“Generative AI and its possibilities” (5/5). Artificial intelligence has necessitated the search for new ways of refining, indexing and storing information. Mathematical vectors are the most suitable objects for storing information from data of all kinds, rationalizing it using mathematical knowledge. This is how the idea of the vector database came about. In this more technical article, we’ll take a look at vector databases, which play a fundamental role in the storage, retrieval and manipulation of complex data.
Understanding vector data in Artificial Intelligence
In AI, vector data are digital representations of complex data such as texts, images, videos or even time sequences. These data are converted into mathematical vectors to enable AI algorithms to process them more efficiently. The vectorization process will have a major impact on the information stored in the vectors. This is why, although many types of data can be found in the same format, it is important to understand how the vectorization was carried out.
Components of an AI vector database
AI vector databases are designed to store, index and query vectors, in addition to other metadata. Here are the main components of an AI vector database:
- Vector storage: vectors representing data are stored in the database. Each vector is associated with a unique key for rapid retrieval.
- Indexing: for fast, efficient searching, index structures are used to organize the vectors. The organization is most commonly related to decision tree logic, with the most similar vectors as trunks.
- Vector queries: vector databases support specific queries such as finding the vectors most similar to a given query vector.
- Metadata: in addition to vectors, databases can store metadata associated with the data, such as information on source, creation date, etc.
Using vector databases
AI vector databases offer many features that are crucial to the development of high-performance AI algorithms: similar value search, summarization, clustering, hierarchization and more.
These features are particularly useful for users, as they simplify the process of operating and scaling their applications. In other words, they make it easier to grow an application while maintaining optimum performance and meeting security requirements.
A concrete example of the use of these features is the creation of a query engine that enables advanced search and filter operations on stored data. This means that developers can create applications that are capable of searching and sorting information in a highly sophisticated way, which is particularly important for artificial intelligence applications.
In addition, vector databases offer the possibility of using hybrid relevance scoring models, which combine traditional text analysis methods with vector techniques to enhance information retrieval. However, it’s important to note that vector databases face similar challenges to other types of database. Developers are constantly working to improve the scalability, approximation accuracy, latency performance and cost-effectiveness of these databases.
Ultimately, as vector database technology continues to develop, it is essential to address these challenges to ensure that they can meet the growing needs of increasingly sophisticated artificial intelligence applications. This includes strengthening security, resilience to failures, operational support and efficient management of different workloads.
The best-known vector databases
– Faiss: a library from Facebook AI Research specially designed to search for similar vectors on a large scale.
– ANNoy: a Python library for approximate vector search.
– Elasticsearch: a distributed search engine that supports vector queries for information retrieval.
– Milvus: a highly scalable open-source vector database.
Vector databases play a central role in many artificial intelligence applications. They enable the efficient storage, indexing and retrieval of complex data in vector form. Whether for information retrieval, content recommendation, image recognition or other domains, vector databases have become an indispensable part of the AI ecosystem, facilitating the development of powerful and efficient AI models.