{"type":"video","version":"1.0","html":"<iframe src=\"https://www.loom.com/embed/bc8ae9c8d28f41b69a8c43a6c851ea7c\" 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/bc8ae9c8d28f41b69a8c43a6c851ea7c-dcc1684ef39ff2b1.gif","duration":300.264,"title":"Agentic Rag chatbot 📊","description":"In this video, I discuss my submission for the Legendic Life Chat about an assignment focused on streamability and file uploads. I demonstrate uploading a quarterly result file and explain the company's plans for upcoming launches. I outline the architecture of the system, which includes three main agents: ingestion, retrieval, and LLM response. I also share my experience using the Google Flend T5 model and the Mistral model, highlighting the superior performance of Mistral. I encourage viewers to check the GitHub repository for the code and further details."}