Automated Document Processing & Q&A with n8n and OpenAI

This n8n workflow facilitates seamless document ingestion, embedding, and question-answering, ideal for automating knowledge retrieval from uploaded files. Users start by uploading PDFs or CSVs via a webhook trigger. The files are then processed and loaded into a vector store leveraging OpenAI embeddings, enabling efficient indexing of document content. The workflow includes two main flows: the ‘Load Data’ flow for ingesting and embedding documents, and the ‘Retriever’ flow for receiving chat messages, retrieving relevant information from the vector store, and generating responses using AI models. The ‘Load Data’ section guides users to upload files and load data into memory, ready for query. The ‘Retriever’ section listens for chat inputs, searches the vector database, and responds with precise answers using either an expensive or cheaper language model depending on the context. This workflow is useful for building intelligent FAQ bots, knowledge bases, or customer support assistants that automatically process documents and answer user inquiries effectively.