Annoy

Annoy is a C++ library designed for approximate nearest neighbor search in high-dimensional vector spaces. It functions as a vector similarity search engine that constructs static, disk-based data structures to facilitate fast lookups. By mapping identifiers to vector data and persisting these structures to disk, the library enables efficient, memory-mapped access to large datasets.

The project distinguishes itself through the use of random projection trees and distance-metric-based partitioning, which organize data into hierarchical binary trees to balance search precision against computational overhead. Because the resulting indices are immutable and memory-mapped, they can be shared across multiple independent system processes without requiring the entire dataset to reside in active memory.

The library supports a broad range of indexing and retrieval capabilities, including the ability to scale to datasets that exceed available system memory. It provides cross-language integration through generated bindings and standard build system support, allowing the core search engine to be utilized across various programming environments.

Features

Approximate Nearest Neighbor Search - Provides a high-performance library for approximate nearest neighbor search using random projection trees and memory-mapped indices.

Memory-Mapped Indexing - Enables efficient access to large datasets by persisting search structures to disk and mapping them directly into process memory.

Vector Search Indexes - Constructs memory-mapped vector search indexes that enable fast, memory-efficient similarity queries.

Similarity Search Engines - Constructs static, disk-based data structures to perform high-dimensional nearest neighbor lookups with configurable precision.

Vector Similarity Search - Performs similarity queries by applying distance metrics to find nearest neighbors in high-dimensional space.

K-Nearest Neighbor Retrieval - Retrieves the closest items to a query vector using pre-built, memory-mapped data structures.

Memory-Disk Layering - Persists search structures to disk, allowing large datasets to be shared across processes without requiring full memory residency.

Trees - Organizes high-dimensional data into hierarchical random projection trees to enable rapid approximate nearest neighbor lookups.

AI & Machine Learning - Approximate nearest neighbors library optimized for memory

General Machine Learning - Library for approximate nearest neighbor search.

Recommender Systems - Approximate Nearest Neighbors in C++/Python optimized for memory usage.

Vector Databases - Library for efficient approximate nearest neighbor search in vector spaces.

Search and Indexing - C++ library for fast approximate nearest neighbor search.

Search Engines - C++ library for searching points in space.

Similarity Search - Memory-optimized approximate nearest neighbor search library.

Vector Databases - Listed in the “Vector Databases” section of the Llm Course awesome list.

Search Index Management - Enables saving and loading of static search indices to disk for distribution and reuse across system environments.

Search and Indexing - Supports scaling indexing to disk to accommodate datasets that exceed available system memory.

Static Content Indexing - Constructs immutable, pre-computed binary search structures that remain fixed after creation for fast, shared lookups.

Native Library Integrations - Exposes high-performance search capabilities to multiple programming environments through native bindings and build system support.

Language Bindings - Generates interface wrappers to allow core search functionality to be utilized from various programming languages.

Distance Metrics - Provides configurable distance metrics to partition vector spaces for efficient similarity search.

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Features

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