The GraphRAG宣言：为GenAI增添知识

We are on the verge of realizing that in order to do anything significantly useful with GenAI, you can’t depend only on autoregressive LLMs to make your decisions. I know what you’re thinking: “RAG is the answer.” Or fine-tuning, or GPT-5.

我们正处于认识到要在GenAI中做任何有意义的事情，不能仅仅依赖自回归LLMs来做决策的边缘。我知道你在想什么：“RAG就是答案。”或者微调，或者GPT-5。

Yes. Techniques like vector-based RAG and fine-tuning can help. And they are good enough for some use cases. But there’s another whole class of use cases where these techniques all bump into a ceiling. Vector-based RAG – in the same way as fine-tuning – increases the probability of a correct answer for many kinds of questions. However neither technique provides the certainty of a correct answer. Oftentimes they also lack context, color, and a connection to what you know to be true. Further, these tools don’t leave you with many clues about why they made a particular decision.

是的。像基于向量的RAG和微调这样的技术可以提供帮助。对于某些用例来说，它们已经足够好了。但是在另一整类用例中，这些技术都会遇到瓶颈。基于向量的RAG和微调技术增加了许多类型问题的正确答案的概率。然而，这两种技术都无法提供正确答案的确定性。它们经常缺乏上下文、色彩以及与您所知道的真实情况的联系。此外，这些工具也没有提供关于它们为什么做出特定决策的线索。

Back in 2012, Google introduced their second-generation search engine with an iconic blog post titled “Introducing the Knowledge Graph: things, not strings1.” They discovered that a huge leap in capability is possible if you use a knowledge graph to organize the things represented by the strings in all these web pages, in addition to also doing all of the string processing. We are seeing this same pattern unfold in GenAI today. Many GenAI projects are bumping up against a ceiling, where the quality of results is gated by the fact that the solutions in use are dealing in strings, not things.

回到2012年，谷歌发布了他们的第二代搜索引擎，标题为“介绍知识图谱：事物而非字符串1”。他们发现，如果使用知识图谱来组织这些网页中字符串所代表的事物，而不仅仅进行字符串处理，就可以实现巨大的能力飞跃。今天，我们在GenAI中也看到了同样的模式。许多GenAI项目都遇到了一个瓶颈，即结果质量受到使用字符串而不是事物的解决方案的限制。

Fast forward to today, AI engineers and academic researchers at the leading edge are discovering the same thing that Google did: that the secret to breaking through this ceiling is knowledge graphs. I...