{"type":"video","version":"1.0","html":"<iframe src=\"https://www.loom.com/embed/d76911e2d84440f58a7032df410bd317\" frameborder=\"0\" width=\"1108\" height=\"831\" webkitallowfullscreen mozallowfullscreen allowfullscreen></iframe>","height":831,"width":1108,"provider_name":"Loom","provider_url":"https://www.loom.com","thumbnail_height":831,"thumbnail_width":1108,"thumbnail_url":"https://cdn.loom.com/sessions/thumbnails/d76911e2d84440f58a7032df410bd317-e09585fa71f45f3a.gif","duration":146.0596,"title":"Exploring Rav Engine X: A Modular Retrieval Augmented Generation Pipeline","description":"In this video, I provide a quick walkthrough of the Rav Engine X, a modular production pipeline designed for retrieval-augmented generation. I explain how the system works, from uploading a PDF document to generating answers using vector similarity and a cross-encoder. You can view the retrieved context, final answers, and evaluation metrics, which can be exported as JSON or CSV files. I encourage you to try the live demo at radenginex.on.dander.com and explore the GitHub repository for the full code. Thank you for watching!"}