Vector databases are indispensable in recommender systems and semantic search engines because they enable searches based on the meaning of words or phrases rather than exact matches. They also serve as repositories for storing complex data types, including geometric and spatial information, and have fundamentally been important in semantic search applications long before the breakthroughs of generative AI.
But it is now, from the exhilarating AI advancements, evident how Generative AI applications and Large Language Models (LLMs) are radically stretching various domains with their ability to generate human-like text. And because most AI applications, by their intrinsic nature, often lack access to recent or proprietary information imperative for contextual responses, this is exactly where retrieval augmented generation (RAG) comes into play—leveraging vector databases to bridge this gap.
Understanding, therefore, the requirements of vector databases is fundamentally essential for AI engineering teams. One important consideration is often determining the Similarity Search features necessary for specific needs. Here are some open-source options practically good for generative AI applications and LLMs.
Faiss (Facebook AI Similarity Search)
Faiss, or Facebook AI Similarity Search by Meta, is incredibly good for efficient similarity search in high-dimensional vector spaces.
It excels in tasks involving quick similarity searches and comparing vectors with very high efficiency. And is particularly valuable for AI applications with support for GPU acceleration—ensuring fast processing and scalability for large datasets usually common in generative AI.
ANNOY (Approximate Nearest Neighbors Oh Yeah)
ANNOY is a C++ library with Python bindings engineered to search for nearby points in space, such as a specific point of interest.
It is widely used in vector-based applications, and is intricately designed to handle large datasets, providing quick and scalable methods for finding approximate similar items in high-dimensional spaces. And its versatility makes it a valuable tool in various machine learning tasks that are often resource intensive, including but not limited to nearest neighbor search, clustering, and dimensionality reduction.
Elasticsearch with Vector Similarity Plugin
The Elasticsearch Vector Similarity Plugin supercharges Elasticsearch, a search and analytics engine, by incorporating vector similarity search capabilities.
It enables efficient querying of high-dimensional vectors, making it valuable for generative AI models and especially RAG based LLMs, where similarity searches are essential, such as image or text retrieval. And this plugin also enables Elasticsearch to handle vector-based tasks and enhances its applicability in the context of generative AI.
NMSLIB (Non Metric Space Library) is an efficient open-source cross-platform similarity search library that provides a framework for the implementation of non-metric space (NMS) algorithms and a toolkit for evaluating similarity search methods.
You can use NMSLIB directly in C++ and Python (via Python bindings). And because it is an extensible library, that makes it possible to add new search methods and distance functions for use in generative AI applications and large language models. It is designed to handle high-dimensional data and is particularly suitable for tasks such as content recommendation, image retrieval, and generative AI applications, offering robust solutions for efficient vector searches.
Tantivy is a full-text search engine library closer to Apache Lucene or Elasticsearch, known for its efficiency and speed in handling similarity searches, making it applicable to various tasks involving text data.
Tantivy’s flexibility makes it suitable for AI applications that involve similarity searches related to text data. It is designed to provide fast and scalable search capabilities, making it practically suitable for quick and accurate retrieval of information from large datasets.
HNSW (Hierarchical Navigable Small World)
HNSW, or Hierarchical Navigable Small World, is an optimized data structure. The algorithm used with these structures is greedy search, which simply looks among all current candidates for the closest target value, in other words, for approximate nearest neighbor search.
HNSW is commonly used in vector databases and applications where quick and approximate similarity searches are crucial. It constructs a multi-layered graph structure, where each layer is simplified, resembling a navigable small-world network, enabling efficient and scalable searches in high-dimensional spaces.
There is no official repository for HNSW on GitHub. However, you may find implementations or information about HNSW by searching through academic sources or consulting official documentation of libraries or frameworks that might include it.