Much has been said about vector databases and their implications in the AI model space. Vector databases are specialised systems for storing, indexing and efficiently retrieving high-dimensional vectors used for machine learning and data analytics. As its popularity increases, so does the number of vector database courses available online.
Vector databases are also essential for supporting large language models and generative AI applications by providing fast and accurate similarity search, scalability, and metadata storage and filtering capabilities. Unlike regular databases that are designed for organised information, vector databases are really good at handling complex sets of numbers, usually representing features from things like images, texts, or different types of data.
The selection of a vector database for generative AI applications is contingent upon the particular needs and features of the generated vectors. Here are some of the vector database options for generative AI applications.
Faiss (Facebook AI Similarity Search)
Faiss, or Facebook AI Similarity Search, is an open-source library by Facebook’s AI Research lab designed for efficient nearest neighbour search in high-dimensional vector spaces. It excels in tasks requiring quick similarity searches, making it valuable in generative AI applications.
Faiss supports GPU acceleration, ensuring fast processing and scalability for large datasets common in generative AI.
Annoy (Approximate Nearest Neighbors Oh Yeah)
Annoy Visualised. Source: GitHub
A C++ library with Python bindings provides a flexible and efficient approach to approximate the nearest neighbour search. Widely used in vector-based applications, Annoy is designed to handle large datasets, providing quick and scalable methods for finding approximate similar items in high-dimensional spaces.
Its versatility makes it a valuable tool in various machine learning tasks, including those within the realm of generative AI.
Elasticsearch with Vector Similarity Plugin
The Elasticsearch Vector Similarity Plugin enhances Elasticsearch, a search and analytics engine, by incorporating vector similarity search capabilities. It allows efficient querying of high-dimensional vectors, making it valuable for generative AI applications where similarity searches are essential, such as image or text retrieval. This plugin enables Elasticsearch to handle vector-based tasks and enhances its applicability in the context of generative AI.
NMSLIB
NMSLIB (Non Metric Space Library) is an open-source similarity search library that provides an efficient implementation of non-metric space (NMS) algorithms. It is designed to handle high-dimensional data and is suitable for use in generative AI applications and large language models.
NMSLIB is crucial for tasks like content recommendation, image retrieval, and generative AI applications, offering robust solutions for efficient vector searches.
Tantivy
Tantivy is a full-text search engine known for its efficiency and speed in handling similarity searches, making it applicable to various tasks involving text data. It is designed to provide fast and scalable search capabilities, making it suitable for applications that require quick and accurate retrieval of information from large datasets.
Tantivy’s flexibility makes it suitable for generative AI applications that involve similarity searches related to text data.
DolphinDB
DolphinDB, primarily recognized as a time-series database, offers features for efficient handling of vector data, making it potentially useful in generative AI applications. Its versatility in managing diverse data types and supporting complex operations positions it for tasks involving high-dimensional vectors, common in generative models.
HNSW (Hierarchical Navigable Small World)
HNSW, or Hierarchical Navigable Small World, is an algorithm for approximate nearest neighbour search. It constructs a hierarchical graph structure, allowing for efficient and scalable searches in high-dimensional spaces. HNSW is commonly used in vector databases and applications, where quick and approximate similarity searches are crucial.
