Vector databases store and retrieve unstructured data (e.g., images, ... For example, while a relational database can efficiently retrieve records based on exact matches or predefined attributes ...

For example, dedicated vector databases provide features such as distributed computing and storage, disaster recovery, and data persistence. Milvus is a primary example.

Vector databases and search aren’t new, but vectorization is essential for generative AI and working with LLMs. Here's what you need to know. One of my first projects as a software developer was ...

Text, images, and videos are all examples of unstructured data. Vector databases capture and store the essence of a particular piece of data that a machine-learning program or LLM can then pull from.

Milvus, a Linux Foundation AI and data project, for example, is a well-known vector database of choice among enterprises that’s easy to try out because of its vibrant open source development.

For example, an agent might use a knowledge graph for task planning or tracking the memory of prior actions taken by other agents, and a vector database for semantic recall.

For example, a vector database can find images similar to a given image, or text similar to a given text. With vectors, LLMs can process requests quickly delivering the performance needed to run ...

DataStax, the well-funded Apache Cassandra-centric database company, is placing a lot of its current bets on AI and its technology’s ability to provide highly scalable vector search capabilities ...

When Pinecone launched a vector database aimed at data scientists in 2021, it was probably ahead of its time. But as the use cases began to take shape last year, the company began pushing AI ...

Why merging vector database and keyword search is more than the sum of two parts. ... Startup Pinecone, for example, announced at the end of April that it had raised $100 million in new funding.