Hierarchical Navigable Small World (HSNW) index for Approximate Nearest Neighbor (ANN) search. The index is based on the with added optimizations. The implementation enables users to run queries on >35M embeddings in less than 1 second.

Unique aspects of Deep Lake's HSNW implementation

  • Rapid index creation with multi-threading optimized for Deep Lake

  • Efficient memory management that reduces RAM usage

Memory Management in Deep Lake

RAM Cost >> On-disk Cost >> Object Storage Cost

Minimizing RAM usage and maximizing object store significantly reduces costs of running a Vector Database. Deep Lake has a unique implementation of memory allocation that minimizes RAM requirement without any performance penalty:

Using the Index

By default, Deep Lake performs linear embedding search for up to 100,000 rows of data. Once the data exceeds that limit, the index is created and the embedding search uses ANN. This index threshold is chosen because linear search is the most accurate, and for less than 100,000 rows, there is almost no performance penalty compared to ANN search.

You may update the threshold for creating the index using the API below:

vectorstore = VectorStore(path, index_params = {threshold: <threshold_int>})

Limitations

The following limitations of the index are being implemented in upcoming releases:

  • If the search is performed using a combination of attribute and vector search, the index is not used and linear search is applied instead.

📚
OSS Hsnwlib package
Memory Architecture for the Deep Lake Vector Store