{"type":"video","version":"1.0","html":"<iframe src=\"https://www.loom.com/embed/c80a402821e04d9ba782eb4b3ea5bfae\" frameborder=\"0\" width=\"1728\" height=\"1296\" webkitallowfullscreen mozallowfullscreen allowfullscreen></iframe>","height":1296,"width":1728,"provider_name":"Loom","provider_url":"https://www.loom.com","thumbnail_height":1296,"thumbnail_width":1728,"thumbnail_url":"https://cdn.loom.com/sessions/thumbnails/c80a402821e04d9ba782eb4b3ea5bfae-00001.gif","duration":200.28,"title":"How to Use Neum AI to bring up-to-date context to your AI applications","description":"Hey everyone, this is Kevin from Neum AI (https://neum.ai), and in this video, I'll show you how to use Neum AI to create data pipelines that automatically update the context of your AI applications. I'll start by demonstrating a simple chatbot application that interacts with my Notion workspace. Without any context, the chatbot won't be able to answer questions accurately. That's where Neum AI comes in. I'll guide you through the process of creating a pipeline that connects to Notion, embeds the data, and stores it in Pinecone, our chosen vector store. By scheduling this pipeline, we can ensure that our source is always updated with our vector store."}