In today's data-driven world, organizations are struggling to extract meaningful insights from vast amounts of unstructured information. Traditional keyword-based search methods often fall short, failing to capture the nuances and relationships between data points.
This is where Vertex AI Vector Search comes in, offering a powerful tool for building enterprise-ready generative AI applications.
Vertex AI Vector Search is a managed vector database on the Google Cloud Platform that enables you to search for similar items across large datasets using vector representations. These vectors encode complex relationships and semantic meaning, allowing for more relevant and accurate search results compared to traditional methods. It was formally called a machine engine.
Embeddings are a way of representing data as n-dimensional vectors, in a space where the locations of those points in space are semantically meaningful. These embeddings can be then used to find similar data points. You can get text embeddings using Vertex AI Embeddings API. These embeddings are managed using a vector database.
In traditional IT systems, most data is organized as structured or tabular data - using simple keywords, labels, and categories in databases and search engines.
In contrast, AI-powered services arrange data into a data structure known as “embeddings.”
Embeddings are a way for AI to organize data based on its meaning, so it can do semantic search instead of keyword search.
Once trained with specific content, AI creates a space called an “embedding space,” which is essentially a map of the content’s meaning, where contents with similar meaning are closer together in the space.
The two-tower model, illustrated below, is a specific type of embedding-based search where queries and database items are mapped to the embedding space by two respective neural networks. In this example, the model responds to natural-language queries for a hypothetical literary database.
Here are some key benefits of using Vertex AI Vector Search for building enterprise-ready generative AI applications:
Here are some real-world examples of how you can use Vertex AI Vector Search to build enterprise-ready generative AI applications:
The following are the steps to implement Vertex AI Vector Search in Google cloud
To learn more about the implementation, please check one of our notebooks [2][3] referenced below.
Vertex AI Vector Search is a key component for Generative applications which helps in grounding your application with enterprise data. The platform provides comprehensive documentation, tutorials, and sample code to get you started quickly. Additionally, Google Cloud offers various resources to support your journey, including:
Vertex AI Vector Search empowers you to build secure, scalable, and enterprise-ready generative AI applications. By leveraging the power of vector representations, you can unlock the true potential of your data and deliver exceptional experiences for your users and customers.