This n8n workflow creates an intelligent question-answering system that integrates with a WordPress site or other platforms to provide accurate responses using a knowledge base of PDFs. It starts with a chat interface that receives user questions, then utilizes a Retrieval-Augmented Generation (RAG) architecture to fetch relevant information from stored documents. Key nodes include a chat trigger, a PDF download and extraction process, vector embeddings for knowledge retrieval, and a reranker to improve search results. The workflow leverages services like Google Drive, OpenAI, and a Supabase vector database to provide a seamless, dynamic Q&A experience. This setup is ideal for organizations seeking to automate customer support, internal knowledge sharing, or educational FAQs based on large document repositories.

Node Count	11 – 20 Nodes
Nodes Used	@n8n/n8n-nodes-langchain.agent, @n8n/n8n-nodes-langchain.chatTrigger, @n8n/n8n-nodes-langchain.documentDefaultDataLoader, @n8n/n8n-nodes-langchain.embeddingsOpenAi, @n8n/n8n-nodes-langchain.lmChatOpenAi, @n8n/n8n-nodes-langchain.memoryBufferWindow, @n8n/n8n-nodes-langchain.rerankerCohere, @n8n/n8n-nodes-langchain.vectorStoreSupabase, extractFromFile, googleDrive, manualTrigger, stickyNote