Deeplake

DeepLake is AI data infrastructure consisting of a multimodal data lake, a hybrid search engine, and a serverless vector database. It provides a PostgreSQL-based AI data runtime that combines multimodal storage with streaming pipelines to load and shuffle datasets from cloud storage directly into deep learning training pipelines.

The system utilizes lazy indexing to store and slice images, audio, and video without loading entire files into memory. It enables retrieval-augmented generation by persisting high-dimensional embeddings in a serverless vector store and implementing hybrid search that combines vector similarity with full-text keyword matching.

The project covers a broad capability surface including structured metadata indexing for numeric and JSON fields, cloud-local data synchronization, and visualization tools for inspecting dataset annotations such as bounding boxes and masks.

Features

Serverless Vector Stores - Provides a serverless vector database for storing high-dimensional embeddings to enable scalable retrieval for language models.

Multimodal Data Storage - Provides a system for saving and organizing mixed-modality data like audio, text, and images using lazy indexing.

Data Loaders - Provides built-in loaders to efficiently load and shuffle datasets for deep learning model training.

AI Data Runtimes - Combines serverless PostgreSQL with multimodal storage to enable scalable retrieval and deep learning training.

Multimodal - Manages multimodal AI data types optimized for deep learning using lazy loading to prevent memory overflow.

Training Data Pipelines - Provides pipelines that load, shuffle, and format diverse multimodal data types for deep learning training.

Full Text Search - Ships a scoring-based full-text search system to find and rank documents based on keyword matches.

Hybrid Search Engines - Integrates vector-based semantic retrieval with traditional keyword-based indexing in a single query.

Training Sample Streaming - Implements streaming of individual training samples from large datasets using random access for training pipelines.

Vector Storage - Provides a specialized storage layer for persisting large-scale high-dimensional embeddings to power retrieval-augmented generation.

Hybrid Retrieval - Combines vector similarity and full text search to improve the accuracy of retrieved data for AI agents.

Stream-Based Data Pipelines - Feeds training pipelines by shuffling and transferring data directly from storage to models via stream-based architectures.

Vector Database Integrations - Integrates high-dimensional vector storage and retrieval to enable scalable similarity search for language models.

Vector Databases - Implements a serverless database optimized for storing and querying high-dimensional vector embeddings.

Vector Similarity Search - Implements vector similarity search using cosine distance metrics to find the most relevant high-dimensional data points.

Cloud-Local Data Interfaces - Synchronizes data transfers between local buffers and multiple cloud providers using a consistent API.

Structured - Uses specialized structures for numeric and JSON fields to accelerate the filtering and retrieval of structured metadata.

Structured Metadata Indexes - Implements optimized indexing for numeric and JSON fields to accelerate the filtering and retrieval of structured metadata.

Cloud-Agnostic Synchronization - Provides a cloud-agnostic synchronization layer to move datasets between local storage and various cloud providers.

Buffered Archival Streams - Moves datasets between local buffers and cloud storage using buffered archival streaming.

Lazy Media Indexing - Loads specific slices of compressed images, audio, and video only when requested to minimize memory usage.

Computer Vision Frameworks - Data infrastructure optimized for computer vision workflows.

Backend and Infrastructure - Vector database for AI data management.

Development Environments - Data lake format optimized for deep learning applications.

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Features

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