Fancy, I think, but again no word on the actual work of turning a few bazillion csv files and pdf's into a knowledge graph.<p>I see a lot of these KG tools pop up, but they never solve the first problem I have, which is actually constructing the KG itself.
"Whitepaper" is guarded behind this:
<a href="https://survey.alipay.com/apps/zhiliao/n33nRj5OV" rel="nofollow">https://survey.alipay.com/apps/zhiliao/n33nRj5OV</a><p>> The white paper is only available for professional developers from different industries. We need to collect your name, contact information, email address, company name, industry type, position and your download purpose to verify your identity...<p>That's new.
LLMs are not that different from humans, in both cases you have some limited working memory and you need to fit the most relevant context into it. This means that if you have a new knowledge base for llms it should be useful for humans too. There should be a lot of cross pollination between these tools.<p>But we need a theory on the differences too. Now it is kind of random how we differentiate the tools. We need ergonomics for llms.
It has come to the point that we need benchmarks for (Graph)-Rag systems now, same as we have for pure LLMs. However vendors will certainly then optimize for the popular ones, so we need a good mix of public, private and dynamic eval datasets.
How does this compare to the Model Context Protocol?<p><a href="https://modelcontextprotocol.io/introduction" rel="nofollow">https://modelcontextprotocol.io/introduction</a>
I like their description/approach for logical problem solving:<p>2.2.<p>"The engine includes three types of operators: planning, reasoning, and retrieval, which transform natural language problems into problem solving processes that combine language and notation.<p>In this process, each step can use different operators, such as exact match retrieval, text retrieval, numerical calculation or semantic reasoning, so as to realize the integration of four different problem solving processes: Retrieval, Knowledge Graph reasoning, language reasoning and numerical calculation."
Somehow the first time I see such pop up in my feed. Glad that someone (judging by the comments that is not the only one project) is working on this, of course I am rather far from the field but to me this feels like a step in the right direction for advancing AI past the hyperadvanced parrot stage that is the current "AI" is (at least per my perception).
Yet another RAG/knowledge graph implementation.<p>At this point, the onus is on the developer to prove it's value through AB comparisons versus traditional RAG. No person/team has the bandwidth to try out this (n + 1) solution.