How to Optimize ChatGPT Knowledge Base using Graph RAG

In this video, I will demonstrate how you can use https://infranodus.com to optimize the knowledge base of a ChatGPT Workspace or any AI tool (e.g. the open source Open-WebUI or Dify for agentic flows). We will be using an example where we will upload. a batch of research papers on GraphRAG to a ChatGPT workspace. Normally, we don't know what 's inside the files and so we don't know whether the model hallucinates or makes things up. We also don't know what questions to ask. To address these issues, we upload those files to InfraNodus and visualize them as a knowledge graph, which allows us to have a high-level overview of the main ideas in our knowledge base and also detect the structural gaps, which can be used to generate interesting research questions. Try it at https://infranodus.com Read more at https://support.noduslabs.com/hc/en-us/articles/18430500689820-Optimize-Your-AI-Knowledge-Base-using-a-Graph Timecodes: 0:00 Why you need to know your knowledge base? 1:13 How are we going to do that? 2:25 Analyzing Your ChatGPT Knowledge Base 4:30 How to enrich your knowledge base structure with more sources 6:28 Finding a topic to develop 8:53 Adding the research found into the knowledge base 10:27 Optimizing by removing the “obvious” ideas from the graph 14:07 Exploring peripheral ideas 16:12 Using the latent topics to augment ChatGPT prompts 17:15 Augmenting your AI Knowldege base with this generated insight 19:26 Adding instructions genated by InfraNodus to ChatGPT prompts 21:35 Generating interesting questions / prompts based on the blind spots in your knowledge base 23:25 Asking those questions to ChatGPT 26:01 Same approach with open-source OpenWebUI — same approach 27:23 Same approach with Dify for building agentic flows #infranodus #chatgpt