No Chunks, No Embeddings OpenAI’s Index‑Free Long RAG
AI Summary
In this video, the speaker discusses OpenAI’s new retrieval augmented generation (RAG) system that uses GPT-4.1 without the need for dedicated indexing. Key points include:
- The introduction of a multi-agent system that mimics human reading behaviors, indexing-free operation, and reliance on long context models capable of handling up to 1 million tokens.
- Pros and cons of the system, including when it’s most effective, particularly for complex Q&A tasks related to long documents such as legal texts.
- A detailed explanation of the implementation steps, including how to process documents into manageable chunks, assess relevance, and generate answers with verification steps to ensure accuracy.
- Insights into cost analysis for using such systems, highlighting potential expenses and the balance between accuracy and latency.