Vector databases and their ramifications in the AI model space have been discussed extensively. Specialized systems called vector databases are used for machine learning and data analytics to store, index, and retrieve high-dimensional vectors quickly and effectively. Online vector database courses are becoming more and more accessible as vector databases gain popularity.
Due to their scalability, rapid and accurate similarity search, metadata storage, and filtering capabilities, vector databases are especially critical for supporting big language models and generative AI applications. Vector databases, which typically represent features from things like photographs, words, or other sorts of data, are very good at managing complex sets of numbers, in contrast to normal databases, which are made for organized information.
The specific requirements and characteristics of the generated vectors determine which vector database should be used for generative AI applications. These are a few possibilities for vector databases that can be used with generative AI applications.
- Facebook AI Similarity Search (FAISS)
Facebook’s AI Research group created the open-source package known as Faiss, or Facebook AI Similarity Search, to facilitate effective nearest-neighbor searches in high-dimensional vector fields. It is useful in generative AI applications since it performs well in tasks requiring rapid similarity searches.
Because Faiss enables GPU acceleration, it can handle big datasets that are prevalent in generative AI quickly and scalably.
- Approximate Nearest Neighbors Oh Yeah (ANNOY)
A technique that is both versatile and efficient for approximating the nearest neighbor search is a C++ library with Python bindings. Annoy is a popular vector-based application tool that can handle enormous datasets and offers scalable and fast ways to locate approximate comparable things in high-dimensional areas. Because of its adaptability, it may be used for a wide range of machine learning tasks, including generative AI tasks.
- Hierarchically Navigable Small World (HNSW)
The approximate nearest neighbor search algorithm is known as HNSW or Hierarchical Navigable Small World. It creates a hierarchical graph structure, making searches in high-dimensional spaces scalable and effective. In vector databases and applications, where fast and approximative similarity searches are essential, HNSW is frequently utilized.
- Using Vector Similarity Plugin in Elasticsearch
The Elasticsearch Vector Similarity Plugin adds vector similarity search features to Elasticsearch, an analytics and search engine. Because it makes querying high-dimensional vectors cheap, it is useful for generative AI applications like picture or text retrieval where similarity searches are crucial. With the help of this plugin, Elasticsearch can now handle vector-based jobs and is more useful when used in conjunction with generative AI.
- DolphinDB
DolphinDB is mainly known as a time-series database, but it also has characteristics that allow it to handle vector data efficiently, which could be helpful for generative AI applications. It is well-suited for jobs involving high-dimensional vectors, which are typical in generative models, because of its adaptability in maintaining various data formats and performing intricate operations.
- Tantivy
Tantivy is a full-text search engine that is useful for various activities requiring text data because of its effectiveness and quickness in processing similarity queries. Because of its speedy and scalable search capabilities, it is appropriate for applications that need to retrieve information from big datasets quickly and accurately.
Tantivy’s adaptability makes it appropriate for generative AI applications involving textual data similarity searches.
- NMSLIB
The non-metric space (NMS) methods can be implemented effectively with the help of the open-source NMSLIB (Non-Metric Space Library) similarity search library. It can be applied to huge language models and generative AI applications because it is made to handle high-dimensional data.
NMSLIB provides strong solutions for effective vector searches, making it essential for jobs like image retrieval, generative AI applications, and content recommendation.
