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Retrieval-Augmented Generation (RAG) and Large Language Models (LLMs) are two distinct yet complementary AI technologies. Understanding the differences between them is crucial for leveraging their ...

As developers look to harness the power of AI in their applications, one of the most exciting advancements is the ability to enrich existing databases with semantic understanding through vector search ...

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

检索找到了某个语义上接近的片段，LLM 围绕它写出一段文字，但是没人发现答案是错的。这是 vector RAG 调参解决不了的失败问题。而现在有2种方法可以解决他： GraphRAG 增加了一层 knowledge graph，用来描绘实体之间的关系。 Vectorless RAG 完全抛弃向量数据库，让 LLM ...

Building retrieval-augmented generation (RAG) systems for AI agents often involves using multiple layers and technologies for structured data, vectors and graph information. In recent months it has ...

Jean Joseph, a data & AI engineer with deep expertise in database development, will explain how to build AI-powered applications with Azure Database for PosgreSQL at at upcoming developer conference.

Startup Zilliz Inc. today debuted a new release of its flagship offering, a managed vector database called Zilliz Cloud that artificial intelligence models can use to hold information. Redwood Shores, ...

If you’re building generative AI applications, you need to control the data used to generate answers to user queries. Simply dropping ChatGPT into your platform isn’t going to work, especially if ...

In today’s data-driven world, the exponential growth of unstructured data is a phenomenon that demands our attention. The rise of generative AI and large language models (LLMs) has added even more ...