{"type":"video","version":"1.0","html":"<iframe src=\"https://www.loom.com/embed/b464a6baf1c24192814c453c2c75ece8\" frameborder=\"0\" width=\"1110\" height=\"832\" webkitallowfullscreen mozallowfullscreen allowfullscreen></iframe>","height":832,"width":1110,"provider_name":"Loom","provider_url":"https://www.loom.com","thumbnail_height":832,"thumbnail_width":1110,"thumbnail_url":"https://cdn.loom.com/sessions/thumbnails/b464a6baf1c24192814c453c2c75ece8-7ab60cf2d20f9c12.gif","duration":360.8743,"title":"Building a RAG Agent for Enhanced Data Retrieval and Analysis 🚀","description":"In this video, I walk you through the RAG agent I built, which stands for Retrieval Augmented Generation. I explain how we process text documents by vectorizing and chunking them for efficient storage in a Postgres database, allowing for quick and accurate information retrieval. I demonstrate the agent's capabilities using various queries, showcasing its speed and accuracy in generating responses, even with complex SQL tasks. This system can be applied across various professional niches, including law, accounting, and healthcare. I encourage you to explore how this technology can enhance your data processing and analysis workflows."}