Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More Today’s complex, unstructured data — text, images, audio and video — are ...
Tiered multitenancy allows users to combine small and large tenants in a single collection and promote growing tenants to dedicated shards. Qdrant has released Qdrant 1.16, an update of the Qdrant ...
Vector databases power RAG, semantic search, recommendations, and memory. Learn how indexing, filtering, and hybrid retrieval ...
PostgreSQL with the pgvector extension allows tables to be used as storage for vectors, each of which is saved as a row. It also allows any number of metadata columns to be added. In an enterprise ...
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For a long time, vector databases were a bit of a niche product, but because they are uniquely suited to provide context and long-term memory to large language models, everybody in the database space ...
Vector databases enable semantic searches but might also store sensitive data. Learn how to build a vector database security ...
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 the ...
In the age of generative AI (genAI), vector databases are becoming increasingly important. They provide a critical capability for storing and retrieving high-dimensional vector representations, ...
A vector database is a type of database technology that's used to store, manage and search vector embeddings, numerical representations of unstructured data that are also referred to simply as vectors ...
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