{"type":"video","version":"1.0","html":"<iframe src=\"https://www.loom.com/embed/e8d4c852300d46678484d22b9debb49a\" frameborder=\"0\" width=\"1920\" height=\"1440\" webkitallowfullscreen mozallowfullscreen allowfullscreen></iframe>","height":1440,"width":1920,"provider_name":"Loom","provider_url":"https://www.loom.com","thumbnail_height":1440,"thumbnail_width":1920,"thumbnail_url":"https://cdn.loom.com/sessions/thumbnails/e8d4c852300d46678484d22b9debb49a-1714969597665.gif","duration":268.441,"title":"Exploring Retrieval Augmentation (RAG) with Langchain","description":"In this video, I demonstrate how to use a rag chartboard to augment document generation. I explain the concept of KPI keys and the three models available through the GROP API. I also discuss the two types of document searches: general docs and Git repositories. I walk through the process of fetching and chunking a document, as well as using file filters for Git repositories. The video provides important context and instructions for viewers to understand and implement document augmentation."}