These dedicated vector databases cater to various
needs in vector storage, retrieval, and similarity search, spanning from
general-purpose solutions to those optimized for specific domains like genomics
or real-time applications. These databases are specifically designed to handle
the unique requirements of vector data, allowing for efficient storage,
retrieval, and analysis.
Milvus:
Open-source vector database optimized for similarity search
of high-dimensional data. Developed by Zilliz.
Faiss:
Library for efficient similarity search and clustering of
dense vectors, developed by Facebook AI Research.
Annoy:
C++ library with Python bindings for approximate nearest
neighbors search, suitable for recommendation systems.
Hnswlib:
Library implementing Hierarchical Navigable Small World
(HNSW) for approximate nearest neighbor search, known for efficiency in
high-dimensional spaces.
PQxx:
Lightweight library for fast approximate nearest neighbor
search, optimized for memory usage and query speed. Developed by Pinterest.
NMSLIB:
Non-Metric Space Library (NMSLIB) is an efficient and
comprehensive library for similarity search. It supports a wide range of
distance measures and similarity functions.
VectoDB:
A distributed vector database designed for large-scale
vector retrieval and similarity search tasks. It's designed to handle
high-throughput queries.
Weaviate:
An open-source, real-time vector search engine that allows
you to store, search, and rank data objects based on their embeddings or
vectors.
TaruDB:
A vector database that is optimized for similarity search,
especially in scenarios where data is constantly evolving. It's designed for
real-time applications.
Genomic Ordered Relational (GOR) Database:
While primarily designed for genomics data, GOR Database
also supports vectors. It's optimized for efficient storage and retrieval of
large-scale biological data.
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