VentureBeat

New DeepMind study reveals a hidden bottleneck in vector search that breaks advanced RAG systems

Vector embeddings are the backbone of modern enterprise AI, powering everything from retrieval-augmented generation (RAG) to semantic search. But a new study from Google DeepMind reveals a fundamental ...
SiliconANGLE

Onehouse’s vector embeddings support aims to cut the cost of AI training

Onehouse Inc., a company that sells a data lakehouse based on Apache Hudi as a managed service, today said it has launched a vector embedding generator to automate embedding pipelines as a part of its ...
InfoWorld

Using PostgreSQL as a vector database in RAG

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 ...
dbta

Unraveling Vector Databases and Vector Search for AI Applications

The emergence of vector databases and vector search for handling massive quantities of complex data have radically transformed the way AI is implemented and managed. As a specialized approach for ...
Computer Weekly

Storage technology explained: Vector databases at the core of AI

Artificial intelligence (AI) processing rests on the use of vectorised data. In other words, AI turns real-world information into data that can be used to gain insight, searched for and manipulated.
dbta

Weaviate Introduces Vector Embedding Service

Weaviate is launching its latest SaaS service, Weaviate Embeddings, to bring freedom and flexibility to a crucial area of AI development: data vectorization. According to the company, Weaviate ...
InfoWorld

Teradata adds Enterprise Vector Store to augment RAG

Teradata’s partnership with Nvidia will allow developers to fine-tune NeMo Retriever microservices with custom models to build document ingestion and RAG applications. Teradata is adding vector ...