Build a RAG app using LangFlow + @streamlitofficial with minimal coding | LangFlow crash course

Video Description

Can AI explain itself? In other words, if I take an AI model and ask it questions about any other AI model, I would expect it to answer my questions. But it turns out that the model simply doesn't know about any other models. Can a simple workaround for this problem lie with Retrieval Augmented Generation or RAG. I developed a chat app using LangFlow and StreamLit with minimal coding and solved this very problem. It uses a RAG pipeline in the backend. I provided the retrieval system with all the papers on the Phi series of models developed by Microsoft and eventually, the system was able to answer my questions about the Phi family of models. So in this video, I will walk you through how I went about developing the pipeline and along the way demonstrate how easy it is to develop a chat app using LangFlow. ⌚️ ⌚️ ⌚️ TIMESTAMPS ⌚️ ⌚️ ⌚️ 0:00 - Intro 1:11 - Problem with existing LLMs (LLAMA2 example) 2:09 - LangFlow Installation 3:18 - LangFlow UI walkthrough 3:45 - Building a pipeline with LangFlow 10:30 - Compiling LangFlow 12:40 - Exporting LangFlow pipeline as JSON 14:00 - Getting StreamLit working with LangFlow 15:04 - StreamLit code walkthrough 19:08 - StreamLit app demo 20:50 - Extro RELATED LINKS github repo for the app: https://github.com/ai-bites/simple-rag-app Phi 1 paper: https://arxiv.org/pdf/2306.11644 Phi 1.5 paper: https://arxiv.org/pdf/2309.05463 Phi 3 report: https://arxiv.org/pdf/2404.14219 LLaVA Phi paper: https://arxiv.org/pdf/2401.02330 MY KEY LINKS YouTube: https://www.youtube.com/@AIBites Twitter: https://twitter.com/ai_bites​ Patreon: https://www.patreon.com/ai_bites​ Github: https://github.com/ai-bites​ WHO AM I? I am a Machine Learning researcher/practitioner who has seen the grind of academia and start-ups. I started my career as a software engineer 15 years ago. Because of my love for Mathematics (coupled with a glimmer of luck), I graduated with a Master's in Computer Vision and Robotics in 2016 when the now happening AI revolution started. Life has changed for the better ever since. #machinelearning #deeplearning #aibites