deeplake

Stores heterogeneous AI data types (embeddings, images, text, audio, video) as hierarchical tensors within a dataset container, using native format compression with lazy loading to minimize storage footprint while maintaining fast random access. The system uses a columnar tensor model where each column represents a distinct data attribute with its own compression codec, enabling efficient partial reads without deserializing entire datasets.

Executes approximate nearest neighbor (ANN) search on embedding tensors combined with structured filtering via Tensor Query Language (TQL), a custom DSL that allows predicates on tensor properties (e.g., 'find embeddings where metadata.source == "pdf" AND embedding_distance < 0.8'). The system uses index structures on vector columns to accelerate search while TQL predicates are evaluated server-side or client-side depending on index availability, enabling hybrid semantic + structured retrieval for RAG applications.

Organizes data using a two-level hierarchy: datasets (containers) hold tensors (columns) representing distinct data attributes, with each tensor supporting a specific data type and optional indices. Tensors are lazily evaluated — queries return tensor views that are only materialized when accessed, enabling efficient handling of large datasets without loading everything into memory. The model mirrors deep learning frameworks' data organization (batch, features, dimensions) rather than forcing relational schemas.

Executes all data transformations, filtering, and aggregations on the client (user's machine or application server) rather than on a dedicated database server, using Python async/await patterns and futures for non-blocking operations. This architecture eliminates server infrastructure costs and allows users to control where computation happens, with built-in support for batch operations, streaming results, and integration with async frameworks like asyncio and Dask.

Tracks changes to datasets using a Git-like version control system with commits, branches, and tags, allowing users to snapshot dataset state, experiment with modifications on branches, and revert to previous versions without duplicating data. The system stores only deltas (changes) between versions, reducing storage overhead, and enables collaborative workflows where multiple users can branch datasets independently and merge changes.

Exposes Deep Lake datasets as native PyTorch DataLoader and TensorFlow Dataset objects, enabling seamless integration with training loops without data format conversion. The system handles batching, shuffling, prefetching, and distributed sampling transparently, with support for lazy loading to stream data from cloud storage during training without downloading the entire dataset upfront.

Provides a domain-specific query language for filtering, transforming, and aggregating tensors using SQL-like syntax extended with tensor-specific operations (e.g., 'SELECT * WHERE embedding.shape[0] > 768 AND text.length() > 100'). TQL supports custom user-defined functions (UDFs) written in Python that operate on tensor columns, enabling complex transformations like embedding distance calculations, image feature extraction, or text processing without materializing intermediate results.

Provides a unified Python API for storing and retrieving datasets across multiple cloud providers (AWS S3, Google Cloud Storage, Azure Blob Storage) and local filesystems, abstracting away provider-specific APIs and authentication. The system handles cloud credentials transparently, supports streaming uploads/downloads, and enables seamless dataset migration between storage backends without data format changes.

Provides native integrations with LangChain and LlamaIndex frameworks, allowing Deep Lake datasets to be used directly as vector stores and document retrievers in RAG pipelines. The integration handles embedding storage, similarity search, and metadata filtering transparently, enabling developers to build RAG applications using framework-native abstractions without writing custom retrieval logic.

Supports storing datasets in-memory (for development and testing) or on local filesystems (for single-machine deployments), in addition to cloud storage. In-memory storage provides fast access for small datasets and rapid prototyping, while local filesystem storage enables offline development and avoids cloud costs for non-production workloads. Both backends use the same API as cloud storage, enabling seamless transitions between development and production environments.

Provides a web-based UI (Deep Lake App) for exploring, visualizing, and analyzing datasets without writing code. The app displays dataset statistics, tensor previews (images, text, embeddings), version history, and search results, enabling non-technical stakeholders to understand dataset contents and quality. The visualization is read-only by default but supports collaborative annotation workflows where team members can label data directly in the UI.