Unlike relational databases, which store data in rows and columns, vector databases represent unstructured data as high-dimensional data points, each representing a vector or an array of numbers.

The vector database is set to dramatically improve our productivity and revolutionize how we field queries to computers. Altogether, this makes vector databases one of the most important emergent ...

Vector databases serve as a sort of long-term memory for LLMs, such as OpenAI ’s GPT-4 and Llama from Meta. By storing and indexing the mathematical representations of pieces of text trained by the ...

A vector database is a specialized system designed to store and retrieve unstructured data —such as images, text, and audio—by converting it into mathematical representations known as vector ...

Rockset rolled out its initial support for vector search and storing vectorized embeddings earlier this year. Like many other SQL and NoSQL databases, the Silicon Valley firm experienced a surge in ...

The new architecture now supports vector clustering on top of blob storage. This results in lower latencies and the ability for Pinecone Serverless to support massive data sizes.

Vector databases haven’t yet been offered in a managed hybrid cloud model because developing this type of service is “a higher effort,” and core products must be compatible, said Zayarni.

Google Cloud on Thursday said it is adding vector support and integrating LangChain with all of its database offerings in an effort to outdo rival cloud service providers, such as Amazon Web ...

More recently, Pinecone, another vector database startup, raised $100 million. My point isn’t to offer a tutorial in vector databases.

Part 1. Create a vector database in Postgres In part 1 we create a vector database in Postgres and populate it with data from a vectorized set of HTML pages. The steps: ...