Pinecone
Managed vector database for AI applications
Open-Source Projects
Discover top open-source software, updated regularly with real-world adoption signals.
Discover top open-source software, updated regularly with real-world adoption signals.
Fast, memory-efficient approximate nearest-neighbor search with shared on-disk indexes
Annoy provides high-speed approximate nearest-neighbor queries using static, mmap-able indexes that can be shared across processes, with low memory footprint and support for multiple distance metrics.
Annoy is designed for developers who need rapid similarity search over large, high‑dimensional datasets. It builds a forest of random projection trees that enable fast approximate nearest‑neighbor queries while keeping memory usage minimal.
The library creates static indexes that are saved to disk and can be memory‑mapped by any number of processes, allowing instant lookups without reloading. It supports Euclidean, Manhattan, cosine (angular), Hamming, and dot‑product metrics, offers multi‑threaded index construction, and provides native Python bindings alongside a C++ API.
Because indexes are immutable after building, they can be generated once and distributed across workers in batch jobs, Hadoop pipelines, or production services. The on‑disk build option lets you handle datasets that exceed available RAM, and the mmap approach enables efficient sharing across multiple processes.
When teams consider Annoy, these hosted platforms usually appear on the same shortlist.
Looking for a hosted option? These are the services engineering teams benchmark against before choosing open source.
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It builds multiple random projection trees and uses memory‑mapped static indexes, reducing I/O and memory overhead.
No, the index is immutable after the build step; you must rebuild to incorporate new vectors.
Euclidean, Manhattan, cosine (angular), Hamming, and dot product.
It can index millions of vectors; on‑disk building allows datasets larger than available RAM.
Annoy is CPU‑only and does not provide GPU acceleration.
Project at a glance
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