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 ...

Most vector search systems struggle with a basic problem: how to break complex documents into searchable pieces. The typical approach is to split text into fixed size chunks of 200 to 500 tokens, this ...

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 ...

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 ...

A new SQL Server 2025 feature lets organizations run vector-based semantic searches on their own data, connecting to local or cloud-hosted AI models without relying on massive general-purpose LLMs. I ...

A new open-source framework called PageIndex solves one of the old problems of retrieval-augmented generation (RAG): handling very long documents. The classic RAG workflow (chunk documents, calculate ...

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 ...

AWS is previewing a specialized storage offering, Amazon S3 Vectors, that it claims can cut the cost of uploading, storing, and querying vectors by up to 90% compared to using a vector database, a ...

Qdrant, the open-source vector search engine built in Rust for production workloads, today announced $50 million in Series B funding led by AVP, with participation from Bosch Ventures, Unusual ...