Have you ever searched for something online, only to feel frustrated when the results didn’t quite match what you had in mind? Maybe you were looking for an image similar to one you had, or trying to ...

Retrieval-augmented generation (RAG) has become the de facto standard for grounding large language models (LLMs) in private ...

For most enterprise applications, vector support is a feature that should be woven into the existing data estate, not a ...

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 ...

When Aquant Inc. was looking to build its platform — an artificial intelligence service that supports field technicians and agents teams with an AI-powered copilot to provide personalized ...

Enterprise data teams moving agentic AI into production are hitting a consistent failure point at the data tier. Agents built across a vector store, a relational database, a graph store and a ...

I recently wrote a Finextra piece entitled 3 GenAI Use Cases for Capital Markets; The Power of the Vector. In it, I discussed the increasing importance of the so-called vector database and vectors ...

Learn how to use vector databases for AI SEO and enhance your content strategy. Find the closest semantic similarity for your target query with efficient vector embeddings. A vector database is a ...

The latest trends and issues around the use of open source software in the enterprise. Data scientists loves vector databases, this year more than ever. Why is this so? Because vector databases have ...

Qdrant, the company behind the eponymous open source vector database, has raised $28 million in a Series A round of funding led by Spark Capital. Founded in 2021, Berlin-based Qdrant is seeking to ...