The field of artificial intelligence and machine learning is evolving as fast as I write this article. Vector databases have emerged as a powerful tool for storing and retrieving high-dimensional data. Our weekly article dealing with the intersection of technology, AI and language translation / localization services today explores the concept of vector databases, their inner workings, and their application in our Deep Adaptive AI Translation, particularly in the context of automatic post-editing.
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A vector database is a specialized database system designed to store, manage, and query high-dimensional vector data (vector embeddings) efficiently. Vector embeddings are numerical representations of data objects, such as text, images, or audio, in a high-dimensional space. Unlike traditional relational databases that store structured data in tables, vector databases are optimized for handling numerical vectors, which are ordered lists of numbers representing various features or characteristics of data points. A vector database stores vectors (fixed-length lists of numbers) along with other data items, typically using Approximate Nearest Neighbor algorithms for search functionalities.
The purpose of a vector database is storing mathematical representations of data in high-dimensional space, where each dimension corresponds to a data feature, supporting complex data representation.
As AI and machine learning applications become more prevalent across industries, they generate vast amounts of data in the form of vectors (mathematical representations of features or attributes in a multi-dimensional space and not word-based descriptions). These vectors, often containing hundreds or thousands of dimensions, pose significant challenges for traditional text-based or number-based database systems.
"Traditional databases were simply not designed to handle the complexity and scale of vector data," explains Jose Miguel Herrera, our Head of Machine Learning. "Vector databases fill a critical gap in our data infrastructure, enabling us to work with AI-generated data more efficiently and effectively, and this has an impact on the way we search and we offer information. And also, of course, the way we can produce AI translations."
Vector databases bring several key features to the table that set them apart from traditional database systems. These are the main features we need to understand and that they are radically different to "usual" databases as we have known them until recently.
At the core of vector databases is their ability to efficiently store and retrieve vectors with hundreds or thousands of dimensions. This capability is crucial for applications like image recognition, natural language processing, and recommendation systems, where data points are often represented as high-dimensional vectors.
"Imagine trying to store and search through millions of images, each represented by a vector with 2,048 dimensions," says Maria Ángeles Garcia, our Head of Machine Translation. "Traditional databases would crumble under the weight of such data, but vector databases handle it elegantly and with ease. So now we can search for words and related synonyms and truly understand why a term fits or needs to fit in a particular sentence in a particular context. What's better, we can even force machine translation systems to translate in a certain way and knowledge systems to retrieve relevant information. The combination of both would create a multilingual Virtual AI Assistant - that is our ECOChat."
Perhaps the most powerful feature of vector databases is their optimization for similarity searches. Unlike exact-match queries common in traditional databases, vector databases excel at finding the most similar vectors to a given query vector.
This capability opens up a world of possibilities for applications such as content recommendation, fraud detection, and semantic search. "With vector databases, we can find 'similar' items in ways that were previously impossible or prohibitively expensive," Maria explains. "In this way, we can admit words like car / vehicle or automobile in context as good translations -and not penalize them- because they fit better in context. With typical machine translation evaluations, the use of a synonym would be penalized, even if it actually improved the translation. This also helps us to interpret queries for our AI Virtual Assistant, so if someone asks "who is the boss at Pangeanic", the system will provide information about the CEO. This is absolutely wonderful when looking for information among hundreds of pages, legislation or documents."
Organizations keep accumulating ever-larger datasets for all types of data. Thus, scalability becomes a critical concern. Vector databases are designed from the ground up to handle large amounts of vector data and perform fast queries even as data volumes grow.
"We're seeing companies with billions of vectors in their databases, and the queries are still blazing fast," notes Jose Miguel. "This scalability is what makes vector databases a game-changer for large-scale AI applications."
To achieve their impressive performance, vector databases employ specialized indexing techniques that dramatically speed up similarity searches. These methods, such as locality-sensitive hashing and hierarchical navigable small world graphs, allow for approximate nearest neighbor searches that are orders of magnitude faster than brute-force approaches.
"The indexing algorithms used in vector databases are a fascinating area of ongoing research," Jose Miguel says. "They're constantly evolving, pushing the boundaries of what's possible in terms of search speed and accuracy."
Vector databases employ various algorithms and data structures to efficiently store and query high-dimensional data. Some common techniques include:
These techniques work together in the new AI Translation workflows:
By combining these techniques, Pangeanic's Deep Adaptive AI Translation system can provide fast, accurate suggestions and translations, even when working with enormous databases of past translations and language data. This leads to faster turnaround times, more consistent translations, and significant cost savings in administration, and management of large-scale localization projects.
Conclusion
Vector databases are revolutionizing the way we store and retrieve high-dimensional data, offering powerful capabilities for AI applications. In the context of Deep Adaptive AI Translation, they provide a robust foundation for improving translation quality, consistency, and style adaptation through automatic post-editing.