Better Retrieval With Reasoning-Based RAG Using PageIndex

Author(s): Dr. Leon Eversberg Originally published on Towards AI. The next generation of RAG: How PageIndex improves retrieval accuracy without semantic search Retrieval-augmented generation (RAG) adds the external knowledge contained in a large collection of documents to an LLM. RAG uses optimized vector databases to efficiently store embedding vectors and find relevant matches to a given query. Reasoning-based RAG versus vector-based RAG. Image by the authorThis article discusses PageIndex, a novel reasoning-based retrieval-augmented generation (RAG) method that improves the relevance of document retrieval without relying on vector representations. It contrasts traditional vector-based RAG workflows with PageIndex’s innovative approach, which methodically assesses a document’s table of contents to enhance retrieval accuracy. The text elaborates on the operational phases of traditional and reasoning-based RAG systems, illustrating how PageIndex’s reasoning process can lead to smarter interactions with structured documents. The article concludes by evaluating the limitations and advantages of both RAG methodologies and suggests potential future hybrid systems combining both approaches for enhanced efficiency. Read the full blog for free on Medium. Join thousands of data leaders on the AI newsletter. Join over 80,000 subscribers and keep up to date with the latest developments in AI. From research to projects and ideas. If you are building an AI startup, an AI-related product, or a service, we invite you to consider becoming a sponsor. Published via Towards AI