Build a RAG agent with n8n, Qdrant & OpenAI

About This Workflow

Build a RAG Agent with n8n, Qdrant & OpenAI

This n8n automation workflow creates an intelligent document assistant that can answer questions from uploaded files using a complete single-vector Retrieval-Augmented Generator (RAG) model. It integrates Qdrant for vector storage and OpenAI for generating human-like responses. This workflow enables users to build a robust and efficient RAG agent for various applications.

This workflow is suitable for developers, data scientists, and business owners who want to leverage AI-powered question-answering capabilities in their applications or services.

• Retrieves relevant information from uploaded files using Qdrant's vector storage
• Uses OpenAI to generate human-like responses to user queries
• Integrates with n8n to create a seamless automation workflow
• Supports single-vector RAG model for efficient information retrieval and response generation

To start using this workflow, import it into your n8n instance and customize the configurations to suit your specific use case, such as integrating with your file storage system or adjusting the OpenAI API settings.