 Today I will demonstrate how to use the Neo4j Knowledge Graph Builder to create a graph for RAC applications using unstructured information like documents from PDFs to YouTube transcripts to Wikipedia pages. We start by connecting to Neo4j. I have already created a Neo4j Aura instance for free. If you don't have one, there's a link that gets started here. So I can take my credentials file and to load this information and connect to our database, which is currently empty. And then I can start adding my documents. I'll start with a Gemini release video from YouTube. So we have some AI related content. I'll take the Google DeepMind page from Wikipedia and a PDF about alpha fold release. So we added all these documents and then we can pick an LLM to extract this information. And we can configure what to extract and how to extract this information from our text. So I already created a graph schema here with people, organizations, technologies, and concepts, and the number of relationships. There's other ways how you can configure the schema from your existing database, or by extracting this information from text that you can paste into the application. Okay, now we can click on generate graph, text that you can paste into the application. Okay, now we can click on generate graph and it will start extracting this information from our PDFs. So we see we have nine pages of PDF and one page of transcripts and Wikipedia. So we can already look at the first extracted data. So this is from the Gemini video. So we see what has been extracted. So the different Gemini models, Google AI, some concepts, Google products, and so on. And then the other two are also done, which allows us now to visualize all of them. So I can just select all of them and we can see the graph. So we see our entities, but then we can also extract and show the chunks that we extracted from the documents and that we extracted from the documents and that we connected the entities to. And you see as well that it cross connects actually entities across the chunks. And you can also see the documents here as well. So now we can also start asking questions of our data. So for instance, we can ask what did DeepMind work on? And it will use the information from the graph and from the answer here for us. And what's especially interesting is that we can actually see what information went into generating this answer by clicking this retrieval information button. And this shows us from the Wikipedia page, this section has been used and from the PDF document, the pages 3 and 7. You can also see which entities have been used to answer this question. We can even visualize them here and look more into it. And we can also see which text chunks have been used to answer this question. have been used to answer this question. If you want to go deeper, you can explore the graph of Neo4j Bloom to visualize, edit and navigate the graph as well.
Neo4j's LLM Knowledge Graph Builder - DEMO
In this demonstration of the LLM Knowledge Graph Builder, we show you how to automagically create a graph from your unstructured text and leverage it for Graph-powered Retrieval-Augmented Generation (GraphRAG).

-upload documents, YouTube videos, and Wikipedia pages.
-configure a graph schema
-extract the lexical and knowledge graph
-visualize the extracted graph
-ask questions and see the details that were used to generate the answers

Try it live: https://bit.ly/4c4HKyp
Learn more: https://bit.ly/3KMRrp6


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