Vector search uses numerical embeddings to capture semantic meaning, excelling in multilingual contexts and meaning-based retrieval, but it can be resource-intensive and struggles with long documents.

As artificial intelligence reshapes marketing technology, vector search has emerged as a critical capability for 2025 data strategies. For marketing leaders, understanding how this technology ...

Semantic search in Oracle Database The company introduced AI Vector Search, which is a collection of semantic search features that include a vector data type, and vector search SQL operators.

Pinecone, a vector database for machine learning, announced the ability to combine keywords with semantic questions in a hybrid search today.

MongoDB Vector Search allows RAG architectures to utilize semantic search in order to bring back relevant context for user queries to reduce hallucinations, bring in real-time private company data ...

On the surface, it looks like new generative AI models are getting better at understanding us and the world. But this glosses over the risks and opportunities. The term Semantic Search makes it easier ...

Vector similarity search looks for matches by comparing the likeness of objects, as captured by machine learning models.

How Search.io’s vector engine enables semantic search Search.io has branded the technology it has developed as Neuralsearch, which provides AI-powered semantic search capabilities.