We often say that there is no artificial intelligence without data. But it can be any kind of data. Consider Large Scale Language Models (LLM), which are deep learning models such as OpenAI’s GPT-4, which can generate text very similar to what humans write.
For LLM to “understand” a word, it must be stored as a text “vector”. This is how numbers are used to capture word meanings and usage patterns. Vectors are the lingua franca of AI, so to speak.
Vector has been around for some time, but the popularity and accessibility of ChatGPT, a generative AI interface, has led to the most popular apps built using these technologies, especially by organizations leveraging their own private data for LLM. Therefore, it has become a hot topic. By constructing your own vector.
But how do they work, how are they stored, how do applications retrieve them, and how do they help enable AI? Vectors, vector lookups, databases that can store and query vectors Let’s take a closer look at the types of
vector
A vector is a numerical representation of an attribute of data. Each data point is represented as a vector containing many numeric values, each value corresponding to a particular feature or attribute of the data.
Converting data such as images or text into a vector representation is known as “embedding”. For example, the choice of image embedding for vector search depends on many factors such as the specific use case, available resources, and characteristics of the image dataset. E-commerce or product image retrieval applications may benefit from using specially trained embeddings for product images. So-called instance searches, on the other hand, involve searching for instances of objects within a larger scene or image.
Storing data as a vector representation allows us to perform various operations and calculations on the data, most importantly retrieval. Choosing vector attributes is important for the kind of questions you want to be able to ask later. For example, if you only store information about the color of an image of a plant, you can’t ask about its care needs. Only visually similar plants can be found.
vector search
By representing data as vectors, you can take advantage of mathematical techniques to efficiently search and compare very large data sets without exact matches. Millions of customer profiles, images and articles represented as vectors (lists of numerical values that capture key features of each product) can be very quickly processed using vector similarity search (or “nearest neighbor search”) can be examined.
Unlike traditional keyword-based search, which matches documents based on the occurrence of specific terms, vector search focuses on query similarity. For example, are their semantic meanings similar?
This feature allows you to find similar items based on their vector representation. Similarity search algorithms can measure the “distance” or similarity between vectors to determine how closely related they are.
A recommendation system can use vector search to find the most similar or dissimilar items or users based on their preferences. Image processing enables tasks such as object recognition and image retrieval. For example, Google, the world’s largest search engine, relies on vector search to power the backend of Google Image Search, YouTube, and other information retrieval services.
Vectors and databases
There are standalone vector search technologies such as Elasticsearch. However, to achieve the responsiveness and scale that AI applications demand, vectors must be stored in and retrieved from scalable, fast databases. There are now several databases that provide vector search as a feature.
The main advantage of databases that allow vector searches is speed. With traditional databases, a query would have to be compared against every item in the database. In contrast, Integrated Vector Search allows for a form of indexing and includes search algorithms that greatly speed up the process, allowing you to search large amounts of data in a fraction of the time it takes a standard database. .
In business, this means using AI applications to recommend products that are similar to past purchases, or to identify fraudulent transactions that resemble known patterns or anomalies that appear to differ from the norm. Very worth it.
An example of a database that offers vector search is DataStax’s Astra DB. It is built on open source Apache Cassandra for its high scalability and high throughput. Cassandra is massively proven to power AI with AI applications from Netflix, Uber, Apple, and more. The addition of vector search makes Astra DB a one-stop shop for large-scale database operations.
Integrating vector search with a scalable data store such as Astra DB enables computation and ranking directly within the database, eliminating the need to transfer large amounts of data to external systems. This reduces latency and improves overall query performance. Vector searches can be combined with other indexes in Astra DB for even more powerful queries.
Growing Importance of Vector Search
Vectors and the databases that store them play a major role in enabling efficient searching, similarity computation, and data exploration in the field of AI. As organizations expand their generative AI efforts and seek to use data to customize end-user experiences, vector representation and the ability to work with scalable, fast databases capable of vector search become increasingly important. .
Learn more about vector search at our free virtual event, Agent X: Architecture for Generative AI, on July 11th. Register now.
