No Chunks, No Embeddings: OpenAI’s Index‑Free Long RAG

In this video, I am taking a look at OpenAI's new long context agentic RAG system that uses GPT-4.1 for retrieval without the need for dedicated index. Blogpost: https://cookbook.openai.com/examples/partners/model_selection_guide/model_selection_guide#adaptation-decision-tr Colab: https://colab.research.google.com/drive/15FuQByD4hVFpqw3Isc_nqVba1Zp_o4Ie?usp=sharing#scrollTo=eyIK33R9iQnu Website: https://engineerprompt.ai/ RAG Beyond Basics Course: https://prompt-s-site.thinkific.com/courses/rag Let's Connect: 🦾 Discord: https://discord.com/invite/t4eYQRUcXB ☕ Buy me a Coffee: https://ko-fi.com/promptengineering |🔴 Patreon: https://www.patreon.com/PromptEngineering 💼Consulting: https://calendly.com/engineerprompt/consulting-call 📧 Business Contact: [email protected] Become Member: http://tinyurl.com/y5h28s6h 💻 Pre-configured localGPT VM: https://bit.ly/localGPT (use Code: PromptEngineering for 50% off). Signup for Newsletter, localgpt: https://tally.so/r/3y9bb0 TimeStamps: 00:00 Retrieval Augmented Generation Systems 00:14 OpenAI's New Multi-Agent System 01:04 Practical Guide and Use Cases 01:48 Step-by-Step System Breakdown 03:29 Detailed Architecture and Implementation 07:13 Example Workflow and Code Walkthrough 18:40 Cost Analysis and Trade-offs 20:51 Future Improvements and Conclusion