Vector databases are all the rage, judging by the number of startups entering the space and the investors ponying up for a piece of the pie. The proliferation of large language models (LLMs) and ...

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.

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.

TL;DR Key Takeaways : Vector databases store and retrieve unstructured data (e.g., images, text, audio) using vector embeddings, allowing semantic search and bridging the “semantic gap ...

“For structured data with straightforward applications, users should stick to traditional relational databases or NoSQL databases. A vector database becomes essential when handling unstructured ...

At the same time, clients are creating large stores of unstructured data – images, audio, PDFs, or email, for example – and employing vector search on top to make sense of their own ...

As such, vector databases prove useful in things like image search or searching within video, audio or other forms of unstructured data to understand the content, not the keywords associated with ...

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 ...

Vector databases are all the rage, judging by the number of startups entering the space and the investors ponying up for a piece of the pie. The proliferation of large language models (LLMs) and ...