Faiss was built to search for multimedia documents that are similar to a query document in a billion-vector database. For evaluation purposes, the developers used Deep1B, a collection of one ...

I equate such search and “similarity search”-type processes being like neurons kicking in, infusing the vector database with proper on-the-fly intelligence.

The traditional way for a database to answer a query is with a list of rows that fit the criteria. If there’s any sorting, it’s done by one field at a time. Vector similarity search looks for ...

Key applications of vector databases include semantic search, retrieval-augmented generation (RAG) for AI systems, and similarity search for recommendations and anomaly detection.

Real-time vector similarity search also opens up new applications in domains for retrieval augmented generation (RAG) that are beyond just language and rich media. Vector embeddings of time series and ...

With the launch of Redis 7.2 today, the startup’s cloud and software-based data stores gain a new scalable search feature that supports something known as vector similarity search.

Vector databases are utilized in natural language processing (NLP) applications to facilitate similarity queries for tasks such as text classification, language translation, and search.

“With vector search for Amazon DocumentDB, you can effectively search the database based on nuanced meaning and context without spending time and cost to manage a separate vector database ...

The platform is gaining support for vector similarity search, hybrid search and support for open-source tools that can help customers connect their data to large language models.

Credit: Oracle. Oracle is planning to add vector search capabilities to its database offering, dubbed Database 23c, the company announced at its ongoing annual CloudWorld conference.