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A library by Google Research for efficient vector similarity search, suitable for large-scale nearest neighbor applications in AI.
An open-source library for approximate nearest neighbor search in high-dimensional spaces, often used as a backend for vector databases and search engines.
HNSWLIB is a C++ library with Python bindings for fast approximate nearest neighbor search using Hierarchical Navigable Small World (HNSW) graphs, commonly used in vector database backends.
Deep Lake is a vector database designed as a data lake for AI, capable of storing and managing vector embeddings, text, images, and videos. It utilizes a tensor format for efficient querying and integration with AI algorithms, making it suitable for similarity search and machine learning workflows. It is open-source and tailored for handling unstructured and multimodal data, with seamless integration with frameworks like PyTorch and TensorFlow.
Marqo is an open-source neural search engine that leverages vector representations to enable semantic search over textual data. It abstracts vector database complexity and provides a high-level interface for building advanced search applications.
Vearch is a distributed vector search engine designed for AI-native applications, enabling scalable and efficient similarity search across large datasets.
ScaNN (Scalable Nearest Neighbors) is an open-source library developed by Google Research for efficient vector similarity search, particularly designed for large-scale nearest neighbor search applications in AI and machine learning. It is optimized for high-dimensional vector data, such as embeddings generated from text, images, or other modalities.
SDKs & Libraries
open-source, ann, vector-search, ai
ScaNN is open-source software and is available for free.