While both Vector Databases and Graph Databases are pivotal technologies in modern artificial intelligence (AI) and data management, they operate on fundamentally different principles of data ...

One of the greatest weaknesses of AI agents that read and understand vast amounts of enterprise data is "hallucination"—the generation of plausible-sounding but factually incorrect information. KAIST ...

Many AI applications start the same way: pick a vector database → generate embeddings → build a retrieval pipeline → ship it. This works for many cases, but what if we can take it a step further by ...

Retrieval-augmented generation (RAG) has become the de facto standard for grounding large language models (LLMs) in private data. The standard architecture — chunking documents, embedding them into a ...

Standard vector RAG finds semantically similar passages. It cannot find the relationship between two entities, trace a multi-hop connection across documents, or know when retrieved documents are ...

To make open source LLM models work better, access to up-to-date information from diverse sources, including private ones currently absent from training data, is essential. Until long context windows ...

One of the greatest weaknesses of AI agents that read and understand vast amounts of enterprise data is "hallucination" — the generation of ...