n8n Just Leveled Up AI Agents (Cohere Reranker)
AI Summary
This video explains how to improve the accuracy of Retrieval-Augmented Generation (RAG) AI agents by using rerankers, specifically the new Coherer 3.5 reranker available in N8N version 1.98. It covers the basics of vector search in RAG, where documents are chunked and embedded into vector stores, and discusses the problem of information loss due to vector compression causing less relevant results.
The video describes a two-stage retrieval approach incorporating rerankers: first retrieving many candidates from the vector store quickly, then refining the top results with a more accurate but slower cross-encoder reranker model that considers query and document chunks together. This method balances recall and accuracy, avoiding issues like context stuffing and lost-in-the-middle problems in large language models.
It also covers how to set up the new rerank results toggle in N8N workflows, including connecting to Coherer’s API, and demonstrates querying with reranked results returned to the LLM. Limitations and future wishlist features for N8N’s reranker node are discussed, such as overriding result counts and metadata handling.
Finally, the video touches on combining reranking with advanced techniques like hybrid search and metadata filtering for even better agent precision, pointing to additional tutorials and a community for learning advanced RAG workflows.