Vector similarity search uses machine learning to translate the similarity of text, images, or audio into a vector space, making search faster, more accurate, and more scalable. Suppose you wanted to ...

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More The traditional way for a database to answer a query is with a list of ...

In previous articles we used Google’s PaLM API to generate a blog post and build a chatbot. In this article, we will explore how to generate text embeddings and perform semantic searches using the ...

The emergence of vector databases and vector search for handling massive quantities of complex data have radically transformed the way AI is implemented and managed. As a specialized approach for ...

Vector databases have emerged to become an integral component of many AI applications. However, as AI applications continue to grow, vector databases are presented with more complicated use cases.

Most vector search systems struggle with a basic problem: how to break complex documents into searchable pieces. The typical approach is to split text into fixed size chunks of 200 to 500 tokens, this ...

The latest trends in software development from the Computer Weekly Application Developer Network. Hazelcast has made sure it keeps its platform progression evolving in line with current major trends ...

I recently wrote a Finextra piece entitled 3 GenAI Use Cases for Capital Markets; The Power of the Vector. In it, I discussed the increasing importance of the so-called vector database and vectors ...

Have you ever searched for something online, only to feel frustrated when the results didn’t quite match what you had in mind? Maybe you were looking for an image similar to one you had, or trying to ...

Unlock the full InfoQ experience by logging in! Stay updated with your favorite authors and topics, engage with content, and download exclusive resources. Vivek Yadav, an engineering manager from ...