A vector database is a database that stores data as high-dimensional vectors, which are mathematical representations of features or attributes. Each vector has a certain number of dimensions, ranging from tens to thousands, depending on the complexity and granularity of the data.
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Both Vector Database and Vector Library are technologies that implement vector similarity search, but they differ in functionality and usability. Vector databases can store and update data, handle various types of data sources, perform queries during data import, and provide user-friendly and enterprise-ready features. Vector libraries can only store data, only process vectors, require all data to be imported before indexing, and require more technical expertise and manual configuration.
Some vector databases are built on top of existing libraries, such as Faiss. This enables them to leverage the library's existing code and functionality, saving development time and effort.
These vector databases and libraries are used in artificial intelligence (AI) applications such as machine learning, natural language processing, and image recognition. They have some common characteristics:
- Supports vector similarity search, which finds the k closest vectors to the query vector, as measured by a similarity metric. Vector similarity search is useful for applications such as image search, natural language processing, recommender systems, and anomaly detection.
- Use vector compression techniques to reduce storage space and improve query performance. Vector compression methods include scalar quantization, product quantization, and anisotropic vector quantization.
- An exact or approximate nearest neighbor search can be performed, depending on the trade-off between accuracy and speed. Exact nearest neighbor search provides perfect recall, but can be slow for large datasets. Approximate nearest neighbor search uses specialized data structures and algorithms to speed up the search, but may sacrifice some recall.
- Different types of similarity measures are supported, such as L2 distance, inner product, and cosine distance. Different similarity measures may be suitable for different use cases and data types.
- Various types of data sources can be processed, such as text, images, audio, video, etc. Data sources can be converted into vector embeddings using machine learning models, such as word embeddings, sentence embeddings, image embeddings, etc.
1、Elasticsearch
ElasticSearch is a distributed search and analysis engine that supports various types of data. One of the data types supported by Elasticsearch is a vector field, which stores dense numeric vectors.
In version 7.10, Elasticsearch added support for indexing vectors into a dedicated data structure to enable fast kNN retrieval via the kNN search API. In version 8.0, Elasticsearch added support for native natural language processing (NLP) with vector fields.
2、Faiss
Meta's Faiss is a library for efficient similarity search and dense vector clustering. It contains algorithms for searching vector sets of arbitrary size up to ones that might not fit in RAM. It also contains supporting code for evaluation and parameter tuning.
3、The kite
Milvus is an open source vector database that can manage trillions of vector datasets, supports multiple vector search indexes and built-in filtering.
4、Weaviate
Weaviate is an open source vector database that allows you to store data objects and vector embeddings from your favorite ML models, and scales seamlessly to billions of data objects.
5、Pinecone
Pinecone is a vector database designed for machine learning applications. It is fast, scalable, and supports a variety of machine learning algorithms.
Pinecone is built on top of Faiss, a library for efficient similarity search on dense vectors.
6、Quadrant
Qdrant is a vector similarity search engine and vector database. It provides a production-ready service with a convenient API to store, search and manage points - vectors with additional payloads.
Qdrant is tailored for extended filtering support. It makes it useful for various neural network or semantic-based matching, faceted search and other applications.
7、Vespa
Vespa is a full-featured search engine and vector database. It supports vector search (ANN), lexical search and structured data search, all within the same query. Integrated machine learning model inference allows you to apply AI to understand your data in real time.
8、Province
Vald is a highly scalable distributed fast approximate nearest neighbor dense vector search engine. Vald is designed and implemented based on the Cloud-Native architecture. It uses the fastest ANN algorithm NGT to search for neighbors.
Vald features automatic vector indexing and index backup, and horizontal scaling to search from billions of feature vector data.
9、ScaNN (Google Research)
ScaNN (Scalable Nearest Neighbors) is a library for efficient vector similarity search, which finds the k closest vectors to the query vector, measured by a similarity measure. Vector similarity search is useful for applications such as image search, natural language processing, recommender systems, and anomaly detection.
10、pgvector
pgvector is an open source extension to PostgreSQL that allows you to store and query vector embeddings in the database. It is built on top of the Faiss library, a popular dense vector efficient similarity search library. pgvector is easy to use and can be installed with just one command.
Original Link: 10 Top Vector Databases—BimAnt