So, my name is Zach. Uh I work for Neo4j. We're a graph intelligence company. You can think of us like a knowledge layer graph database at the core. We help connect and resolve information so AI systems can be a little bit more accurate and explainable. So, I'm going to talk to you today about context graphs. ... Basically we saw a lot of noise around this idea of creating decision traces and reasoning um to help agents make better decisions. And so, to kind of think actually about what a context graph is, we need to ask ourselves, "Would you agents really be accurate?" Right? And so, um for one thing, doing a lot of retrieval, you're going to need a knowledge base, obviously. Um so, a knowledge base for something like rag or graph retrieval basically helps an agent or a chatbot answer questions correctly. What a context graph does in the evolution of this is really the information required to not only answer questions correctly, but make better decisions. Um systems of record, really about facts, entities, current state, [Context graphs are] about precedents, causal chains, expected outcomes, and enabling the agent to act with subject matter expertise, um and telling the agent really [how to reason]. Um the entities, about things that exist, there'll be uh events, so decisions, transactions, approvals, things like that. And then context, policies that are um in different reasoning by AI that records memory, but um by employees and and past humans that have made decisions.

Then it does this thing called find precedents, which is going to be more in a second, but it's going to look at structural information in the graph to pull a bunch of [data]. ... connect to each other in these causal chains. So, you build off of each other. Then we did a special of hybrid search, especially around those precedents, both on semantic similarity and structural similarity inside of the graph that actually looked at how the decision traces were made um from previous decisions, and it tried to match that, and I'll talk a little bit about how that works with graph embeddings in the next slide to ultimately come up with the recommendation. And so, we have a we have a vector index, so we're able to search like fraud rejection, for example, sort of on the semantics. But then we have this concept called graph embedding. ... A graph embedding is the same concept except those green nodes that I was showing you before everything was connected. We actually embedded those into a vector. And so, what that means is similar decision traces are now going to be able to be looked up by vector similarity. Um GDS is called graph data science. That's what we use for the uh for the graph embeddings themselves.

But what I really wanted to show you today um was another tool that was just recently created a little month ago, um which allows you to create a full um stack application to start with with just a one-line command in the terminal to actually create the context graph and the front end and the back end. ... Basically you can think about this like um create React app or create Next app, like you basically get this boilerplate. There's just a UVX command, UVX create context graph, um specify the name of the app, the domain, um the framework I want to use. ... It'll give you um you know data inside of a graph and then um you know, you can go ahead and ask questions um and it will go and and you know, basically do this type of retrieval with Cypher and and everything else. Cypher's a graph query language um to pull data back. Underpinning this, one of the big dependencies is our Neo4j agent memory package. Um so, this is a complete memory API. Uh it includes and context graphs really need all three of these things. Um a short-term memory, a long-term memory and reasoning. ... So, here uh we actually implemented this inside of the package. Um where it goes through a few different stages on raw text. ... stages where you go from spaCy to gliner and and more advanced to an LLM fallback. Um and then there's a separate merging, deduplication, and enrichment strategy.