“A vector database organizes and stores vectors - numerical representations of words or phrases generated by LLMs - in a structured way,” explained Mark Nijmeijer, senior director of product ...

Vector embeddings represent data in multi-dimensional space, with specialized models like CLIP, GloVe, and Wav2Vec generating embeddings for images, text, and audio, respectively.

Zilliz, the company behind the Milvus database, has launched a new Zilliz Cloud region in Azure Central India. This expansion ...

“For example, a vector database can quickly find all images in a database that are visually similar to a given image by comparing their respective vectors within seconds. The unique advantage of ...

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 ...

Vector databases, on the other hand, store and process data in the form of vector embeddings, which convert text, documents, images, and other data into numerical representations that capture the ...

Unstructured data — such as images, video, audio, and user behaviors — generally don’t fit the relational database model; it can’t be easily sorted into row and column relationships.

Timescale Inc., the creator of a cloud time-series database that’s based on PostgreSQL, is looking to cater to artificial intelligence developers with the launch of its latest vector capabilities.

There is no universal ‘best’ vector database—the choice depends on your needs. Evaluating scalability, functionality, performance, and compatibility with your use cases is vital.

Qdrant, the company behind the eponymous open source vector database, has raised $28 million in a Series A round of funding led by Spark Capital.. Founded in 2021, Berlin-based Qdrant is seeking ...