Simple LightRAG Chatbot Setup

LightRAG Chatbot with Automated Knowledge Base Management Workflow Overview This workflow creates and maintains a complete, production-ready AI chatbot system. It's built around a core concept called Retrieval-Augmented Generation (RAG), where an AI's knowledge is dynamically supplemented with information from a specific, controlled knowledge base. The workflow is divided into three distinct, automated processes: 1. AI Chatbot Interface: A user-facing chatbot that answers questions based on a knowledge base hosted on a LightRAG server. 2. Data Ingestion Pipeline: An automated system that watches a specific Google Drive folder. When files are added or updated, it automatically processes them and uploads them to the LightRAG knowledge base, keeping the AI's information current. 3. Data Cleanup Routine: A scheduled process that synchronizes the LightRAG knowledge base with the Google Drive folder. It identifies and deletes documents from LightRAG that no longer exist in the Drive folder, preventing the AI from using outdated information. Technologies Used - n8n: The core automation platform connecting all services. - LangChain: Used via n8n's LangChain nodes to build the AI Agent, manage memory, and connect tools. - OpenAI: Provides the core Large Language Model (LLM) (gpt-4.1-nano) that powers the AI Agent's reasoning. - Google Drive: Acts as the "source of truth" and user-friendly content management system for the AI's knowledge base. - LightRAG: A custom knowledge graph RAG service that indexes documents and provides an API for the AI to query. Automation Value This workflow provides an end-to-end solution for a common business need: a chatbot that can answer questions accurately about a specific set of documents (e.g., internal policies, product documentation, support articles). The value is multifaceted: - Effortless Knowledge Management: Non-technical users can update the AI's knowledge simply by adding, editing, or deleting files in a Google Drive folder.