{"type":"video","version":"1.0","html":"<iframe src=\"https://www.loom.com/embed/cb6c432060f94d1eb4a384a1f03f3a0a\" 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/cb6c432060f94d1eb4a384a1f03f3a0a-aeaacd17af7d4ae2.gif","duration":80.21,"title":"Google Drive RAG to Vector Database","description":"This Loom demonstrates a simple RAG automation that converts a document from Google Drive into a vector database for an AI agent’s memory. The example takes a FAQ file, downloads it from Google Drive, and uses OpenAI Embeddings to turn it into a MINECON vector database under a namespace named FAQ. It then shows a chatbot that queries this vector database to retrieve relevant information, such as answering a user’s question about the return policy based on the document."}